On the global CRISPR array behavior in class I systems

Alice Toms, Rodolphe Barrangou

Much effort is underway to build and upgrade databases and tools related to occurrence, diversity, and characterization of CRISPR-Cas systems. As microbial communities and their genome complements are unearthed, much emphasis has been placed on details of individual strains and model systems within the CRISPR-Cas classification, and that collection of information as a whole affords the opportunity to analyze CRISPR-Cas systems from a quantitative perspective to gain insight into distribution of CRISPR array sizes across the different classes, types and subtypes. CRISPR diversity, nomenclature, occurrence, and biological functions have generated a plethora of data that created a need to understand the size and distribution of these various systems to appreciate their features and complexity. By utilizing a statistical framework and visual analytic techniques, we have been able to test several hypotheses about CRISPR loci in bacterial class I systems. Quantitatively, though CRISPR loci can expand to hundreds of spacers, the mean and median sizes are 40 and 25, respectively, reflecting rather modest acquisition and/or retention overall. Histograms uncovered that CRISPR array size displayed a parametric distribution, which was confirmed by a goodness-of fit test. Mapping the frequency of CRISPR loci on a standardized chromosome plot revealed that CRISPRs have a higher probability of occurring at clustered locations along the positive or negative strand. Lastly, when multiple arrays occur in a particular system, the size of a particular CRISPR array varies with its distance from the cas operon, reflecting acquisition and expansion biases. This study establishes that bacterial Class I CRISPR array size tends to follow a geometric distribution; these CRISPRs are not randomly distributed along the chromosome; and the CRISPR array closest to the cas genes is typically larger than loci in trans. Overall, we provide an analytical framework to understand the features and behavior of CRISPR-Cas systems through a quantitative lens. This article was reviewed by Eugene Koonin (NIH-NCBI) and Uri Gophna (Tel Aviv University).