Site-specific mutagenesis screening in KRASG12D mutant library to uncover resistance mechanisms to KRASG12D inhibitors.

KRAS plays a crucial role in regulating cell survival and proliferation and is one of the most commonly mutated oncogenes in human cancers. The novel KRASG12D inhibitor, MRTX1133, demonstrates promising antitumor efficacy in vitro and in vivo. However, the development of acquired resistance in treated patients presents a considerable challenge to sustained therapeutic effectiveness. In response to this challenge, we conducted site-specific mutagenesis screening to identify potential secondary mutations that could induce resistance to MRTX1133. We screened a range of KRASG12D variants harboring potential secondary mutations, and 44 representative variants were selected for in-depth validation of the pooled screening outcomes. We identified eight variants (G12D with V9E, V9W, V9Q, G13P, T58Y, R68G, Y96W, and Q99L) that exhibited substantial resistance, with V9W showing notable resistance, and downstream signaling analyses and structural modeling were conducted. We observed that secondary mutations in KRASG12D can lead to acquired resistance to MRTX1133 and BI-2865, a novel pan-KRAS inhibitor, in human cancer cell lines. This evidence is critical for devising new strategies to counteract resistance mechanisms and, ultimately, enhance treatment outcomes in patients with KRASG12D-mutant cancers.

Massively parallel evaluation and computational prediction of the activities and specificities of 17 small Cas9s.

Recently, various small Cas9 orthologs and variants have been reported for use in in vivo delivery applications. Although small Cas9s are particularly suited for this purpose, selecting the most optimal small Cas9 for use at a specific target sequence continues to be challenging. Here, to this end, we have systematically compared the activities of 17 small Cas9s for thousands of target sequences. For each small Cas9, we have characterized the protospacer adjacent motif and determined optimal single guide RNA expression formats and scaffold sequence. High-throughput comparative analyses revealed distinct high- and low-activity groups of small Cas9s. We also developed DeepSmallCas9, a set of computational models predicting the activities of the small Cas9s at matched and mismatched target sequences. Together, this analysis and these computational models provide a useful guide for researchers to select the most suitable small Cas9 for specific applications.

Prediction of efficiencies for diverse prime editing systems in multiple cell types.

Applications of prime editing are often limited due to insufficient efficiencies, and it can require substantial time and resources to determine the most efficient pegRNAs and prime editors (PEs) to generate a desired edit under various experimental conditions. Here, we evaluated prime editing efficiencies for a total of 338,996 pairs of pegRNAs including 3,979 epegRNAs and target sequences in an error-free manner. These datasets enabled a systematic determination of factors affecting prime editing efficiencies. Then, we developed computational models, named DeepPrime and DeepPrime-FT, that can predict prime editing efficiencies for eight prime editing systems in seven cell types for all possible types of editing of up to 3 base pairs. We also extensively profiled the prime editing efficiencies at mismatched targets and developed a computational model predicting editing efficiencies at such targets. These computational models, together with our improved knowledge about prime editing efficiency determinants, will greatly facilitate prime editing applications.

Prediction of Base Editing Efficiencies and Outcomes Using DeepABE and DeepCBE.

Adenine base editors (ABEs) and cytosine base editors (CBEs) have been widely used to introduce disease-relevant point mutations at target DNA sites of interest. However, the introduction of point mutations using base editors can be difficult due to low editing efficiencies and/or the existence of multiple target nucleotides within the base editing window at the target site. Thus, previous works have relied heavily on experimentally evaluating the base editing efficiencies and outcomes using time-consuming and labor-intensive multi-step experimental processes. DeepABE and DeepCBE are deep learning-based computational models to predict the efficiencies and outcome frequencies of ABE and CBE at given target DNA sites, in silico. Here, we describe the step-by-step procedure for the accurate determination of specific target nucleotides for ABE or CBE editing on the online available web tool, (DeepBaseEditor, https://deepcrispr.info/DeepBaseEditor ).

Recording of elapsed time and temporal information about biological events using Cas9.

DNA has not been utilized to record temporal information, although DNA has been used to record biological information and to compute mathematical problems. Here, we found that indel generation by Cas9 and guide RNA can occur at steady rates, in contrast to typical dynamic biological reactions, and the accumulated indel frequency can be a function of time. By measuring indel frequencies, we developed a method for recording and measuring absolute time periods over hours to weeks in mammalian cells. These time-recordings were conducted in several cell types, with different promoters and delivery vectors for Cas9, and in both cultured cells and cells of living mice. As applications, we recorded the duration of chemical exposure and the lengths of elapsed time since the onset of biological events (e.g., heat exposure and inflammation). We propose that our systems could serve as synthetic "DNA clocks."

Predicting the efficiency of prime editing guide RNAs in human cells.

Prime editing enables the introduction of virtually any small-sized genetic change without requiring donor DNA or double-strand breaks. However, evaluation of prime editing efficiency requires time-consuming experiments, and the factors that affect efficiency have not been extensively investigated. In this study, we performed high-throughput evaluation of prime editor 2 (PE2) activities in human cells using 54,836 pairs of prime editing guide RNAs (pegRNAs) and their target sequences. The resulting data sets allowed us to identify factors affecting PE2 efficiency and to develop three computational models to predict pegRNA efficiency. For a given target sequence, the computational models predict efficiencies of pegRNAs with different lengths of primer binding sites and reverse transcriptase templates for edits of various types and positions. Testing the accuracy of the predictions using test data sets that were not used for training, we found Spearman's correlations between 0.47 and 0.81. Our computational models and information about factors affecting PE2 efficiency will facilitate practical application of prime editing.

Sequence-specific prediction of the efficiencies of adenine and cytosine base editors.

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.

Prediction of the sequence-specific cleavage activity of Cas9 variants.

