Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data.

The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation in genome editing. Here we develop a computational pipeline that was used to interrogate a massively expanded dataset of metagenome and virome assemblies for accurate and comprehensive PAM predictions. This procedure allows the identification and isolation of sequence-tailored Cas9 nucleases by using the target sequence as bait. As proof of concept, starting from the disease-causing mutation P23H in the RHO gene, we find, isolate and experimentally validate a Cas9 which uses the mutated sequence as PAM. Our PAM prediction pipeline will be instrumental to generate a Cas9 nuclease repertoire responding to any PAM requirement.

Exosomal telomerase transcripts reprogram the microRNA transcriptome profile of fibroblasts and partially contribute to CAF formation.

It is now well accepted that cancer cells change their microenvironment from normal to tumor-supportive state to provide sustained tumor growth, metastasis and drug resistance. These processes are partially carried out by exosomes, nano-sized vesicles secreted from cells, shuttled from donor to recipient cells containing a cargo of nucleic acids, proteins and lipids. By transferring biologically active molecules, cancer-derived exosomes may transform microenvironmental cells to become tumor supportive. Telomerase activity is regarded as a hallmark of cancer. We have recently shown that the transcript of human telomerase reverse transcriptase (hTERT), is packaged in cancer cells derived- exosomes. Following the engulfment of the hTERT transcript into fibroblasts, it is translated into a fully active enzyme [after assembly with its RNA component (hTERC) subunit]. Telomerase activity in the recipient, otherwise telomerase negative cells, provides them with a survival advantage. Here we show that exosomal telomerase might play a role in modifying normal fibroblasts into cancer associated fibroblasts (CAFs) by upregulating [Formula: see text]SMA and Vimentin, two CAF markers. We also show that telomerase activity changes the transcriptome of microRNA in these fibroblasts. By ectopically expressing microRNA 342, one of the top identified microRNAs, we show that it may mediate the proliferative phenotype that these cells acquire upon taking-up exosomal hTERT, providing them with a survival advantage.

Protein self-entanglement modulates successful folding to the native state: A multi-scale modeling study.

The computer-aided investigation of protein folding has greatly benefited from coarse-grained models, that is, simplified representations at a resolution level lower than atomistic, providing access to qualitative and quantitative details of the folding process that would be hardly attainable, via all-atom descriptions, for medium to long molecules. Nonetheless, the effectiveness of low-resolution models is itself hampered by the presence, in a small but significant number of proteins, of nontrivial topological self-entanglements. Features such as native state knots or slipknots introduce conformational bottlenecks, affecting the probability to fold into the correct conformation; this limitation is particularly severe in the context of coarse-grained models. In this work, we tackle the relationship between folding probability, protein folding pathway, and protein topology in a set of proteins with a nontrivial degree of topological complexity. To avoid or mitigate the risk of incurring in kinetic traps, we make use of the elastic folder model, a coarse-grained model based on angular potentials optimized toward successful folding via a genetic procedure. This light-weight representation allows us to estimate in silico folding probabilities, which we find to anti-correlate with a measure of topological complexity as well as to correlate remarkably well with experimental measurements of the folding rate. These results strengthen the hypothesis that the topological complexity of the native state decreases the folding probability and that the force-field optimization mimics the evolutionary process these proteins have undergone to avoid kinetic traps.