The EcoCyc Database

Control of pili synthesis and putrescine homeostasis in Escherichia coli

Polyamines are biologically ubiquitous cations that bind to nucleic acids, ribosomes, and phospholipids and, thereby, modulate numerous processes, including surface motility in Escherichia coli. We characterized the metabolic pathways that contribute to polyamine-dependent control of surface motility in the commonly used strain W3110 and the transcriptome of a mutant lacking a putrescine synthetic pathway that was required for surface motility. Genetic analysis showed that surface motility required type 1 pili, the simultaneous presence of two independent putrescine anabolic pathways, and modulation by putrescine transport and catabolism. An immunological assay for FimA-the major pili subunit, reverse transcription quantitative PCR of fimA, and transmission electron microscopy confirmed that pili synthesis required putrescine. Comparative RNAseq analysis of a wild type and ΔspeB mutant which exhibits impaired pili synthesis showed that the latter had fewer transcripts for pili structural genes and for fimB which codes for the phase variation recombinase that orients the fim operon promoter in the ON phase, although loss of speB did not affect the promoter orientation. Results from the RNAseq analysis also suggested (a) changes in transcripts for several transcription factor genes that affect fim operon expression, (b) compensatory mechanisms for low putrescine which implies a putrescine homeostatic network, and (c) decreased transcripts of genes for oxidative energy metabolism and iron transport which a previous genetic analysis suggests may be sufficient to account for the pili defect in putrescine synthesis mutants. We conclude that pili synthesis requires putrescine and putrescine concentration is controlled by a complex homeostatic network that includes the genes of oxidative energy metabolism.

Spatio-temporal organization of the E. coli chromosome from base to cellular length scales

Escherichia coli has been a vital model organism for studying chromosomal structure, thanks, in part, to its small and circular genome (4.6 million base pairs) and well-characterized biochemical pathways. Over the last several decades, we have made considerable progress in understanding the intricacies of the structure and subsequent function of the E. coli nucleoid. At the smallest scale, DNA, with no physical constraints, takes on a shape reminiscent of a randomly twisted cable, forming mostly random coils but partly affected by its stiffness. This ball-of-spaghetti-like shape forms a structure several times too large to fit into the cell. Once the physiological constraints of the cell are added, the DNA takes on overtwisted (negatively supercoiled) structures, which are shaped by an intricate interplay of many proteins carrying out essential biological processes. At shorter length scales (up to about 1 kb), nucleoid-associated proteins organize and condense the chromosome by inducing loops, bends, and forming bridges. Zooming out further and including cellular processes, topological domains are formed, which are flanked by supercoiling barriers. At the megabase-scale both large, highly self-interacting regions (macrodomains) and strong contacts between distant but co-regulated genes have been observed. At the largest scale, the nucleoid forms a helical ellipsoid. In this review, we will explore the history and recent advances that pave the way for a better understanding of E. coli chromosome organization and structure, discussing the cellular processes that drive changes in DNA shape, and what contributes to compaction and formation of dynamic structures, and in turn how bacterial chromatin affects key processes such as transcription and replication.

Designing a simple and efficient phage biocontainment system using the amber suppressor initiator tRNA

Multidrug-resistant infections are becoming increasingly prevalent worldwide. One of the fastest-emerging alternative and adjuvant therapies being proposed is phage therapy. Naturally isolated phages are used in the vast majority of phage therapy treatments today. Engineered phages are being developed to enhance the effectiveness of phage therapy, but concerns over their potential escape remain a salient issue. To address this problem, we designed a biocontained phage system based on conditional replication using amber stop codon suppression. This system can be easily installed on any natural phage with a known genome sequence. To test the system, we individually mutated the start codons of three essential capsid genes in phage φX174 to the amber stop codon (UAG). These phages were able to efficiently infect host cells expressing the amber initiator tRNA, which suppresses the amber stop codon and initiates translation at TAG stop codons. The amber phage mutants were also able to successfully infect host cells and reduce their population on solid agar and liquid culture but could not produce infectious particles in the absence of the amber initiator tRNA or complementing capsid gene. We did not detect any growth-inhibiting effects on E. coli strains known to lack a receptor for φX174 and we showed that engineered phages have a limited propensity for reversion. The approach outlined here may be useful to control engineered phage replication in both the lab and clinic.

Deciphering transcript architectural complexity in bacteria and archaea

RNA transcripts are potential therapeutic targets, yet bacterial transcripts have uncharacterized biodiversity. We developed an algorithm for transcript prediction called tp.py using it to predict transcripts (mRNA and other RNAs) in Escherichia coli K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria), Listeria monocytogenes strains Scott A and RO15 (Bacteria:Firmicute), Pseudomonas aeruginosa strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and Haloferax volcanii (Archaea:Halobacteria). From >5 million E. coli K12 and >3 million E. coli E2348/69 newly generated Oxford Nanopore Technologies direct RNA sequencing reads, 2,487 K12 mRNAs and 1,844 E2348/69 mRNAs were predicted, with the K12 mRNAs containing more than half of the predicted E. coli K12 proteins. While the number of predicted transcripts varied by strain based on the amount of sequence data used, across all strains examined, the predicted average size of the mRNAs was 1.6-1.7 kbp, while the median size of the 5'- and 3'-untranslated regions (UTRs) were 30-90 bp. Given the lack of bacterial and archaeal transcript annotation, most predictions were of novel transcripts, but we also predicted many previously characterized mRNAs and ncRNAs, including post-transcriptionally generated transcripts and small RNAs associated with pathogenesis in the E. coli E2348/69 LEE pathogenicity islands. We predicted small transcripts in the 100-200 bp range as well as >10 kbp transcripts for all strains, with the longest transcript for two of the seven strains being the nuo operon transcript, and for another two strains it was a phage/prophage transcript. This quick, easy, and reproducible method will facilitate the presentation of transcripts, and UTR predictions alongside coding sequences and protein predictions in bacterial genome annotation as important resources for the research community.IMPORTANCEOur understanding of bacterial and archaeal genes and genomes is largely focused on proteins since there have only been limited efforts to describe bacterial/archaeal RNA diversity. This contrasts with studies on the human genome, where transcripts were sequenced prior to the release of the human genome over two decades ago. We developed software for the quick, easy, and reproducible prediction of bacterial and archaeal transcripts from Oxford Nanopore Technologies direct RNA sequencing data. These predictions are urgently needed for more accurate studies examining bacterial/archaeal gene regulation, including regulation of virulence factors, and for the development of novel RNA-based therapeutics and diagnostics to combat bacterial pathogens, like those with extreme antimicrobial resistance.

Core and accessory genomic traits of Vibrio cholerae O1 drive lineage transmission and disease severity

In Bangladesh, Vibrio cholerae lineages are undergoing genomic evolution, with increased virulence and spreading ability. However, our understanding of the genomic determinants influencing lineage transmission and disease severity remains incomplete. Here, we developed a computational framework using machine-learning, genome scale metabolic modelling (GSSM) and 3D structural analysis, to identify V. cholerae genomic traits linked to lineage transmission and disease severity. We analysed in-patients isolates from six Bangladeshi regions (2015-2021), and uncovered accessory genes and core SNPs unique to the most recent dominant lineage, with virulence, motility and bacteriophage resistance functions. We also found a strong correlation between V. cholerae genomic traits and disease severity, with some traits overlapping those driving lineage transmission. GSMM and 3D structure analysis unveiled a complex interplay between transcription regulation, protein interaction and stability, and metabolic networks, associated to lifestyle adaptation, intestinal colonization, acid tolerance and symptom severity. Our findings support advancing therapeutics and targeted interventions to mitigate cholera spread.

