Impact of early preeclampsia prediction on medication adherence and behavior change: a survey of pregnant and recently-delivered individuals.

BACKGROUND: Behavior change and medication adherence represent potential barriers to optimal prevention of pregnancy complications including preeclampsia. We sought to evaluate baseline sentiments on pregnancy care and medication amenability, and how these measures would be impacted by early predictive testing for preeclampsia. METHODS: We developed a digital survey to query participants' baseline sentiments on pregnancy care, knowledge about pregnancy complications, and views on a hypothetical test to predict preeclampsia. The survey was administered online to pregnant and recently-delivered individuals in the United States. Survey data were analyzed using pooled two-sample proportion z-tests with adjustment for multiple comparisons. RESULTS: One thousand and twenty-two people completed the survey. 84% reported they were satisfied with their pregnancy care. Self-assessed knowledge about preeclampsia was high, with 75% of respondents reporting they have a "good understanding" of preeclampsia, but measured knowledge was low, with only 10% able to identify five common signs/symptoms of preeclampsia. Notably, 40% of participants with prior preeclampsia believed they were at average or below-average risk for recurrence. 91% of participants desired early pregnancy predictive testing for preeclampsia. If found to be at high risk for preeclampsia, 88% reported they would be more motivated to follow their provider's medication recommendations and 94% reported they would desire home blood pressure monitoring. Increased motivation to follow clinicians' medication and monitoring recommendations was observed across the full spectrum of medication amenability. Individuals who are more medication-hesitant still reported high rates of motivation to change behavior and adhere to medication recommendations if predictive testing showed a high risk of preeclampsia. Importantly, a high proportion of medication-hesitant individuals reported that if a predictive test demonstrated they were at high risk of preeclampsia, they would feel more motivated to take medications (83.0%) and aspirin (75.9%) if recommended. CONCLUSION: While satisfaction with care is high, participants desire more information about their pregnancy health, would value predictive testing for preeclampsia, and report they would act on this information. Improved detection of at-risk individuals through objective testing combined with increased adherence to their recommended care plan may be an important step to remedy the growing gap in prevention.

RNA profiles reveal signatures of future health and disease in pregnancy.

Maternal morbidity and mortality continue to rise, and pre-eclampsia is a major driver of this burden1. Yet the ability to assess underlying pathophysiology before clinical presentation to enable identification of pregnancies at risk remains elusive. Here we demonstrate the ability of plasma cell-free RNA (cfRNA) to reveal patterns of normal pregnancy progression and determine the risk of developing pre-eclampsia months before clinical presentation. Our results centre on comprehensive transcriptome data from eight independent prospectively collected cohorts comprising 1,840 racially diverse pregnancies and retrospective analysis of 2,539 banked plasma samples. The pre-eclampsia data include 524 samples (72 cases and 452 non-cases) from two diverse independent cohorts collected 14.5 weeks (s.d., 4.5 weeks) before delivery. We show that cfRNA signatures from a single blood draw can track pregnancy progression at the placental, maternal and fetal levels and can robustly predict pre-eclampsia, with a sensitivity of 75% and a positive predictive value of 32.3% (s.d., 3%), which is superior to the state-of-the-art method2. cfRNA signatures of normal pregnancy progression and pre-eclampsia are independent of clinical factors, such as maternal age, body mass index and race, which cumulatively account for less than 1% of model variance. Further, the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia.

Identification and Characterization of Pleiotropic High-Persistence Mutations in the Beta Subunit of the Bacterial RNA Polymerase.

Mutations conferring resistance to bactericidal antibiotics reduce the average susceptibility of mutant populations. It is unknown, however, how those mutations affect the survival of individual bacteria. Since surviving bacteria can be a reservoir for recurring infections, it is important to know how survival rates may be affected by resistance mutations and by the choice of antibiotics. Here, we present evidence that (i) Escherichia coli mutants with 100 to 1,000 times increased frequency of survival in ciprofloxacin, an archetypal fluoroquinolone antibiotic, can be readily obtained in a stepwise selection; (ii) the high survival frequency is conferred by mutations in the switch region of the beta subunit of the RNA polymerase; (iii) the switch-region mutations are (p)ppGpp mimics, partially analogous to rpoB stringent mutations; (iv) the stringent and switch region rpoB mutations frequently occur in clinical isolates of E. coli, Acinetobacter baumannii, Mycobacterium tuberculosis, and Staphylococcus aureus, and at least one of them, RpoB S488L, which is a common rifampicin resistance mutations, dramatically increases the survival of a clinical methicillin-resistant S. aureus (MRSA) strain in ampicillin; and (v) the RpoB-associated high-survival phenotype can be reversed by subinhibitory concentrations of chloramphenicol.

Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics.

BACKGROUND: Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses. RESULTS: We show that alternative pathway definitions can alter enrichment p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p-values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3-2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p-value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question. CONCLUSIONS: The choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses.

Analysis of mutational patterns in quinolone resistance-determining regions of GyrA and ParC of clinical isolates.

Fluoroquinolone (FQ)-resistant bacteria pose a major global health threat. Unanalysed genomic data from thousands of sequenced microbes likely contain important hints regarding the evolution of FQ resistance, yet this information lies fallow. Here we analysed the co-occurrence patterns of quinolone resistance mutations in genes encoding the FQ drug targets DNA gyrase (gyrase) and topoisomerase IV (topo-IV) from 36,402 bacterial genomes, representing 10 Gram-positive and 10 Gram-negative species. For 19 species, the likeliest routes toward resistance mutations in both targets were determined, and for 5 species those mutations necessary and sufficient to predict FQ resistance were also determined. Target mutation hierarchy was fixed in all examined Gram-negative species, with gyrase being the primary and topo-IV the secondary quinolone target, as well as in six of nine Gram-positive species, with topo-IV being the primary and gyrase the secondary target. By contrast, in three Gram-positive species (Staphylococcus haemolyticus, Streptococcus pneumoniae and Streptococcus suis), under some conditions gyrase became the primary and topo-IV the secondary target. The path through individual resistance mutations varied by species. Both linear and branched paths were identified in Gram-positive and Gram-negative organisms alike. Finally, FQ resistance could be predicted based solely on target gene quinolone resistance mutations for Acinetobacter baumannii, Escherichia coli and Staphylococcus aureus, but not Klebsiella pneumoniae or Pseudomonas aeruginosa. These findings have important implications both for sequence-based diagnostics and for understanding the emergence of FQ resistance.

De Novo Characterization of Genes That Contribute to High-Level Ciprofloxacin Resistance in Escherichia coli.

Sensitization of resistant bacteria to existing antibiotics depends on the identification of candidate targets whose activities contribute to resistance. Using a transposon insertion library in an Escherichia coli mutant that was 2,000 times less susceptible to ciprofloxacin than its parent and the relative fitness scores, we identified 19 genes that contributed to the acquired ciprofloxacin resistance and mapped the shortest genetic path that increased the antibiotic susceptibility of the resistant bacteria back to a near wild-type level.

Thymineless Death Lives On: New Insights into a Classic Phenomenon.

The primary mechanisms by which bacteria lose viability when deprived of thymine have been elusive for over half a century. Early research focused on stalled replication forks and the deleterious effects of uracil incorporation into DNA from thymidine-deficient nucleotide pools. The initiation of the replication cycle and origin-proximal DNA degradation during thymine starvation have now been quantified via whole-genome microarrays and other approaches. These advances have fostered innovative models and informative experiments in bacteria since this topic was last reviewed. Given that thymineless death is similar in mammalian cells and that certain antibacterial and chemotherapeutic drugs elicit thymine deficiency, a mechanistic understanding of this phenomenon might have valuable biomedical applications.

Perturbed states of the bacterial chromosome: a thymineless death case study.

Spatial patterns of transcriptional activity in the living genome of Escherichia coli represent one of the more peculiar aspects of the E. coli chromosome biology. Spatial transcriptional correlations can be observed throughout the chromosome, and their formation depends on the state of replication in the cell. The condition of thymine starvation leading to thymineless death (TLD) is at the "cross-roads" of replication and transcription. According to a current view, e.g., (Cagliero et al., 2014), one of the cellular objectives is to segregate the processes of transcription and replication in time and space. An ultimate segregation would take place when one process is inhibited and another is not, as it happens during thymine starvation, which results in numerous molecular and physiological abnormalities associated with TLD. One of such abnormalities is the loss of spatial correlations in the vicinity of the origin of replication. We review the transcriptional consequences of replication inhibition by thymine starvation in a context of the state of DNA template in the starved cells and opine about a possible significance of normal physiological coupling between the processes of replication and transcription.

