Antibiotic-induced degradation of antitoxin enhances the transcription of acetyltransferase-type toxin-antitoxin operon.

BACKGROUND: Bacterial toxin-antitoxin (TA) modules respond to various stressful conditions. The Gcn5-related N-acetyltransferase-type toxin (GNAT) protein encoded by the GNAT-RHH TA locus is involved in the antibiotic tolerance of Klebsiella pneumoniae. OBJECTIVES: To investigate the transcriptional mechanism of the GNAT-RHH operon kacAT under antibiotic stress. METHODS: The transcriptional level of the kacAT operon of K. pneumoniae was measured by quantitative real-time (qRT) PCR assay. The degradation of antitoxin KacA was examined by western blot and fluorescent protein. The ratio of [KacA]:[KacT] was calculated by the fluorescence intensity of KacA-eGFP and mCherry-KacT. Mathematical modelling predicted protein and transcript synthesis dynamics. RESULTS: A meropenem-induced increase in transcript levels of kacA and kacT resulted from the relief from transcriptional autoregulation of the kacAT operon. Meropenem induces the degradation of KacA through Lon protease, resulting in a reduction in the ratio of [KacA]:[KacT]. The decreased ratio causes the dissociation of the KacAT complex from its promoter region, which eliminates the repression of kacAT transcription. In addition, our dynamic model of kacAT expression regulation quantitatively reproduced the experimentally observed reduction of the [KacA]:[KacT] ratio and a large increase in kacAT transcript levels under the condition of strong promoter autorepression by the KacAT complex. CONCLUSIONS: Meropenem promotes the degradation of antitoxin by enhancing the expression of Lon protease. Degradation of antitoxin reduces the ratio of intracellular [antitoxin]:[toxin], leading to detachment of the TA complex from its promoter, and releasing repression of TA operon transcription. These results may provide an important insight into the transcriptional mechanism of GNAT-RHH TA modules under antibiotic stress.

Understanding risk factors of a new variant outburst through global analysis of Omicron transmissibility.

The emergence of a new virus variant is generally recognized by its usually sudden and rapid spread (outburst) in a certain world region. Due to the near-exponential rate of initial expansion, the new strain may not be detected at its true geographical origin but in the area with the most favorable conditions leading to the fastest exponential growth. Therefore, it is crucial to understand better the factors that promote such outbursts, which we address in the example of analyzing global Omicron transmissibility during its global emergence/outburst in November 2021-February 2022. As predictors, we assemble a number of potentially relevant factors: vaccinations (both full and boosters), different measures of population mobility (provided by Google), estimated stringency of measures, the prevalence of chronic diseases, population age, the timing of the outburst, and several other socio-demographic variables. As a proxy for natural immunity (prevalence of prior infections in population), we use cumulative numbers of COVID-19 deaths. As a response variable (transmissibility measure), we use the estimated effective reproduction number (Re) averaged in the vicinity of the outburst maxima. To select significant predictors of Re, we use machine learning regressions that employ feature selection, including methods based on ensembles of decision trees (Random Forest and Gradient Boosting). We identify the young population, earlier infection onset, higher mobility, low natural immunity, and low booster prevalence as likely direct risk factors. Interestingly, we find that all these risk factors were significantly higher for Africa, though curiously somewhat lower in Southern African countries (where the outburst emerged) compared to other African countries. Therefore, while the risk factors related to the virus transmissibility clearly promote the outburst of a new virus variant, specific regions/countries where the outburst actually happens may be related to less evident factors, possibly random in nature.

PM2.5 as a major predictor of COVID-19 basic reproduction number in the USA.

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number (R0) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM2.5 is a major predictor of R0 in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in R0, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.

Controller protein of restriction-modification system Kpn2I affects transcription of its gene by acting as a transcription elongation roadblock.

C-proteins control restriction-modification (R-M) systems' genes transcription to ensure sufficient levels of restriction endonuclease to allow protection from foreign DNA while avoiding its modification by excess methyltransferase. Here, we characterize transcription regulation in C-protein dependent R-M system Kpn2I. The Kpn2I restriction endonuclease gene is transcribed from a constitutive, weak promoter, which, atypically, is C-protein independent. Kpn2I C-protein (C.Kpn2I) binds upstream of the strong methyltransferase gene promoter and inhibits it, likely by preventing the interaction of the RNA polymerase sigma subunit with the -35 consensus element. Diminished transcription from the methyltransferase promoter increases transcription from overlapping divergent C-protein gene promoters. All known C-proteins affect transcription initiation from R-M genes promoters. Uniquely, the C.Kpn2I binding site is located within the coding region of its gene. C.Kpn2I acts as a roadblock stalling elongating RNA polymerase and decreasing production of full-length C.Kpn2I mRNA. Mathematical modeling shows that this unusual mode of regulation leads to the same dynamics of accumulation of R-M gene transcripts as observed in systems where C-proteins act at transcription initiation stage only. Bioinformatics analyses suggest that transcription regulation through binding of C.Kpn2I-like proteins within the coding regions of their genes may be widespread.

