EcoGene 3.0

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.

Plasticity and Stereotypic Rewiring of the Transcriptome Upon Bacterial Evolution of Antibiotic Resistance

Bacterial evolution of antibiotic resistance frequently has deleterious side effects on microbial growth, virulence, and susceptibility to other antimicrobial agents. However, it is unclear how these trade-offs could be utilized for manipulating antibiotic resistance in the clinic, not least because the underlying molecular mechanisms are poorly understood. Using laboratory evolution, we demonstrate that clinically relevant resistance mutations in Escherichia coli constitutively rewire a large fraction of the transcriptome in a repeatable and stereotypic manner. Strikingly, lineages adapted to functionally distinct antibiotics and having no resistance mutations in common show a wide range of parallel gene expression changes that alter oxidative stress response, iron homeostasis, and the composition of the bacterial outer membrane and cell surface. These common physiological alterations are associated with changes in cell morphology and enhanced sensitivity to antimicrobial peptides. Finally, the constitutive transcriptomic changes induced by resistance mutations are largely distinct from those induced by antibiotic stresses in the wild type. This indicates a limited role for genetic assimilation of the induced antibiotic stress response during resistance evolution. Our work suggests that diverse resistance mutations converge on similar global transcriptomic states that shape genetic susceptibility to antimicrobial compounds.

The absence of the queuosine tRNA modification leads to pleiotropic phenotypes revealing perturbations of metal and oxidative stress homeostasis in Escherichia coli K12

Queuosine (Q) is a conserved hypermodification of the wobble base of tRNA containing GUN anticodons but the physiological consequences of Q deficiency are poorly understood in bacteria. This work combines transcriptomic, proteomic and physiological studies to characterize a Q-deficient Escherichia coli K12 MG1655 mutant. The absence of Q led to an increased resistance to nickel and cobalt, and to an increased sensitivity to cadmium, compared to the wild-type (WT) strain. Transcriptomic analysis of the WT and Q-deficient strains, grown in the presence and absence of nickel, revealed that the nickel transporter genes (nikABCDE) are downregulated in the Q- mutant, even when nickel is not added. This mutant is therefore primed to resist to high nickel levels. Downstream analysis of the transcriptomic data suggested that the absence of Q triggers an atypical oxidative stress response, confirmed by the detection of slightly elevated reactive oxygen species (ROS) levels in the mutant, increased sensitivity to hydrogen peroxide and paraquat, and a subtle growth phenotype in a strain prone to accumulation of ROS.

A Survey of Biological Data in a Big Data Perspective

The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.

Comprehensive analysis of PNA-based antisense antibiotics targeting various essential genes in uropathogenic Escherichia coli

Antisense peptide nucleic acids (PNAs) that target mRNAs of essential bacterial genes exhibit specific bactericidal effects in several microbial species, but our mechanistic understanding of PNA activity and their target gene spectrum is limited. Here, we present a systematic analysis of PNAs targeting 11 essential genes with varying expression levels in uropathogenic Escherichia coli (UPEC). We demonstrate that UPEC is susceptible to killing by peptide-conjugated PNAs, especially when targeting the widely-used essential gene acpP. Our evaluation yields three additional promising target mRNAs for effective growth inhibition, i.e.dnaB, ftsZ and rpsH. The analysis also shows that transcript abundance does not predict target vulnerability and that PNA-mediated growth inhibition is not universally associated with target mRNA depletion. Global transcriptomic analyses further reveal PNA sequence-dependent but also -independent responses, including the induction of envelope stress response pathways. Importantly, we show that 9mer PNAs are generally as effective in inhibiting bacterial growth as their 10mer counterparts. Overall, our systematic comparison of a range of PNAs targeting mRNAs of different essential genes in UPEC suggests important features for PNA design, reveals a general bacterial response to PNA conjugates and establishes the feasibility of using PNA antibacterials to combat UPEC.

T-G-A Deficiency Pattern in Protein-Coding Genes and Its Potential Reason

If a stop codon appears within one gene, then its translation will be terminated earlier than expected. False folding of premature protein will be adverse to the host; hence, all functional genes would tend to avoid the intragenic stop codons. Therefore, we hypothesize that there will be less frequency of nucleotides corresponding to stop codons at each codon position of genes. Here, we validate this inference by investigating the nucleotide frequency at a large scale and results from 19,911 prokaryote genomes revealed that nucleotides coinciding with stop codons indeed have the lowest frequency in most genomes. Interestingly, genes with three types of stop codons all tend to follow a T-G-A deficiency pattern, suggesting that the property of avoiding intragenic termination pressure is the same and the major stop codon TGA plays a dominant role in this effect. Finally, a positive correlation between the TGA deficiency extent and the base length was observed in start-experimentally verified genes of Escherichia coli (E. coli). This strengthens the proof of our hypothesis. The T-G-A deficiency pattern observed would help to understand the evolution of codon usage tactics in extant organisms.

Engineering of the Recombinant Expression and PEGylation Efficiency of the Therapeutic Enzyme Human Thymidine Phosphorylase

Human thymidine phosphorylase (HsTP) is an enzyme with important implications in the field of rare metabolic diseases. Defective mutations of HsTP lead to mitochondrial neurogastrointestinal encephalomyopathy (MNGIE), a disease with a high unmet medical need that is associated with severe neurological and gastrointestinal complications. Current efforts focus on the development of an enzyme replacement therapy (ERT) using the Escherichia coli ortholog (EcTP). However, bacterial enzymes are counter-indicated for human therapeutic applications because they are recognized as foreign by the human immune system, thereby eliciting adverse immune responses and raising significant safety and efficacy risks. Thus, it is critical to utilize the HsTP enzyme as starting scaffold for pre-clinical drug development, thus de-risking the safety concerns associated with the use of bacterial enzymes. However, HsTP expresses very poorly in E. coli, whereas its PEGylation, a crucial chemical modification for achieving long serum persistence of therapeutic enzymes, is highly inefficient and negatively affects its catalytic activity. Here we focused on the engineering of the recombinant expression profile of HsTP in E. coli cells, as well as on the optimization of its PEGylation efficiency aiming at the development of an alternative therapeutic approach for MNGIE. We show that phylogenetic and structural analysis of proteins can provide important insights for the rational design of N’-terminus-truncation constructs which exhibit significantly improved recombinant expression levels. In addition, we developed and implemented a criteria-driven rational surface engineering strategy for the substitution of arginine-to-lysine and lysine-to-arginine residues to achieve more efficient, homogeneous and reproducible PEGylation without negatively affecting the enzymatic catalytic activity upon PEGylation. Collectively, our proposed strategies provide an effective way to optimize enzyme PEGylation and E. coli recombinant expression and are likely applicable for other proteins and enzymes.