Global regulation via modulation of ribosome pausing by the ABC-F protein EttA

Having multiple rounds of translation of the same mRNA creates dynamic complexities along with opportunities for regulation related to ribosome pausing and stalling at specific sequences. Yet, mechanisms controlling these critical processes and the principles guiding their evolution remain poorly understood. Through genetic, genomic, physiological, and biochemical approaches, we demonstrate that regulating ribosome pausing at specific amino acid sequences can produce ~2-fold changes in protein expression levels which strongly influence cell growth and therefore evolutionary fitness. We demonstrate, both in vivo and in vitro, that the ABC-F protein EttA directly controls the translation of mRNAs coding for a subset of enzymes in the tricarboxylic acid (TCA) cycle and its glyoxylate shunt, which modulates growth in some chemical environments. EttA also modulates expression of specific proteins involved in metabolically related physiological and stress-response pathways. These regulatory activities are mediated by EttA rescuing ribosomes paused at specific patterns of negatively charged residues within the first 30 amino acids of nascent proteins. We thus establish a unique global regulatory paradigm based on sequence-specific modulation of translational pausing.

Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers in Escherichia coli and their application for microbial source tracking

Microbial source tracking leverages a wide range of approaches designed to trace the origins of fecal contamination in aquatic environments. Although source tracking methods are typically employed within the laboratory setting, computational techniques can be leveraged to advance microbial source tracking methodology. Herein, we present a logic regression-based supervised learning approach for the discovery of source-informative genetic markers within intergenic regions across the Escherichia coli genome that can be used for source tracking. With just single intergenic loci, logic regression was able to identify highly source-specific (i.e., exceeding 97.00%) biomarkers for a wide range of host and niche sources, with sensitivities reaching as high as 30.00%-50.00% for certain source categories, including pig, sheep, mouse, and wastewater, depending on the specific intergenic locus analyzed. Restricting the source range to reflect the most prominent zoonotic sources of E. coli transmission (i.e., bovine, chicken, human, and pig) allowed for the generation of informative biomarkers for all host categories, with specificities of at least 90.00% and sensitivities between 12.50% and 70.00%, using the sequence data from key intergenic regions, including emrKY-evgAS, ibsB-(mdtABCD-baeSR), ompC-rcsDB, and yedS-yedR, that appear to be involved in antibiotic resistance. Remarkably, we were able to use this approach to classify 48 out of 113 river water E. coli isolates collected in Northwestern Sweden as either beaver, human, or reindeer in origin with a high degree of consensus-thus highlighting the potential of logic regression modeling as a novel approach for augmenting current source tracking efforts.IMPORTANCEThe presence of microbial contaminants, particularly from fecal sources, within water poses a serious risk to public health. The health and economic burden of waterborne pathogens can be substantial-as such, the ability to detect and identify the sources of fecal contamination in environmental waters is crucial for the control of waterborne diseases. This can be accomplished through microbial source tracking, which involves the use of various laboratory techniques to trace the origins of microbial pollution in the environment. Building on current source tracking methodology, we describe a novel workflow that uses logic regression, a supervised machine learning method, to discover genetic markers in Escherichia coli, a common fecal indicator bacterium, that can be used for source tracking efforts. Importantly, our research provides an example of how the rise in prominence of machine learning algorithms can be applied to improve upon current microbial source tracking methodology.

Emergence of potentially disinfection-resistant, naturalized Escherichia coli populations across food- and water-associated engineered environments

The Escherichia coli species is comprised of several 'ecotypes' inhabiting a wide range of host and natural environmental niches. Recent studies have suggested that novel naturalized ecotypes have emerged across wastewater treatment plants and meat processing facilities. Phylogenetic and multilocus sequence typing analyses clustered naturalized wastewater and meat plant E. coli strains into two main monophyletic clusters corresponding to the ST635 and ST399 sequence types, with several serotypes identified by serotyping, potentially representing distinct lineages that have naturalized across wastewater treatment plants and meat processing facilities. This evidence, taken alongside ecotype prediction analyses that distinguished the naturalized strains from their host-associated counterparts, suggests these strains may collectively represent a novel ecotype that has recently emerged across food- and water-associated engineered environments. Interestingly, pan-genomic analyses revealed that the naturalized strains exhibited an abundance of biofilm formation, defense, and disinfection-related stress resistance genes, but lacked various virulence and colonization genes, indicating that their naturalization has come at the cost of fitness in the original host environment.

Genetic evidence for functional diversification of gram-negative intermembrane phospholipid transporters

The outer membrane of Gram-negative bacteria is a barrier to chemical and physical stress. Phospholipid transport between the inner and outer membranes has been an area of intense investigation and, in E. coli K-12, it has recently been shown to be mediated by YhdP, TamB, and YdbH, which are suggested to provide hydrophobic channels for phospholipid diffusion, with YhdP and TamB playing the major roles. However, YhdP and TamB have different phenotypes suggesting distinct functions. We investigated these functions using synthetic cold sensitivity (at 30 °C) caused by deletion of yhdP and fadR, a transcriptional regulator controlling fatty acid degradation and unsaturated fatty acid production, but not by ΔtamB ΔfadR or ΔydbH ΔfadR,. Deletion of tamB suppresses the ΔyhdP ΔfadR cold sensitivity suggesting this phenotype is related to phospholipid transport. The ΔyhdP ΔfadR strain shows a greater increase in cardiolipin upon transfer to the non-permissive temperature and genetically lowering cardiolipin levels can suppress cold sensitivity. These data also reveal a qualitative difference between cardiolipin synthases in E. coli, as deletion of clsA and clsC suppresses cold sensitivity but deletion of clsB does not despite lower cardiolipin levels. In addition to increased cardiolipin, increased fatty acid saturation is necessary for cold sensitivity and lowering this level genetically or through supplementation of oleic acid suppresses the cold sensitivity of the ΔyhdP ΔfadR strain. Although indirect effects are possible, we favor the parsimonious hypothesis that YhdP and TamB have differential substrate transport preferences, most likely with YhdP preferentially transporting more saturated phospholipids and TamB preferentially transporting more unsaturated phospholipids. We envision cardiolipin contributing to this transport preference by sterically clogging TamB-mediated transport of saturated phospholipids. Thus, our data provide a potential mechanism for independent control of the phospholipid composition of the inner and outer membranes in response to changing conditions.

ORBIT for E. coli: kilobase-scale oligonucleotide recombineering at high throughput and high efficiency

Microbiology and synthetic biology depend on reverse genetic approaches to manipulate bacterial genomes; however, existing methods require molecular biology to generate genomic homology, suffer from low efficiency, and are not easily scaled to high throughput. To overcome these limitations, we developed a system for creating kilobase-scale genomic modifications that uses DNA oligonucleotides to direct the integration of a non-replicating plasmid. This method, Oligonucleotide Recombineering followed by Bxb-1 Integrase Targeting (ORBIT) was pioneered in Mycobacteria, and here we adapt and expand it for Escherichia coli. Our redesigned plasmid toolkit for oligonucleotide recombineering achieved significantly higher efficiency than λ Red double-stranded DNA recombineering and enabled precise, stable knockouts (≤134 kb) and integrations (≤11 kb) of various sizes. Additionally, we constructed multi-mutants in a single transformation, using orthogonal attachment sites. At high throughput, we used pools of targeting oligonucleotides to knock out nearly all known transcription factor and small RNA genes, yielding accurate, genome-wide, single mutant libraries. By counting genomic barcodes, we also show ORBIT libraries can scale to thousands of unique members (>30k). This work demonstrates that ORBIT for E. coli is a flexible reverse genetic system that facilitates rapid construction of complex strains and readily scales to create sophisticated mutant libraries.

