Prokaryotes encode adaptive immune systems, called CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR associated), to provide resistance against mobile invaders, such as viruses and plasmids. Host immunity is based on incorporation of invader DNA sequences in a memory locus (CRISPR), the formation of guide RNAs from this locus, and the degradation of cognate invader DNA (protospacer). Invaders can escape type I-E CRISPRCas immunity in Escherichia coli K12 by making point mutations in the seed region of the protospacer or its adjacent motif (PAM), but hosts quickly restore immunity by integrating new spacers in a positive-feedback process termed "priming." Here, by using a randomized protospacer and PAM library and high-throughput plasmid loss assays, we provide a systematic analysis of the constraints of both direct interference and subsequent priming in E. coli. We have defined a high-resolution genetic map of direct interference by Cascade and Cas3, which includes five positions of the protospacer at 6-nt intervals that readily tolerate mutations. Importantly, we show that priming is an extremely robust process capable of using degenerate target regions, with up to 13 mutations throughout the PAM and protospacer region. Priming is influenced by the number of mismatches, their position, and is nucleotide dependent. Our findings imply that even outdated spacers containing many mismatches can induce a rapid primed CRISPR response against diversified or related invaders, giving microbes an advantage in the coevolutionary arms race with their invaders.adaptive immunity | phage resistance | crRNA | next-generation sequencing | horizontal gene transfer

The composition and activity of the microbiota in the human gastrointestinal tract are primarily shaped by nutrients derived from either food or the host. Bacteria colonizing the mucus layer have evolved to use mucin as a carbon and energy source. One of the members of the mucosa-associated microbiota is , which is capable of producing an extensive repertoire of mucin-degrading enzymes. To further study the substrate utilization abilities of, we constructed a genome-scale metabolic model to test amino acid auxotrophy, vitamin biosynthesis, and sugar-degrading capacities. The model-supported predictions were validated by experiments, which showed to be able to utilize the mucin-derived monosaccharides fucose, galactose, and -acetylglucosamine. Growth was also observed on-acetylgalactosamine, even though the metabolic model did not predict this. The uptake of these sugars, as well as the nonmucin sugar glucose, was enhanced in the presence of mucin, indicating that additional mucin-derived components are needed for optimal growth. An analysis of whole-transcriptome sequencing (RNA-Seq) comparing the gene expression of grown on mucin with that of the same bacterium grown on glucose confirmed the activity of the genes involved in mucin degradation and revealed most of these to be upregulated in the presence of mucin. The transcriptional response was confirmed by a proteome analysis, altogether revealing a hierarchy in the use of sugars and reflecting the adaptation of to the mucosal environment. In conclusion, these findings provide molecular insights into the lifestyle of and further confirm its role as a mucin specialist in the gut. is among the most abundant mucosal bacteria in humans and in a wide range of other animals. Recently, has attracted considerable attention because of its capacity to protect against diet-induced obesity in mouse models. However, the physiology of has not been studied in detail. Hence, we constructed a genome-scale model and describe its validation by transcriptomic and proteomic approaches on bacterial cells grown on mucus and glucose, a nonmucus sugar. The results provide detailed molecular insight into the mucus-degrading lifestyle of and further confirm the role of this mucin specialist in producing propionate and acetate under conditions of the intestinal tract.

Summary 14Prokaryotes use a mechanism called priming to update their CRISPR immunological memory to rapidly counter 15 revisiting, mutated viruses and plasmids. Here we have determined how new spacers are produced and 16 selected for integration into the CRISPR array during priming. We show that Cas3 couples CRISPR interference 17 to adaptation by producing DNA breakdown products that fuel the spacer integration process in a two-step,

The complex nature of the mechanisms behind cardiovascular diseases prevents the detection of latent early risk conditions. Network representations are ideally suited to investigate the complex interconnections between the individual components of a biological system that underlies complex diseases. Here, we investigate the patterns of correlations of an array of 29 metabolites identified and quantified in the plasma of 864 healthy blood donors and use a systems biology approach to define metabolite probabilistic networks specific for low and high latent cardiovascular risk. We adapted methods based on the likelihood of correlation and methods from information theory and combined them with resampling techniques. Our results show that plasma metabolite networks can be defined that associate with latent cardiovascular disease risk. The analysis of the networks supports our previous finding of a possible association between cardiovascular risk and impaired mitochondrial activity and highlights post-translational modifications (glycosilation and oxidation) of lipoproteins as a possible target-mechanism for early detection of latent cardiovascular risk.

Despite various efforts to develop tools to detect and compare the catabolic potential and activity for pollutant degradation in environmental samples, there is still a need for an open-source, curated and reliable array method. We developed a custom array system including a novel normalization strategy that can be applied to any microarray design, allowing the calculation of the reliability of signals and make cross-experimental comparisons. Array probes, which are fully available to the scientific community, were designed from knowledge-based curated databases for key aromatic catabolic gene families and key alkane degradation genes. This design assigns signals to the respective protein subfamilies, thus directly inferring function and substrate specificity. Experimental procedures were optimized using DNA of four genome sequenced biodegradation strains and reliability of signals assessed through a novel normalization procedure, where a plasmid containing four artificial targets in increased copy numbers and co-amplified with the environmental DNA served as an internal calibration curve. The array system was applied to assess the catabolic gene landscape and transcriptome of aromatic contaminated environmental samples, confirming the abundance of catabolic gene subfamilies previously detected by functional metagenomics but also revealing the presence of previously undetected catabolic groups and specifically their expression under pollutant stress.