StartLink and StartLink+: Prediction of Gene Starts in Prokaryotic Genomes

State-of-the-art algorithms of ab initio gene prediction for prokaryotic genomes were shown to be sufficiently accurate. A pair of algorithms would agree on predictions of gene 3'ends. Nonetheless, predictions of gene starts would not match for 15-25% of genes in a genome. This discrepancy is a serious issue that is difficult to be resolved due to the absence of sufficiently large sets of genes with experimentally verified starts. We have introduced StartLink that infers gene starts from conservation patterns revealed by multiple alignments of homologous nucleotide sequences. We also have introduced StartLink+ combining both ab initio and alignment-based methods. The ability of StartLink to predict the start of a given gene is restricted by the availability of homologs in a database. We observed that StartLink made predictions for 85% of genes per genome on average. The StartLink+ accuracy was shown to be 98-99% on the sets of genes with experimentally verified starts. In comparison with database annotations, we observed that the annotated gene starts deviated from the StartLink+ predictions for ∼5% of genes in AT-rich genomes and for 10-15% of genes in GC-rich genomes on average. The use of StartLink+ has a potential to significantly improve gene start annotation in genomic databases.

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.

Differential Packaging Into Outer Membrane Vesicles Upon Oxidative Stress Reveals a General Mechanism for Cargo Selectivity

Selective cargo packaging into bacterial extracellular vesicles has been reported and implicated in many biological processes, however, the mechanism behind the selectivity has remained largely unexplored. In this study, proteomic analysis of outer membrane (OM) and OM vesicle (OMV) fractions from enterotoxigenic E. coli revealed significant differences in protein abundance in the OMV and OM fractions for cultures shifted to oxidative stress conditions. Analysis of sequences of proteins preferentially packaged into OMVs showed that proteins with oxidizable residues were more packaged into OMVs in comparison with those retained in the membrane. In addition, the results indicated two distinct classes of OM-associated proteins were differentially packaged into OMVs as a function of peroxide treatment. Implementing a Bayesian hierarchical model, OM lipoproteins were determined to be preferentially exported during stress whereas integral OM proteins were preferentially retained in the cell. Selectivity was determined to be independent of transcriptional regulation of the proteins upon oxidative stress and was validated using randomly selected protein candidates from the different cargo classes. Based on these data, a hypothetical functional and mechanistic basis for cargo selectivity was tested using OmpA constructs. Our study reveals a basic mechanism for cargo selectivity into OMVs that may be useful for the engineering of OMVs for future biotechnological applications.

digIS: towards detecting distant and putative novel insertion sequence elements in prokaryotic genomes

Background

The insertion sequence elements (IS elements) represent the smallest and the most abundant mobile elements in prokaryotic genomes. It has been shown that they play a significant role in genome organization and evolution. To better understand their function in the host genome, it is desirable to have an effective detection and annotation tool. This need becomes even more crucial when considering rapid-growing genomic and metagenomic data. The existing tools for IS elements detection and annotation are usually based on comparing sequence similarity with a database of known IS families. Thus, they have limited ability to discover distant and putative novel IS elements.

Results

In this paper, we present digIS, a software tool based on profile hidden Markov models assembled from catalytic domains of transposases. It shows a very good performance in detecting known IS elements when tested on datasets with manually curated annotation. The main contribution of digIS is in its ability to detect distant and putative novel IS elements while maintaining a moderate level of false positives. In this category it outperforms existing tools, especially when tested on large datasets of archaeal and bacterial genomes.

Conclusion

We provide digIS, a software tool using a novel approach based on manually curated profile hidden Markov models, which is able to detect distant and putative novel IS elements. Although digIS can find known IS elements as well, we expect it to be used primarily by scientists interested in finding novel IS elements. The tool is available at https://github.com/janka2012/digIS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04177-6.

Three Ribosomal Operons of Escherichia coli Contain Genes Encoding Small RNAs That Interact With Hfq and CsrA in vitro

Three out of the seven ribosomal RNA operons in Escherichia coli end in dual terminator structures. Between the two terminators of each operon is a short sequence that we report here to be an sRNA gene, transcribed as part of the ribosomal RNA primary transcript by read-through of the first terminator. The sRNA genes (rrA, rrB and rrF) from the three operons (rrnA, rrnB and rrnD) are more than 98% identical, and pull-down experiments show that their transcripts interact with Hfq and CsrA. Deletion of rrA, B, F, as well as overexpression of rrB, only modestly affect known CsrA-regulated phenotypes like biofilm formation, pgaA translation and glgC translation, and the role of the sRNAs in vivo may not yet be fully understood. Since RrA, B, F are short-lived and transcribed along with the ribosomal RNA components, their concentration reflect growth-rate regulation at the ribosomal RNA promoters and they could function to fine-tune other growth-phase-dependent processes in the cell. The primary and secondary structure of these small RNAs are conserved among species belonging to different genera of Enterobacteriales.

Network pharmacology-based study to explore the mechanism of the Yiqi Gubiao pill in lung cancer treatment

Lung cancer (LC) has been one of the most prevalent and fatal malignancies in the past 5 years. Yiqi Gubiao pills have a good clinical effect against LC. However, their complex composition limits proper understanding of their pharmacological mechanism. Therefore, the present study aimed to systemically explore the underlying mechanisms of Yiqi Gubiao pills in treatment of LC. The network pharmacology approach was employed to identify the active ingredients and LC targets associated with Yiqi Gubiao pills. Prediction of potential active ingredients and action targets was then conducted through protein-protein interaction (PPI), Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. In vitro experiments were then performed to further verify the mechanism of action of Yiqi Gubiao pills, revealing that the anti-LC effects were mediated by regulating the expression of IL6, TP53, albumin (ALB), MAPK3 and AKT1. In total, 102 active ingredients and 229 targets of Yiqi Gubiao pills were identified. The PPI network further revealed that AKT1, TP53, ALB, IL6 and MAPK3 were the top five hub genes associated with LC treatment. Targets of the Yiqi Gubiao pills were mainly enriched in the PI3K-Akt and Advanced glycation end products (AGE)-receptors for AGEs (RAGE) signaling pathways. Overall, network pharmacology deciphered the active ingredients and potential targets of the Yiqi Gubiao pills. Yiqi Gubiao pills partially inhibited the progression of LC by regulating the expression of hub genes (AKT1, TP53, ALB, IL6 and MAPK3) through the PI3K-Akt and AGE-RAGE signaling pathways. The findings of the present study may provide a theoretical basis for the clinical application of Yiqi Gubiao pills in LC treatment.