Impact of suppression of the SOS response on protein expression in clinical isolates of Escherichia coli under antimicrobial pressure of ciprofloxacin

Introduction/objective

Suppression of the SOS response in combination with drugs damaging DNA has been proposed as a potential target to tackle antimicrobial resistance. The SOS response is the pathway used to repair bacterial DNA damage induced by antimicrobials such as quinolones. The extent of lexA-regulated protein expression and other associated systems under pressure of agents that damage bacterial DNA in clinical isolates remains unclear. The aim of this study was to assess the impact of this strategy consisting on suppression of the SOS response in combination with quinolones on the proteome profile of Escherichia coli clinical strains.

Materials and methods

Five clinical isolates of E. coli carrying different chromosomally- and/or plasmid-mediated quinolone resistance mechanisms with different phenotypes were selected, with E. coli ATCC 25922 as control strain. In addition, from each clinical isolate and control, a second strain was created, in which the SOS response was suppressed by deletion of the recA gene. Bacterial inocula from all 12 strains were then exposed to 1xMIC ciprofloxacin treatment (relative to the wild-type phenotype for each isogenic pair) for 1 h. Cell pellets were collected, and proteins were digested into peptides using trypsin. Protein identification and label-free quantification were done by liquid chromatography-mass spectrometry (LC-MS) in order to identify proteins that were differentially expressed upon deletion of recA in each strain. Data analysis and statistical analysis were performed using the MaxQuant and Perseus software.

Results

The proteins with the lowest expression levels were: RecA (as control), AphA, CysP, DinG, DinI, GarL, PriS, PsuG, PsuK, RpsQ, UgpB and YebG; those with the highest expression levels were: Hpf, IbpB, TufB and RpmH. Most of these expression alterations were strain-dependent and involved DNA repair processes and nucleotide, protein and carbohydrate metabolism, and transport. In isolates with suppressed SOS response, the number of underexpressed proteins was higher than overexpressed proteins.

Conclusion

High genomic and proteomic variability was observed among clinical isolates and was not associated with a specific resistant phenotype. This study provides an interesting approach to identify new potential targets to combat antimicrobial resistance.

Combined Study of Gene Expression and Chromosome Three-Dimensional Structure in Escherichia coli During Growth Process

The chromosome structure of different bacteria has its unique organization pattern, which plays an important role in maintaining the spatial location relationship between genes and regulating gene expression. Conversely, transcription also plays a global role in regulating the three-dimensional structure of bacterial chromosomes. Therefore, we combine RNA-Seq and Hi-C technology to explore the relationship between chromosome structure changes and transcriptional regulation in E. coli at different growth stages. Transcriptome analysis indicates that E. coli synthesizes many ribosomes and peptidoglycan in the exponential phase. In contrast, E. coli undergoes more transcriptional regulation and catabolism during the stationary phase, reflecting its adaptability to changes in environmental conditions during growth. Analyzing the Hi-C data shows that E. coli has a higher frequency of global chromosomal interaction in the exponential phase and more defined chromosomal interaction domains (CIDs). Still, the long-distance interactions at the replication termination region are lower than in the stationary phase. Combining transcriptome and Hi-C data analysis, we conclude that highly expressed genes are more likely to be distributed in CID boundary regions during the exponential phase. At the same time, most high-expression genes distributed in the CID boundary regions are ribosomal gene clusters, forming clearer CID boundaries during the exponential phase. The three-dimensional structure of chromosome and expression pattern is altered during the growth of E. coli from the exponential phase to the stationary phase, clarifying the synergy between the two regulatory aspects.

Multidrug resistance plasmids commonly reprogram the expression of metabolic genes in Escherichia coli

Multidrug-resistant Escherichia coli is a leading cause of global mortality. Transfer of plasmids carrying genes encoding beta-lactamases, carbapenamases, and colistin resistance between lineages is driving the rising rates of hard-to-treat nosocomial and community infections. Multidrug resistance (MDR) plasmid acquisition commonly causes transcriptional disruption, and while a number of studies have shown strain-specific fitness and transcriptional effects of an MDR plasmid across diverse bacterial lineages, fewer studies have compared the impacts of different MDR plasmids in a common bacterial host. As such, our ability to predict which MDR plasmids are the most likely to be maintained and spread in bacterial populations is limited. Here, we introduced eight diverse MDR plasmids encoding resistances against a range of clinically important antibiotics into E. coli K-12 MG1655 and measured their fitness costs and transcriptional impacts. The scale of the transcriptional responses varied substantially between plasmids, ranging from >650 to <20 chromosomal genes being differentially expressed. However, the scale of regulatory disruption did not correlate significantly with the magnitude of the plasmid fitness cost, which also varied between plasmids. The identities of differentially expressed genes differed between transconjugants, although the expression of certain metabolic genes and functions were convergently affected by multiple plasmids, including the downregulation of genes involved in L-methionine transport and metabolism. Our data show the complexity of the interaction between host genetic background and plasmid genetic background in determining the impact of MDR plasmid acquisition on E. coli.

IMPORTANCE

The increase in infections that are resistant to multiple classes of antibiotics, including those isolates that carry carbapenamases, beta-lactamases, and colistin resistance genes, is of global concern. Many of these resistances are spread by conjugative plasmids. Understanding more about how an isolate responds to an incoming plasmid that encodes antibiotic resistance will provide information that could be used to predict the emergence of MDR lineages. Here, the identification of metabolic networks as being particularly sensitive to incoming plasmids suggests the possible targets for reducing plasmid transfer.

Identification of crucial roles of transcription factor IhfA on high production of free fatty acids in Escherichia coli

Transcription factor engineering has unique advantages in improving the performance of microbial cell factories due to the global regulation of gene transcription. Omics analyses and reverse engineering enable learning and subsequent incorporation of novel design strategies for further engineering. Here, we identify the role of the global regulator IhfA for overproduction of free fatty acids (FFAs) using CRISPRi-facilitated reverse engineering and cellular physiological characterization. From the differentially expressed genes in the ihfAL- strain, a total of 14 beneficial targets that enhance FFAs production by above 20 % are identified, which involve membrane function, oxidative stress, and others. For membrane-related genes, the engineered strains obtain lower cell surface hydrophobicity and increased average length of membrane lipid tails. For oxidative stress-related genes, the engineered strains present decreased reactive oxygen species (ROS) levels. These gene modulations enhance cellular robustness and save cellular resources, contributing to FFAs production. This study provides novel targets and strategies for engineering microbial cell factories with improved FFAs bioproduction.

Why and how to use the SeqCode

The SeqCode, formally called the Code of Nomenclature of Prokaryotes Described from Sequence Data, is a new code of nomenclature in which genome sequences are the nomenclatural types for the names of prokaryotic species. While similar to the International Code of Nomenclature of Prokaryotes (ICNP) in structure and rules of priority, it does not require the deposition of type strains in international culture collections. Thus, it allows for the formation of permanent names for uncultured prokaryotes whose nearly complete genome sequences have been obtained directly from environmental DNA as well as other prokaryotes that cannot be deposited in culture collections. Because the diversity of uncultured prokaryotes greatly exceeds that of readily culturable prokaryotes, the SeqCode is the only code suitable for naming the majority of prokaryotic species. The start date of the SeqCode was January 1, 2022, and the online Registry (https://seqco.de/) was created to ensure valid publication of names. The SeqCode recognizes all names validly published under the ICNP before 2022. After that date, names validly published under the SeqCode compete with ICNP names for priority. As a result, species can have only one name, either from the SeqCode or ICNP, enabling effective communication and the creation of unified taxonomies of uncultured and cultured prokaryotes. The SeqCode is administered by the SeqCode Committee, which is comprised of the SeqCode Community and elected administrative components. Anyone with an interest in the systematics of prokaryotes is encouraged to join the SeqCode Community and participate in the development of this resource.