From biophysics to 'omics and systems biology.

Recent decades brought a revolution to biology, driven mainly by exponentially increasing amounts of data coming from "'omics" sciences. To handle these data, bioinformatics often has to combine biologically heterogeneous signals, for which methods from statistics and engineering (e.g. machine learning) are often used. While such an approach is sometimes necessary, it effectively treats the underlying biological processes as a black box. Similarly, systems biology deals with inherently complex systems, characterized by a large number of degrees of freedom, and interactions that are highly non-linear. To deal with this complexity, the underlying physical interactions are often (over)simplified, such as in Boolean modelling of network dynamics. In this review, we argue for the utility of applying a biophysical approach in bioinformatics and systems biology, including discussion of two examples from our research which address sequence analysis and understanding intracellular gene expression dynamics.

Endogenous Gene Regulation as a Predicted Main Function of Type I-E CRISPR/Cas System in E. coli.

CRISPR/Cas is an adaptive bacterial immune system, whose CRISPR array can actively change in response to viral infections. However, Type I-E CRISPR/Cas in E. coli (an established model system), appears not to exhibit such active adaptation, which suggests that it might have functions other than immune response. Through computational analysis, we address the involvement of the system in non-canonical functions. To assess targets of CRISPR spacers, we align them against both E. coli genome and an exhaustive (~230) set of E. coli viruses. We systematically investigate the obtained alignments, such as hit distribution with respect to genome annotation, propensity to target mRNA, the target functional enrichment, conservation of CRISPR spacers and putative targets in related bacterial genomes. We find that CRISPR spacers have a statistically highly significant tendency to target i) host compared to phage genomes, ii) one of the two DNA strands, iii) genomic dsDNA rather than mRNA, iv) transcriptionally active regions, and v) sequences (cis-regulatory elements) with slower turn-over rate compared to CRISPR spacers (trans-factors). The results suggest that the Type I-E CRISPR/Cas system has a major role in transcription regulation of endogenous genes, with a potential to rapidly rewire these regulatory interactions, with targets being selected through naïve adaptation.

Effects of Population Dynamics on Establishment of a Restriction-Modification System in a Bacterial Host.

In vivo dynamics of protein levels in bacterial cells depend on both intracellular regulation and relevant population dynamics. Such population dynamics effects, e.g., interplay between cell and plasmid division rates, are, however, often neglected in modeling gene expression regulation. Including them in a model introduces additional parameters shared by the dynamical equations, which can significantly increase dimensionality of the parameter inference. We here analyse the importance of these effects, on a case of bacterial restriction-modification (R-M) system. We redevelop our earlier minimal model of this system gene expression regulation, based on a thermodynamic and dynamic system modeling framework, to include the population dynamics effects. To resolve the problem of effective coupling of the dynamical equations, we propose a "mean-field-like" procedure, which allows determining only part of the parameters at a time, by separately fitting them to expression dynamics data of individual molecular species. We show that including the interplay between kinetics of cell division and plasmid replication is necessary to explain the experimental measurements. Moreover, neglecting population dynamics effects can lead to falsely identifying non-existent regulatory mechanisms. Our results call for advanced methods to reverse-engineer intracellular regulation from dynamical data, which would also take into account the population dynamics effects.

Temporal dynamics of methyltransferase and restriction endonuclease accumulation in individual cells after introducing a restriction-modification system.

