The EcoCyc database: reflecting new knowledge about Escherichia coli K-12

Large-Scale Functional Analysis of CRP-Mediated Feed-Forward Loops

Feed-forward loops (FFLs) represent an important and basic network motif to understand specific biological functions. Cyclic-AMP (cAMP) receptor protein (CRP), a transcription factor (TF), mediates catabolite repression and regulates more than 400 genes in response to changes in intracellular concentrations of cAMP in Escherichia coli. CRP participates in some FFLs, such as araBAD and araFGH operons and adapts to fluctuating environmental nutrients, thereby enhancing the survivability of E. coli. Although computational simulations have been conducted to explore the potential functionality of FFLs, a comprehensive study on the functions of all structural types on the basis of in vivo data is lacking. Moreover, the regulatory role of CRP-mediated FFLs (CRP-FFLs) remains obscure. We identified 393 CRP-FFLs in E. coli using EcoCyc and RegulonDB. Dose⁻response genomic microarray of E. coli revealed dynamic gene expression of each target gene of CRP-FFLs in response to a range of cAMP dosages. All eight types of FFLs were present in CRP regulon with various expression patterns of each CRP-FFL, which were further divided into five functional groups. The microarray and reported regulatory relationships identified 202 CRP-FFLs that were directly regulated by CRP in these eight types of FFLs. Interestingly, 34% (147/432) of genes were directly regulated by CRP and CRP-regulated TFs, which indicates that these CRP-regulated genes were also regulated by other CRP-regulated TFs responding to environmental signals through CRP-FFLs. Furthermore, we applied gene ontology annotation to reveal the biological functions of CRP-FFLs.

On the Origin of Compositional Features of Ribosomes

Ribosomes are highly abundant in cells and comprise, besides RNAs of varying lengths, 55–80 similarly sized, short proteins. This seemingly unusual composition is thought to have resulted from selection for rapid autocatalytic ribosome production. Here, we demonstrate that ribosomal protein-splitting mutations cannot accelerate ribosome production. The autocatalytic explanation is also unnecessary, because protein lengths generally decline with expression levels. Although ribosomal proteins are shorter than expected from their expression levels, they are not outliers among members of large protein complexes in mean protein length or coefficient of variation. These observations are explainable because 1) shortening proteins lowers their synthetic cost and reduces the waste from mistranslation-induced protein dysfunction and degradation, 2) such benefits rise with expression levels, and 3) members of large complexes participate in more protein–protein interactions so are less tolerant to mistranslation. These and other considerations suggest that the compositional features of ribosomes originate from cellular energy economics.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

Optimal Allocation of Bacterial Protein Resources under Nonlethal Protein Maturation Stress

Under different environmental stresses, bacteria optimize the allocation of cellular resources through a variety of mechanisms. Recently, researchers have used phenomenological models to quantitatively characterize the allocation of bacterial protein resources under metabolic and translational limitations. Some stresses interfere with protein maturation, thereby enhancing the expression of chaperones and proteases. However, the reallocation of protein resources caused by such environmental stresses has not been modeled quantitatively. Here, we developed a dynamic model of coarse-grained protein resource fluxes based on a self-replicator that includes protein maturation and degradation. Through flux balance analysis, it produces a constrained optimization problem that can be solved analytically. Accordingly, we predicted protein allocation fractions as functions of growth rate under different limitations, which are basically in line with empirical data. We cultured Escherichia coli in media containing different concentrations of chloramphenicol, acetic acid, and paraquat and measured the functional relationship between the expression level of β-galactosidase driven by a constitutive promoter and the bacterial growth rate, respectively. Taking into account the possible mode of stress limitation on the fluxes, our model reproduces this experimentally measured relationship. In addition, our model is in good agreement with the experimental relationship between growth rate and proteome fraction of unnecessary protein in E. coli, considering the unoptimized upregulation of chaperones with useless protein overexpression. The results provide a more systematic view of bacterial stress adaptation that may help in designing for bioengineering or medical interventions.

Transposon Insertion Sequencing Elucidates Novel Gene Involvement in Susceptibility and Resistance to Phages T4 and T7 in Escherichia coli O157

Experiments using bacteriophage (phage) to infect bacterial strains have helped define some basic genetic concepts in microbiology, but our understanding of the complexity of bacterium-phage interactions is still limited. As the global threat of antibiotic resistance continues to increase, phage therapy has reemerged as an attractive alternative or supplement to treating antibiotic-resistant bacterial infections. Further, the long-used method of phage typing to classify bacterial strains is being replaced by molecular genetic techniques. Thus, there is a growing need for a complete understanding of the precise molecular mechanisms underpinning phage-bacterium interactions to optimize phage therapy for the clinic as well as for retrospectively interpreting phage typing data on the molecular level. In this study, a genomics-based fitness assay (TraDIS) was used to identify all host genes involved in phage susceptibility and resistance for a T4 phage infecting Shiga-toxigenic Escherichia coli O157. The TraDIS results identified both established and previously unidentified genes involved in phage infection, and a subset were confirmed by site-directed mutagenesis and phenotypic testing of 14 T4 and 2 T7 phages. For the first time, the entire sap operon was implicated in phage susceptibility and, conversely, the stringent starvation protein A gene (sspA) was shown to provide phage resistance. Identifying genes involved in phage infection and replication should facilitate the selection of bespoke phage combinations to target specific bacterial pathogens.IMPORTANCE Antibiotic resistance has diminished treatment options for many common bacterial infections. Phage therapy is an alternative option that was once popularly used across Europe to kill bacteria within humans. Phage therapy acts by using highly specific viruses (called phages) that infect and lyse certain bacterial species to treat the infection. Whole-genome sequencing has allowed modernization of the investigations into phage-bacterium interactions. Here, using E. coli O157 and T4 bacteriophage as a model, we have exploited a genome-wide fitness assay to investigate all genes involved in defining phage resistance or susceptibility. This knowledge of the genetic determinants of phage resistance and susceptibility can be used to design bespoke phage combinations targeted to specific bacterial infections for successful infection eradication.