Randomly barcoded transposon mutant libraries for gut commensals II: Applying libraries for functional genetics

The critical role of the intestinal microbiota in human health and disease is well recognized. Nevertheless, there are still large gaps in our understanding of the functions and mechanisms encoded in the genomes of most members of the gut microbiota. Genome-scale libraries of transposon mutants are a powerful tool to help us address this gap. Recent advances in barcoded transposon mutagenesis have dramatically lowered the cost of mutant fitness determination in hundreds of in vitro and in vivo experimental conditions. In an accompanying review, we discuss recent advances and caveats for the construction of pooled and arrayed barcoded transposon mutant libraries in human gut commensals. In this review, we discuss how these libraries can be used across a wide range of applications, the technical aspects involved, and expectations for such screens.

The EcoCyc Database (2023)

EcoCyc is a bioinformatics database available online at EcoCyc.org that describes the genome and the biochemical machinery of Escherichia coli K-12 MG1655. The long-term goal of the project is to describe the complete molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists and for biologists who work with related microorganisms. The database includes information pages on each E. coli gene product, metabolite, reaction, operon, and metabolic pathway. The database also includes information on the regulation of gene expression, E. coli gene essentiality, and nutrient conditions that do or do not support the growth of E. coli. The website and downloadable software contain tools for the analysis of high-throughput data sets. In addition, a steady-state metabolic flux model is generated from each new version of EcoCyc and can be executed online. The model can predict metabolic flux rates, nutrient uptake rates, and growth rates for different gene knockouts and nutrient conditions. Data generated from a whole-cell model that is parameterized from the latest data on EcoCyc are also available. This review outlines the data content of EcoCyc and of the procedures by which this content is generated.

Control of glnA (glutamine synthetase) expression by urea in non-pathogenic and uropathogenic Escherichia coli

IMPORTANCE

The bacteria that cause urinary tract infections often become resistant to antibiotic treatment, and genes expressed during an infection could suggest non-antibiotic targets. During growth in urine, glnA (specifying glutamine synthetase) expression is high, but our results show that urea induces glnA expression independent of the regulation that responds to nitrogen limitation. Although our results suggest that glnA is an unlikely target for therapy because of variation in urinary components between individuals, our analysis of glnA expression in urine-like environments has revealed previously undescribed layers of regulation. In other words, regulatory mechanisms that are discovered in a laboratory environment do not necessarily operate in the same way in nature.

The roles of nucleoid-associated proteins and topoisomerases in chromosome structure, strand segregation, and the generation of phenotypic heterogeneity in bacteria

How to adapt to a changing environment is a fundamental, recurrent problem confronting cells. One solution is for cells to organize their constituents into a limited number of spatially extended, functionally relevant, macromolecular assemblies or hyperstructures, and then to segregate these hyperstructures asymmetrically into daughter cells. This asymmetric segregation becomes a particularly powerful way of generating a coherent phenotypic diversity when the segregation of certain hyperstructures is with only one of the parental DNA strands and when this pattern of segregation continues over successive generations. Candidate hyperstructures for such asymmetric segregation in prokaryotes include those containing the nucleoid-associated proteins (NAPs) and the topoisomerases. Another solution to the problem of creating a coherent phenotypic diversity is by creating a growth-environment-dependent gradient of supercoiling generated along the replication origin-to-terminus axis of the bacterial chromosome. This gradient is modulated by transcription, NAPs, and topoisomerases. Here, we focus primarily on two topoisomerases, TopoIV and DNA gyrase in Escherichia coli, on three of its NAPs (H-NS, HU, and IHF), and on the single-stranded binding protein, SSB. We propose that the combination of supercoiling-gradient-dependent and strand-segregation-dependent topoisomerase activities result in significant differences in the supercoiling of daughter chromosomes, and hence in the phenotypes of daughter cells.

The global anaerobic metabolism regulator fnr is necessary for the degradation of food dyes and drugs by Escherichia coli

IMPORTANCE

This work has broad relevance due to the ubiquity of dyes containing azo bonds in food and drugs. We report that azo dyes can be degraded by human gut bacteria through both enzymatic and nonenzymatic mechanisms, even from a single gut bacterial species. Furthermore, we revealed that environmental factors, oxygen, and L-Cysteine control the ability of E. coli to degrade azo dyes due to their impacts on bacterial transcription and metabolism. These results open up new opportunities to manipulate the azoreductase activity of the gut microbiome through the manipulation of host diet, suggest that azoreductase potential may be altered in patients suffering from gastrointestinal disease, and highlight the importance of studying bacterial enzymes for drug metabolism in their natural cellular and ecological context.

The distinct transcriptome of virulence-associated phylogenetic group B2 Escherichia coli

Escherichia coli strains of phylogenetic group B2 are often associated with urinary tract infections (UTIs) and several other diseases. Recent genomic and transcriptomic analyses have not suggested or identified specific genes required for virulence, but have instead suggested multiple virulence strategies and complex host-pathogen interactions. Previous analyses have not compared core gene expression between phylogenetic groups or between pathogens and nonpathogens within phylogenetic groups. We compared the core gene expression of 35 strains from three phylogenetic groups that included both pathogens and nonpathogens after growth in a medium that allowed comparable growth of both types of strains. K-means clustering suggested a B2 cluster with 17 group B2 strains and two group A strains; an AD cluster with six group A strains, five group D strains and one B2 strain; and four outliers which included the highly studied model uropathogenic E. coli strains UTI89 and CFT073. Half of the core genes were differentially expressed between B2 and AD cluster strains, including transcripts of genes for all aspects of macromolecular synthesis-replication, transcription, translation, and peptidoglycan synthesis-energy metabolism, and environmental-sensing transcriptional regulators. Notably, core gene expression between nonpathogenic and uropathogenic transcriptomes within phylogenetic groups did not differ. If differences between pathogens and nonpathogens exist, then the differences do not require transcriptional reprogramming. In summary, B2 cluster strains have a distinct transcription pattern that involves hundreds of genes. We propose that this transcription pattern is one factor that contributes to virulence. IMPORTANCE Escherichia coli is a diverse species and an opportunistic pathogen that is associated with various diseases, such as urinary tract infections. When examined, phylogenetic group B2 strains are more often associated with these diseases, but the specific properties that contribute to their virulence are not known. From a comparative transcriptomic analysis, we found that group B2 strains grown in a nutrient-rich medium had a distinct transcription pattern, which is the first evidence that core gene expression differs between phylogenetic groups. Understanding the consequences of group B2 transcription pattern will provide important information on basic E. coli biology, the basis for E. coli virulence, and possibly for developing therapies for a majority of urinary tract infections and other group B2-associated diseases.

Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe

Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

Leveraging artificial intelligence in the fight against infectious diseases

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

Transcription-replication interactions reveal principles of bacterial genome regulation

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome1. These modes interact with a changing cellular environment to yield highly dynamic expression patterns2. In bacteria, the relationship between a gene's regulatory architecture and its expression is well understood for individual model gene circuits3,4. However, a broader perspective of these dynamics at the genome-scale is lacking, in part because bacterial transcriptomics have hitherto captured only a static snapshot of expression averaged across millions of cells5. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on each gene's transcriptional response to its own replication, which we term the Transcription-Replication Interaction Profile (TRIP). We found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal a gene's local regulatory context. While the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, including altered timing or amplitude of expression, and this is shaped by factors such as intra-operon position, repression state, or presence on mobile genetic elements. Our transcriptome analysis also simultaneously captures global properties, such as the rates of replication and transcription, as well as the nestedness of replication patterns. This work challenges previous notions of the drivers of expression heterogeneity within a population of cells, and unearths a previously unseen world of gene transcription dynamics.

A study on L-threonine and L-serine uptake in Escherichia coli K-12

In the current study, we report the identification and characterization of the yifK gene product as a novel amino acid carrier in E. coli K-12 cells. Both phenotypic and biochemical analyses showed that YifK acts as a permease specific to L-threonine and, to a lesser extent, L-serine. An assay of the effect of uncouplers and composition of the reaction medium on the transport activity indicates that YifK utilizes a proton motive force to energize substrate uptake. To identify the remaining threonine carriers, we screened a genomic library prepared from the yifK-mutant strain and found that brnQ acts as a multicopy suppressor of the threonine transport defect caused by yifK disruption. Our results indicate that BrnQ is directly involved in threonine uptake as a low-affinity but high-flux transporter, which forms the main entry point when the threonine concentration in the external environment reaches a toxic level. By abolishing YifK and BrnQ activity, we unmasked and quantified the threonine transport activity of the LIV-I branched chain amino acid transport system and demonstrated that LIV-I contributes significantly to total threonine uptake. However, this contribution is likely smaller than that of YifK. We also observed the serine transport activity of LIV-I, which was much lower compared with that of the dedicated SdaC carrier, indicating that LIV-I plays a minor role in the serine uptake. Overall, these findings allow us to propose a comprehensive model of the threonine/serine uptake subsystem in E. coli cells.

About the dark corners in the gene function space of Escherichia coli remaining without illumination by scientific literature

BACKGROUND

Although Escherichia coli (E. coli) is the most studied prokaryote organism in the history of life sciences, many molecular mechanisms and gene functions encoded in its genome remain to be discovered. This work aims at quantifying the illumination of the E. coli gene function space by the scientific literature and how close we are towards the goal of a complete list of E. coli gene functions.

RESULTS

The scientific literature about E. coli protein-coding genes has been mapped onto the genome via the mentioning of names for genomic regions in scientific articles both for the case of the strain K-12 MG1655 as well as for the 95%-threshold softcore genome of 1324 E. coli strains with known complete genome. The article match was quantified with the ratio of a given gene name's occurrence to the mentioning of any gene names in the paper. The various genome regions have an extremely uneven literature coverage. A group of elite genes with ≥ 100 full publication equivalents (FPEs, FPE = 1 is an idealized publication devoted to just a single gene) attracts the lion share of the papers. For K-12, ~ 65% of the literature covers just 342 elite genes; for the softcore genome, ~ 68% of the FPEs is about only 342 elite gene families (GFs). We also find that most genes/GFs have at least one mentioning in a dedicated scientific article (with the exception of at least 137 protein-coding transcripts for K-12 and 26 GFs from the softcore genome). Whereas the literature growth rates were highest for uncharacterized or understudied genes until 2005-2010 compared with other groups of genes, they became negative thereafter. At the same time, literature for anyhow well-studied genes started to grow explosively with threshold T10 (≥ 10 FPEs). Typically, a body of ~ 20 actual articles generated over ~ 15 years of research effort was necessary to reach T10. Lineage-specific co-occurrence analysis of genes belonging to the accessory genome of E. coli together with genomic co-localization and sequence-analytic exploration hints previously completely uncharacterized genes yahV and yddL being associated with osmotic stress response/motility mechanisms.

CONCLUSION

If the numbers of scientific articles about uncharacterized and understudied genes remain at least at present levels, full gene function lists for the strain K-12 MG1655 and the E. coli softcore genome are in reach within the next 25-30 years. Once the literature body for a gene crosses 10 FPEs, most of the critical fundamental research risk appears overcome and steady incremental research becomes possible.

Improved 13C metabolic flux analysis in Escherichia coli metabolism: application of a high-resolution MS (GC-EI-QTOF) for comprehensive assessment of MS/MS fragments

Gas chromatography-tandem mass spectrometry with electron ionization (GC-EI-MS/MS) provides rich information on stable-isotope labeling for 13C-metabolic flux analysis (13C-MFA). To pave the way for the routine application of tandem MS data for metabolic flux quantification, we aimed to compile a comprehensive library of GC-EI-MS/MS fragments of tert-butyldimethylsilyl (TBDMS) derivatized proteinogenic amino acids. First, we established an analytical workflow that combines high-resolution gas chromatography-quadrupole time-of-flight mass spectrometry and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments. Application of the high-mass accuracy MS procedure resulted into the identification of 129 validated precursor-product ion pairs of 13 amino acids with 30 fragments being accepted for 13C-MFA. The practical benefit of the novel tandem MS data was demonstrated by a proof-of-concept study, which confirmed the importance of the compiled library for high-resolution 13C-MFA.

ONE SENTENCE SUMMARY

An analytical workflow that combines high-resolution mass spectrometry (MS) and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments, which provide positional information and therefore offering significant advantages over traditional MS to improve 13C-metabolic flux analysis.

Enzyme expression kinetics by Escherichia coli during transition from rich to minimal media depends on proteome reserves

Bacterial fitness depends on adaptability to changing environments. In rich growth medium, which is replete with amino acids, Escherichia coli primarily expresses protein synthesis machineries, which comprise ~40% of cellular proteins and are required for rapid growth. Upon transition to minimal medium, which lacks amino acids, biosynthetic enzymes are synthesized, eventually reaching ~15% of cellular proteins when growth fully resumes. We applied quantitative proteomics to analyse the timing of enzyme expression during such transitions, and established a simple positive relation between the onset time of enzyme synthesis and the fractional enzyme 'reserve' maintained by E. coli while growing in rich media. We devised and validated a coarse-grained kinetic model that quantitatively captures the enzyme recovery kinetics in different pathways, solely on the basis of proteomes immediately preceding the transition and well after its completion. Our model enables us to infer regulatory strategies underlying the 'as-needed' gene expression programme adopted by E. coli.

Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria

Protein concentrations are set by a complex interplay between gene-specific regulatory processes and systemic factors, including cell volume and shared gene expression machineries. Elucidating this interplay is crucial for discerning and designing gene regulatory systems. We quantitatively characterized gene-specific and systemic factors that affect transcription and translation genome-wide for Escherichia coli across many conditions. The results revealed two design principles that make regulation of gene expression insulated from concentrations of shared machineries: RNA polymerase activity is fine-tuned to match translational output, and translational characteristics are similar across most messenger RNAs (mRNAs). Consequently, in bacteria, protein concentration is set primarily at the promoter level. A simple mathematical formula relates promoter activities and protein concentrations across growth conditions, enabling quantitative inference of gene regulation from omics data.

Metabolic control analysis enables rational improvement of E. coli L-tryptophan producers but methylglyoxal formation limits glycerol-based production

BACKGROUND

Although efficient L-tryptophan production using engineered Escherichia coli is established from glucose, the use of alternative carbon sources is still very limited. Through the application of glycerol as an alternate, a more sustainable substrate (by-product of biodiesel preparation), the well-studied intracellular glycolytic pathways are rerouted, resulting in the activity of different intracellular control sites and regulations, which are not fully understood in detail. Metabolic analysis was applied to well-known engineered E. coli cells with 10 genetic modifications. Cells were withdrawn from a fed-batch production process with glycerol as a carbon source, followed by metabolic control analysis (MCA). This resulted in the identification of several additional enzymes controlling the carbon flux to L-tryptophan.