Dynamics of chromosomal target search by a membrane-integrated one-component receptor

Membrane proteins account for about one third of the cellular proteome, but it is still unclear how dynamic they are and how they establish functional contacts with cytoplasmic interaction partners. Here, we consider a membrane-integrated one-component receptor that also acts as a transcriptional activator, and analyze how it kinetically locates its specific binding site on the genome. We focus on the case of CadC, the pH receptor of the acid stress response Cad system in E. coli. CadC is a prime example of a one-component signaling protein that directly binds to its cognate target site on the chromosome to regulate transcription. We combined fluorescence microscopy experiments, mathematical analysis, and kinetic Monte Carlo simulations to probe this target search process. Using fluorescently labeled CadC, we measured the time from activation of the receptor until successful binding to the DNA in single cells, exploiting that stable receptor-DNA complexes are visible as fluorescent spots. Our experimental data indicate that CadC is highly mobile in the membrane and finds its target by a 2D diffusion and capture mechanism. DNA mobility is constrained due to the overall chromosome organization, but a labeled DNA locus in the vicinity of the target site appears sufficiently mobile to randomly come close to the membrane. Relocation of the DNA target site to a distant position on the chromosome had almost no effect on the mean search time, which was between four and five minutes in either case. However, a mutant strain with two binding sites displayed a mean search time that was reduced by about a factor of two. This behavior is consistent with simulations of a coarse-grained lattice model for the coupled dynamics of DNA within a cell volume and proteins on its surface. The model also rationalizes the experimentally determined distribution of search times. Overall our findings reveal that DNA target search does not present a much bigger kinetic challenge for membrane-integrated proteins than for cytoplasmic proteins. More generally, diffusion and capture mechanisms may be sufficient for bacterial membrane proteins to establish functional contacts with cytoplasmic targets.

Adaptation to changing environments is vital to bacteria and is enabled by sophisticated signal transduction systems. While signal transduction by two-component systems is well studied, the signal transduction of membrane-integrated one-component systems, where one protein performs both sensing and response regulation, are insufficiently understood. How can a membrane-integrated protein bind to specific sites on the genome to regulate transcription? Here, we study the kinetics of this process, which involves both protein diffusion within the membrane and conformational fluctuations of the genomic DNA. A well-suited model system for this question is CadC, the signaling protein of the E. coli Cad system involved in pH stress response. Fluorescently labeled CadC forms visible spots in single cells upon stable DNA-binding, marking the end of the protein-DNA search process. Moreover, the start of the search is triggered by a medium shift exposing cells to pH stress. We probe the underlying mechanism by varying the number and position of DNA target sites. We combine these experiments with mathematical analysis and kinetic Monte Carlo simulations of lattice models for the search process. Our results suggest that CadC diffusion in the membrane is pivotal for this search, while the DNA target site is just mobile enough to reach the membrane.

Experimental determination of evolutionary barriers to horizontal gene transfer

BACKGROUND

Horizontal gene transfer, the acquisition of genes across species boundaries, is a major source of novel phenotypes that enables microbes to rapidly adapt to new environments. How the transferred gene alters the growth - fitness - of the new host affects the success of the horizontal gene transfer event and how rapidly the gene spreads in the population. Several selective barriers - factors that impact the fitness effect of the transferred gene - have been suggested to impede the likelihood of horizontal transmission, however experimental evidence is scarce. The objective of this study was to determine the fitness effects of orthologous genes transferred from Salmonella enterica serovar Typhimurium to Escherichia coli to identify the selective barriers using highly precise experimental measurements.

RESULTS

We found that most gene transfers result in strong fitness costs. Previously identified evolutionary barriers - gene function and the number of protein-protein interactions - did not predict the fitness effects of transferred genes. In contrast, dosage sensitivity, gene length, and the intrinsic protein disorder significantly impact the likelihood of a successful horizontal transfer.

CONCLUSION

While computational approaches have been successful in describing long-term barriers to horizontal gene transfer, our experimental results identified previously underappreciated barriers that determine the fitness effects of newly transferred genes, and hence their short-term eco-evolutionary dynamics.

Properties of protein unfolded states suggest broad selection for expanded conformational ensembles

Much attention is being paid to conformational biases in the ensembles of intrinsically disordered proteins. However, it is currently unknown whether or how conformational biases within the disordered ensembles of foldable proteins affect function in vivo. Recently, we demonstrated that water can be a good solvent for unfolded polypeptide chains, even those with a hydrophobic and charged sequence composition typical of folded proteins. These results run counter to the generally accepted model that protein folding begins with hydrophobicity-driven chain collapse. Here we investigate what other features, beyond amino acid composition, govern chain collapse. We found that local clustering of hydrophobic and/or charged residues leads to significant collapse of the unfolded ensemble of pertactin, a secreted autotransporter virulence protein from Bordetella pertussis, as measured by small angle X-ray scattering (SAXS). Sequence patterns that lead to collapse also correlate with increased intermolecular polypeptide chain association and aggregation. Crucially, sequence patterns that support an expanded conformational ensemble enhance pertactin secretion to the bacterial cell surface. Similar sequence pattern features are enriched across the large and diverse family of autotransporter virulence proteins, suggesting sequence patterns that favor an expanded conformational ensemble are under selection for efficient autotransporter protein secretion, a necessary prerequisite for virulence. More broadly, we found that sequence patterns that lead to more expanded conformational ensembles are enriched across water-soluble proteins in general, suggesting protein sequences are under selection to regulate collapse and minimize protein aggregation, in addition to their roles in stabilizing folded protein structures.

SOLart: a structure-based method to predict protein solubility and aggregation

MOTIVATION

The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools.

RESULTS

We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists.

AVAILABILITY AND IMPLEMENTATION

The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome-wide high-throughput screening of interactive bacterial metabolite in the algal population using Escherichia coli K-12 Keio collection

Algae-bacteria interaction is one of the main factors underlying the formation of harmful algal blooms (HABs). The aim of this study was to develop a genome-wide high-throughput screening method to identify HAB-influenced specific interactive bacterial metabolites using a comprehensive collection of gene-disrupted E. coli K-12 mutants (Keio collection). The screening revealed that a total of 80 gene knockout mutants in E. coli K-12 resulted in an approximately 1.5-fold increase in algal growth relative to that in wild-type E. coli. Five bacterial genes (lpxL, lpxM, kdsC, kdsD, gmhB) involved in the lipopolysaccharide (LPS) (or lipooligosaccharide, LOS) biosynthesis were identified from the screen. Relatively lower levels of LPS were detected in these bacteria compared to that in the wild-type. Moreover, the concentration-dependent decrease in microalgal growth after synthetic LPS supplementation indicated that LPS inhibits algal growth. LPS supplementation increased the 2,7-dichlorodihydrofluorescein diacetate fluorescence, as well as the levels of lipid peroxidation-mediated malondialdehyde formation, in a concentration-dependent manner, indicating that oxidative stress can result from LPS supplementation. Furthermore, supplementation with LPS also remarkably reduced the growth of diverse bloom-forming dinoflagellates and green algae. Our findings indicate that the Keio collection-based high-throughput in vitro screening is an effective approach for the identification of interactive bacterial metabolites and related genes.

Cooperative and sequence-dependent model for RNAP dynamics: Application to ribosomal gene transcription

Escherichia coli ribosomal genes are a well-established experimental model used to investigate the transcription process. These genes are essential to cell physiology and are therefore strongly expressed. Multiple transcription units collaborate in rrn expression. Experiments involving electron microscopy have shown the non-uniform density of the RNA polymerases transcribing these ribosomal operons. Here, we investigate RNAP collaborative transcription in E. coli ribosomal genes using a stochastic sequence-dependent model that included interactions among the RNAPs. We achieved results consistent with experimental data using a model with variable parametrization for genic and intergenic regions, compared with previous attempts that used uniform parameters for genic and intergenic regions. Our model also showed that cooperative behaviour reduced the dwell times in pause sites predicted by the single-round approach but induced a new pausing event at an upstream position. This work may stimulate new experimental research and provide other scenarios to test our model predictions.

iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

Background:

Constraint-based metabolic network models have been widely used in pheno-typic prediction and metabolic engineering design. In recent years, researchers have attempted to im-prove prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.