An evolutionarily biased distribution of miRNA sites toward regulatory genes with high promoter-driven intrinsic transcriptional noise.

BACKGROUND: miRNAs are a major class of regulators of gene expression in metazoans. By targeting cognate mRNAs, miRNAs are involved in regulating most, if not all, biological processes in different cell and tissue types. To better understand how this regulatory potential is allocated among different target gene sets, we carried out a detailed and systematic analysis of miRNA target sites distribution in the mouse genome. RESULTS: We used predicted conserved and non-conserved sites for 779 miRNAs in 3' UTR of 18440 genes downloaded from TargetScan website. Our analysis reveals that 3' UTRs of genes encoding regulatory proteins harbor significantly greater number of miRNA sites than those of non-regulatory, housekeeping and structural, genes. Analysis of miRNA sites for orthologous 3'UTR's in 10 other species indicates that the regulatory genes were maintaining or accruing miRNA sites while non-regulatory genes gradually shed them in the course of evolution. Furthermore, we observed that 3' UTR of genes with higher gene expression variability driven by their promoter sequence content are targeted by many more distinct miRNAs compared to genes with low transcriptional noise. CONCLUSIONS: Based on our results we envision a model, which we dubbed "selective inclusion", whereby non-regulatory genes with low transcription noise and stable expression profile lost their sites, while regulatory genes which endure higher transcription noise retained and gained new sites. This adaptation is consistent with the requirements that regulatory genes need to be tightly controlled in order to have precise and optimum protein level to properly function.

Transient growth arrest in Escherichia coli induced by chromosome condensation.

MukB is a bacterial SMC (structural maintenance of chromosome) protein that regulates the global folding of the Escherichia coli chromosome by bringing distant DNA segments together. We report that moderate overproduction of MukB may lead, depending on strain and growth conditions, to transient growth arrest. In DH5α cells, overproduction of MukB or MukBEF using pBAD expression system triggered growth arrest 2.5 h after induction. The exit from growth arrest was accompanied by the loss of the overproducing plasmid and a decline in the abundance of MukBEF. The arrested cells showed a compound gene expression profile which can be characterized by the following features: (i) a broad and deep downregulation of ribosomal proteins (up to 80-fold); (ii) downregulation of groups of genes encoding enzymes involved in nucleotide metabolism, respiration, and central metabolism; (iii) upregulation of some of the genes responsive to general stress; and (iv) degradation of the patterns of spatial correlations in the transcriptional activity of the chromosome. The transcriptional state of the MukB induced arrest is most similar to stationary cells and cells recovered from stationary phase into a nutrient deprived medium, to amino acid starved cells and to the cells shifting from glucose to acetate. The mukB++ state is dissimilar from all examined transcriptional states generated by protein overexpression with the possible exception of RpoE and RpoH overexpression. Thus, the transcription profile of MukB-arrested cells can be described as a combination of responses typical for other growth-arrested cells and those for overproducers of DNA binding proteins with a particularly deep down-regulation of ribosomal genes.

The association of DNA damage response and nucleotide level modulation with the antibacterial mechanism of the anti-folate drug trimethoprim.