Type II restriction-modification (R-M) systems encode a restriction endonuclease that cleaves DNA at specific sites, and a methyltransferase that modifies same sites protecting them from restriction endonuclease cleavage. Type II R-M systems benefit bacteria by protecting them from bacteriophages. Many type II R-M systems are plasmid-based and thus capable of horizontal transfer. Upon the entry of such plasmids into a naïve host with unmodified genomic recognition sites, methyltransferase should be synthesized first and given sufficient time to methylate recognition sites in the bacterial genome before the toxic restriction endonuclease activity appears. Here, we directly demonstrate a delay in restriction endonuclease synthesis after transformation of Escherichia coli cells with a plasmid carrying the Esp1396I type II R-M system, using single-cell microscopy. We further demonstrate that before the appearance of the Esp1396I restriction endonuclease the intracellular concentration of Esp1396I methyltransferase undergoes a sharp peak, which should allow rapid methylation of host genome recognition sites. A mathematical model that satisfactorily describes the observed dynamics of both Esp1396I enzymes is presented. The results reported here were obtained using a functional Esp1396I type II R-M system encoding both enzymes fused to fluorescent proteins. Similar approaches should be applicable to the studies of other R-M systems at single-cell level.

Mix-and-matching as a promoter recognition mechanism by ECF σ factors.

BACKGROUND: Transcription initiation is in bacteria exhibited by different σ factors, most of which fall within σ70 family. This family is diverse, ranging from the housekeeping Group I (RpoDs), to Group IV (ECF) σ factors, that transcribe smaller regulons under more stringent conditions. RpoDs employ a kinetic mix-and-match mechanism, where promoter elements complement each other binding strengths in achieving sufficient transcription activity. On the other hand, it is assumed that ECF σs, which are the most distant from the housekeeping σ factors, cannot exhibit mix-and-matching. However, mix-and-matching for ECF σ factors was not quantitatively checked before, and recent results show a much larger flexibility in the promoter recognition by the members of this group. RESULTS: To this end, we quantitatively investigate mix-and-matching in two canonical ECF σ family members (σE and σW), for which we use a biophysics based model of transcription initiation. For σE, we perform a separate analysis for in-vitro active and in-vitro inactive promoters, which allows us investigating how mix-and-matching depends on the external factors that may control transcription activity in the in-vitro inactive set. We show that the promoter elements of canonical ECF σs significantly complement each other strengths, where such mix-and-matching is in the in-vitro active set even stronger compared to the correlations observed for the housekeeping σs. This complementation however significantly decreases for the in-vitro inactive set, which we propose is due to mix-and-matching with regulatory sequences outside of the canonical promoter elements. In line with this proposition, we show that a conserved spacer element, which appears in the in-vitro inactive promoter set, significantly increases the promoter element complementation. While RpoD promoter elements mix-and-match to achieve sufficient total transcription activity, for σE they complement each other to achieve sufficiently strong total binding affinity, which we relate to differences in physiological responses between the two groups of σ factors. CONCLUSION: Despite a common notion that smaller σ factor specificity leads to a larger mix-and-matching, we here obtain a larger promoter element complementation for σE compared to RpoDs. Finally, to explain this finding, we propose a simple model which relates the size of σ factor regulon with the extent of mix-and-matching, based on an assumption of a selection pressure on promoters that are near the non-specific binding boundary to remain functional.

Promoter Recognition by Extracytoplasmic Function σ Factors: Analyzing DNA and Protein Interaction Motifs.

UNLABELLED: Extracytoplasmic function (ECF) σ factors are the largest and the most diverse group of alternative σ factors, but their mechanisms of transcription are poorly studied. This subfamily is considered to exhibit a rigid promoter structure and an absence of mixing and matching; both -35 and -10 elements are considered necessary for initiating transcription. This paradigm, however, is based on very limited data, which bias the analysis of diverse ECF σ subgroups. Here we investigate DNA and protein recognition motifs involved in ECF σ factor transcription by a computational analysis of canonical ECF subfamily members, much less studied ECF σ subgroups, and the group outliers, obtained from recently sequenced bacteriophages. The analysis identifies an extended -10 element in promoters for phage ECF σ factors; a comparison with bacterial σ factors points to a putative 6-amino-acid motif just C-terminal of domain σ2, which is responsible for the interaction with the identified extension of the -10 element. Interestingly, a similar protein motif is found C-terminal of domain σ2 in canonical ECF σ factors, at a position where it is expected to interact with a conserved motif further upstream of the -10 element. Moreover, the phiEco32 ECF σ factor lacks a recognizable -35 element and σ4 domain, which we identify in a homologous phage, 7-11, indicating that the extended -10 element can compensate for the lack of -35 element interactions. Overall, the results reveal greater flexibility in promoter recognition by ECF σ factors than previously recognized and raise the possibility that mixing and matching also apply to this group, a notion that remains to be biochemically tested. IMPORTANCE: ECF σ factors are the most numerous group of alternative σ factors but have been little studied. Their promoter recognition mechanisms are obscured by the large diversity within the ECF σ factor group and the limited similarity with the well-studied housekeeping σ factors. Here we extensively compare bacterial and bacteriophage ECF σ factors and their promoters in order to infer DNA and protein recognition motifs involved in transcription initiation. We predict a more flexible promoter structure than is recognized by the current paradigm, which assumes rigidness, and propose that ECF σ promoter elements may complement (mix and match with) each other's strengths. These results warrant the refocusing of research efforts from the well-studied housekeeping σ factors toward the physiologically highly important, but insufficiently understood, alternative σ factors.

