Comparative analysis of essential genes and nonessential genes in Escherichia coli K12

The identification of essential cellular genes is critical for validating drug targets

Accurately identifying biological targets is crucial for advancing treatment options. Essential genes, vital for cell or organism survival, hold promise as potential drug targets in disease treatment. Although many studies have sought to identify essential genes as therapeutic targets in medicine and bioinformatics, systematic reviews on their relationship with drug targets are relatively rare. This work presents a comprehensive analysis to aid in identifying essential genes as potential targets for drug discovery, encompassing their relevance, identification methods, successful case studies, and challenges. This work will facilitate the identification of essential genes as therapeutic targets, thereby boosting new drug development.

An open-access dashboard to interrogate the genetic diversity of Mycobacterium tuberculosis clinical isolates

Tuberculosis (TB) remains one of the leading infectious disease killers in the world. The ongoing development of novel anti-TB medications has yielded potent compounds that often target single sites with well-defined mechanisms of action. However, despite the identification of resistance-associated mutations through target deconvolution studies, comparing these findings with the diverse Mycobacterium tuberculosis populations observed in clinical settings is often challenging. To address this gap, we constructed an open-access database encompassing genetic variations from > 50,000 clinical isolates, spanning the entirety of the M. tuberculosis protein-encoding genome. This resource offers a valuable tool for investigating the prevalence of target-based resistance mutations in any drug target within clinical contexts. To demonstrate the practical application of this dataset in drug discovery, we focused on drug targets currently undergoing phase II clinical trials. By juxtaposing genetic variations of these targets with resistance mutations derived from laboratory-adapted strains, we identified multiple positions across three targets harbouring resistance-associated mutations already present in clinical isolates. Furthermore, our analysis revealed a discernible correlation between genetic diversity within each protein and their predicted essentiality. This meta-analysis, openly accessible via a dedicated dashboard, enables comprehensive exploration of genetic diversity pertaining to any drug target or resistance determinant in M. tuberculosis.

Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network

The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational principles resemble an artificial neural network (ANN), which can pave the way for the development of wet-neuromorphic computing systems. Genes are integrated into gene-perceptrons with transcription factors (TFs) as input, where the TF concentration relative to half-maximal RNA concentration and gene product copy number influences transcription and translation via weighted multiplication before undergoing a nonlinear activation function. This process yields protein concentration as the output, effectively turning the entire GRN into a gene regulatory neural network (GRNN). In this paper, we establish nonlinear classifiers for molecular machine learning using the inherent sigmoidal nonlinear behavior of gene expression. The eigenvalue-based stability analysis, tailored to system parameters, confirms maximum-stable concentration levels, minimizing concentration fluctuations and computational errors. Given the significance of the stabilization phase in GRNN computing and the dynamic nature of the GRN, alongside potential changes in system parameters, we utilize the Lyapunov stability theorem for temporal stability analysis. Based on this GRN-to-GRNN mapping and stability analysis, three classifiers are developed utilizing two generic multilayer sub-GRNNs and a sub-GRNN extracted from the Escherichia coli GRN. Our findings also reveal the adaptability of different sub-GRNNs to suit different application requirements.

CRISPR-Cas system as a promising player against bacterial infection and antibiotic resistance

The phenomenon of antibiotic resistance (AR) and its increasing global trends and destructive waves concerns patients and the healthcare system. In order to combat AR, it is necessary to explore new strategies when the current antibiotics fail to be effective. Thus, knowing the resistance mechanisms and appropriate diagnosis of bacterial infections may help enhance the sensitivity and specificity of novel strategies. On the other hand, resistance to antimicrobial compounds can spread from resistant populations to susceptible ones. Antimicrobial resistance genes (ARGs) significantly disseminate AR via horizontal and vertical gene transfer. The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas system is a member of the bacterial immune system with the ability to remove the ARGs; therefore, it can be introduced as an effective and innovative strategy in the battle against AR. Here, we reviewed CRISPR-based bacterial diagnosis technologies. Moreover, the strategies to battle AR based on targeting bacterial chromosomes and resistance plasmids using the CRISPR-Cas system have been explained. Besides, we have presented the limitations of CRISPR delivery and potential solutions to help improve the future development of CRISPR-based platforms.

Sequence-structure-function relationships in the microbial protein universe

For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don't rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.