Transposon Insertion Sequencing Elucidates Novel Gene Involvement in Susceptibility and Resistance to Phages T4 and T7 in Escherichia coli O157

Experiments using bacteriophage (phage) to infect bacterial strains have helped define some basic genetic concepts in microbiology, but our understanding of the complexity of bacterium-phage interactions is still limited. As the global threat of antibiotic resistance continues to increase, phage therapy has reemerged as an attractive alternative or supplement to treating antibiotic-resistant bacterial infections. Further, the long-used method of phage typing to classify bacterial strains is being replaced by molecular genetic techniques. Thus, there is a growing need for a complete understanding of the precise molecular mechanisms underpinning phage-bacterium interactions to optimize phage therapy for the clinic as well as for retrospectively interpreting phage typing data on the molecular level. In this study, a genomics-based fitness assay (TraDIS) was used to identify all host genes involved in phage susceptibility and resistance for a T4 phage infecting Shiga-toxigenic Escherichia coli O157. The TraDIS results identified both established and previously unidentified genes involved in phage infection, and a subset were confirmed by site-directed mutagenesis and phenotypic testing of 14 T4 and 2 T7 phages. For the first time, the entire sap operon was implicated in phage susceptibility and, conversely, the stringent starvation protein A gene (sspA) was shown to provide phage resistance. Identifying genes involved in phage infection and replication should facilitate the selection of bespoke phage combinations to target specific bacterial pathogens.IMPORTANCE Antibiotic resistance has diminished treatment options for many common bacterial infections. Phage therapy is an alternative option that was once popularly used across Europe to kill bacteria within humans. Phage therapy acts by using highly specific viruses (called phages) that infect and lyse certain bacterial species to treat the infection. Whole-genome sequencing has allowed modernization of the investigations into phage-bacterium interactions. Here, using E. coli O157 and T4 bacteriophage as a model, we have exploited a genome-wide fitness assay to investigate all genes involved in defining phage resistance or susceptibility. This knowledge of the genetic determinants of phage resistance and susceptibility can be used to design bespoke phage combinations targeted to specific bacterial infections for successful infection eradication.

Thermal proteome profiling in bacteria: probing protein state in vivo

Increasing antibiotic resistance urges for new technologies for studying microbes and antimicrobial mechanism of action. We adapted thermal proteome profiling (TPP) to probe the thermostability of Escherichia coli proteins in vivoE. coli had a more thermostable proteome than human cells, with protein thermostability depending on subcellular location-forming a high-to-low gradient from the cell surface to the cytoplasm. While subunits of protein complexes residing in one compartment melted similarly, protein complexes spanning compartments often had their subunits melting in a location-wise manner. Monitoring the E. coli meltome and proteome at different growth phases captured changes in metabolism. Cells lacking TolC, a component of multiple efflux pumps, exhibited major physiological changes, including differential thermostability and levels of its interaction partners, signaling cascades, and periplasmic quality control. Finally, we combined in vitro and in vivo TPP to identify targets of known antimicrobial drugs and to map their downstream effects. In conclusion, we demonstrate that TPP can be used in bacteria to probe protein complex architecture, metabolic pathways, and intracellular drug target engagement.

Using a Chemical Genetic Screen to Enhance Our Understanding of the Antibacterial Properties of Silver

It is essential to understand the mechanisms by which a toxicant is capable of poisoning the bacterial cell. The mechanism of action of many biocides and toxins, including numerous ubiquitous compounds, is not fully understood. For example, despite the widespread clinical and commercial use of silver (Ag), the mechanisms describing how this metal poisons bacterial cells remains incomplete. To advance our understanding surrounding the antimicrobial action of Ag, we performed a chemical genetic screen of a mutant library of Escherichia coli—the Keio collection, in order to identify Ag sensitive or resistant deletion strains. Indeed, our findings corroborate many previously established mechanisms that describe the antibacterial effects of Ag, such as the disruption of iron-sulfur clusters containing proteins and certain cellular redox enzymes. However, the data presented here demonstrates that the activity of Ag within the bacterial cell is more extensive, encompassing genes involved in cell wall maintenance, quinone metabolism and sulfur assimilation. Altogether, this study provides further insight into the antimicrobial mechanism of Ag and the physiological adaption of E. coli to this metal.

