The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. Integrating these with complementary published data, we train a deep learning model, CRISPRon, on 23,902 gRNAs. Compared to existing tools, CRISPRon exhibits significantly higher prediction performances on four test datasets not overlapping with training data used for the development of these tools. Furthermore, we present an interactive gRNA design webserver based on the CRISPRon standalone software, both available via https://rth.dk/resources/crispr/. CRISPRon advances CRISPR applications by providing more accurate gRNA efficiency predictions than the existing tools.

Background Extrachromosomal circular deoxyribonucleic acid (eccDNA) is evolving as a valuable biomarker, while little is known about its presence in urine. Methods Here, we report the discovery and analysis of urinary cell‐free eccDNAs (ucf‐eccDNAs) in healthy controls and patients with advanced chronic kidney disease (CKD) by Circle‐Seq. Results Millions of unique ucf‐eccDNAs were identified and comprehensively characterised. The ucf‐eccDNAs are GC‐rich. Most ucf‐eccDNAs are less than 1000 bp and are enriched in four pronounced peaks at 207, 358, 553 and 732 bp. Analysis of the genomic distribution of ucf‐eccDNAs shows that eccDNAs are found on all chromosomes but enriched on chromosomes 17, 19 and 20 with a high density of protein‐coding genes, CpG islands, short interspersed transposable elements (SINEs) and simple repeat elements. Analysis of eccDNA junction sequences further suggests that microhomology and palindromic repeats might be involved in eccDNA formation. The ucf‐eccDNAs in CKD patients are significantly higher than those in healthy controls. Moreover, eccDNA with miRNA genes is highly enriched in CKD ucf‐eccDNA. Conclusions This work discovers and provides the first deep characterisation of ucf‐eccDNAs and suggests ucf‐eccDNA as a valuable noninnvasive biomarker for urogenital disorder diagnosis and monitoring.

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Plants biosynthesize a great diversity of biologically active small molecules of interest for fragrances, flavors, and pharmaceuticals. Among specialized metabolites, terpenoids represent the greatest molecular diversity. Many terpenoids are very complex, and total chemical synthesis often requires many steps and difficult chemical reactions, resulting in a low final yield or incorrect stereochemistry. Several drug candidates with terpene skeletons are difficult to obtain by chemical synthesis due to their large number of chiral centers. Thus, biological production remains the preferred method for industrial production for many of these compounds. However, because these chemicals are often found in low abundance in the native plant, or are produced in plants which are difficult to cultivate, there is great interest in engineering increased production or expression of the biosynthetic pathways in heterologous hosts. Although there are many examples of successful engineering of microbes such as yeast or bacteria to produce these compounds, this often requires extensive changes to the host organism's metabolism. Optimization of plant gene expression, post-translational protein modifications, subcellular localization, and other factors often present challenges. To address the future demand for natural products used as drugs, new platforms are being established that are better suited for heterologous production of plant metabolites. Specifically, direct metabolic engineering of plants can provide effective heterologous expression for production of valuable plant-derived natural products. In this review, our primary focus is on small terpenoids and we discuss the benefits of plant expression platforms and provide several successful examples of stable production of small terpenoids in plants.

A major challenge of CRISPR/Cas9-mediated genome engineering is that not all guide RNAs (gRNAs) cleave the DNA efficiently. Although the heterogeneity of gRNA activity is well recognized, the current understanding of how CRISPR/Cas9 activity is regulated remains incomplete. Here, we identify a sweet spot range of binding free energy change for optimal efficiency which largely explains why gRNAs display changes in efficiency at on- and off-target sites, including why gRNAs can cleave an off-target with higher efficiency than the on-target. Using an energy-based model, we show that local gRNA-DNA interactions resulting from Cas9 “sliding” on overlapping protospacer adjacent motifs (PAMs) profoundly impact gRNA activities. Combining the effects of local sliding for a given PAM context with global off-targets allows us to better identify highly specific, and thus efficient, gRNAs. We validate the effects of local sliding on gRNA efficiency using both public data and in-house data generated by measuring SpCas9 cleavage efficiency at 1024 sites designed to cover all possible combinations of 4-nt PAM and context sequences of 4 gRNAs. Our results provide insights into the mechanisms of Cas9-PAM compatibility and cleavage activation, underlining the importance of accounting for local sliding in gRNA design.