Ribosome-controlled transcription termination is essential for the production of antibiotic microcin C.

Microcin C (McC) is a peptide-nucleotide antibiotic produced by Escherichia coli cells harboring a plasmid-borne operon mccABCDE. The heptapeptide MccA is converted into McC by adenylation catalyzed by the MccB enzyme. Since MccA is a substrate for MccB, a mechanism that regulates the MccA/MccB ratio likely exists. Here, we show that transcription from a promoter located upstream of mccA directs the synthesis of two transcripts: a short highly abundant transcript containing the mccA ORF and a longer minor transcript containing mccA and downstream ORFs. The short transcript is generated when RNA polymerase terminates transcription at an intrinsic terminator located in the intergenic region between the mccA and mccB genes. The function of this terminator is strongly attenuated by upstream mcc sequences. Attenuation is relieved and transcription termination is induced when ribosome binds to the mccA ORF. Ribosome binding also makes the mccA RNA exceptionally stable. Together, these two effects-ribosome-induced transcription termination and stabilization of the message-account for very high abundance of the mccA transcript that is essential for McC production. The general scheme appears to be evolutionary conserved as ribosome-induced transcription termination also occurs in a homologous operon from Helicobacter pylori.

Inferring bacteriophage infection strategies from genome sequence: analysis of bacteriophage 7-11 and related phages.

BACKGROUND: Analyzing regulation of bacteriophage gene expression historically lead to establishing major paradigms of molecular biology, and may provide important medical applications in the future. Temporal regulation of bacteriophage transcription is commonly analyzed through a labor-intensive combination of biochemical and bioinformatic approaches and macroarray measurements. We here investigate to what extent one can understand gene expression strategies of lytic phages, by directly analyzing their genomes through bioinformatic methods. We address this question on a recently sequenced lytic bacteriophage 7 - 11 that infects bacterium Salmonella enterica. RESULTS: We identify novel promoters for the bacteriophage-encoded σ factor, and test the predictions through homology with another bacteriophage (phiEco32) that has been experimentally characterized in detail. Interestingly, standard approach based on multiple local sequence alignment (MLSA) fails to correctly identify the promoters, but a simpler procedure that is based on pairwise alignment of intergenic regions identifies the desired motifs; we argue that such search strategy is more effective for promoters of bacteriophage-encoded σ factors that are typically well conserved but appear in low copy numbers, which we also verify on two additional bacteriophage genomes. Identifying promoters for bacteriophage encoded σ factors together with a more straightforward identification of promoters for bacterial encoded σ factor, allows clustering the genes in putative early, middle and late class, and consequently predicting the temporal regulation of bacteriophage gene expression, which we demonstrate on phage 7-11. CONCLUSIONS: While MLSA algorithms proved highly useful in computational analysis of transcription regulation, we here established that a simpler procedure is more successful for identifying promoters that are recognized by bacteriophage encoded σ factor/RNA polymerase. We here used this approach for predicting sequence specificity of a novel (bacteriophage encoded) σ factor, and consequently inferring phage 7-11 transcription strategy. Therefore, direct analysis of bacteriophage genome sequences is a plausible first-line approach for efficiently inferring phage transcription strategies, and may provide a wealth of information on transcription initiation by diverse σ factors/RNA polymerases.

Systems Biology Approaches to Understanding COVID-19 Spread in the Population.

In essence, the COVID-19 pandemic can be regarded as a systems biology problem, with the entire world as the system, and the human population as the element transitioning from one state to another with certain transition rates. While capturing all the relevant features of such a complex system is hardly possible, compartmental epidemiological models can be used as an appropriate simplification to model the system's dynamics and infer its important characteristics, such as basic and effective reproductive numbers of the virus. These measures can later be used as response variables in feature selection methods to uncover the main factors contributing to disease transmissibility. We here demonstrate that a combination of dynamic modeling and machine learning approaches can represent a powerful tool in understanding the spread, not only of COVID-19, but of any infectious disease of epidemiological proportions.

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity.