BACKGROUND: Trimethoprim is a widely prescribed antibiotic for a variety of bacterial infections. It belongs to a class of anti-metabolites - antifolates - which includes drugs used against malarial parasites and in cancer therapy. However, spread of bacterial resistance to the drug has severely hampered its clinical use and has necessitated further investigations into its mechanism of action and treatment regimen. Trimethoprim selectively starves bacterial cells for tetrahydrofolate, a vital cofactor necessary for the synthesis of several metabolites. The outcome (bacteriostatic or bactericidal) of such starvation, however, depends on the availability of folate-dependent metabolites in the growth medium. To characterize this dependency, we investigated in detail the regulatory and structural components of Escherichia coli cellular response to trimethoprim in controlled growth and supplementation conditions. RESULTS: We surveyed transcriptional responses to trimethoprim treatment during bacteriostatic and bactericidal conditions and analyzed associated gene sets/pathways. Concurrent starvation of all folate dependent metabolites caused growth arrest, and this was accompanied by induction of general stress and stringent responses. Three gene sets were significantly associated with the bactericidal effect of TMP in different media including LB: genes of the SOS regulon, genes of the pyrimidine nucleotide biosynthetic pathway and members of the multiple antibiotic resistance (mar) regulon controlled by the MarR repressor. However, the SOS response was identified as the only universal transcriptional signature associated with the loss of viability by direct thymine starvation or by folate stress. We also used genome-wide gene knock-out screen to uncover means of sensitization of bacteria to the drug. We observed that among a number of candidate genes and pathways, the effect of knock-outs in the deoxyribose nucleotide salvage pathway, encoded by the deoCABD operon and under the control of the DeoR repressor, was most informative. CONCLUSION: Transcriptional induction of DNA damage response is an essential feature of the bactericidal effect of trimethoprim. Either the observation of the transcriptional response or DNA damage itself, or both, is made possible by thymine starvation when other folate-dependent metabolites are not limited. The effect of DNA damage by the drug takes place prior to its bactericidal effect, at the beginning of the lag stage of the treatment. Mutations in the deoxyribose nucleotide salvage pathway can affect duration of the lag as well as the rate of killing. This information can be used to postulate certain mechanistic differences between direct thymine starvation in thymidylate synthase deficient mutants and thymine starvation by anti-folate inhibitors.

Inferring a transcriptional regulatory network from gene expression data using nonlinear manifold embedding.

Transcriptional networks consist of multiple regulatory layers corresponding to the activity of global regulators, specialized repressors and activators as well as proteins and enzymes shaping the DNA template. Such intrinsic complexity makes uncovering connections difficult and it calls for corresponding methodologies, which are adapted to the available data. Here we present a new computational method that predicts interactions between transcription factors and target genes using compendia of microarray gene expression data and documented interactions between genes and transcription factors. The proposed method, called Kernel Embedding of Regulatory Networks (KEREN), is based on the concept of gene-regulon association, and captures hidden geometric patterns of the network via manifold embedding. We applied KEREN to reconstruct transcription regulatory interactions on a genome-wide scale in the model bacteria Escherichia coli (E. coli). Application of the method not only yielded accurate predictions of verifiable interactions, which outperformed on certain metrics comparable methodologies, but also demonstrated the utility of a geometric approach in the analysis of high-dimensional biological data. We also described possible applications of kernel embedding techniques to other function and network discovery algorithms.

Modeling Three-Dimensional Chromosome Structures Using Gene Expression Data.

Recent genomic studies have shown that significant chromosomal spatial correlation exists in gene expression of many organisms. Interestingly, coexpression has been observed among genes separated by a fixed interval in specific regions of a chromosome chain, which is likely caused by three-dimensional (3D) chromosome folding structures. Modeling such spatial correlation explicitly may lead to essential understandings of 3D chromosome structures and their roles in transcriptional regulation. In this paper, we explore chromosomal spatial correlation induced by 3D chromosome structures, and propose a hierarchical Bayesian method based on helical structures to formally model and incorporate the correlation into the analysis of gene expression microarray data. It is the first study to quantify and infer 3D chromosome structures in vivo using expression microarrays. Simulation studies show computing feasibility of the proposed method and that, under the assumption of helical chromosome structures, it can lead to precise estimation of structural parameters and gene expression levels. Real data applications demonstrate an intriguing biological phenomenon that functionally associated genes, which are far apart along the chromosome chain, are brought into physical proximity by chromosomal folding in 3D space to facilitate their coexpression. It leads to important biological insight into relationship between chromosome structure and function.

Thymineless death is associated with loss of essential genetic information from the replication origin.