Interaction of lipoprotein QseG with sensor kinase QseE in the periplasm controls the phosphorylation state of the two-component system QseE/QseF in Escherichia coli

Histidine kinase QseE and response regulator QseF compose a two-component system in Enterobacteriaceae. In Escherichia coli K-12 QseF activates transcription of glmY and of rpoE from Sigma 54-dependent promoters by binding to upstream activating sequences. Small RNA GlmY and RpoE (Sigma 24) are important regulators of cell envelope homeostasis. In pathogenic Enterobacteriaceae QseE/QseF are required for virulence. In enterohemorrhagic E. coli QseE was reported to sense the host hormone epinephrine and to regulate virulence genes post-transcriptionally through employment of GlmY. The qseEGF operon contains a third gene, qseG, which encodes a lipoprotein attached to the inner leaflet of the outer membrane. Here, we show that QseG is essential and limiting for activity of QseE/QseF in E. coli K-12. Metabolic 32P-labelling followed by pull-down demonstrates that phosphorylation of the receiver domain of QseF in vivo requires QseE as well as QseG. Accordingly, QseG acts upstream and through QseE/QseF by stimulating activity of kinase QseE. 32P-labelling also reveals an additional phosphorylation in the QseF C-terminus of unknown origin, presumably at threonine/serine residue(s). Pulldown and two-hybrid assays demonstrate interaction of QseG with the periplasmic loop of QseE. A mutational screen identifies the Ser58Asn exchange in the periplasmic loop of QseE, which decreases interaction with QseG and concomitantly lowers QseE/QseF activity, indicating that QseG activates QseE by interaction. Finally, epinephrine is shown to have a moderate impact on QseE activity in E. coli K-12. Epinephrine slightly stimulates QseF phosphorylation and thereby glmY transcription, but exclusively during stationary growth and this requires both, QseE and QseG. Our data reveal a three-component signaling system, in which the phosphorylation state of QseE/QseF is governed by interaction with lipoprotein QseG in response to a signal likely derived from the cell envelope.

Selective labeling and unlabeling strategies in protein solid-state NMR spectroscopy

Selective isotope labeling is central in NMR experiments and often allows to push the limits on the systems investigated. It has the advantage to supply additional resolution by diminishing the number of signals in the spectra. This is particularly interesting when dealing with the large protein systems which are currently becoming accessible to solid-state NMR studies. Isotope labeled proteins for NMR experiments are most often expressed in E. coli systems, where bacteria are grown in minimal media supplemented with ¹⁵NH₄Cl and ¹³C-glucose as sole source of nitrogen and carbon. For amino acids selective labeling or unlabeling, specific amino acids are supplemented in the minimal medium. The aim is that they will be incorporated in the protein by the bacteria. However, E. coli amino-acid anabolism and catabolism tend to interconnect different pathways, remnant of a subway system. These connections lead to inter conversion between amino acids, called scrambling. A thorough understanding of the involved pathways is thus important to obtain the desired labeling schemes, as not all combinations of amino acids are adapted. We present here a detailed overview of amino-acid metabolism in this context. Each amino-acid pathway is described in order to define accessible combinations for ¹³C or ¹⁵N specific labeling or unlabeling. Using as example the ABC transporter BmrA, a membrane protein of 600 residues, we demonstrate how these strategies can be applied. Indeed, even though there is no size limit in solid-state NMR, large (membrane) proteins are still a challenge due to heavy signal overlap. To initiate resonance assignment in these large systems, we describe how selectively labeled samples can be obtained with the addition of labeled or unlabeled amino acids in the medium. The reduced spectral overlap enabled us to identify typical spectral fingerprints and to initiate sequential assignment using the more sensitive 2D DARR experiments with long mixing time showing inter-residue correlations.

Mining Novel Constitutive Promoter Elements in Soil Metagenomic Libraries in Escherichia coli

Although functional metagenomics has been widely employed for the discovery of genes relevant to biotechnology and biomedicine, its potential for assessing the diversity of transcriptional regulatory elements of microbial communities has remained poorly explored. Here, we experimentally mined novel constitutive promoter sequences in metagenomic libraries by combining a bi-directional reporter vector, high-throughput fluorescence assays and predictive computational methods. Through the expression profiling of fluorescent clones from two independent soil sample libraries, we have analyzed the regulatory dynamics of 260 clones with candidate promoters as a set of active metagenomic promoters in the host Escherichia coli. Through an in-depth analysis of selected clones, we were able to further explore the architecture of metagenomic fragments and to report the presence of multiple promoters per fragment with a dominant promoter driving the expression profile. These approaches resulted in the identification of 33 novel active promoters from metagenomic DNA originated from very diverse phylogenetic groups. The in silico and in vivo analysis of these individual promoters allowed the generation of a constitutive promoter consensus for exogenous sequences recognizable by E. coli in metagenomic studies. The results presented here demonstrates the potential of functional metagenomics for exploring environmental bacterial communities as a source of novel regulatory genetic parts to expand the toolbox for microbial engineering.

