CRISPR/Cas9 System to Knockdown MicroRNA In Vitro and In Vivo

Small extracellular vesicle-mediated CRISPR-Cas9 RNP delivery for cardiac-specific genome editing

Myocardial infarction (MI) is a major cause of morbidity and mortality worldwide. Although clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) gene editing holds immense potential for genetic manipulation, its clinical application is hindered by the absence of an efficient heart-targeted drug delivery system. Herein, we developed CRISPR-Cas9 ribonucleoprotein (RNP)-loaded extracellular vesicles (EVs) conjugated with cardiac-targeting peptide (T) for precise cardiac-specific genome editing. RNP complexes containing Cas9 and single guide RNA targeting miR-34a, an MI-associated molecular target, were loaded into EVs (EV@RNP). Gene editing by EV@RNP attenuated hydrogen peroxide-induced apoptosis in cardiomyocytes via miR-34a inhibition, evidenced by increased B-cell lymphoma 2 levels, decreased Bcl-2-associated X protein levels, and the cleavage of caspase-3. Additionally, to improve cardiac targeting in vivo, we used click chemistry to form functional T-EV@RNP by conjugating T peptides to EV@RNP. Consequently, T-EV@RNP-mediated miR-34a genome editing might exert a protective effect against MI, reducing apoptosis, ameliorating MI injury, and facilitating the recovery of cardiac function. In conclusion, the genome editing delivery system established by loading CRISPR/Cas9 RNP with cardiac-targeting EVs is a powerful approach for precise and tissue-specific gene therapy for cardiovascular disease.

The microRNAs in the antennae of Apolygus lucorum (Hemiptera: Miridae): Expression properties and targets prediction

MicroRNAs (miRNAs) regulate gene expression and contribute to numerous physiological processes. However, little is known about the functions of miRNAs in insect chemosensation. In this study, nine small RNA libraries were constructed and sequenced from the antennae of nymphs, adult males, and adult females of Apolygus lucorum. In total, 399 (275 known and 124 novel) miRNAs were identified. miR-7-5p_1 was the most abundant miRNA. Altogether, 69,708 target genes related to biogenesis, membrane, and binding activities were predicted. In particular, 15 miRNAs targeted 16 olfactory genes. Comparing the antennae of nymphs and adult males and females, 94 miRNAs were differentially expressed. Alternatively, a subset of differentially expressed miRNAs was verified by qPCR, supporting the reliability of the sequencing results. This study provides a global miRNA transcriptome for the antennae of A. lucorum and valuable information for further investigations of the functions of miRNAs in the regulation of chemosensation.

Uncertainty-aware and interpretable evaluation of Cas9-gRNA and Cas12a-gRNA specificity for fully matched and partially mismatched targets with Deep Kernel Learning

The choice of guide RNA (gRNA) for CRISPR-based gene targeting is an essential step in gene editing applications, but the prediction of gRNA specificity remains challenging. Lack of transparency and focus on point estimates of efficiency disregarding the information on possible error sources in the model limit the power of existing Deep Learning-based methods. To overcome these problems, we present a new approach, a hybrid of Capsule Networks and Gaussian Processes. Our method predicts the cleavage efficiency of a gRNA with a corresponding confidence interval, which allows the user to incorporate information regarding possible model errors into the experimental design. We provide the first utilization of uncertainty estimation in computational gRNA design, which is a critical step toward accurate decision-making for future CRISPR applications. The proposed solution demonstrates acceptable confidence intervals for most test sets and shows regression quality similar to existing models. We introduce a set of criteria for gRNA selection based on off-target cleavage efficiency and its variance and present a collection of pre-computed gRNAs for human chromosome 22. Using Neural Network Interpretation methods, we show that our model rediscovers an established biological factor underlying cleavage efficiency, the importance of the seed region in gRNA.