Bacterial genome reductions: Tools, applications, and challenges

Bacterial cells are widely used to produce value-added products due to their versatility, ease of manipulation, and the abundance of genome engineering tools. However, the efficiency of producing these desired biomolecules is often hindered by the cells’ own metabolism, genetic instability, and the toxicity of the product. To overcome these challenges, genome reductions have been performed, making strains with the potential of serving as chassis for downstream applications. Here we review the current technologies that enable the design and construction of such reduced-genome bacteria as well as the challenges that limit their assembly and applicability. While genomic reductions have shown improvement of many cellular characteristics, a major challenge still exists in constructing these cells efficiently and rapidly. Computational tools have been created in attempts at minimizing the time needed to design these organisms, but gaps still exist in modelling these reductions in silico. Genomic reductions are a promising avenue for improving the production of value-added products, constructing chassis cells, and for uncovering cellular function but are currently limited by their time-consuming construction methods. With improvements to and the creation of novel genome editing tools and in silico models, these approaches could be combined to expedite this process and create more streamlined and efficient cell factories.

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.

Gradients in gene essentiality reshape antibacterial research

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

Cross-species prediction of essential genes in insects

MOTIVATION

Insects possess a vast phenotypic diversity and key ecological roles. Several insect species also have medical, agricultural and veterinary importance as parasites and disease vectors. Therefore, strategies to identify potential essential genes in insects may reduce the resources needed to find molecular players in central processes of insect biology. However, most predictors of essential genes in multicellular eukaryotes using machine learning rely on expensive and laborious experimental data to be used as gene features, such as gene expression profiles or protein-protein interactions, even though some of this information may not be available for the majority of insect species with genomic sequences available.

RESULTS

Here, we present and validate a machine learning strategy to predict essential genes in insects using sequence-based intrinsic attributes (statistical and physicochemical data) together with the predictions of subcellular location and transcriptomic data, if available. We gathered information available in public databases describing essential and non-essential genes for Drosophila melanogaster (fruit fly, Diptera) and Tribolium castaneum (red flour beetle, Coleoptera). We proceeded by computing intrinsic and extrinsic attributes that were used to train statistical models in one species and tested by their capability of predicting essential genes in the other. Even models trained using only intrinsic attributes are capable of predicting genes in the other insect species, including the prediction of lineage-specific essential genes. Furthermore, the inclusion of RNA-Seq data is a major factor to increase classifier performance.

AVAILABILITY AND IMPLEMENTATION

The code, data and final models produced in this study are freely available at https://github.com/g1o/GeneEssentiality/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deciphering the physiological response of Escherichia coli under high ATP demand

One long-standing question in microbiology is how microbes buffer perturbations in energy metabolism. In this study, we systematically analyzed the impact of different levels of ATP demand in Escherichia coli under various conditions (aerobic and anaerobic, with and without cell growth). One key finding is that, under all conditions tested, the glucose uptake increases with rising ATP demand, but only to a critical level beyond which it drops markedly, even below wild-type levels. Focusing on anaerobic growth and using metabolomics and proteomics data in combination with a kinetic model, we show that this biphasic behavior is induced by the dual dependency of the phosphofructokinase on ATP (substrate) and ADP (allosteric activator). This mechanism buffers increased ATP demands by a higher glycolytic flux but, as shown herein, it collapses under very low ATP concentrations. Model analysis also revealed two major rate-controlling steps in the glycolysis under high ATP demand, which could be confirmed experimentally. Our results provide new insights on fundamental mechanisms of bacterial energy metabolism and guide the rational engineering of highly productive cell factories.

BING, a novel antimicrobial peptide isolated from Japanese medaka plasma, targets bacterial envelope stress response by suppressing cpxR expression

Antimicrobial peptides (AMPs) have emerged as a promising alternative to small molecule antibiotics. Although AMPs have previously been isolated in many organisms, efforts on the systematic identification of AMPs in fish have been lagging. Here, we collected peptides from the plasma of medaka (Oryzias latipes) fish. By using mass spectrometry, 6399 unique sequences were identified from the isolated peptides, among which 430 peptides were bioinformatically predicted to be potential AMPs. One of them, a thermostable 13-residue peptide named BING, shows a broad-spectrum toxicity against pathogenic bacteria including drug-resistant strains, at concentrations that presented relatively low toxicity to mammalian cell lines and medaka. Proteomic analysis indicated that BING treatment induced a deregulation of periplasmic peptidyl-prolyl isomerases in gram-negative bacteria. We observed that BING reduced the RNA level of cpxR, an upstream regulator of envelope stress responses. cpxR is known to play a crucial role in the development of antimicrobial resistance, including the regulation of genes involved in drug efflux. BING downregulated the expression of efflux pump components mexB, mexY and oprM in P. aeruginosa and significantly synergised the toxicity of antibiotics towards these bacteria. In addition, exposure to sublethal doses of BING delayed the development of antibiotic resistance. To our knowledge, BING is the first AMP shown to suppress cpxR expression in Gram-negative bacteria. This discovery highlights the cpxR pathway as a potential antimicrobial target.