Understanding sociodemographic factors behind COVID-19 severity relates to significant methodological difficulties, such as differences in testing policies and epidemics phase, as well as a large number of predictors that can potentially contribute to severity. To account for these difficulties, we assemble 115 predictors for more than 3,000 US counties and employ a well-defined COVID-19 severity measure derived from epidemiological dynamics modeling. We then use a number of advanced feature selection techniques from machine learning to determine which of these predictors significantly impact the disease severity. We obtain a surprisingly simple result, where only two variables are clearly and robustly selected-population density and proportion of African Americans. Possible causes behind this result are discussed. We argue that the approach may be useful whenever significant determinants of disease progression over diverse geographic regions should be selected from a large number of potentially important factors.

SecReT6 update: a comprehensive resource of bacterial Type VI Secretion Systems.

Type VI Secretion System (T6SS) plays significant roles in microbial activities via injecting effectors into adjacent cells or environments. T6SS increasingly gained attention due to its important influence on pathogenesis, microbial competition, etc. T6SS-associated research is explosively expanding on numerous grounds that call for an efficient resource. The SecReT6 version 3 provides comprehensive information on T6SS and the interactions between T6SS and T6SS-related proteins such as T6SS regulators and T6SS effectors. To assist T6SS researches like microbial competition and regulatory mechanisms, SecReT6 v3 developed online tools for detection and analysis of T6SS and T6SS-related proteins and estimation of T6SS-dependent killing risk. We have identified a novel T6SS regulator and T6SS-dependent killing capacity in Acinetobacter baumannii clinical isolates with the aid of SecReT6 v3. 17,212 T6SSs and plentiful T6SS-related proteins in 26,573 bacterial complete genomes were also detected, analyzed and incorporated into the database. The database is freely available at https://bioinfo-mml.sjtu.edu.cn/SecReT6/ .

A simple biosynthetic pathway for large product generation from small substrate amounts.

A recently emerging discipline of synthetic biology has the aim of constructing new biosynthetic pathways with useful biological functions. A major application of these pathways is generating a large amount of the desired product. However, toxicity due to the possible presence of toxic precursors is one of the main problems for such production. We consider here the problem of generating a large amount of product from a potentially toxic substrate. To address this, we propose a simple biosynthetic pathway, which can be induced in order to produce a large number of the product molecules, by keeping the substrate amount at low levels. Surprisingly, we show that the large product generation crucially depends on fast non-specific degradation of the substrate molecules. We derive an optimal induction strategy, which allows as much as three orders of magnitude increase in the product amount through biologically realistic parameter values. We point to a recently discovered bacterial immune system (CRISPR/Cas in E. coli) as a putative example of the pathway analysed here. We also argue that the scheme proposed here can be used not only as a stand-alone pathway, but also as a strategy to produce a large amount of the desired molecules with small perturbations of endogenous biosynthetic pathways.

Analyzing the GHSI puzzle of whether highly developed countries fared worse in COVID-19.

Global Health Security Index (GHSI) categories are formulated to assess the capacity of world countries to deal with infectious disease risks. Thus, higher values of these indices were expected to translate to lower COVID-19 severity. However, it turned out to be the opposite, surprisingly suggesting that higher estimated country preparedness to epidemics may lead to higher disease mortality. To address this puzzle, we: (i) use a model-derived measure of COVID-19 severity; (ii) employ a range of statistical learning approaches, including non-parametric machine learning methods; (iii) consider the overall excess mortality, in addition to official COVID-19 fatality counts. Our results suggest that the puzzle is, to a large extent, an artifact of oversimplified data analysis and a consequence of misclassified COVID-19 deaths, combined with the higher median age of the population and earlier epidemics onset in countries with high GHSI scores.

Comparative Analysis of Diverse Acetyltransferase-Type Toxin-Antitoxin Loci in Klebsiella pneumoniae.