RESULTS

These controlling enzyme activities were addressed stepwise by the targeted overexpression of 4 additional enzymes (trpC, trpB, serB, aroB). Their efficacy regarding L-tryptophan productivity was evaluated under consistent fed-batch cultivation conditions. Although process comparability was impeded by process variances related to a temporal, unpredictable break-off in L-tryptophan production, process improvements of up to 28% with respect to the L-tryptophan produced were observed using the new producer strains. The intracellular effects of these targeted genetic modifications were revealed by metabolic analysis in combination with MCA and expression analysis. Furthermore, it was discovered that the E. coli cells produced the highly toxic metabolite methylglyoxal (MGO) during the fed-batch process. A closer look at the MGO production and detoxification on the metabolome, fluxome, and transcriptome level of the engineered E. coli indicated that the highly toxic metabolite plays a critical role in the production of aromatic amino acids with glycerol as a carbon source.

CONCLUSIONS

A detailed process analysis of a new L-tryptophan producer strain revealed that several of the 4 targeted genetic modifications of the E. coli L-tryptophan producer strain proved to be effective, and, for others, new engineering approaches could be derived from the results. As a starting point for further strain and process optimization, the up-regulation of MGO detoxifying enzymes and a lowering of the feeding rate during the last third of the cultivation seems reasonable.

An expanded whole-cell model of E. coli links cellular physiology with mechanisms of growth rate control

Growth and environmental responses are essential for living organisms to survive and adapt to constantly changing environments. In order to simulate new conditions and capture dynamic responses to environmental shifts in a developing whole-cell model of E. coli, we incorporated additional regulation, including dynamics of the global regulator guanosine tetraphosphate (ppGpp), along with dynamics of amino acid biosynthesis and translation. With the model, we show that under perturbed ppGpp conditions, small molecule feedback inhibition pathways, in addition to regulation of expression, play a role in ppGpp regulation of growth. We also found that simulations with dysregulated amino acid synthesis pathways provide average amino acid concentration predictions that are comparable to experimental results but on the single-cell level, concentrations unexpectedly show regular fluctuations. Additionally, during both an upshift and downshift in nutrient availability, the simulated cell responds similarly with a transient increase in the mRNA:rRNA ratio. This additional simulation functionality should support a variety of new applications and expansions of the E. coli Whole-Cell Modeling Project.

Escherichia coli YigI is a Conserved Gammaproteobacterial Acyl-CoA Thioesterase Permitting Metabolism of Unusual Fatty Acid Substrates

Thioesterases play a critical role in metabolism, membrane biosynthesis, and overall homeostasis for all domains of life. In this present study, we characterize a putative thioesterase from Escherichia coli MG1655 and define its role as a cytosolic enzyme. Building on structure-guided functional predictions, we show that YigI is a medium- to long-chain acyl-CoA thioesterase that is involved in the degradation of conjugated linoleic acid (CLA) in vivo, showing overlapping specificity with two previously defined E. coli thioesterases TesB and FadM. We then bioinformatically identify the regulatory relationships that induce YigI expression, which include: an acidic environment, high oxygen availability, and exposure to aminoglycosides. Our findings define a role for YigI and shed light on why the E. coli genome harbors numerous thioesterases with closely related functions. IMPORTANCE Previous research has shown that long chain acyl-CoA thioesterases are needed for E. coli to grow in the presence of carbon sources such as conjugated linoleic acid, but that E. coli must possess at least one such enzyme that had not previously been characterized. Building off structure-guided function predictions, we showed that the poorly annotated protein YigI is indeed the previously unidentified third acyl CoA thioesterase. We found that the three potentially overlapping acyl-CoA thioesterases appear to be induced by nonoverlapping conditions and use that information as a starting point for identifying the precise reactions catalyzed by each such thioesterase, which is an important prerequisite for their industrial application and for more accurate metabolic modeling of E. coli.

Escherichia coli transcription factors of unknown function: sequence features and possible evolutionary relationships

Organisms need mechanisms to perceive the environment and respond accordingly to environmental changes or the presence of hazards. Transcription factors (TFs) are required for cells to respond to the environment by controlling the expression of genes needed. Escherichia coli has been the model bacterium for many decades, and still, there are features embedded in its genome that remain unstudied. To date, 58 TFs remain poorly characterized, although their binding sites have been experimentally determined. This study showed that these TFs have sequence variation at the third codon position G+C content but maintain the same Codon Adaptation Index (CAI) trend as annotated functional transcription factors. Most of these transcription factors are in areas of the genome where abundant repetitive and mobile elements are present. Sequence divergence points to groups with distinctive sequence signatures but maintaining the same type of DNA binding domain. Finally, the analysis of the promoter sequences of the 58 TFs showed A+T rich regions that agree with the features of horizontally transferred genes. The findings reported here pave the way for future research of these TFs that may uncover their role as spare factors in case of lose-of-function mutations in core TFs and trace back their evolutionary history.

Gene regulation in Escherichia coli is commonly selected for both high plasticity and low noise

Bacteria often respond to dynamically changing environments by regulating gene expression. Despite this regulation being critically important for growth and survival, little is known about how selection shapes gene regulation in natural populations. To better understand the role natural selection plays in shaping bacterial gene regulation, here we compare differences in the regulatory behaviour of naturally segregating promoter variants from Escherichia coli (which have been subject to natural selection) to randomly mutated promoter variants (which have never been exposed to natural selection). We quantify gene expression phenotypes (expression level, plasticity and noise) for hundreds of promoter variants across multiple environments and show that segregating promoter variants are enriched for mutations with minimal effects on expression level. In many promoters, we infer that there is strong selection to maintain high levels of plasticity, and direct selection to decrease or increase cell-to-cell variability in expression. Taken together, these results expand our knowledge of how gene regulation is affected by natural selection and highlight the power of comparing naturally segregating polymorphisms to de novo random mutations to quantify the action of selection.

Reactive oxygen species accumulation is synchronised with growth inhibition of temperature-sensitive recAts polA Escherichia coli

When combined with recombinase defects, chromosome breakage and double-strand break repair deficiencies render cells inviable. However, cells are viable when an SOS response occurs in recAts polA cells in Escherichia coli. Here, we aimed to elucidate the underlying mechanisms of this process. Transposon mutagenesis revealed that the hslO gene, a redox chaperone Hsp33 involved in reactive oxidative species (ROS) metabolism, was required for the suppression of recAts polA lethality at a restricted temperature. Recently, it has been reported that lethal treatments trigger ROS accumulation. We also found that recAts polA cells accumulated ROS at the restricted temperature. A catalase addition to the medium alleviates the temperature sensitivity of recAts polA cells and decreases ROS accumulation. These results suggest that the SOS response and hslO manage oxidative insult to an acceptable level in cells with oxidative damage and rescue cell growth. Overall, ROS might regulate several cellular processes.