Methods and Results:

We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction bounda-ries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.

Conclusion:

Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise pre-dictions of metabolic fluxes by using expression values normalized based on GO.

Chemical-genetic profiling reveals limited cross-resistance between antimicrobial peptides with different modes of action

Antimicrobial peptides (AMPs) are key effectors of the innate immune system and promising therapeutic agents. Yet, knowledge on how to design AMPs with minimal cross-resistance to human host-defense peptides remains limited. Here, we systematically assess the resistance determinants of Escherichia coli against 15 different AMPs using chemical-genetics and compare to the cross-resistance spectra of laboratory-evolved AMP-resistant strains. Although generalizations about AMP resistance are common in the literature, we find that AMPs with different physicochemical properties and cellular targets vary considerably in their resistance determinants. As a consequence, cross-resistance is prevalent only between AMPs with similar modes of action. Finally, our screen reveals several genes that shape susceptibility to membrane- and intracellular-targeting AMPs in an antagonistic manner. We anticipate that chemical-genetic approaches could inform future efforts to minimize cross-resistance between therapeutic and human host AMPs.

Density of σ70 promoter-like sites in the intergenic regions dictates the redistribution of RNA polymerase during osmotic stress in Escherichia coli

RNA polymerase (RNAP), the transcription machinery, shows dynamic binding across the genomic DNA under different growth conditions. The genomic features that selectively redistribute the limited RNAP molecules to dictate genome-wide transcription in response to environmental cues remain largely unknown. We chose the bacterial osmotic stress response model to determine genomic features that direct genome-wide redistribution of RNAP during the stress. Genomic mapping of RNAP and transcriptome profiles corresponding to the different temporal states after salt shock were determined. We found rapid redistribution of RNAP across the genome, primarily at σ70 promoters. Three subsets of genes exhibiting differential salt sensitivities were identified. Sequence analysis using an information-theory based σ70 model indicates that the intergenic regions of salt-responsive genes are enriched with a higher density of σ70 promoter-like sites than those of salt-sensitive genes. In addition, the density of promoter-like sites has a positive linear correlation with RNAP binding at different salt concentrations. The RNAP binding contributed by the non-initiating promoter-like sites is important for gene transcription at high salt concentration. Our study demonstrates that hyperdensity of σ70 promoter-like sites in the intergenic regions of salt-responsive genes drives the RNAP redistribution for reprograming the transcriptome to counter osmotic stress.

DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns

Annotation of gene expression in prokaryotes often finds itself corrected due to small variations of the annotated gene regions observed between different (sub)-species. It has become apparent that traditional sequence alignment algorithms, used for the curation of genomes, are not able to map the full complexity of the genomic landscape. We present DeepRibo, a novel neural network utilizing features extracted from ribosome profiling information and binding site sequence patterns that shows to be a precise tool for the delineation and annotation of expressed genes in prokaryotes. The neural network combines recurrent memory cells and convolutional layers, adapting the information gained from both the high-throughput ribosome profiling data and ribosome binding translation initiation sequence region into one model. DeepRibo is designed as a single model trained on a variety of ribosome profiling experiments, used for the identification of open reading frames in prokaryotes without a priori knowledge of the translational landscape. Through extensive validation of the model trained on various sets of data, multiple species sequence similarity, mass spectrometry and Edman degradation verified proteins, the effectiveness of DeepRibo is highlighted.

The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function

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

AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions

MOTIVATION

A core task of genomics is to identify the boundaries of protein coding genes, which may cover over 90% of a prokaryote's genome. Several programs are available for gene finding, yet it is currently unclear how well these programs perform and whether any offers superior accuracy. This is in part because there is no universal benchmark for gene finding and, therefore, most developers select their own benchmarking strategy.

RESULTS

Here, we introduce AssessORF, a new approach for benchmarking prokaryotic gene predictions based on evidence from proteomics data and the evolutionary conservation of start and stop codons. We applied AssessORF to compare gene predictions offered by GenBank, GeneMarkS-2, Glimmer and Prodigal on genomes spanning the prokaryotic tree of life. Gene predictions were 88-95% in agreement with the available evidence, with Glimmer performing the worst but no clear winner. All programs were biased towards selecting start codons that were upstream of the actual start. Given these findings, there remains considerable room for improvement, especially in the detection of correct start sites.

AVAILABILITY AND IMPLEMENTATION

AssessORF is available as an R package via the Bioconductor package repository.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Spatiotemporal Organization of the E. coli Transcriptome: Translation Independence and Engagement in Regulation

RNA localization in eukaryotes is a mechanism to regulate transcripts fate. Conversely, bacterial transcripts were not assumed to be specifically localized. We previously demonstrated that E. coli mRNAs may localize to where their products localize in a translation-independent manner, thus challenging the transcription-translation coupling extent. However, the scope of RNA localization in bacteria remained unknown. Here, we report the distribution of the E. coli transcriptome between the membrane, cytoplasm, and poles by combining cell fractionation with deep-sequencing (Rloc-seq). Our results reveal asymmetric RNA distribution on a transcriptome-wide scale, significantly correlating with proteome localization and prevalence of translation-independent RNA localization. The poles are enriched with stress-related mRNAs and small RNAs, the latter becoming further enriched upon stress in an Hfq-dependent manner. Genome organization may play a role in localizing membrane protein-encoding transcripts. Our results show an unexpected level of intricacy in bacterial transcriptome organization and highlight the poles as hubs for regulation.

Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation

The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.

Simultaneously inhibiting undecaprenyl phosphate production and peptidoglycan synthases promotes rapid lysis in Escherichia coli

Peptidoglycan (PG) is a highly cross-linked polysaccharide that encases bacteria, resists the effects of turgor and confers cell shape. PG precursors are translocated across the cytoplasmic membrane by the lipid carrier undecaprenyl phosphate (Und-P) where they are incorporated into the PG superstructure. Previously, we found that one of our Escherichia coli laboratory strains (CS109) harbors a missense mutation in uppS, which encodes an enzymatically defective Und-P(P) synthase. Here, we show that CS109 cells lacking the bifunctional aPBP PBP1B (penicillin binding protein 1B) lyse during exponential growth at elevated temperature. PBP1B lysis was reversed by: (i) reintroducing wild-type uppS, (ii) increasing the availability of PG precursors or (iii) overproducing PBP1A, a related bifunctional PG synthase. In addition, inhibiting the catalytic activity of PBP2 or PBP3, two monofunctional bPBPs, caused CS109 cells to lyse. Limiting the precursors required for Und-P synthesis in MG1655, which harbors a wild-type allele of uppS, also promoted lysis in mutants lacking PBP1B or bPBP activity. Thus, simultaneous inhibition of Und-P production and PG synthases provokes a synergistic response that leads to cell lysis. These findings suggest a biological connection that could be exploited in combination therapies.