The Complex Rcs Regulatory Cascade

RcsB, a response regulator of the FixJ/NarL family, is at the center of a complex network of regulatory inputs and outputs. Cell surface stress is sensed by an outer membrane lipoprotein, RcsF, which regulates interactions of the inner membrane protein IgaA, lifting negative regulation of a phosphorelay. In vivo evidence supports a pathway in which histidine kinase RcsC transfers phosphate to phosphotransfer protein RcsD, resulting in phosphorylation of RcsB. RcsB acts either alone or in combination with RcsA to positively regulate capsule synthesis and synthesis of small RNA (sRNA) RprA as well as other genes, and to negatively regulate motility. RcsB in combination with other FixJ/NarL auxiliary proteins regulates yet other functions, independent of RcsB phosphorylation. Proper expression of Rcs and its targets is critical for success of Escherichia coli commensal strains, for proper development of biofilm, and for virulence in some pathogens. New understanding of how the Rcs phosphorelay works provides insight into the flexibility of the two-component system paradigm.

Non-equilibrium repressor binding kinetics link DNA damage dose to transcriptional timing within the SOS gene network

Biochemical pathways are often genetically encoded as simple transcription regulation networks, where one transcription factor regulates the expression of multiple genes in a pathway. The relative timing of each promoter’s activation and shut-off within the network can impact physiology. In the DNA damage repair pathway (known as the SOS response) of Escherichia coli, approximately 40 genes are regulated by the LexA repressor. After a DNA damaging event, LexA degradation triggers SOS gene transcription, which is temporally separated into subsets of ‘early’, ‘middle’, and ‘late’ genes. Although this feature plays an important role in regulating the SOS response, both the range of this separation and its underlying mechanism are not experimentally defined. Here we show that, at low doses of DNA damage, the timing of promoter activities is not separated. Instead, timing differences only emerge at higher levels of DNA damage and increase as a function of DNA damage dose. To understand mechanism, we derived a series of synthetic SOS gene promoters which vary in LexA-operator binding kinetics, but are otherwise identical, and then studied their activity over a large dose-range of DNA damage. In distinction to established models based on rapid equilibrium assumptions, the data best fit a kinetic model of repressor occupancy at promoters, where the drop in cellular LexA levels associated with higher doses of DNA damage leads to non-equilibrium binding kinetics of LexA at operators. Operators with slow LexA binding kinetics achieve their minimal occupancy state at later times than operators with fast binding kinetics, resulting in a time separation of peak promoter activity between genes. These data provide insight into this remarkable feature of the SOS pathway by demonstrating how a single transcription factor can be employed to control the relative timing of each gene’s transcription as a function of stimulus dose.

As the precise timing of gene expression is critical for cells to respond and adapt to new environments, it is important to understand the underlying mechanisms which control this timing. In this report, we studied the timing of transcription for genes in the bacterial DNA damage repair pathway (known as the SOS response), a regulatory system where each gene is controlled by the same transcriptional repressor, LexA. By specifically isolating the role of the LexA binding interaction at SOS gene promoters, we found a relationship between the amount of DNA damage incurred by the cell, LexA binding kinetics at a promoter, and the timing of promoter activation. Our data fit a kinetic model that reveals how a disequilibrium between the LexA-operator binding reaction and cellular LexA concentrations causes timing differences between genes to emerge only at higher doses of DNA damage. Taken together, we show that non-equilibrium DNA binding kinetics is the mechanism by which a single transcription factor can modulate timing differences across an entire network of genes as a function of stimulus dose.

Influence of Glucose Availability and CRP Acetylation on the Genome-Wide Transcriptional Response of Escherichia coli: Assessment by an Optimized Factorial Microarray Analysis

Background: While in eukaryotes acetylation/deacetylation regulation exerts multiple pleiotropic effects, in Escherichia coli it seems to be more limited and less known. Hence, we aimed to progress in the characterization of this regulation by dealing with three convergent aspects: the effector enzymes involved, the master regulator CRP, and the dependence on glucose availability.

Methods: The transcriptional response of E. coli BW25113 was analyzed across 14 relevant scenarios. These conditions arise when the wild type and four isogenic mutants (defective in deacetylase CobB, defective in N(ε)-lysine acetyl transferase PatZ, Q- and R-type mutants of protein CRP) are studied under three levels of glucose availability (glucose-limited chemostat and glucose-excess or glucose-exhausted in batch culture). The Q-type emulates a permanent stage of CRP^acetylated, whereas the R-type emulates a permanent stage of CRP^deacetylated. The data were analyzed by an optimized factorial microarray method (Q-GDEMAR).

Results: (a) By analyzing one mutant against the other, we were able to unravel the true genes that participate in the interaction between ΔcobB/ΔpatZ mutations and glucose availability; (b) Increasing stages of glucose limitation appear to be associated with the up-regulation of specific sets of target genes rather than with the loss of genes present when glucose is in excess; (c) Both CRP^deacetylated and CRP^acetylated produce extensive changes in specific subsets of genes, but their number and identity depend on the glucose availability; (d) In other sub-sets of genes, the transcriptional effect of CRP seems to be independent of its acetylation or deacetylation; (e) Some specific ontology functions can be associated with each of the different sets of genes detected herein.

