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Base editors, including adenine base editors (ABEs) 1 and cytosine base editors (CBEs) 2 , 3 , are widely used to induce point mutations. However, determining whether a specific nucleotide in its genomic context can be edited requires time-consuming experiments. Furthermore, when the editable window contains multiple target nucleotides, various genotypic products can be generated. To develop computational tools to predict base-editing efficiency and outcome product frequencies, we first evaluated the efficiencies of an ABE and a CBE and the outcome product frequencies at 13,504 and 14,157 target sequences, respectively, in human cells. We found that there were only modest asymmetric correlations between the activities of the base editors and Cas9 at the same targets. Using deep-learning-based computational modeling, we built tools to predict the efficiencies and outcome frequencies of ABE- and CBE-directed editing at any target sequence, with Pearson correlations ranging from 0.50 to 0.95. These tools and results will facilitate modeling and therapeutic correction of genetic diseases by base editing. The activity of adenine or cytosine base editors at specific target nucleotides is predicted computationally.

The applications of clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing can be limited by a lack of compatible protospacer adjacent motifs (PAMs), insufficient on-target activity and off-target effects. Here, we report an extensive comparison of the PAM-sequence compatibilities and the on-target and off-target activities of Cas9 from Streptococcus pyogenes (SpCas9) and the SpCas9 variants xCas9 and SpCas9-NG (which are known to have broader PAM compatibility than SpCas9) at 26,478 lentivirally integrated target sequences and 78 endogenous target sites in human cells. We found that xCas9 has the lowest tolerance for mismatched target sequences and that SpCas9-NG has the broadest PAM compatibility. We also show, on the basis of newly identified non-NGG PAM sequences, that SpCas9-NG and SpCas9 can edit six previously unedited endogenous sites associated with genetic diseases. Moreover, we provide deep-learning models that predict the activities of xCas9 and SpCas9-NG at the target sequences. The resulting deeper understanding of the activities of xCas9, SpCas9-NG and SpCas9 in human cells should facilitate their use. A comparison of compatibilities in protospacer adjacent motifs and of on-target and off-target activities of Streptococcus pyogenes Cas9 variants at endogenous sites in human cells enables the editing of new genomic sites associated with genetic diseases.

Atmospheric mercury (Hg(0), Hg(II)) and riverine exported Hg (Hg(II)) are proposed as important Hg sources to the Arctic Ocean. As plankton cannot passively uptake Hg(0), gaseous Hg(0) has to be oxidized to be bioavailable. Here, we measured Hg isotope ratios in zooplankton, Arctic cod, total gaseous Hg, sediment, seawater, and snowpack from the Bering Strait, the Chukchi Sea, and the Beaufort Sea. The Δ 200 Hg, used to differentiate between Hg(0) and Hg(II), shows, on average, 70% of Hg(0) in all biota and differs with seawater Δ 200 Hg (Hg(II)). Since Δ 200 Hg anomalies occur via tropospheric Hg(0) oxidation, we propose that near-surface Hg(0) oxidation via terrestrial vegetation, coastally evaded halogens, and sea salt aerosols, which preserve Δ 200 Hg of Hg(0) upon oxidation, supply bioavailable Hg(II) pools in seawater. Our study highlights sources and pathways in which Hg(0) poses potential ecological risks to the Arctic Ocean biota. This study finds that atmospheric mercury is rapidly oxidized near the surface via terrestrial vegetation and sea salt aerosols, generating bioavailable mercury pools for the Arctic Ocean biota.

Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.

The objective of this study was to investigate effects of silicate (SIL) supplementation on growth performance, nutrient digestibility, organ weight, immune traits, fecal microbiota, rectal temperature, and diarrhea score in weaning to growing pigs with challenged lipopolysaccharide (LPS). In experiment 1, a total of 72 piglets [(Landrace × Yorkshire) × Duroc] with initial body weight (BW) of 7.53 ± 0.1 kg were used in 14 weeks. Treatment groups: (1) Basal diet (CON), and (2) Basal diet + 0.1% silicate (SIL). In experiment 2, after reaching an average BW of 28.12 ± 0.79 kg at end of this experiment, 24 piglets in each treatment (12 pigs per treatment and 2 pigs per pen) were challenged with 100 μg/kg LPS. In experiment 1, SIL diets improved (P < 0.05) growth performance, crude protein (CP) digestibility and reduced (P < 0.05) NH 3 emission compared to CON diet. In experiment 2, after piglets were fed SIL diets, numbers of white blood cells (WBCs) such as WBC, neutrophil, monocyte, and lymphocyte were increased (P < 0.05). SIL diets decreased (P < 0.05) cortisol level, Escherichia coli count, diarrhea incidence, and rectal temperature compared to CON diet.

