33 research outputs found

    CRISPR performance for two virus species.

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    <p>Plot of the survival probability <i>E</i>(<i>t</i>) as a function of crRNA decay coefficient <i>δ</i> and the number of spacers <i>S</i> of a cell confronting two different viruses with equal population sizes, <i>ν</i><sub>1</sub> = <i>ν</i><sub>2</sub> = 0.5. The binding efficiency is <i>β</i> = 1 and the interference efficiency is <i>χ</i> = 1.4. Viral mutation probability 1 − <i>μ</i> is equal to 0.1 and <i>rNt</i> = 5.</p

    Optimal number of spacers in CRISPR arrays

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    <div><p>Prokaryotic organisms survive under constant pressure of viruses. CRISPR-Cas system provides its prokaryotic host with an adaptive immune defense against viruses that have been previously encountered. It consists of two components: Cas-proteins that cleave the foreign DNA and CRISPR array that suits as a virus recognition key. CRISPR array consists of a series of spacers, short pieces of DNA that originate from and match the corresponding parts of viral DNA called protospacers. Here we estimate the number of spacers in a CRISPR array of a prokaryotic cell which maximizes its protection against a viral attack. The optimality follows from a competition between two trends: too few distinct spacers make host vulnerable to an attack by a virus with mutated corresponding protospacers, while an excessive variety of spacers dilutes the number of the CRISPR complexes armed with the most recent and thus most useful spacers. We first evaluate the optimal number of spacers in a simple scenario of an infection by a single viral species and later consider a more general case of multiple viral species. We find that depending on such parameters as the concentration of CRISPR-Cas interference complexes and its preference to arm with more recently acquired spacers, the rate of viral mutation, and the number of viral species, the predicted optimal number of spacers lies within a range that agrees with experimentally-observed values.</p></div

    Effect of parameters on the optimal number of spacers and the maximal survival probability.

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    <p>The optimal number of spacers and corresponding survival probability as functions of one of the array-unrelated parameters: (A) As function of mutation probability 1 − <i>μ</i>, other parameters are <i>β</i> = 1 and <i>χ</i> = 1.4. (B) As function of binding efficiency <i>β</i>, other parameters are <i>μ</i> = 0.9 and <i>χ</i> = 1.4. (C) As function of interference efficiency <i>χ</i>, other parameters <i>μ</i> = 0.9 and <i>β</i> = 1. The average number of viral infections was <i>rNt</i> = 5 in all panels.</p

    Survival probability vs diversity of the virus pool.

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    <p>Plots of the optimized over <i>δ</i> and <i>S</i> cell survival probability and the number of spacers vs the number of viral species and the composition of a two-virus pool for <i>β</i> = 1, <i>χ</i> = 1.4, <i>μ</i> = 0.9 and <i>rNt</i> = 5. (A) Maximal survival probability <i>E</i>(<i>t</i>) (outer plot) and optimal number of spacers <i>S</i><sub><i>opt</i></sub> (inner plot) as a function of the number of virus species <i>n</i>. The abundance of virions belonging to different species in the viral pool are the same for all species, <i>ν</i><sub>1</sub> = … = <i>ν</i><sub><i>n</i></sub> = 1/<i>n</i>. (B) The maximal survival probability vs the relative abundance of one of the viruses in a two-virus pool.</p

    Functioning of CRISPR-Cas system.

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    <p>Three spacers are colored according to their age from the time of their acquisition, from dark green marking the youngest (the most recently acquired) spacer to yellow marking the oldest one (which was acquired the earliest). Phages carry protospacers colored similarly to their matching spacers; mutated protospacers are colored white. There are more mutated protospacers among protospacers matching older spacers than among protospacers matching younger ones. Inside the cell, bean-shaped objects are CRISPR effector complexes armed with individual crRNAs. Complexes with crRNA of younger spacers are more abundant than those with older ones. Viral DNA is shown to be simultaneously assessed by two CRISPR effector complexes: the dark green CRISPR spacer matches the non-mutated corresponding protospacer while the protospacer corresponding to the yellow spacer has mutated. The former interaction results in destruction of viral DNA while the latter leaves it intact.</p

    The optimal number of spacers and maximal cell survival probability.

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    <p>The optimal number of spacers (A) and the maximal cell survival probability (B) are shown vs. a range of binding efficiencies <i>β</i> and mutation probabilities 1 − <i>μ</i> for <i>rNt</i> = 5 and <i>χ</i> = 1.4.</p

    Scheme of calculations.

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    <p>A cell with <i>S</i> = 3 CRISPR spacers encounters viruses as a Poisson process with an average rate <i>rN</i>. During each encounter there is either a successful interference with probability <i>I</i> or the cell dies with probability 1 − <i>I</i>. We evaluate the probability <i>E</i>(<i>t</i>) of the cell to survive till time <i>t</i> as the measure of performance of its CRISPR-Cas system.</p

    Typical survival probability profile.

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    <p>(A) Plot of survival probability <i>E</i>(<i>t</i>) vs. the crRNA decay coefficient <i>δ</i> and the number of spacers in CRISPR array <i>S</i>. Other parameters are: <i>β</i> = 1, <i>χ</i> = 1.4, <i>μ</i> = 0.9, and <i>rNt</i> = 5. (B) Six curves of <i>E</i>(<i>t</i>) vs. <i>S</i> for various values of <i>δ</i> and same <i>β</i>, <i>χ</i>, <i>m</i>, and <i>rNt</i> as in the panel A.</p

    Effects of mutation rate and binding efficiency.

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    <p>A set of 25 panels illustrating how the survival probability depends on <i>S</i> and <i>δ</i> for various values of protospacer mutation probability 1 − <i>μ</i> and binding efficiency of effectors <i>β</i>. The <i>δ</i> and <i>S</i> axes in each small panel have the same range as in the panel A in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005891#pcbi.1005891.g003" target="_blank">Fig 3</a>, while the scale of the heat-map varies and is indicated to the right of each panel. The external axes describe the variation of mutation probability 1 − <i>μ</i> and effector binding efficiency <i>β</i>. In all panels <i>χ</i> = 1.4 and <i>rNt</i> = 5.</p

    The Product of <i>Yersinia pseudotuberculosis mcc</i> Operon Is a Peptide-Cytidine Antibiotic Activated Inside Producing Cells by the TldD/E Protease

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    Microcin C is a heptapeptide-adenylate antibiotic produced by some strains of <i>Escherichia coli</i>. Its peptide part is responsible for facilitated transport inside sensitive cells where it is proteolyzed with release of a toxic warheada nonhydrolyzable aspartamidyl-adenylate, which inhibits aspartyl-tRNA synthetase. Recently, a microcin C homologue from <i>Bacillus amyloliquefaciens</i> containing a longer peptide part modified with carboxymethyl-cytosine instead of adenosine was described, but no biological activity of this compound was revealed. Here, we characterize modified peptide-cytidylate from <i>Yersinia pseudotuberculosis</i>. As reported for <i>B. amyloliquefaciens</i> homologue, the initially synthesized compound contains a long peptide that is biologically inactive. This compound is subjected to endoproteolytic processing inside producing cells by the evolutionary conserved TldD/E protease. As a result, an 11-amino acid long peptide with C-terminal modified cytosine residue is produced. This compound is exported outside the producing cell and is bioactive, inhibiting sensitive cells in the same way as <i>E. coli</i> microcin C. Proteolytic processing inside producing cells is a novel strategy of peptide–nucleotide antibiotics biosynthesis that may help control production levels and avoid toxicity to the producer
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