Conclusions: CRP cannot be thought of only as an effector of catabolite repression, because it acts along all the glucose conditions tested (excess, limited, and exhausted), exerting both positive and negative effects through different sets of genes. Acetylation of CRP does not seem to be a binary form of regulation, as there is not a univocal relationship between its activation/inhibitory effect and its acetylation/deacetylation stage. All the combinatorial possibilities are observed. During the exponential growth phase, CRP also exerts a very significant transcriptional effect, mainly on flagellar assembly and chemotaxis (FDR = 7.2 × 10⁻⁴⁴).

Simulating biological processes: stochastic physics from whole cells to colonies

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a 'minimal cell'. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.

High-Level dCas9 Expression Induces Abnormal Cell Morphology in Escherichia coli

Along with functional advances in the use of CRISPR/Cas9 for genome editing, endonuclease-deficient Cas9 (dCas9) has provided a versatile molecular tool for exploring gene functions. In principle, differences in cell phenotypes that result from the RNA-guided modulation of transcription levels by dCas9 are critical for inferring with gene function; however, the effect of intracellular dCas9 expression on bacterial morphology has not been systematically elucidated. Here, we observed unexpected morphological changes in Escherichia coli mediated by dCas9, which were then characterized using RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq). Growth rates were severely decreased, to approximately 50% of those of wild type cells, depending on the expression levels of dCas9. Cell shape was changed to abnormal filamentous morphology, indicating that dCas9 affects bacterial cell division. RNA-Seq revealed that 574 genes were differentially transcribed in the presence of high expression levels of dCas9. Genes associated with cell division were upregulated, which was consistent with the observed atypical morphologies. In contrast, 221 genes were downregulated, and these mostly encoded proteins located in the cell membrane. Further, ChIP-Seq results showed that dCas9 directly binds upstream of 37 genes without single-guide RNA, including fimA, which encodes bacterial fimbriae. These results support the fact that dCas9 has critical effects on cell division as well as inner and outer membrane structure. Thus, to precisely understand gene functions using dCas9-driven transcriptional modulation, the regulation of intracellular levels of dCas9 is pivotal to avoid unexpected morphological changes in E. coli.

CapZyme-Seq Comprehensively Defines Promoter-Sequence Determinants for RNA 5' Capping with NAD<sup/>

Nucleoside-containing metabolites such as NAD⁺ can be incorporated as 5' caps on RNA by serving as non-canonical initiating nucleotides (NCINs) for transcription initiation by RNA polymerase (RNAP). Here, we report CapZyme-seq, a high-throughput-sequencing method that employs NCIN-decapping enzymes NudC and Rai1 to detect and quantify NCIN-capped RNA. By combining CapZyme-seq with multiplexed transcriptomics, we determine efficiencies of NAD⁺ capping by Escherichia coli RNAP for ∼16,000 promoter sequences. The results define preferred transcription start site (TSS) positions for NAD⁺ capping and define a consensus promoter sequence for NAD⁺ capping: HRRASWW (TSS underlined). By applying CapZyme-seq to E. coli total cellular RNA, we establish that sequence determinants for NCIN capping in vivo match the NAD⁺-capping consensus defined in vitro, and we identify and quantify NCIN-capped small RNAs (sRNAs). Our findings define the promoter-sequence determinants for NCIN capping with NAD⁺ and provide a general method for analysis of NCIN capping in vitro and in vivo.

Evolutionary Convergence of Pathway-Specific Enzyme Expression Stoichiometry

Coexpression of proteins in response to pathway-inducing signals is the founding paradigm of gene regulation. However, it remains unexplored whether the relative abundance of co-regulated proteins requires precise tuning. Here, we present large-scale analyses of protein stoichiometry and corresponding regulatory strategies for 21 pathways and 67-224 operons in divergent bacteria separated by 0.6-2 billion years. Using end-enriched RNA-sequencing (Rend-seq) with single-nucleotide resolution, we found that many bacterial gene clusters encoding conserved pathways have undergone massive divergence in transcript abundance and architectures via remodeling of internal promoters and terminators. Remarkably, these evolutionary changes are compensated post-transcriptionally to maintain preferred stoichiometry of protein synthesis rates. Even more strikingly, in eukaryotic budding yeast, functionally analogous proteins that arose independently from bacterial counterparts also evolved to convergent in-pathway expression. The broad requirement for exact protein stoichiometries despite regulatory divergence provides an unexpected principle for building biological pathways both in nature and for synthetic activities.

Functional Prediction of Hypothetical Transcription Factors of Escherichia coli K-12 Based on Expression Data

The repertoire of 304 DNA-binding transcription factors (TFs) in Escherichia coli K-12 has been described recently, with 196 TFs experimentally characterized and 108 proteins predicted by sequence comparisons. Based on 303 expression profile patterns retrieved from the Colombos database 12 clusters were identified, including hypothetical and experimentally characterized TFs, using a spectral clustering algorithm based on a 3NN graph built using 14 principal components that represent 65% of the variance of the expression data. In a posterior step, clusters were characterized in terms of their associated overrepresented functions, based on KEGG, Supfam annotations and Pfam assignments among other functional categories using an enrichment test, reinforcing the notion that the identified clusters are functionally similar among them. Based on these data, the we identified 12 clusters in which hypothetical and known TFs share similar regulatory and physiological functions, such as module associations of toxin-antitoxin (TA) systems with DNA repair mechanisms, amino acid biosynthesis, and carbon metabolism/transport, among others. This analysis has increased our knowledge about gene regulation in E. coli K-12 and can be further expanded to other organisms.