Ribosome profiling reveals multiple roles of SecA in cotranslational protein export

SecA, an ATPase known to posttranslationally translocate secretory proteins across the bacterial plasma membrane, also binds ribosomes, but the role of SecA’s ribosome interaction has been unclear. Here, we used a combination of ribosome profiling methods to investigate the cotranslational actions of SecA. Our data reveal the widespread accumulation of large periplasmic loops of inner membrane proteins in the cytoplasm during their cotranslational translocation, which are specifically recognized and resolved by SecA in coordination with the proton motive force (PMF). Furthermore, SecA associates with 25% of secretory proteins with highly hydrophobic signal sequences at an early stage of translation and mediates their cotranslational transport. In contrast, the chaperone trigger factor (TF) delays SecA engagement on secretory proteins with weakly hydrophobic signal sequences, thus enforcing a posttranslational mode of their translocation. Our results elucidate the principles of SecA-driven cotranslational protein translocation and reveal a hierarchical network of protein export pathways in bacteria.

Using a combination of ribosome profiling methods, Zhu et al. investigate the principles governing the cotranslational interaction of SecA with nascent proteins and reveal a hierarchical organization of protein export pathways in bacteria.

Transducer Cascades for Biological Literature-Based Discovery

G protein-coupled receptors (GPCRs) control the response of cells to many signals, and as such, are involved in most cellular processes. As membrane receptors, they are accessible at the surface of the cell. GPCRs are also the largest family of membrane receptors, with more than 800 representatives in mammal genomes. For this reason, they are ideal targets for drugs. Although about one third of approved drugs target GPCRs, only about 16% of GPCRs are targeted by drugs. One of the difficulties comes from the lack of knowledge on the intra-cellular events triggered by these molecules. In the last two decades, scientists have started mapping the signaling networks triggered by GPCRs. However, it soon appeared that the system is very complex, which led to the publication of more than 320,000 scientific papers. Clearly, a human cannot take into account such massive sources of information. These papers represent a mine of information about both ontological knowledge and experimental results related to GPCRs, which have to be exploited in order to build signaling networks. The ABLISS project aims at the automatic building of GPCRs networks using automated deductive reasoning, allowing to integrate all available data. Therefore, we processed the automatic extraction of network information from the literature using Natural Language Processing (NLP). We mainly focused on the experimental results about GPCRs reported in the scientific papers, as so far there is no source gathering all these experimental results. We designed a relational database in order to make them available to the scientific community later. After introducing the more general objectives of the ABLISS project, we describe the formalism in detail. We then explain the NLP program using the finite state methods (Unitex graph cascades) we implemented and discuss the extracted facts obtained. Finally, we present the design of the relational database that stores the facts extracted from the selected papers.

Transitional genomes and nutritional role reversals identified for dual symbionts of adelgids (Aphidoidea: Adelgidae)

Many plant-sap-feeding insects have maintained a single, obligate, nutritional symbiont over the long history of their lineage. This senior symbiont may be joined by one or more junior symbionts that compensate for gaps in function incurred through genome-degradative forces. Adelgids are sap-sucking insects that feed solely on conifer trees and follow complex life cycles in which the diet fluctuates in nutrient levels. Adelgids are unusual in that both senior and junior symbionts appear to have been replaced repeatedly over their evolutionary history. Genomes can provide clues to understanding symbiont replacements, but only the dual symbionts of hemlock adelgids have been examined thus far. Here, we sequence and compare genomes of four additional dual-symbiont pairs in adelgids. We show that these symbionts are nutritional partners originating from diverse bacterial lineages and exhibiting wide variation in general genome characteristics. Although dual symbionts cooperate to produce nutrients, the balance of contributions varies widely across pairs, and total genome contents reflect a range of ages and degrees of degradation. Most symbionts appear to be in transitional states of genome reduction. Our findings support a hypothesis of periodic symbiont turnover driven by fluctuating selection for nutritional provisioning related to gains and losses of complex life cycles in their hosts.

Combined Transcriptomic and Proteomic Profiling of E. coli under Microaerobic versus Aerobic Conditions: The Multifaceted Roles of Noncoding Small RNAs and Oxygen-Dependent Sensing in Global Gene Expression Control

Adaptive mechanisms that facilitate intestinal colonization by the human microbiota, including Escherichia coli, may be better understood by analyzing the physiology and gene expression of bacteria in low-oxygen environments. We used high-throughput transcriptomics and proteomics to compare the expression profiles of E. coli grown under aerobic versus microaerobic conditions. Clustering of high-abundance transcripts under microaerobiosis highlighted genes controlling acid-stress adaptation (gadAXW, gadAB, hdeAB-yhiD and hdeD operons), cell adhesion/biofilm formation (pgaABCD and csgDEFG operons), electron transport (cydAB), oligopeptide transport (oppABCDF), and anaerobic respiration/fermentation (hyaABCDEF and hycABCDEFGHI operons). In contrast, downregulated genes were involved in iron transport (fhuABCD, feoABC and fepA-entD operons), iron-sulfur cluster assembly (iscRSUA and sufABCDSE operons), aerobic respiration (sdhDAB and sucABCDSE operons), and de novo nucleotide synthesis (nrdHIEF). Additionally, quantitative proteomics showed that the products (proteins) of these high- or low-abundance transcripts were expressed consistently. Our findings highlight interrelationships among energy production, carbon metabolism, and iron homeostasis. Moreover, we have identified and validated a subset of differentially expressed noncoding small RNAs (i.e., CsrC, RyhB, RprA and GcvB), and we discuss their regulatory functions during microaerobic growth. Collectively, we reveal key changes in gene expression at the transcriptional and post-transcriptional levels that sustain E. coli growth when oxygen levels are low.

ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

The E. coli Whole-Cell Modeling Project

The Escherichia coli whole-cell modeling project seeks to create the most detailed computational model of an E. coli cell in order to better understand and predict the behavior of this model organism. Details about the approach, framework, and current version of the model are discussed. Currently, the model includes the functions of 43% of characterized genes, with ongoing efforts to include additional data and mechanisms. As additional information is incorporated in the model, its utility and predictive power will continue to increase, which means that discovery efforts can be accelerated by community involvement in the generation and inclusion of data. This project will be an invaluable resource to the E. coli community that could be used to verify expected physiological behavior, to predict new outcomes and testable hypotheses for more efficient experimental design iterations, and to evaluate heterogeneous data sets in the context of each other through deep curation.

PRODORIC: state-of-the-art database of prokaryotic gene regulation

PRODORIC is worldwide one of the largest collections of prokaryotic transcription factor binding sites from multiple bacterial sources with corresponding interpretation and visualization tools. With the introduction of PRODORIC2 in 2017, the transition to a modern web interface and maintainable backend was started. With this latest PRODORIC release the database backend is now fully API-based and provides programmatical access to the complete PRODORIC data. The visualization tools Genome Browser and ProdoNet from the original PRODORIC have been reintroduced and were integrated into the PRODORIC website. Missing input and output options from the original Virtual Footprint were added again for position weight matrix pattern-based searches. The whole PRODORIC dataset was reannotated. Every transcription factor binding site was re-evaluated to increase the overall database quality. During this process, additional parameters, like bound effectors, regulation type and different types of experimental evidence have been added for every transcription factor. Additionally, 109 new transcription factors and 6 new organisms have been added. PRODORIC is publicly available at https://www.prodoric.de.