Identification of a Formate-Dependent Uric Acid Degradation Pathway in Escherichia coli

Purine is a nitrogen-containing compound that is abundant in nature. In organisms that utilize purine as a nitrogen source, purine is converted to uric acid, which is then converted to allantoin. Allantoin is then converted to ammonia. In Escherichia coli, neither urate-degrading activity nor a gene encoding an enzyme homologous to the known urate-degrading enzymes had previously been found. Here, we demonstrate urate-degrading activity in E. coli We first identified aegA as an E. coli gene involved in oxidative stress tolerance. An examination of gene expression revealed that both aegA and its paralog ygfT are expressed under both microaerobic and anaerobic conditions. The ygfT gene is localized within a chromosomal gene cluster presumably involved in purine catabolism. Accordingly, the expression of ygfT increased in the presence of exogenous uric acid, suggesting that ygfT is involved in urate degradation. Examination of the change of uric acid levels in the growth medium with time revealed urate-degrading activity under microaerobic and anaerobic conditions in the wild-type strain but not in the aegA ygfT double-deletion mutant. Furthermore, AegA- and YgfT-dependent urate-degrading activity was detected only in the presence of formate and formate dehydrogenase H. Collectively, these observations indicate the presence of urate-degrading activity in E. coli that is operational under microaerobic and anaerobic conditions. The activity requires formate, formate dehydrogenase H, and either aegA or ygfT We also identified other putative genes which are involved not only in formate-dependent but also in formate-independent urate degradation and may function in the regulation or cofactor synthesis in purine catabolism.IMPORTANCE The metabolic pathway of uric acid degradation to date has been elucidated only in aerobic environments and is not understood in anaerobic and microaerobic environments. In the current study, we showed that Escherichia coli, a facultative anaerobic organism, uses uric acid as a sole source of nitrogen under anaerobic and microaerobic conditions. We also showed that formate, formate dehydrogenase H, and either AegA or YgfT are involved in uric acid degradation. We propose that formate may act as an electron donor for a uric acid-degrading enzyme in this bacterium.

Machine learning-powered antibiotics phenotypic drug discovery

Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. Deployment of the semi-automated bacterial phenotypic fingerprint (BPF) profiling platform in conjunction with a machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA). The method identifies weak antibacterial hits allowing full exploitation of low potency hits frequently discovered by routine antibacterial screening. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, hence widening the known antibacterial chemical space of existing pharmaceutical compound libraries. More generally, beyond the specific objective of the present work, the proposed approach could be profitably applied to a broader range of diseases amenable to phenotypic drug discovery.

Retapamulin-Assisted Ribosome Profiling Reveals the Alternative Bacterial Proteome

The use of alternative translation initiation sites enables production of more than one protein from a single gene, thereby expanding the cellular proteome. Although several such examples have been serendipitously found in bacteria, genome-wide mapping of alternative translation start sites has been unattainable. We found that the antibiotic retapamulin specifically arrests initiating ribosomes at start codons of the genes. Retapamulin-enhanced Ribo-seq analysis (Ribo-RET) not only allowed mapping of conventional initiation sites at the beginning of the genes, but strikingly, it also revealed putative internal start sites in a number of Escherichia coli genes. Experiments demonstrated that the internal start codons can be recognized by the ribosomes and direct translation initiation in vitro and in vivo. Proteins, whose synthesis is initiated at internal in-frame and out-of-frame start sites, can be functionally important and contribute to the "alternative" bacterial proteome. The internal start sites may also play regulatory roles in gene expression.

Mutations in sigma 70 transcription factor improves expression of functional eukaryotic membrane proteins in Escherichia coli

Eukaryotic integral membrane proteins (IMPs) are difficult to study due to low functional expression levels. To investigate factors for efficient biogenesis of eukaryotic IMPs in the prokaryotic model organism Escherichia coli, important, e.g., for isotope-labeling for NMR, we selected for E. coli cells expressing high levels of functional G protein-coupled receptors (GPCRs) by FACS. Utilizing an E. coli strain library with all non-essential genes systematically deleted, we unexpectedly discovered upon whole-genome sequencing that the improved phenotype was not conferred by the deleted genes but by various subtle alterations in the “housekeeping” sigma 70 factor (RpoD). When analyzing effects of the rpoD mutations at the transcriptome level we found that toxic effects incurred on wild-type E. coli during receptor expression were diminished by two independent and synergistic effects: a slower but longer-lasting GPCR biosynthesis and an optimized transcriptional pattern, augmenting growth and expression at low temperature, setting the basis for further bacterial strain engineering.

DNA-binding directs the localization of a membrane-integrated receptor of the ToxR family

All living cells have a large number of proteins that are anchored with one transmembrane helix in the cytoplasmic membrane. Almost nothing is known about their spatiotemporal organization in whole cells. Here we report on the localization and dynamics of one representative, the pH sensor and transcriptional regulator CadC in Escherichia coli. Fluorophore-tagged CadC was detectable as distinct cluster only when the receptor was activated by external stress, which results in DNA-binding. Clusters immediately disappeared under non-stress conditions. CadC variants that mimic the active state of CadC independent of environmental stimuli corroborated the correlation between CadC clustering and binding to the DNA, as did altering the number or location of the DNA-binding site(s) in whole cells. These studies reveal a novel diffusion-and-capture mechanism to organize a membrane-integrated receptor dependent on the DNA in a rod-shaped bacterium.

The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain

The homeostasis of the proteome depends on the tight regulation of the mRNA and protein abundances, of the translation rates, and of the protein lifetimes. Results from several studies on prokaryotes or eukaryotic cell cultures have suggested that protein homeostasis is connected to, and perhaps regulated by, the protein and the codon sequences. However, this has been little investigated for mammals in vivo. Moreover, the link between the coding sequences and one critical parameter, the protein lifetime, has remained largely unexplored, both in vivo and in vitro. We tested this in the mouse brain, and found that the percentages of amino acids and codons in the sequences could predict all of the homeostasis parameters with a precision approaching experimental measurements. A key predictive element was the wobble nucleotide. G-/C-ending codons correlated with higher protein lifetimes, protein abundances, mRNA abundances and translation rates than A-/U-ending codons. Modifying the proportions of G-/C-ending codons could tune these parameters in cell cultures, in a proof-of-principle experiment. We suggest that the coding sequences are strongly linked to protein homeostasis in vivo, albeit it still remains to be determined whether this relation is causal in nature.