Comprehensive phenotypic characterization of the many mutations found in cancer tissues is one of the biggest challenges in cancer genomics. In this study, we evaluated the functional effects of 29,060 cancer-related transition mutations that result in protein variants on the survival and proliferation of non-tumorigenic lung cells using cytosine and adenine base editors and single guide RNA (sgRNA) libraries. By monitoring base editing efficiencies and outcomes using surrogate target sequences paired with sgRNA-encoding sequences on the lentiviral delivery construct, we identified sgRNAs that induced a single primary protein variant per sgRNA, enabling linking those mutations to the cellular phenotypes caused by base editing. The functions of the vast majority of the protein variants (28,458 variants, 98%) were classified as neutral or likely neutral; only 18 (0.06%) and 157 (0.5%) variants caused outgrowing and likely outgrowing phenotypes, respectively. We expect that our approach can be extended to more variants of unknown significance and other tumor types. Cancer-associated variants of unknown significance are identified using base editing.

Using deep learning to combine target sequence and chromatin accessibility data boosts the accuracy of CRISPR–Cpf1 guide RNA activity We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

Variants of uncertain significance (VUS) hamper the clinical application of genetic information. For example, in treating lung cancer with tyrosine kinase inhibitors (TKIs), many epidermal growth factor receptor (EGFR) variants remain classified as VUS with respect to TKI sensitivity. Such incomplete resistance profiles hinder clinicians from selecting optimal therapeutic agents. A high-throughput approach that can evaluate the functional effects of single nucleotide variants (SNVs) could reduce the number of VUS. Here we introduce SynPrime, a method based on prime editing that enabled the generation and functional evaluation of 2,476 SNVs in the EGFR gene, including 99% of all possible variants in the canonical tyrosine kinase domain (exons 18 to 21). We determined resistance profiles of 95% (= 1,726/1,817) of all possible EGFR protein variants encoded in the whole tyrosine kinase domain (exons 18 to 24) against afatinib, osimertinib, and osimertinib in the presence of the co-occurring mutation T790M, in PC-9 cells. SynPrime, which uses direct sequencing of endogenous regions to identify SNVs, provided more accurate functional evaluations than a guide RNA abundance-based approach. Our study has the potential to substantially improve the precision of therapeutic choices in clinical settings and contribute to addressing the issue of VUS by being applied to other genes.Competing Interest StatementYonsei University has filed a patent application based on this work, in which Y.K., H.C.O., S.L. and H.H.K. are listed as inventors.

We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing sgRNA-encoding and target sequence pairs. Deep learning-based training on this large data set of SpCas9-induced indel frequencies led to the development of a SpCas9-activity predicting model named DeepSpCas9. When tested against independently generated data sets (our own and those published by other groups), DeepSpCas9 showed unprecedentedly high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepCas9. Footnotes * We changed the name of our tool, from \"DeepCas9\" to \"DeepSpCas9\". We changed the name of our tool throughout the manuscript and figure legends.

The asymmetric simple exclusion process (ASEP) is an important particle model with deep connections to orthogonal polynomials. Motivated by this connection, Corteel and Williams introduced the Koornwinder moments \\(M^Z_\\) at \\( t=q \\), which generalize the moments of Askey--Wilson polynomials. They showed that the partition function of the two-species ASEP is equal to \\(M^Z_\\) for a one-row partition \\( \\). In this paper, we investigate a conjecture of Rains on the positivity of the minimal numerator of the Koornwinder moment \\(M^Z_\\). We derive the first explicit formula for this moment, thereby obtaining a precise formulation of the conjecture by determining the minimal denominator of \\(M^Z_\\). We also propose a generalization of the conjecture for the more general Koornwinder moments \\(M^Z_ \\) indexed by two partitions at special parameter values. We prove the generalized Rains' conjecture in two special cases: \\(( q)=(1,0)\\) and \\(( q)=(1,1)\\). For \\(( q)=(1,0)\\), we construct a lattice path model and obtain a combinatorial formula for \\(M^Z_ \\) in terms of non-intersecting lattice paths. For \\(( q)=(1,1)\\), we establish an explicit product formula for \\(M^Z_ \\) and give a combinatorial interpretation using lecture hall tableaux.