A Genome-Scale Antibiotic Screen in Serratia marcescens Identifies YdgH as a Conserved Modifier of Cephalosporin and Detergent Susceptibility

Serratia marcescens, a member of the order Enterobacterales, is adept at colonizing health care environments and is an important cause of invasive infections. Antibiotic resistance is a daunting problem in S. marcescens because, in addition to plasmid-mediated mechanisms, most isolates have considerable intrinsic resistance to multiple antibiotic classes. To discover endogenous modifiers of antibiotic susceptibility in S. marcescens, a high-density transposon insertion library was subjected to sub-MICs of two cephalosporins, cefoxitin, and cefepime, as well as the fluoroquinolone ciprofloxacin. Comparisons of transposon insertion abundance before and after antibiotic exposure identified hundreds of potential modifiers of susceptibility to these agents. Using single-gene deletions, we validated several candidate modifiers of cefoxitin susceptibility and chose ydgH, a gene of unknown function, for further characterization. In addition to cefoxitin, deletion of ydgH in S. marcescens resulted in decreased susceptibility to multiple third-generation cephalosporins and, in contrast, to increased susceptibility to both cationic and anionic detergents. YdgH is highly conserved throughout the Enterobacterales, and we observed similar phenotypes in Escherichia coli O157:H7 and Enterobacter cloacae mutants. YdgH is predicted to localize to the periplasm, and we speculate that it may be involved there in cell envelope homeostasis. Collectively, our findings provide insight into chromosomal mediators of antibiotic resistance in S. marcescens and will serve as a resource for further investigations of this important pathogen.

A Shift to Human Body Temperature (37°C) Rapidly Reprograms Multiple Adaptive Responses in Escherichia coli That Would Facilitate Niche Survival and Colonization

One of the first environmental cues sensed by a microbe as it enters a human host is an upshift in temperature to 37°C. In this dynamic time point analysis, we demonstrate that this environmental transition rapidly signals a multitude of gene expression changes in Escherichia coli. Bacteria grown at 23°C under aerobic conditions were shifted to 37°C, and mRNA expression was measured at time points after the shift to 37°C (t = 0.5, 1, and 4 h). The first hour is characterized by a transient shift to anaerobic respiration strategies and stress responses, particularly acid resistance, indicating that temperature serves as a sentinel cue to predict and prepare for various niches within the host. The temperature effects on a subset of stress response genes were shown to be mediated by RpoS and directly correlated with RpoS, DsrA, and RprA levels, and increased acid resistance was observed that was dependent on 23°C growth and RpoS. By 4 h, gene expression shifted to aerobic respiration pathways and decreased stress responses, coupled with increases in genes associated with biosynthesis (amino acid and nucleotides), iron uptake, and host defense. ompT, a gene that confers resistance to antimicrobial peptides, was highly thermoregulated, with a pattern conserved in enteropathogenic and uropathogenic E. coli strains. An immediate decrease in curli gene expression concomitant with an increase in flagellar gene expression implicates temperature in this developmental decision. Together, our studies demonstrate that temperature signals a reprogramming of gene expression immediately upon an upshift that may predict, prepare, and benefit the survival of the bacterium within the host.

IMPORTANCE As one of the first cues sensed by the microbe upon entry into a human host, understanding how bacteria like E. coli modulate gene expression in response to temperature improves our understanding of how bacteria immediately initiate responses beneficial for survival and colonization. For pathogens, understanding the various pathways of thermal regulation could yield valuable targets for anti-infective chemotherapeutic drugs or disinfection measures. In addition, our data provide a dynamic examination of the RpoS stress response, providing genome-wide support for how temperature impacts RpoS through changes in RpoS stability and modulation by small regulatory RNAs.

Global connectivity in genome-scale metabolic networks revealed by comprehensive FBA-based pathway analysis

Background

Graph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks.

Results

Here, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks.

Conclusions

The FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02357-1.

Escherichia coli Small Proteome

Escherichia coli was one of the first species to have its genome sequenced and remains one of the best-characterized model organisms. Thus, it is perhaps surprising that recent studies have shown that a substantial number of genes have been overlooked. Genes encoding more than 140 small proteins, defined as those containing 50 or fewer amino acids, have been identified in E. coli in the past 10 years, and there is substantial evidence indicating that many more remain to be discovered. This review covers the methods that have been successful in identifying small proteins and the short open reading frames that encode them. The small proteins that have been functionally characterized to date in this model organism are also discussed. It is hoped that the review, along with the associated databases of known as well as predicted but undetected small proteins, will aid in and provide a roadmap for the continued identification and characterization of these proteins in E. coli as well as other bacteria.

Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria

BACKGROUND

Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the question whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression can be predicted from symmetries in the gene regulatory networks described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models-with gene input functions differencing between genes-predict symmetry breaking and desynchronization.

RESULTS

To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the gene regulatory networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes.

CONCLUSIONS

Thus, gene fibrations provide a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.

Consolidated Bioprocessing: Synthetic Biology Routes to Fuels and Fine Chemicals

The long road from emerging biotechnologies to commercial “green” biosynthetic routes for chemical production relies in part on efficient microbial use of sustainable and renewable waste biomass feedstocks. One solution is to apply the consolidated bioprocessing approach, whereby microorganisms convert lignocellulose waste into advanced fuels and other chemicals. As lignocellulose is a highly complex network of polymers, enzymatic degradation or “saccharification” requires a range of cellulolytic enzymes acting synergistically to release the abundant sugars contained within. Complications arise from the need for extracellular localisation of cellulolytic enzymes, whether they be free or cell-associated. This review highlights the current progress in the consolidated bioprocessing approach, whereby microbial chassis are engineered to grow on lignocellulose as sole carbon sources whilst generating commercially useful chemicals. Future perspectives in the emerging biofoundry approach with bacterial hosts are discussed, where solutions to existing bottlenecks could potentially be overcome though the application of high throughput and iterative Design-Build-Test-Learn methodologies. These rapid automated pathway building infrastructures could be adapted for addressing the challenges of increasing cellulolytic capabilities of microorganisms to commercially viable levels.

The Nutrient and Energy Pathway Requirements for Surface Motility of Nonpathogenic and Uropathogenic Escherichia coli

Uropathogenic E. coli (UPEC) is the causative pathogen for most uncomplicated urinary tract infections. Motility is likely to contribute to these infections, and E. coli possesses flagella-dependent swimming motility, flagella-dependent surface motility (often called swarming), and the recently observed pili-dependent surface motility. Surface motility has not been extensively studied, but for the strains that have been tested nonpathogenic E. coli (NPEC) lab strains use pili, NPEC hypermotile derivatives of these lab strains use flagella, and UPEC strains use flagella. Using a representative of these three types of strains, we showed differences in the nutritional and pathway requirements for surface motility with respect to the glucose concentration, the glycolytic pathway utilized, acetogenesis, and the TCA cycle. In addition, glucose controlled flagella synthesis for the NPEC strain, but not for the hypermotile NPEC variant or the UPEC strain. The requirements for surface motility are likely to reflect major metabolic differences between strains for the pathways and regulation of energy metabolism.IMPORTANCEUrinary tract infections (UTIs) are one of the most common bacterial infections and are an increasing burden on the healthcare system because of recurrence and antibiotic resistance (1, 2). The most common uropathogen is E. coli (3, 4), which is responsible for about 80-90% of community acquired UTIs and 40-50% of nosocomial acquired UTIs (2). Virulence requires both pili and flagella, and either appendage can contribute to surface motility, although surface motility of uropathogenic E. coli has not been examined. We found different appendage, nutrient and pathway requirements for surface motility of a nonpathogenic E. coli lab strain and a uropathogenic E. coli We propose that these differences are the result of differences in the pathways and regulation of energy metabolism.

Leveraging heterogeneous network embedding for metabolic pathway prediction

MOTIVATION

Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible.

RESULTS

Here, we present pathway2vec, a software package consisting of six representational learning modules used to automatically generate features for pathway inference. Specifically, we build a three-layered network composed of compounds, enzymes and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve prediction outcomes.

AVAILABILITY AND IMPLEMENTATION

The software package and installation instructions are published on http://github.com/pathway2vec.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.