Genome-wide CRISPR-dCas9 screens in E. coli identify essential genes and phage host factors

High-throughput genetic screens are powerful methods to identify genes linked to a given phenotype. The catalytic null mutant of the Cas9 RNA-guided nuclease (dCas9) can be conveniently used to silence genes of interest in a method also known as CRISPRi. Here, we report a genome-wide CRISPR-dCas9 screen using a starting pool of ~ 92,000 sgRNAs which target random positions in the chromosome of E. coli. To benchmark our method, we first investigate its utility to predict gene essentiality in the genome of E. coli during growth in rich medium. We could identify 79% of the genes previously reported as essential and demonstrate the non-essentiality of some genes annotated as essential. In addition, we took advantage of the intermediate repression levels obtained when targeting the template strand of genes to show that cells are very sensitive to the expression level of a limited set of essential genes. Our data can be visualized on CRISPRbrowser, a custom web interface available at crispr.pasteur.fr. We then apply the screen to discover E. coli genes required by phages λ, T4 and 186 to kill their host, highlighting the involvement of diverse host pathways in the infection process of the three tested phages. We also identify colanic acid capsule synthesis as a shared resistance mechanism to all three phages. Finally, using a plasmid packaging system and a transduction assay, we identify genes required for the formation of functional λ capsids, thus covering the entire phage cycle. This study demonstrates the usefulness and convenience of pooled genome-wide CRISPR-dCas9 screens in bacteria and paves the way for their broader use as a powerful tool in bacterial genomics.

Over the past few years, CRISPR-Cas technologies have emerged as powerful tools to edit genomes and modulate gene expression. They have been applied to perform high-throughput genetic screens with the purpose to understand the function of genes in a systematic manner, but the application of these screens to bacteria have so far remained limited. Here, we present the use of a library of ~92,000 guide RNAs directing the dCas9 protein to silence one by one all the genes in the chromosome of E. coli. To benchmark our method, we first investigate the performance of the technique to identify essential genes, highlighting several non-essential genes also found to be essential by other methods. We then apply our method to detect bacterial genes required by three different bacteriophages to kill E. coli and for the production of functional progeny by phage λ. Our screens highlight previously known and new genetic interactions between phages and their host’s pathways and emphasize the importance of bacterial capsule in the resistance to multiple phages. Altogether, our results demonstrate the usefulness of genome-wide CRISPR-dCas9 screens in bacteria to uncover genes involved in various phenotypes.

Synthesis and degradation of FtsZ quantitatively predict the first cell division in starved bacteria

In natural environments, microbes are typically non-dividing and gauge when nutrients permit division. Current models are phenomenological and specific to nutrient-rich, exponentially growing cells, thus cannot predict the first division under limiting nutrient availability. To assess this regime, we supplied starving Escherichia coli with glucose pulses at increasing frequencies. Real-time metabolomics and microfluidic single-cell microscopy revealed unexpected, rapid protein, and nucleic acid synthesis already from minuscule glucose pulses in non-dividing cells. Additionally, the lag time to first division shortened as pulsing frequency increased. We pinpointed division timing and dependence on nutrient frequency to the changing abundance of the division protein FtsZ. A dynamic, mechanistic model quantitatively relates lag time to FtsZ synthesis from nutrient pulses and FtsZ protease-dependent degradation. Lag time changed in model-congruent manners, when we experimentally modulated the synthesis or degradation of FtsZ. Thus, limiting abundance of FtsZ can quantitatively predict timing of the first cell division.

Bacterial Lipoproteins Constitute the TLR2-Stimulating Activity of Serum Amyloid A

Studies comparing endogenous and recombinant serum amyloid A (SAA) have generated conflicting data on the proinflammatory function of these proteins. In exploring this discrepancy, we found that in contrast to commercially sourced recombinant human SAA1 (hSAA1) proteins produced in Escherichia coli, hSAA1 produced from eukaryotic cells did not promote proinflammatory cytokine production from human or mouse cells, induce Th17 differentiation, or stimulate TLR2. Proteomic analysis of E. coli-derived hSAA1 revealed the presence of numerous bacterial proteins, with several being reported or probable lipoproteins. Treatment of hSAA1 with lipoprotein lipase or addition of a lipopeptide to eukaryotic cell-derived hSAA1 inhibited or induced the production of TNF-α from macrophages, respectively. Our results suggest that a function of SAA is in the binding of TLR2-stimulating bacterial proteins, including lipoproteins, and demand that future studies of SAA employ a recombinant protein derived from eukaryotic cells.

Genome-wide analysis of fitness data and its application to improve metabolic models

Background

Synthetic biology and related techniques enable genome scale high-throughput investigation of the effect on organism fitness of different gene knock-downs/outs and of other modifications of genomic sequence.

Results

We develop statistical and computational pipelines and frameworks for analyzing high throughput fitness data over a genome scale set of sequence variants. Analyzing data from a high-throughput knock-down/knock-out bacterial study, we investigate differences and determinants of the effect on fitness in different conditions. Comparing fitness vectors of genes, across tens of conditions, we observe that fitness consequences strongly depend on genomic location and more weakly depend on gene sequence similarity and on functional relationships. In analyzing promoter sequences, we identified motifs associated with conditions studied in bacterial media such as Casaminos, D-glucose, Sucrose, and other sugars and amino-acid sources.

We also use fitness data to infer genes associated with orphan metabolic reactions in the iJO1366 E. coli metabolic model. To do this, we developed a new computational method that integrates gene fitness and gene expression profiles within a given reaction network neighborhood to associate this reaction with a set of genes that potentially encode the catalyzing proteins. We then apply this approach to predict candidate genes for 107 orphan reactions in iJO1366. Furthermore - we validate our methodology with known reactions using a leave-one-out approach. Specifically, using top-20 candidates selected based on combined fitness and expression datasets, we correctly reconstruct 39.7% of the reactions, as compared to 33% based on fitness and to 26% based on expression separately, and to 4.02% as a random baseline. Our model improvement results include a novel association of a gene to an orphan cytosine nucleosidation reaction.

Conclusion

Our pipeline for metabolic modeling shows a clear benefit of using fitness data for predicting genes of orphan reactions. Along with the analysis pipelines we developed, it can be used to analyze similar high-throughput data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2341-9) contains supplementary material, which is available to authorized users.

Computational analysis of the amino acid interactions that promote or decrease protein solubility

The solubility of globular proteins is a basic biophysical property that is usually a prerequisite for their functioning. In this study, we probed the solubility of globular proteins with the help of the statistical potential formalism, in view of objectifying the connection of solubility with structural and energetic properties and of the solubility-dependence of specific amino acid interactions. We started by setting up two independent datasets containing either soluble or aggregation-prone proteins with known structures. From these two datasets, we computed solubility-dependent distance potentials that are by construction biased towards the solubility of the proteins from which they are derived. Their analysis showed the clear preference of amino acid interactions such as Lys-containing salt bridges and aliphatic interactions to promote protein solubility, whereas others such as aromatic, His-π, cation-π, amino-π and anion-π interactions rather tend to reduce it. These results indicate that interactions involving delocalized π-electrons favor aggregation, unlike those involving no (or few) dispersion forces. Furthermore, using our potentials derived from either highly or weakly soluble proteins to compute protein folding free energies, we found that the difference between these two energies correlates better with solubility than other properties analyzed before such as protein length, isoelectric point and aliphatic index. This is, to the best of our knowledge, the first comprehensive in silico study of the impact of residue-residue interactions on protein solubility properties.The results of this analysis provide new insights that will facilitate future rational protein design applications aimed at modulating the solubility of targeted proteins.