Several Streptococcus pyogenes Cas9 (SpCas9) variants have been developed to improve an enzyme's specificity or to alter or broaden its protospacer-adjacent motif (PAM) compatibility, but selecting the optimal variant for a given target sequence and application remains difficult. To build computational models to predict the sequence-specific activity of 13 SpCas9 variants, we first assessed their cleavage efficiency at 26,891 target sequences. We found that, of the 256 possible four-nucleotide NNNN sequences, 156 can be used as a PAM by at least one of the SpCas9 variants. For the high-fidelity variants, overall activity could be ranked as SpCas9 ≥ Sniper-Cas9 > eSpCas9(1.1) > SpCas9-HF1 > HypaCas9 ≈ xCas9 >> evoCas9, whereas their overall specificities could be ranked as evoCas9 >> HypaCas9 ≥ SpCas9-HF1 ≈ eSpCas9(1.1) > xCas9 > Sniper-Cas9 > SpCas9. Using these data, we developed 16 deep-learning-based computational models that accurately predict the activity of these variants at any target sequence.

High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells.

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.

SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance.

We evaluated SpCas9 activities at 12,832 target sequences using a high-throughput approach based on a human cell library containing single-guide RNA-encoding and target sequence pairs. Deep learning-based training on this large dataset of SpCas9-induced indel frequencies led to the development of a SpCas9 activity-predicting model named DeepSpCas9. When tested against independently generated datasets (our own and those published by other groups), DeepSpCas9 showed high generalization performance. DeepSpCas9 is available at http://deepcrispr.info/DeepSpCas9.

Deep learning improves prediction 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.

The effects of combined dietary probiotics Lactococcus lactis BFE920 and Lactobacillus plantarum FGL0001 on innate immunity and disease resistance in olive flounder (Paralichthys olivaceus).

The effects of a dietary probiotic mixture containing Lactococcus (Lc.) lactis BFE920 isolated from bean sprout and autochthonous Lactobacillus (Lb.) plantarum FGL0001 originally isolated from the hindgut of olive flounder (Paralichthys olivaceus) were investigated for the purpose of improving the probiotic effects of Lc. lactis BFE920 on the olive flounder. The immunostimulatory, disease protective, and weight gain effects of Lc. lactis BFE920 were significantly improved when olive flounder (average weight 37.5±1.26 g) were fed the probiotic mixture (log10 7.0 CFU each/g feed pellet) for 30 days. Flounder fed the mixture showed improved skin mucus lysozyme activity and phagocytic activity of innate immune cells compared to flounder fed a single probiotic agent or a control diet. While the levels of neutrophil activity in flounder fed the single probiotic agent or the mixture were similar, they were significantly higher than levels in a control group. Additionally, probiotic-fed flounder showed significantly increased expressions of IL-6, IL-8, and TNF-α in the intestine compared to the control group. Following a 30-day period of being fed probiotics or a control diet, the olive flounder were challenged with an i.p. injection of Streptococcus iniae (log10 6.0 CFU/fish). The groups fed the mixed probiotics, Lc. lactis BFE920, Lb. plantarum FGL0001, and the control diet had survival rates of 55%, 45%, 35%, and 20%, respectively. Flounder fed the probiotic mixture gained 38.1±2.8% more body weight compared to flounder fed the control diet during the 30-day study period. These data strongly suggest that a mixture of Lc. lactis BFE920 and Lb. plantarum FGL0001 may serve as an immunostimulating feed additive useful for disease protection in the fish farming industry.

Identification of a novel mutation in a patient with pseudohypoparathyroidism type Ia.

Pseudohypoparathyroidism type Ia (PHP Ia) is a disorder characterized by multiform hormonal resistance including parathyroid hormone (PTH) resistance and Albright hereditary osteodystrophy (AHO). It is caused by heterozygous inactivating mutations within the Gs alpha-encoding GNAS exons. A 9-year-old boy presented with clinical and laboratory abnormalities including hypocalcemia, hyperphosphatemia, PTH resistance, multihormone resistance and AHO (round face, short stature, obesity, brachydactyly and osteoma cutis) which were typical of PHP Ia. He had a history of repeated convulsive episodes that started from the age of 2 months. A cranial computed tomography scan showed bilateral calcifications in the basal ganglia and his intelligence quotient testing indicated mild mental retardation. Family history revealed that the patient's maternal relatives, including his grandmother and 2 of his mother's siblings, had features suggestive of AHO. Sequencing of the GNAS gene of the patient identified a heterozygous nonsense mutation within exon 11 (c.637 C>T). The C>T transversion results in an amino acid substitution from Gln to stop codon at codon 213 (p.Gln213(*)). To our knowledge, this is a novel mutation in GNAS.

Hutchinson-Gilford progeria syndrome with G608G LMNA mutation.

Hutchinson-Gilford progeria syndrome (HGPS) is a rare condition originally described by Hutchinson in 1886. Death result from cardiac complications in the majority of cases and usually occurs at average age of thirteen years. A 4-yr old boy had typical clinical findings such as short stature, craniofacial disproportion, alopecia, prominent scalp veins and sclerodermatous skin. This abnormal appearance began at age of 1 yr. On serological and hormonal evaluation, all values are within normal range. He was neurologically intact with motor and mental development. An echocardiogram showed calcification of aortic and mitral valves. Hypertrophy of internal layer at internal carotid artery suggesting atherosclerosis was found by carotid doppler sonography. He is on low dose aspirin to prevent thromboembolic episodes and on regular follow up. Gene study showed typical G608G (GGC- > GGT) point mutation at exon 11 in LMNA gene. This is a rare case of Hutchinson-Gilford progeria syndrome confirmed by genetic analysis in Korea.