Burden-driven feedback control of gene expression

Cells use feedback regulation to ensure robust growth despite fluctuating demands for resources and differing environmental conditions. However, the expression of foreign proteins from engineered constructs is an unnatural burden that cells are not adapted for. Here we combined RNA-seq with an in vivo assay to identify the major transcriptional changes that occur in Escherichia coli when inducible synthetic constructs are expressed. We observed that native promoters related to the heat-shock response activated expression rapidly in response to synthetic expression, regardless of the construct. Using these promoters, we built a dCas9-based feedback-regulation system that automatically adjusts the expression of a synthetic construct in response to burden. Cells equipped with this general-use controller maintained their capacity for native gene expression to ensure robust growth and thus outperformed unregulated cells in terms of protein yield in batch production. This engineered feedback is to our knowledge the first example of a universal, burden-based biomolecular control system and is modular, tunable and portable.

Evaluation of reaction gap-filling accuracy by randomization

Background

Completion of genome-scale flux-balance models using computational reaction gap-filling is a widely used approach, but its accuracy is not well known.

Results

We report on computational experiments of reaction gap filling in which we generated degraded versions of the EcoCyc-20.0-GEM model by randomly removing flux-carrying reactions from a growing model. We gap-filled the degraded models and compared the resulting gap-filled models with the original model. Gap-filling was performed by the Pathway Tools MetaFlux software using its General Development Mode (GenDev) and its Fast Development Mode (FastDev). We explored 12 GenDev variants including two linear solvers (SCIP and CPLEX) for solving the Mixed Integer Linear Programming (MILP) problems for gap filling; three different sets of linear constraints were applied; and two MILP methods were implemented. We compared these 13 variants according to accuracy, speed, and amount of information returned to the user.

Conclusions

We observed large variation among the performance of the 13 gap-filling variants. Although no variant was best in all dimensions, we found one variant that was fast, accurate, and returned more information to the user. Some gap-filling variants were inaccurate, producing solutions that were non-minimum or invalid (did not enable model growth). The best GenDev variant showed a best average precision of 87% and a best average recall of 61%. FastDev showed an average precision of 71% and an average recall of 59%. Thus, using the most accurate variant, approximately 13% of the gap-filled reactions were incorrect (were not the reactions removed from the model), and 39% of gap-filled reactions were not found, suggesting that curation is still an important aspect of metabolic-model development.

Transcriptome Profiling Reveals Interplay of Multifaceted Stress Response in Escherichia coli on Exposure to Glutathione and Ciprofloxacin

The emergence and spread of multidrug-resistant bacterial strains have serious medical and clinical consequences. In addition, the rate of discovery of new therapeutic antibiotics has been inadequate in last few decades. Fluoroquinolone antibiotics such as ciprofloxacin represent a precious therapeutic resource in the fight against bacterial pathogens. However, these antibiotics have been gradually losing their appeal due to the emergence and buildup of resistance to them. In this report, we shed light on the genome-level expression changes in bacteria with respect to glutathione (GSH) exposure which act as a trigger for fluoroquinolone antibiotic resistance. The knowledge about different bacterial stress response pathways under conditions of exposure to the conditions described above and potential points of cross talk between them could help us in understanding and formulating the conditions under which buildup and spread of antibiotic resistance could be minimized. Our findings are also relevant because GSH-induced genome-level expression changes have not been reported previously for E. coli.

We have previously reported that supplementation of exogenous glutathione (GSH) promotes ciprofloxacin resistance in Escherichia coli by neutralizing antibiotic-induced oxidative stress and by enhancing the efflux of antibiotic. In the present study, we used a whole-genome microarray as a tool to analyze the system-level transcriptomic changes of E. coli on exposure to GSH and/or ciprofloxacin. The microarray data revealed that GSH supplementation affects redox function, transport, acid shock, and virulence genes of E. coli. The data further highlighted the interplay of multiple underlying stress response pathways (including those associated with the genes mentioned above and DNA damage repair genes) at the core of GSH, offsetting the effect of ciprofloxacin in E. coli. The results of a large-scale validation of the transcriptomic data using reverse transcription-quantitative PCR (RT-qPCR) analysis for 40 different genes were mostly in agreement with the microarray results. The altered growth profiles of 12 different E. coli strains carrying deletions in the specific genes mentioned above with GSH and/or ciprofloxacin supplementation implicate these genes in the GSH-mediated phenotype not only at the molecular level but also at the functional level. We further associated GSH supplementation with increased acid shock survival of E. coli on the basis of our transcriptomic data. Taking the data together, it can be concluded that GSH supplementation influences the expression of genes of multiple stress response pathways apart from its effect(s) at the physiological level to counter the action of ciprofloxacin in E. coli.