Pseudogene product YqiG is important for pflB expression and biohydrogen production in Escherichia coli BW25113

Pseudogenes in the Escherichia coli genome are assumed to be non-functional. In this study, Keio collection BW25113∆yqiG and YqiG-producing strain (BW25113/pCA24N-YqiG) were used to evaluate the importance of pseudogene yqiG in hydrogen metabolism. Our results show pseudogene protein YqiG was identified as an essential protein in the production of biohydrogen from glucose. The mutant yqiG decreased biohydrogen production from 37 µmol mg^-1 protein to 6 µmol mg^-1 protein compared to the wild-type strain, and glucose consumption was reduced by 80%. Through transcriptional analysis, we found that the yqiG mutation represses pflB transcription tenfold; pflB encodes pyruvate-formate lyase, one of the key enzymes in the anaerobic metabolism of E. coli. Moreover, production of YqiG stimulated glycolysis and increased biohydrogen productivity 1.5-fold compared to that of the wild-type strain. Thus, YqiG is important for the central glycolysis reaction and is able to influence hydrogen metabolism activity in E. coli.

Codon usage and modular interactions between messenger RNA coding regions and small RNAs in Escherichia coli

Background

Small RNAs (sRNAs) are key regulators of gene expression in bacteria. In addition to modulating translation initiation, sRNAs can interact with mRNA coding regions to regulate mRNA stability and translation efficiency, enhancing or impeding progression of the ribosome along the mRNA. Since most amino acids are decoded by more than one codon (synonymous) we asked as to whether there is a codon bias in the interaction of sRNAs with coding regions of mRNAs. Therefore, we explored whether there are differences in codon usage or tRNA availability according to whether an mRNA is regulated by sRNAs or not. We also explored these parameters in the coding interaction regions in mRNAs. We focused our analysis on sRNAs that regulate multiple mRNAs.

Results

We found differences in codon adaptation index and tRNA adaptation index between sRNA-regulated and non-sRNA-regulated mRNAs. Interestingly, the sRNA-mRNA interacting regions tended to be enriched in unpreferred codons decoded by scarce tRNAs. We also found that sRNAs with multiple targets often contained modular segments capable of recognizing conserved motifs among these mRNAs.

Conclusions

Our results show that sRNAs in E. coli tend to recognize mRNA coding regions in which the ribosome is predicted to advance at low speeds. Identified motifs in interacting regions are conserved among mRNAs that are recognized by the same sRNA.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5038-6) contains supplementary material, which is available to authorized users.

A unified resource for transcriptional regulation in Escherichia coli K-12 incorporating high-throughput-generated binding data into RegulonDB version 10.0

BACKGROUND

Our understanding of the regulation of gene expression has benefited from the availability of high-throughput technologies that interrogate the whole genome for the binding of specific transcription factors and gene expression profiles. In the case of widely used model organisms, such as Escherichia coli K-12, the new knowledge gained from these approaches needs to be integrated with the legacy of accumulated knowledge from genetic and molecular biology experiments conducted in the pre-genomic era in order to attain the deepest level of understanding possible based on the available data.

RESULTS

In this paper, we describe an expansion of RegulonDB, the database containing the rich legacy of decades of classic molecular biology experiments supporting what we know about gene regulation and operon organization in E. coli K-12, to include the genome-wide dataset collections from 32 ChIP and 19 gSELEX publications, in addition to around 60 genome-wide expression profiles relevant to the functional significance of these datasets and used in their curation. Three essential features for the integration of this information coming from different methodological approaches are: first, a controlled vocabulary within an ontology for precisely defining growth conditions; second, the criteria to separate elements with enough evidence to consider them involved in gene regulation from isolated transcription factor binding sites without such support; and third, an expanded computational model supporting this knowledge. Altogether, this constitutes the basis for adequately gathering and enabling the comparisons and integration needed to manage and access such wealth of knowledge.

CONCLUSIONS

This version 10.0 of RegulonDB is a first step toward what should become the unifying access point for current and future knowledge on gene regulation in E. coli K-12. Furthermore, this model platform and associated methodologies and criteria can be emulated for gathering knowledge on other microbial organisms.

Quantitative RNA-seq Analysis Unveils Osmotic and Thermal Adaptation Mechanisms Relevant for Ectoine Production in Chromohalobacter salexigens

Quantitative RNA sequencing (RNA-seq) and the complementary phenotypic assays were implemented to investigate the transcriptional responses of Chromohalobacter salexigens to osmotic and heat stress. These conditions trigger the synthesis of ectoine and hydroxyectoine, two compatible solutes of biotechnological interest. Our findings revealed that both stresses make a significant impact on C. salexigens global physiology. Apart from compatible solute metabolism, the most relevant adaptation mechanisms were related to “oxidative- and protein-folding- stress responses,” “modulation of respiratory chain and related components,” and “ion homeostasis.” A general salt-dependent induction of genes related to the metabolism of ectoines, as well as repression of ectoine degradation genes by temperature, was observed. Different oxidative stress response mechanisms, secondary or primary, were induced at low and high salinity, respectively, and repressed by temperature. A higher sensitivity to H₂O₂ was observed at high salinity, regardless of temperature. Low salinity induced genes involved in “protein-folding-stress response,” suggesting disturbance of protein homeostasis. Transcriptional shift of genes encoding three types of respiratory NADH dehydrogenases, ATP synthase, quinone pool, Na⁺/H⁺ antiporters, and sodium-solute symporters, was observed depending on salinity and temperature, suggesting modulation of the components of the respiratory chain and additional systems involved in the generation of H⁺ and/or Na⁺ gradients. Remarkably, the Na⁺ intracellular content remained constant regardless of salinity and temperature. Disturbance of Na⁺- and H⁺-gradients with specific ionophores suggested that both gradients influence ectoine production, but with differences depending on the solute, salinity, and temperature conditions. Flagellum genes were strongly induced by salinity, and further induced by temperature. However, salt-induced cell motility was reduced at high temperature, possibly caused by an alteration of Na⁺ permeability by temperature, as dependence of motility on Na⁺-gradient was observed. The transcriptional induction of genes related to the synthesis and transport of siderophores correlated with a higher siderophore production and intracellular iron content only at low salinity. An excess of iron increased hydroxyectoine accumulation by 20% at high salinity. Conversely, it reduced the intracellular content of ectoines by 50% at high salinity plus high temperature. These findings support the relevance of iron homeostasis for osmoadaptation, thermoadaptation and accumulation of ectoines, in C. salexigens.

