Quantifying allele-specific CRISPR editing activity with CRISPECTOR2.0.

Off-target effects present a significant impediment to the safe and efficient use of CRISPR-Cas genome editing. Since off-target activity is influenced by the genomic sequence, the presence of sequence variants leads to varying on- and off-target profiles among different alleles or individuals. However, a reliable tool that quantifies genome editing activity in an allelic context is not available. Here, we introduce CRISPECTOR2.0, an extended version of our previously published software tool CRISPECTOR, with an allele-specific editing activity quantification option. CRISPECTOR2.0 enables reference-free, allele-aware, precise quantification of on- and off-target activity, by using de novo sample-specific single nucleotide variant (SNV) detection and statistical-based allele-calling algorithms. We demonstrate CRISPECTOR2.0 efficacy in analyzing samples containing multiple alleles and quantifying allele-specific editing activity, using data from diverse cell types, including primary human cells, plants, and an original extensive human cell line database. We identified instances where an SNV induced changes in the protospacer adjacent motif sequence, resulting in allele-specific editing. Intriguingly, differential allelic editing was also observed in regions carrying distal SNVs, hinting at the involvement of additional epigenetic factors. Our findings highlight the importance of allele-specific editing measurement as a milestone in the adaptation of efficient, accurate, and safe personalized genome editing.

Rapid super resolution for infrared imagery.

Infrared (IR) imagery is used in agriculture for irrigation monitoring and early detection of disease in plants. The common IR cameras in this field typically have low resolution. This work offers a method to obtain the super-resolution of IR images from low-power devices to enhance plant traits. The method is based on deep learning (DL). Most calculations are done in the low-resolution domain. The results of each layer are aggregated together to allow a better flow of information through the network. This work shows that good results can be achieved using depthwise separable convolution with roughly 300K multiply-accumulate computations (MACs), while state-of-the-art convolutional neural network-based super-resolution algorithms are performed with around 1500K MACs. MTF analysis of the proposed method shows a real ×4 improvement in the spatial resolution of the system, out-preforming the diffraction limit. The method is demonstrated on real agricultural images.

Silicon promotes cytokinin biosynthesis and delays senescence in Arabidopsis and Sorghum.

Silicate minerals are dominant soil components. Thus, plant roots are constantly exposed to silicic acid. High silicon intake, enabled by root silicon transporters, correlates with increased tolerance to many biotic and abiotic stresses. However, the underlying protection mechanisms are largely unknown. Here, we tested the hypothesis that silicon interacts with the plant hormones, and specifically, that silicic acid intake increases cytokinin biosynthesis. The reaction of sorghum (Sorghum bicolor) and Arabidopsis plants, modified to absorb high versus low amounts of silicon, to dark-induced senescence was monitored, by quantifying expression levels of genes along the senescence pathway and measuring tissue cytokinin levels. In both species, detached leaves with high silicon content senesced more slowly than leaves that were not exposed to silicic acid. Expression levels of genes along the senescence pathway suggested increased cytokinin biosynthesis with silicon exposure. Mass spectrometry measurements of cytokinin suggested a positive correlation between silicon exposure and active cytokinin concentrations. Our results indicate a similar reaction to silicon treatment in distantly related plants, proposing a general function of silicon as a stress reliever, acting via increased cytokinin biosynthesis.

Arabidopsis thaliana plants with different levels of aliphatic- and indolyl-glucosinolates affect host selection and performance of Bemisia tabaci.

Generalist insects show reduced selectivity when subjected to similar, but not identical, host plant chemical signatures. Here, we produced transgenic Arabidopsis thaliana plants that over-express genes regulating the aliphatic- and indolyl- glucosinolates biosynthetic pathways with either a constitutive (CaMV 35S) or a phloem-specific promoter (AtSUC2). This allowed us to examine how exposure to high levels of aliphatic- or indolyl-glucosinolates in homogenous habitats (leaf cage apparatus containing two wild-type or two transgenic leaves) and heterogeneous habitats (leaf cage apparatus containing one wild-type and one transgenic leaf) affects host selection and performance of Bemsia tabaci, a generalist phloem-feeding insect. Data from homogenous habitats indicated that exposure to A. thaliana plants accumulating high levels of aliphatic- or indolyl-glucosinolates negatively affected the performance of both adult females and nymphs of B. tabaci. Data from heterogeneous habitats indicated that B. tabaci adult females selected for oviposition plants on which their offspring perform better (preference-performance relationship). However, the combinations of wild-type and transgenic plants in heterogeneous habitats increased the period of time until the first choice was made and led to increased movement rate on transgenic plants, and reduced fecundity on wild-type plants. Overall, our findings are consistent with the view that both performance and selectivity of B. tabaci decrease in heterogeneous habitats that contain plants with closely-related chemical signatures.