Summary: 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.

Identification of transcription factor binding sites within regulatory segments of genomic DNA is an important step toward understanding of the regulatory circuits that control expression of genes. Here, we describe a novel bioinformatics method that bases classification of potential binding sites explicitly on the estimate of sequence-specific binding energy of a given transcription factor. The method also estimates the chemical potential of the factor that defines the threshold of binding. In contrast with the widely used information-theoretic weight matrix method, the new approach correctly describes saturation in the transcription factor/DNA binding probability. This results in a significant improvement in the number of expected false positives, particularly in the ubiquitous case of low-specificity factors. In the strong binding limit, the algorithm is related to the “support vector machine” approach to pattern recognition. The new method is used to identify likely genomic binding sites for the E. coli transcription factors collected in the DPInteract database. In addition, for CRP (a global regulatory factor), the likely regulatory modality (i.e., repressor or activator) of predicted binding sites is determined.

Jet suppression of light and heavy flavor observables is considered to be an excellent tool to study the properties of QCD matter created in ultra-relativistic heavy ion collisions. We calculate the suppression patterns of light hadrons, D mesons, non-photonic single electrons and non-prompt J/ψ in Pb + Pb collisions at the LHC. We use a theoretical formalism that takes into account finite size dynamical QCD medium with finite magnetic mass effects and running coupling, which is integrated into a numerical procedure that uses no free parameters in model testing. We obtain a good agreement with the experimental results across different experiments/particle species. Our results show that the developed theoretical formalism can robustly explain suppression data in ultra relativistic heavy ion collisions, which strongly suggests that pQCD in Quark–Gluon Plasma is able to provide a reasonable description of the underlying jet physics at LHC

Recognition by transcription factors of the regulatory DNA elements upstream of genes is the fundamental step in controlling gene expression. How does the necessity to provide stability with respect to mutation constrain the organization of transcription control networks? We examine the mutation load of a transcription factor interacting with a set of n regulatory response elements as a function of the factor/DNA binding specificity and conclude on theoretical grounds that the optimal specificity decreases with n . The predicted correlation between variability of binding sites (for a given transcription factor) and their number is supported by the genomic data for Escherichia coli . The analysis of E. coli genomic data was carried out using an algorithm suggested by the biophysical model of transcription factor/DNA binding. Complete results of the search for candidate transcription factor binding sites are available at http://www.physics.rockefeller.edu/∼boris/public/search_ecoli .