IMPORTANCE The emergence and spread of multidrug-resistant bacterial strains have serious medical and clinical consequences. In addition, the rate of discovery of new therapeutic antibiotics has been inadequate in last few decades. Fluoroquinolone antibiotics such as ciprofloxacin represent a precious therapeutic resource in the fight against bacterial pathogens. However, these antibiotics have been gradually losing their appeal due to the emergence and buildup of resistance to them. In this report, we shed light on the genome-level expression changes in bacteria with respect to glutathione (GSH) exposure which act as a trigger for fluoroquinolone antibiotic resistance. The knowledge about different bacterial stress response pathways under conditions of exposure to the conditions described above and potential points of cross talk between them could help us in understanding and formulating the conditions under which buildup and spread of antibiotic resistance could be minimized. Our findings are also relevant because GSH-induced genome-level expression changes have not been reported previously for E. coli.

Proteome Data Improves Protein Function Prediction in the Interactome of Helicobacter pylori

Helicobacter pylori is a common pathogen that is estimated to infect half of the human population, causing several diseases such as duodenal ulcer. Despite one of the first pathogens to be sequenced, its proteome remains poorly characterized as about one-third of its proteins have no functional annotation. Here, we integrate and analyze known protein interactions with proteomic and genomic data from different sources. We find that proteins with similar abundances tend to interact. Such an observation is accompanied by a trend of interactions to appear between proteins of similar functions, although some show marked cross-talk to others. Protein function prediction with protein interactions is significantly improved when interactions from other bacteria are included in our network, allowing us to obtain putative functions of more than 300 poorly or previously uncharacterized proteins. Proteins that are critical for the topological controllability of the underlying network are significantly enriched with genes that are up-regulated in the spiral compared with the coccoid form of H. pylori Determining their evolutionary conservation, we present evidence that 80 protein complexes are identical in composition with their counterparts in Escherichia coli, while 85 are partially conserved and 120 complexes are completely absent. Furthermore, we determine network clusters that coincide with related functions, gene essentiality, genetic context, cellular localization, and gene expression in different cellular states.

Reconfigurable Analog Signal Processing by Living Cells

Living cells are known for their capacity for versatile signal processing, particularly the ability to respond differently to the same stimuli using biochemical networks that integrate environmental signals and reconfigure their dynamic responses. However, the complexity of natural biological networks confounds the discovery of fundamental mechanisms behind versatile signaling. Here, we study one specific aspect of reconfigurable signal processing in which a minimal biological network integrates two signals, using one to reconfigure the network's transfer function with respect to the other, producing an emergent switch between induction and repression. In contrast to known mechanisms, the new mechanism reconfigures transfer functions through genetic networks without extensive protein-protein interactions. These results provide a novel explanation for the versatility of genetic programs, and suggest a new mechanism of signal integration that may govern flexibility and plasticity of gene expression.

Bacterial RNA Biology on a Genome Scale

Bacteria are an exceedingly diverse group of organisms whose molecular exploration is experiencing a renaissance. While the classical view of bacterial gene expression was relatively simple, the emerging view is more complex, encompassing extensive post-transcriptional control involving riboswitches, RNA thermometers, and regulatory small RNAs (sRNAs) associated with the RNA-binding proteins CsrA, Hfq, and ProQ, as well as CRISPR/Cas systems that are programmed by RNAs. Moreover, increasing interest in members of the human microbiota and environmental microbial communities has highlighted the importance of understudied bacterial species with largely unknown transcriptome structures and RNA-based control mechanisms. Collectively, this creates a need for global RNA biology approaches that can rapidly and comprehensively analyze the RNA composition of a bacterium of interest. We review such approaches with a focus on RNA-seq as a versatile tool to investigate the different layers of gene expression in which RNA is made, processed, regulated, modified, translated, and turned over.

Improving the success and impact of the metabolic engineering design, build, test, learn cycle by addressing proteins of unknown function

Rational, predictive metabolic engineering of organisms requires an ability to associate biological activity to the corresponding gene(s). Despite extensive advances in the 20 years since the Escherichia coli genome was published, there are still gaps in our knowledge of protein function. The substantial amount of data that has been published, such as: omics-level characterization in a myriad of conditions; genome-scale libraries; and evolution and genome sequencing, provide means of identifying and prioritizing proteins for characterization. This review describes the scale of this knowledge gap, demonstrates the benefit of addressing the knowledge gap, and demonstrates the availability of interesting candidates for characterization.

Roles of elusive translational GTPases come to light and inform on the process of ribosome biogenesis in bacteria

Protein synthesis relies on several translational GTPases (trGTPases), related proteins that couple the hydrolysis of GTP to specific molecular events on the ribosome. Most bacterial trGTPases, including IF2, EF-Tu, EF-G and RF3, play well-known roles in translation. The cellular functions of LepA (also termed EF4) and BipA (also termed TypA), conversely, have remained enigmatic. Recent studies provide compelling in vivo evidence that LepA and BipA function in biogenesis of the 30S and 50S subunit respectively. These findings have important implications for ribosome biogenesis in bacteria. Because the GTPase activity of each of these proteins depends on interactions with both ribosomal subunits, some portion of 30S and 50S assembly must occur in the context of the 70S ribosome. In this review, we introduce the trGTPases of bacteria, describe the new functional data on LepA and BipA, and discuss the how these findings shape our current view of ribosome biogenesis in bacteria.