YtfB, an OapA Domain-Containing Protein, Is a New Cell Division Protein in Escherichia coli

While screening the Pfam database for novel peptidoglycan (PG) binding modules, we identified the OapA domain, which is annotated as a LysM-like domain. LysM domains bind PG and mediate localization to the septal ring. In the Gram-negative bacterium Escherichia coli, an OapA domain is present in YtfB, an inner membrane protein of unknown function but whose overproduction causes cells to filament. Together, these observations suggested that YtfB directly affects cell division, most likely through its OapA domain. Here, we show that YtfB accumulates at the septal ring and that its action requires the division-initiating protein FtsZ and, to a lesser extent, ZipA, an early recruit to the septalsome. While the loss of YtfB had no discernible impact, a mutant lacking both YtfB and DedD (a known cell division protein) grew as filamentous cells. The YtfB OapA domain by itself also localized to sites of division, and this localization was enhanced by the presence of denuded PGs. Finally, the OapA domain bound PG, though binding did not depend on the formation of denuded glycans. Collectively, our findings demonstrate that YtfB is a cell division protein whose function is related to cell wall hydrolases.IMPORTANCE All living cells must divide in order to thrive. In bacteria, this involves the coordinated activities of a large number of proteins that work in concert to constrict the cell. Knowing which proteins contribute to this process and how they function is fundamental. Here, we identify a new member of the cell division apparatus in the Gram-negative bacterium Escherichia coli whose function is related to the generation of a transient cell wall structure. These findings deepen our understanding of bacterial cell division.

The Odd "RB" Phage-Identification of Arabinosylation as a New Epigenetic Modification of DNA in T4-Like Phage RB69

In bacteriophages related to T4, hydroxymethylcytosine (hmC) is incorporated into the genomic DNA during DNA replication and is then further modified to glucosyl-hmC by phage-encoded glucosyltransferases. Previous studies have shown that RB69 shares a core set of genes with T4 and relatives. However, unlike the other “RB” phages, RB69 is unable to recombine its DNA with T4 or with the other “RB” isolates. In addition, despite having homologs to the T4 enzymes used to synthesize hmC, RB69 has no identified homolog to known glucosyltransferase genes. In this study we sought to understand the basis for RB69’s behavior using high-pH anion exchange chromatography (HPAEC) and mass spectrometry. Our analyses identified a novel phage epigenetic DNA sugar modification in RB69 DNA, which we have designated arabinosyl-hmC (ara-hmC). We sought a putative glucosyltranserase responsible for this novel modification and determined that RB69 also has a novel transferase gene, ORF003c, that is likely responsible for the arabinosyl-specific modification. We propose that ara-hmC was responsible for RB69 being unable to participate in genetic exchange with other hmC-containing T-even phages, and for its described incipient speciation. The RB69 ara-hmC also likely protects its DNA from some anti-phage type-IV restriction endonucleases. Several T4-related phages, such as E. coli phage JS09 and Shigella phage Shf125875 have homologs to RB69 ORF003c, suggesting the ara-hmC modification may be relatively common in T4-related phages, highlighting the importance of further work to understand the role of this modification and the biochemical pathway responsible for its production.

Antibiotic-resistant bacteria show widespread collateral sensitivity to antimicrobial peptides

Antimicrobial peptides are promising alternative antimicrobial agents. However, little is known about whether resistance to small-molecule antibiotics leads to cross-resistance (decreased sensitivity) or collateral sensitivity (increased sensitivity) to antimicrobial peptides. We systematically addressed this question by studying the susceptibilities of a comprehensive set of 60 antibiotic-resistant Escherichia coli strains towards 24 antimicrobial peptides. Strikingly, antibiotic-resistant bacteria show a high frequency of collateral sensitivity to antimicrobial peptides, whereas cross-resistance is relatively rare. We identify clinically relevant multidrug-resistance mutations that increase bacterial sensitivity to antimicrobial peptides. Collateral sensitivity in multidrug-resistant bacteria arises partly through regulatory changes shaping the lipopolysaccharide composition of the bacterial outer membrane. These advances allow the identification of antimicrobial peptide-antibiotic combinations that enhance antibiotic activity against multidrug-resistant bacteria and slow down de novo evolution of resistance. In particular, when co-administered as an adjuvant, the antimicrobial peptide glycine-leucine-amide caused up to 30-fold decrease in the antibiotic resistance level of resistant bacteria. Our work provides guidelines for the development of efficient peptide-based therapies of antibiotic-resistant infections.

Modeling leaderless transcription and atypical genes results in more accurate gene prediction in prokaryotes

In a conventional view of the prokaryotic genome organization, promoters precede operons and ribosome binding sites (RBSs) with Shine-Dalgarno consensus precede genes. However, recent experimental research suggesting a more diverse view motivated us to develop an algorithm with improved gene-finding accuracy. We describe GeneMarkS-2, an ab initio algorithm that uses a model derived by self-training for finding species-specific (native) genes, along with an array of precomputed “heuristic” models designed to identify harder-to-detect genes (likely horizontally transferred). Importantly, we designed GeneMarkS-2 to identify several types of distinct sequence patterns (signals) involved in gene expression control, among them the patterns characteristic for leaderless transcription as well as noncanonical RBS patterns. To assess the accuracy of GeneMarkS-2, we used genes validated by COG (Clusters of Orthologous Groups) annotation, proteomics experiments, and N-terminal protein sequencing. We observed that GeneMarkS-2 performed better on average in all accuracy measures when compared with the current state-of-the-art gene prediction tools. Furthermore, the screening of ∼5000 representative prokaryotic genomes made by GeneMarkS-2 predicted frequent leaderless transcription in both archaea and bacteria. We also observed that the RBS sites in some species with leadered transcription did not necessarily exhibit the Shine-Dalgarno consensus. The modeling of different types of sequence motifs regulating gene expression prompted a division of prokaryotic genomes into five categories with distinct sequence patterns around the gene starts.

The evolutionary impact of intragenic FliA promoters in proteobacteria

In Escherichia coli, one sigma factor recognizes the majority of promoters, and six 'alternative' sigma factors recognize specific subsets of promoters. The alternative sigma factor FliA (σ28 ) recognizes promoters upstream of many flagellar genes. We previously showed that most E. coli FliA binding sites are located inside genes. However, it was unclear whether these intragenic binding sites represent active promoters. Here, we construct and assay transcriptional promoter-lacZ fusions for all 52 putative FliA promoters previously identified by ChIP-seq. These experiments, coupled with integrative analysis of published genome-scale transcriptional datasets, strongly suggest that most intragenic FliA binding sites are active promoters that transcribe highly unstable RNAs. Additionally, we show that widespread intragenic FliA-dependent transcription may be a conserved phenomenon, but that specific promoters are not themselves conserved. We conclude that intragenic FliA-dependent promoters and the resulting RNAs are unlikely to have important regulatory functions. Nonetheless, one intragenic FliA promoter is broadly conserved and constrains evolution of the overlapping protein-coding gene. Thus, our data indicate that intragenic regulatory elements can influence bacterial protein evolution and suggest that the impact of intragenic regulatory sequences on genome evolution should be considered more broadly.