Thymine starvation results in a terminal cellular condition known as thymineless death (TLD), which is the basis of action for several common antibiotics and anticancer drugs. We characterized the onset and progression of TLD in Escherichia coli and found that DNA damage is the only salient property that distinguishes cells irreversibly senesced under thymine starvation from cells reversibly arrested by the nucleotide limitation. The damage is manifested as the relative loss of genetic material spreading outward from the replication origin: the extent of TLD correlates with the progression of damage. The reduced lethality in mutants deficient in the RecFOR/JQ repair pathway also correlates with the extent of damage, which explains most of the observed variance in cell killing. We propose that such spatially localized and persistent DNA damage is the consequence of transcription-dependent initiation of replication in the thymine-starved cells and may be the underlying cause of TLD.

A Bayesian approach to joint modeling of protein-DNA binding, gene expression and sequence data.

The genome-wide DNA-protein-binding data, DNA sequence data and gene expression data represent complementary means to deciphering global and local transcriptional regulatory circuits. Combining these different types of data can not only improve the statistical power, but also provide a more comprehensive picture of gene regulation. In this paper, we propose a novel statistical model to augment protein-DNA-binding data with gene expression and DNA sequence data when available. We specify a hierarchical Bayes model and use Markov chain Monte Carlo simulations to draw inferences. Both simulation studies and an analysis of an experimental data set show that the proposed joint modeling method can significantly improve the specificity and sensitivity of identifying target genes as compared with conventional approaches relying on a single data source.

Reconstruction of Escherichia coli transcriptional regulatory networks via regulon-based associations.

BACKGROUND: Network reconstruction methods that rely on covariance of expression of transcription regulators and their targets ignore the fact that transcription of regulators and their targets can be controlled differently and/or independently. Such oversight would result in many erroneous predictions. However, accurate prediction of gene regulatory interactions can be made possible through modeling and estimation of transcriptional activity of groups of co-regulated genes. RESULTS: Incomplete regulatory connectivity and expression data are used here to construct a consensus network of transcriptional regulation in Escherichia coli (E. coli). The network is updated via a covariance model describing the activity of gene sets controlled by common regulators. The proposed model-selection algorithm was used to annotate the likeliest regulatory interactions in E. coli on the basis of two independent sets of expression data, each containing many microarray experiments under a variety of conditions. The key regulatory predictions have been verified by an experiment and literature survey. In addition, the estimated activity profiles of transcription factors were used to describe their responses to environmental and genetic perturbations as well as drug treatments. CONCLUSION: Information about transcriptional activity of documented co-regulated genes (a core regulon) should be sufficient for discovering new target genes, whose transcriptional activities significantly co-vary with the activity of the core regulon members. Our ability to derive a highly significant consensus network by applying the regulon-based approach to two very different data sets demonstrated the efficiency of this strategy. We believe that this approach can be used to reconstruct gene regulatory networks of other organisms for which partial sets of known interactions are available.

In vivo and in vitro patterns of the activity of simocyclinone D8, an angucyclinone antibiotic from Streptomyces antibioticus.

Simocyclinone D8 (SD8) exhibits antibiotic activity against gram-positive bacteria but not against gram-negative bacteria. The molecular basis of the cytotoxicity of SD8 is not fully understood, although SD8 has been shown to inhibit the supercoiling activity of Escherichia coli gyrase. To understand the mechanism of SD8, we have employed biochemical assays to directly measure the sensitivities of E. coli and Staphylococcus aureus type II topoisomerases to SD8 and microarray analysis to monitor the cellular responses to SD8 treatment. SD8 is a potent inhibitor of either E. coli or S. aureus gyrase. In contrast, SD8 exhibits only a moderate inhibitory effect on S. aureus topoisomerase IV, and E. coli topoisomerase IV is virtually insensitive to SD8. The antimicrobial effect of SD8 against E. coli has become evident in the absence of the AcrB multidrug efflux pump. As expected, SD8 treatment exhibits the signature responses to the loss of supercoiling activity in E. coli: upregulation of gyrase genes and downregulation of the topoisomerase I gene. Unlike quinolone treatment, however, SD8 treatment does not induce the SOS response. These results suggest that DNA gyrase is the target of SD8 in both gram-positive and gram-negative bacteria and that the lack of the antibacterial effect against gram-negative bacteria is due, in part, to the activity of the AcrB efflux pump.

Improved detection of differentially expressed genes through incorporation of gene locations.