Ultraviolet-induced RNA:DNA hybrids interfere with chromosomal DNA synthesis

Ultraviolet (UV) induces pyrimidine dimers (PDs) in DNA and replication-dependent fragmentation in chromosomes. The rnhAB mutants in Escherichia coli, accumulating R-loops and single DNA-rNs, are generally resistant to DNA damage, but are surprisingly UV-sensitive, even though they remove PDs normally, suggesting irreparable chromosome lesions. We show here that the RNase H defect does not cause additional chromosome fragmentation after UV, but inhibits DNA synthesis after replication restart. Genetic analysis implies formation of R-loop-anchored transcription elongation complexes (R-loop-aTECs) in UV-irradiated rnhAB mutants, predicting that their chromosomal DNA will accumulate: (i) RNA:DNA hybrids; (ii) a few slow-to-remove PDs. We confirm both features and also find that both, surprisingly, depend on replication restart. Finally, enriching for the UV-induced RNA:DNA hybrids in the rnhAB uvrA mutants also co-enriches for PDs, showing their co-residence in the same structures. We propose that PD-triggered R-loop-aTECs block head-on replication in RNase H-deficient mutants.

Signal Recognition Particle Suppressor Screening Reveals the Regulation of Membrane Protein Targeting by the Translation Rate

The signal recognition particle (SRP) is conserved in all living organisms, and it cotranslationally delivers proteins to the inner membrane or endoplasmic reticulum. Recently, SRP loss was found not to be lethal in either the eukaryote Saccharomyces cerevisiae or the prokaryote Streptococcus mutans In Escherichia coli, the role of SRP in mediating inner membrane protein (IMP) targeting has long been studied. However, the essentiality of SRP remains a controversial topic, partly hindered by the lack of strains in which SRP is completely absent. Here we show that the SRP was nonessential in E. coli by suppressor screening. We identified two classes of extragenic suppressors-two translation initiation factors and a ribosomal protein-all of which are involved in translation initiation. The translation rate and inner membrane proteomic analyses were combined to define the mechanism that compensates for the lack of SRP. The primary factor that contributes to the efficiency of IMP targeting is the extension of the time window for targeting by pausing the initiation of translation, which further reduces translation initiation and elongation rates. Furthermore, we found that easily predictable features in the nascent chain determine the specificity of protein targeting. Our results show why the loss of the SRP pathway does not lead to lethality. We report a new paradigm in which the time delay in translation initiation is beneficial during protein targeting in the absence of SRP.IMPORTANCE Inner membrane proteins (IMPs) are cotranslationally inserted into the inner membrane or endoplasmic reticulum by the signal recognition particle (SRP). Generally, the deletion of SRP can result in protein targeting defects in Escherichia coli Suppressor screening for loss of SRP reveals that pausing at the translation start site is likely to be critical in allowing IMP targeting and avoiding aggregation. In this work, we found for the first time that SRP is nonessential in E. coli The time delay in initiation is different from the previous mechanism that only slows down the elongation rate. It not only maximizes the opportunity for untranslated ribosomes to be near the inner membrane but also extends the time window for targeting translating ribosomes by decreasing the speed of translation. We anticipate that our work will be a starting point for a more delicate regulatory mechanism of protein targeting.

Essentiality, conservation, evolutionary pressure and codon bias in bacterial genomes

Essential genes constitute the core of genes which cannot be mutated too much nor lost along the evolutionary history of a species. Natural selection is expected to be stricter on essential genes and on conserved (highly shared) genes, than on genes that are either nonessential or peculiar to a single or a few species. In order to further assess this expectation, we study here how essentiality of a gene is connected with its degree of conservation among several unrelated bacterial species, each one characterised by its own codon usage bias. Confirming previous results on E. coli, we show the existence of a universal exponential relation between gene essentiality and conservation in bacteria. Moreover, we show that, within each bacterial genome, there are at least two groups of functionally distinct genes, characterised by different levels of conservation and codon bias: i) a core of essential genes, mainly related to cellular information processing; ii) a set of less conserved nonessential genes with prevalent functions related to metabolism. In particular, the genes in the first group are more retained among species, are subject to a stronger purifying conservative selection and display a more limited repertoire of synonymous codons. The core of essential genes is close to the minimal bacterial genome, which is in the focus of recent studies in synthetic biology, though we confirm that orthologs of genes that are essential in one species are not necessarily essential in other species. We also list a set of highly shared genes which, reasonably, could constitute a reservoir of targets for new anti-microbial drugs.