Antibiotic-Induced Changes to the Host Metabolic Environment Inhibit Drug Efficacy and Alter Immune Function

Bactericidal antibiotics alter microbial metabolism as part of their lethality and can damage mitochondria in mammalian cells. In addition, antibiotic susceptibility is sensitive to extracellular metabolites, but it remains unknown whether metabolites present at an infection site can affect either treatment efficacy or immune function. Here, we quantify local metabolic changes in the host microenvironment following antibiotic treatment for a peritoneal Escherichia coli infection. Antibiotic treatment elicits microbiome-independent changes in local metabolites, but not those distal to the infection site, by acting directly on host cells. The metabolites induced during treatment, such as AMP, reduce antibiotic efficacy and enhance phagocytic killing. Moreover, antibiotic treatment impairs immune function by inhibiting respiratory activity in immune cells. Collectively, these results highlight the immunomodulatory potential of antibiotics and reveal the local metabolic microenvironment to be an important determinant of infection resolution.

Knowledge-based biomedical Data Science

Computational manipulation of knowledge is an important, and often under-appreciated, aspect of biomedical Data Science. The first Data Science initiative from the US National Institutes of Health was entitled "Big Data to Knowledge (BD2K)." The main emphasis of the more than $200M allocated to that program has been on "Big Data;" the "Knowledge" component has largely been the implicit assumption that the work will lead to new biomedical knowledge. However, there is long-standing and highly productive work in computational knowledge representation and reasoning, and computational processing of knowledge has a role in the world of Data Science. Knowledge-based biomedical Data Science involves the design and implementation of computer systems that act as if they knew about biomedicine. There are many ways in which a computational approach might act as if it knew something: for example, it might be able to answer a natural language question about a biomedical topic, or pass an exam; it might be able to use existing biomedical knowledge to rank or evaluate hypotheses; it might explain or interpret data in light of prior knowledge, either in a Bayesian or other sort of framework. These are all examples of automated reasoning that act on computational representations of knowledge. After a brief survey of existing approaches to knowledge-based data science, this position paper argues that such research is ripe for expansion, and expanded application.

Discovery of new RNA classes and global RNA-binding proteins

The identification of new RNA functions and the functional annotation of transcripts in genomes represent exciting yet challenging endeavours of modern biology. Crucial insights into the biological roles of RNA molecules can be gained from the identification of the proteins with which they form specific complexes. Modern interactome techniques permit to profile RNA-protein interactions in a genome-wide manner and identify new RNA classes associated with globally acting RNA-binding proteins. Applied to a variety of organisms, these methods are already revolutionising our understanding of RNA-mediated biological processes. Here, we focus on one such approach-Gradient sequencing or Grad-seq-which has recently guided the discovery of protein ProQ and its associated small RNAs as a new domain of post-transcriptional control in bacteria.

Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach

Computational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems.

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

The multiple antibiotic resistance operon of enteric bacteria controls DNA repair and outer membrane integrity

The multiple antibiotic resistance (mar) operon of Escherichia coli is a paradigm for chromosomally encoded antibiotic resistance in enteric bacteria. The locus is recognised for its ability to modulate efflux pump and porin expression via two encoded transcription factors, MarR and MarA. Here we map binding of these regulators across the E. coli genome and identify an extensive mar regulon. Most notably, MarA activates expression of genes required for DNA repair and lipid trafficking. Consequently, the mar locus reduces quinolone-induced DNA damage and the ability of tetracyclines to traverse the outer membrane. These previously unrecognised mar pathways reside within a core regulon, shared by most enteric bacteria. Hence, we provide a framework for understanding multidrug resistance, mediated by analogous systems, across the Enterobacteriaceae. Transcription factors MarR and MarA confer multidrug resistance in enteric bacteria by modulating efflux pump and porin expression. Here, Sharma et al. show that MarA also upregulates genes required for lipid trafficking and DNA repair, thus reducing antibiotic entry and quinolone-induced DNA damage.

Eukaryotic and prokaryotic promoter databases as valuable tools in exploring the regulation of gene transcription: a comprehensive overview

The complete exploration of the regulation of gene expression remains one of the top-priority goals for researchers. As the regulation is mainly controlled at the level of transcription by promoters, study on promoters and findings are of great importance. This review summarizes forty selected databases that centralize experimental and theoretical knowledge regarding the organization of promoters, interacting transcription factors (TFs) and microRNAs (miRNAs) in many eukaryotic and prokaryotic species. The presented databases offer researchers valuable support in elucidating the regulation of gene transcription.

Update notifications for the BioCyc collection of databases

We describe the BioCyc update notifications service, a new mechanism to keep researchers informed of the latest developments in their areas of interest. BioCyc.org combines databases for 9,300 sequenced organisms that integrate genome, metabolic pathway, and regulatory information with extensive bioinformatics tools. Users of the BioCyc website can register their specific areas of interest online by specifying a set of genes, pathways and/or Gene Ontology terms. Then, when significant new information becomes available in a BioCyc database in a user’s interest areas (usually due to curation), an email notification is sent to the user. The BioCyc ontology is leveraged to identify changes that are both relevant to a user’s specified interests and worthy of notification. Every effort is made to ensure that the resulting email text is both concise and informative, with links to relevant BioCyc pages.

Database URL:https://BioCyc.org, https://EcoCyc.org, https://MetaCyc.org