Toxin-antitoxin (TA) modules containing a Gcn5-related N-acetyltransferase (GNAT) toxin domain regulate bacterial physiology under adverse environmental stresses. Multiple GNAT-ribbon-helix-helix domain (RHH) TA loci have been identified in single bacterial genomes. However, their diversity and interactions are still obscure. Our previous analysis showed that the GNAT toxin of Klebsiella pneumoniae, KacT, introduces antibiotic tolerance and the toxicity of GNAT is neutralized by KacA, an RHH antitoxin. We here present a phylogenetic analysis of GNAT toxins of more than 1,000 GNAT-RHH pairs detected in completely sequenced K. pneumoniae genomes, revealing that the GNAT toxins are diverse and grouped into four distinct clades. Overexpression of GNAT toxins representative of each of the four clades halts the cell growth of K. pneumoniae, while the coexpression of the cognate RHH antitoxin neutralizes GNAT toxicity. We also identify point mutations that inactivate the GNAT toxins. Moreover, we observe a cross-interaction between GNAT-RHH pairs encoded by different replicons, where a chromosomal toxin (KacT2) can be neutralized by its cognate RHH antitoxin (KacA2 on a chromosome) and another antitoxin (KacA3 on a plasmid). Finally, statistical analysis of the distribution of GNAT-RHH loci in K. pneumoniae strains shows pronounced deviation from random distribution within the same clades. Moreover, we also obtain statistically significant correlations between different clades, which we discuss in terms of the experimental results. IMPORTANCE Elucidating the roles of multifaceted GNAT-RHH TA loci is essential for understanding how these TAs interact among themselves. Recently, the reaction mechanisms and structures of several GNAT-RHH pairs have been reported. While bacterial strains can carry multiple GNAT-RHH loci with diverse origins, studies on the possible cross-interactions of these TA pairs are still limited. Here, we find that 1,000 predicted GNAT toxins of K. pneumoniae can be grouped into four distinct clades. The distributions of TA loci within these clades in K. pneumoniae strains are highly nonrandom, with the presence of a single locus of each clade per strain being highly overrepresented. Moreover, the toxicity of a GNAT toxin encoded by a chromosome was alleviated by a noncognate RHH antitoxin on a plasmid. These results might yield a profound understanding of the widespread GNAT-RHH TA pairs and the cross-interactions between noncognate TA pairs located on different replicons.

Redefining Escherichia coli σ(70) promoter elements: -15 motif as a complement of the -10 motif.

Classical elements of σ(70) bacterial promoters include the -35 element ((-35)TTGACA(-30)), the -10 element ((-12)TATAAT(-7)), and the extended -10 element ((-15)TG(-14)). Although the -35 element, the extended -10 element, and the upstream-most base in the -10 element ((-12)T) interact with σ(70) in double-stranded DNA (dsDNA) form, the downstream bases in the -10 motif ((-11)ATAAT(-7)) are responsible for σ(70)-single-stranded DNA (ssDNA) interactions. In order to directly reflect this correspondence, an extension of the extended -10 element to a so-called -15 element ((-15)TGnT(-12)) has been recently proposed. I investigated here the sequence specificity of the proposed -15 element and its relationship to other promoter elements. I found a previously undetected significant conservation of (-13)G and a high degeneracy at (-15)T. I therefore defined the -15 element as a degenerate motif, which, together with the conserved stretch of sequence between -15 and -12, allows treating this element analogously to -35 and -10 elements. Furthermore, the strength of the -15 element inversely correlates with the strengths of the -35 element and -10 element, whereas no such complementation between other promoter elements was found. Despite the direct involvement of -15 element in σ(70)-dsDNA interactions, I found a significantly stronger tendency of this element to complement weak -10 elements that are involved in σ(70)-ssDNA interactions. This finding is in contrast to the established view, according to which the -15 element provides a sufficient number of σ(70)-dsDNA interactions, and suggests that the main parameter determining a functional promoter is the overall promoter strength.

Transcription, processing and function of CRISPR cassettes in Escherichia coli.

CRISPR/Cas, bacterial and archaeal systems of interference with foreign genetic elements such as viruses or plasmids, consist of DNA loci called CRISPR cassettes (a set of variable spacers regularly separated by palindromic repeats) and associated cas genes. When a CRISPR spacer sequence exactly matches a sequence in a viral genome, the cell can become resistant to the virus. The CRISPR/Cas systems function through small RNAs originating from longer CRISPR cassette transcripts. While laboratory strains of Escherichia coli contain a functional CRISPR/Cas system (as judged by appearance of phage resistance at conditions of artificial co-overexpression of Cas genes and a CRISPR cassette engineered to target a λ-phage), no natural phage resistance due to CRISPR system function was observed in this best-studied organism and no E. coli CRISPR spacer matches sequences of well-studied E. coli phages. To better understand the apparently 'silent'E. coli CRISPR/Cas system, we systematically characterized processed transcripts from CRISPR cassettes. Using an engineered strain with genomically located spacer matching phage λ we show that endogenous levels of CRISPR cassette and cas genes expression allow only weak protection against infection with the phage. However, derepression of the CRISPR/Cas system by disruption of the hns gene leads to high level of protection.