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes

Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features

We propose an integrated machine learning process to predict gene essentiality in Escherichia coli K-12 MG1655 metabolism that outperforms known methods.

Essential Genes Embody Increased Mutational Robustness to Compensate for the Lack of Backup Genetic Redundancy

Genetic robustness is a hallmark of cells, occurring through many mechanisms and at many levels. Essential genes lack the common robustness mechanism of genetic redundancy (i.e., existing alongside other genes with the same function), and thus appear at first glance to leave cells highly vulnerable to genetic or environmental perturbations. Here we explore a hypothesis that cells might protect against essential gene loss through mechanisms that occur at various cellular levels aside from the level of the gene. Using Escherichia coli and Saccharomyces cerevisiae as models, we find that essential genes are enriched over non-essential genes for properties we call "coding efficiency" and "coding robustness", denoting respectively a gene's efficiency of translation and robustness to non-synonymous mutations. The coding efficiency levels of essential genes are highly positively correlated with their evolutionary conservation levels, suggesting that this feature plays a key role in protecting conserved, evolutionarily important genes. We then extend our hypothesis into the realm of metabolic networks, showing that essential metabolic reactions are encoded by more "robust" genes than non-essential reactions, and that essential metabolites are produced by more reactions than non-essential metabolites. Taken together, these results testify that robustness at the gene-loss level and at the mutation level (and more generally, at two cellular levels that are usually treated separately) are not decoupled, but rather, that cellular vulnerability exposed due to complete gene loss is compensated by increased mutational robustness. Why some genes are backed up primarily against loss and others against mutations still remains an open question.

Evolutionary Conservation of Bacterial Essential Metabolic Genes across All Bacterial Culture Media

One of the basic postulates of molecular evolution is that functionally important genes should evolve slower than genes of lesser significance. Essential genes, whose knockout leads to a lethal phenotype are considered of high functional importance, yet whether they are truly more conserved than nonessential genes has been the topic of much debate, fuelled by a host of contradictory findings. Here we conduct the first large-scale study utilizing genome-scale metabolic modeling and spanning many bacterial species, which aims to answer this question. Using the novel Media Variation Analysis, we examine the range of conservation of essential vs. nonessential metabolic genes in a given species across all possible media. We are thus able to obtain for the first time, exact upper and lower bounds on the levels of differential conservation of essential genes for each of the species studied. The results show that bacteria do exhibit an overall tendency for differential conservation of their essential genes vs. their non-essential ones, yet this tendency is highly variable across species. We show that the model bacterium E. coli K12 may or may not exhibit differential conservation of essential genes depending on its growth medium, shedding light on previous experimental studies showing opposite trends.

Three computational tools for predicting bacterial essential genes

Essential genes are those genes indispensable for the survival of any living cell. Bacterial essential genes constitute the cornerstones of synthetic biology and are often attractive targets in the development of antibiotics and vaccines. Because identification of essential genes with wet-lab ways often means expensive economic costs and tremendous labor, scientists changed to seek for alternative way of computational prediction. Aiming to help to solve this issue, our research group (CEFG: group of Computational, Comparative, Evolutionary and Functional Genomics, http://cefg.uestc.edu.cn) has constructed three online services to predict essential genes in bacterial genomes. These freely available tools are applicable for single gene sequences without annotated functions, single genes with definite names, and complete genomes of bacterial strains. To ensure reliable predictions, the investigated species should belong to the same family (for EGP) or phylum (for CEG_Match and Geptop) with one of the reference species, respectively. As the pilot software for the issue, predicting accuracies of them have been assessed and compared with existing algorithms, and note that all of other published algorithms have not any formed online services. We hope these services at CEFG will help scientists and researchers in the field of essential genes.

Geptop: a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny

Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.

Three independent determinants of protein evolutionary rate

One of the most widely accepted ideas related to the evolutionary rates of proteins is that functionally important residues or regions evolve slower than other regions, a reasonable outcome of which should be a slower evolutionary rate of the proteins with a higher density of functionally important sites. Oddly, the role of functional importance, mainly measured by essentiality, in determining evolutionary rate has been challenged in recent studies. Several variables other than protein essentiality, such as expression level, gene compactness, protein-protein interactions, etc., have been suggested to affect protein evolutionary rate. In the present review, we try to refine the concept of functional importance of a gene, and consider three factors-functional importance, expression level, and gene compactness, as independent determinants of evolutionary rate of a protein, based not only on their known correlation with evolutionary rate but also on a reasonable mechanistic model. We suggest a framework based on these mechanistic models to correctly interpret the correlations between evolutionary rates and the various variables as well as the interrelationships among the variables.