In determining differential expression in cDNA microarray experiments, the expression level of an individual gene is usually assumed to be independent of the expression levels of other genes, but many recent studies have shown that a gene's expression level tends to be similar to that of its neighbors on a chromosome, and differentially expressed (DE) genes are likely to form clusters of similar transcriptional activity along the chromosome. When modeled as a one-dimensional spatial series, the expression level of genes on the same chromosome frequently exhibit significant spatial correlation, reflecting spatial patterns in transcription. By modeling these spatial correlations, we can obtain improved estimates of transcript levels. Here, we demonstrate the existence of spatial correlations in transcriptional activity in the Escherichia coli (E. coli) chromosome across more than 50 experimental conditions. Based on this finding, we propose a hierarchical Bayesian model that borrows information from neighboring genes to improve the estimation of the expression level of a given gene and hence the detection of DE genes. Furthermore, we extend the model to account for the circular structure of E. coli chromosome and the intergenetic distance between gene neighbors. The simulation studies and analysis of real data examples in E. coli and yeast Saccharomyces cerevisiae show that the proposed method outperforms the commonly used significant analysis of microarray (SAM) t-statistic in detecting DE genes.

Gene expression patterns of sulfur starvation in Synechocystis sp. PCC 6803.

BACKGROUND: The unicellular cyanobacterium Synechocystis sp. PCC 6803 is a model microbe for studying biochemistry, genetics and molecular biology of photobiological processes. Importance of this bacterium in basic and applied research calls for a systematic, genome-wide description of its transcriptional regulatory capacity. Characteristic transcriptional responses to changes in the growth environment are expected to provide a scaffold for describing the Synechocystis transcriptional regulatory network as well as efficient means for functional annotation of genes in the genome. RESULTS: We designed, validated and used Synechocystis genome-wide oligonucleotide (70-mer) microarray (representing 96.7% of all chromosomal ORFs annotated at the time of the beginning of this project) to study transcriptional activity of the cyanobacterial genome in response to sulfur (S) starvation. The microarray data were verified by quantitative RT-PCR. We made five main observations: 1) Transcriptional changes upon sulfate starvation were relatively moderate, but significant and consistent with growth kinetics; 2) S acquisition genes encoding for a high-affinity sulfate transporter were significantly induced, while decreased transcription of genes for phycobilisome, photosystems I and II, cytochrome b6/f, and ATP synthase indicated reduced light-harvesting and photosynthetic activity; 3) S starvation elicited transcriptional responses associated with general growth arrest and stress; 4) A large number of genes regulated by S availability encode hypothetical proteins or proteins of unknown function; 5) Hydrogenase structural and maturation accessory genes were not identified as differentially expressed, even though increased hydrogen evolution was observed. CONCLUSION: The expression profiles recorded by using this oligonucleotide-based microarray platform revealed that during transition from the condition of plentiful S to S starvation, Synechocystis undergoes coordinated transcriptional changes, including changes in gene expression whose products are involved in sensing nutrient limitations and tuning bacterial metabolism. The transcriptional profile of the nutrient starvation was dominated by a decrease in abundances of many transcripts. However, these changes were unlikely due to the across-the-board, non-specific shut down of transcription in a condition of growth arrest. Down-regulation of transcripts encoding proteins whose function depends on a cellular S status indicated that the observed repression has a specific regulatory component. The repression of certain S-related genes was paralleled by activation of genes involved in internal and external S scavenging.

Overexpression of phage HK022 Nun protein is toxic for Escherichia coli.

The Nun protein of coliphage HK022 excludes superinfecting lambda phage. Nun recognizes and binds to the N utilization (nut) sites on phage lambda nascent RNA and induces transcription termination. Overexpression of Nun from a high-copy plasmid is toxic for Escherichia coli, despite the fact that nut sites are not encoded in the E. coli genome. Cells expressing Nun cannot exit stationary phase. Toxicity is related to transcription termination, since host and nun mutations that block termination also suppress cell killing. Nun inhibits expression of wild-type lacZ, but not lacZ expressed from the Crp/cAMP-independent lacUV5 promoter. Microarray and proteomic analyses show that Nun down-regulates crp and tnaA. Crp overexpression and high indole concentrations partially reverse Nun-mediated toxicity and restore lacZ expression.