Loss of genetic redundancy in reductive genome evolution

Biological systems evolved to be functionally robust in uncertain environments, but also highly adaptable. Such robustness is partly achieved by genetic redundancy, where the failure of a specific component through mutation or environmental challenge can be compensated by duplicate components capable of performing, to a limited extent, the same function. Highly variable environments require very robust systems. Conversely, predictable environments should not place a high selective value on robustness. Here we test this hypothesis by investigating the evolutionary dynamics of genetic redundancy in extremely reduced genomes, found mostly in intracellular parasites and endosymbionts. By combining data analysis with simulations of genome evolution we show that in the extensive gene loss suffered by reduced genomes there is a selective drive to keep the diversity of protein families while sacrificing paralogy. We show that this is not a by-product of the known drivers of genome reduction and that there is very limited convergence to a common core of families, indicating that the repertoire of protein families in reduced genomes is the result of historical contingency and niche-specific adaptations. We propose that our observations reflect a loss of genetic redundancy due to a decreased selection for robustness in a predictable environment.

Relationship between operon preference and functional properties of persistent genes in bacterial genomes

BACKGROUND

Genes in bacteria may be organised into operons, leading to strict co-expression of the genes that participate in the same operon. However, comparisons between different bacterial genomes have shown that much of the operon structure is dynamic on an evolutionary time scale. This indicates that there are opposing effects influencing the tendency for operon formation, and these effects may be reflected in properties like evolutionary rate, complex formation, metabolic pathways and gene fusion.

RESULTS

We have used multi-species protein-protein comparisons to generate a high-quality set of genes that are persistent in bacterial genomes (i.e. they have close to universal distribution). We have analysed these genes with respect to operon participation and important functional properties, including evolutionary rate and protein-protein interactions.

CONCLUSIONS

Genes for ribosomal proteins show a very slow rate of evolution. This is consistent with a strong tendency for the genes to participate in operons and for their proteins to be involved in essential and well defined complexes. Persistent genes for non-ribosomal proteins can be separated into two classes according to tendency to participate in operons. Those with a strong tendency for operon participation make proteins with fewer interaction partners that seem to participate in relatively static complexes and possibly linear pathways. Genes with a weak tendency for operon participation tend to produce proteins with more interaction partners, but possibly in more dynamic complexes and convergent pathways. Genes that are not regulated through operons are therefore more evolutionary constrained than the corresponding operon-associated genes and will on average evolve more slowly.

Transcript stability in the protein interaction network of Escherichia coli

Gene expression is a dynamic process which can be controlled by a number of mechanisms as genetic information flows from nucleic acids to proteins. The study of gene expression in the steady state, while informative, overlooks the underlying dynamics of the processes. Steady-state transcript levels are a result of both RNA synthesis and degradation, and as such, measurements of degradation rates can be used to determine their rates of synthesis as well as reveal regulation that occurs via changes in RNA stability. Messenger RNA degradation plays a central role in diverse cellular processes and is controlled primarily by the activity of the degradosome in prokaryotes. In this study, we use the currently available network of protein-protein interactions (PPIs) and mRNA half-lives in Escherichia coli to demonstrate that centrality of a protein in the PPI network is strongly correlated with its mRNA half-life. We find that interacting proteins tend to show similar half-lives, commonly referred to as assortative behavior in networks, which is frequently found in biological and social networks. While a major fraction of the interacting proteins show significantly lower differences in mRNA stabilities, a smaller but significant number of protein pairs tend to show higher differences than expected by chance. Higher differences in transcript stabilities often involved those that encode for transcription factors and enzymes, suggesting a feedback link at the post-translational level. We also note that although essential genes, which act as a proxy for in vivo centrality in PPI networks, are highly expressed compared to non-essential ones, they do not encode for more stable transcripts than non-essential genes. Our results provide a direct link between mRNA stability and centrality of a protein in PPI network indicating the importance of post-transcriptional mechanisms on nascent RNAs in the cell.

Novel approaches to discovery of antibacterial agents

Antimicrobial resistance is a rapidly increasing problem impacting the successful treatment of bacterial infectious disease. To combat resistance, the development of new treatment options is required. Recent advances in technology have aided in the discovery of novel antibacterial agents, specifically through genome mining for novel natural product biosynthetic gene clusters and improved small molecule high-throughput screening methods. Novel targets such as lipopolysaccharide and fatty acid biosyntheses have been identified by essential gene studies, representing a shift from traditional antibiotic targets. Finally, inhibiting non-essential genes with small molecules is being explored as a method for rescuing the activity of ‘old’ antibiotics, providing a novel synergistic approach to antimicrobial discovery.