CRISPRtracrRNA: robust approach for CRISPR tracrRNA detection

Disparate mechanisms counteract extraneous CRISPR RNA production in type II-C CRISPR-Cas systems

CRISPR-Cas adaptive immune systems in bacteria and archaea enable precise targeting and elimination of invading genetic elements. An inherent feature of these systems is the 'extraneous' CRISPR RNA (ecrRNA), which is produced via the extra repeat in a CRISPR array lacking a corresponding spacer. As ecrRNAs would interact with the Cas machinery yet not direct acquired immunity, they pose a potential barrier to defence. Type II-A CRISPR-Cas systems resolve this barrier through the leader sequence upstream of a CRISPR array, which forms a hairpin structure with the extra repeat that inhibits ecrRNA production. However, the fate of ecrRNAs in other CRISPR types and subtypes remains to be explored. Here, we report that II-C systems likely employ disparate strategies to resolve the ecrRNA due to their distinct configuration in comparison to II-A. Applying bioinformatics analyses to over 650 II-C systems followed by experimental validation, we identified three strategies applicable to these systems: formation of an upstream Rho-independent terminator, formation of a hairpin that sequesters the ecrRNA guide, and mutations in the repeat expected to disrupt ecrRNA formation. These findings expand the list of mechanisms in CRISPR-Cas systems that could resolve the ecrRNA to optimize immune response.

PAMPHLET: PAM Prediction HomoLogous-Enhancement Toolkit for precise PAM prediction in CRISPR-Cas systems

CRISPR-Cas technology has revolutionized our ability to understand and engineer organisms, evolving from a singular Cas9 model to a diverse CRISPR toolbox. A critical bottleneck in developing new Cas proteins is identifying protospacer adjacent motif (PAM) sequences. Due to the limitations of experimental methods, bioinformatics approaches have become essential. However, existing PAM prediction programs are limited by the small number of spacers in CRISPR-Cas systems, resulting in low accuracy. To address this, we develop PAMPHLET, a pipeline that uses homology searches to identify additional spacers, significantly increasing the number of spacers up to 18-fold. PAMPHLET is validated on 20 CRISPR-Cas systems and successfully predicts PAM sequences for 18 protospacers. These predictions are further validated using the DocMF platform, which characterizes protein-DNA recognition patterns via next-generation sequencing. The high consistency between PAMPHLET predictions and DocMF results for Cas proteins demonstrates the potential of PAMPHLET to enhance PAM sequence prediction accuracy, expedite the discovery process, and accelerate the development of CRISPR tools.

CRISPR-Cas9 Gene Therapy: Non-Viral Delivery and Stimuli-Responsive Nanoformulations

The CRISPR-Cas9 technology, one of the groundbreaking genome editing methods for addressing genetic disorders, has emerged as a powerful, precise, and efficient tool. However, its clinical translation remains hindered by challenges in delivery efficiency and targeting specificity. This review provides a comprehensive analysis of the structural features, advantages, and potential applications of various non-viral and stimuli-responsive systems, examining recent progress to emphasize the potential to address these limitations and advance CRISPR-Cas9 therapeutics. We describe how recent reports emphasize that nonviral vectors, including lipid-based nanoparticles, extracellular vesicles, polymeric nanoparticles, gold nanoparticles, and mesoporous silica nanoparticles, can offer diverse advantages to enhance stability, cellular uptake, and biocompatibility, based on their structures and physio-chemical stability. We also summarize recent progress on stimuli-responsive nanoformulations, a type of non-viral vector, to introduce precision and control in CRISPR-Cas9 delivery. Stimuli-responsive nanoformulations are designed to respond to pH, redox states, and external triggers, facilitate controlled and targeted delivery, and minimize off-target effects. The insights in our review suggest future challenges for clinical applications of gene therapy technologies and highlight the potential of delivery systems to enhance CRISPR-Cas9's clinical efficacy, positioning them as pivotal tools for future gene-editing therapies.

Sequence modeling and design from molecular to genome scale with Evo

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

Deepdefense: annotation of immune systems in prokaryotes using deep learning

BACKGROUND

Due to a constant evolutionary arms race, archaea and bacteria have evolved an abundance and diversity of immune responses to protect themselves against phages. Since the discovery and application of CRISPR-Cas adaptive immune systems, numerous novel candidates for immune systems have been identified. Previous approaches to identifying these new immune systems rely on hidden Markov model (HMM)-based homolog searches or use labor-intensive and costly wet-lab experiments. To aid in finding and classifying immune systems genomes, we use machine learning to classify already known immune system proteins and discover potential candidates in the genome. Neural networks have shown promising results in classifying and predicting protein functionality in recent years. However, these methods often operate under the closed-world assumption, where it is presumed that all potential outcomes or classes are already known and included in the training dataset. This assumption does not always hold true in real-world scenarios, such as in genomics, where new samples can emerge that were not previously accounted for in the training phase.

RESULTS

In this work, we explore neural networks for immune protein classification, deal with different methods for rejecting unrelated proteins in a genome-wide search, and establish a benchmark. Then, we optimize our approach for accuracy. Based on this, we develop an algorithm called Deepdefense to predict immune cassette classes based on a genome. This design facilitates the differentiation between immune system-related and unrelated proteins by analyzing variations in model-predicted confidence values, aiding in the identification of both known and potentially novel immune system proteins. Finally, we test our approach for detecting immune systems in the genome against an HMM-based method.

CONCLUSIONS

Deepdefense can automatically detect genes and define cassette annotations and classifications using 2 model classifications. This is achieved by creating an optimized deep learning model to annotate immune systems, in combination with calibration methods, and a second model to enable the scanning of an entire genome.

Comparative RNA Genomics

Over the last quarter of a century it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large-scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible non-coding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of non-coding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

CvkR is a MerR-type transcriptional repressor of class 2 type V-K CRISPR-associated transposase systems

Certain CRISPR-Cas elements integrate into Tn7-like transposons, forming CRISPR-associated transposon (CAST) systems. How the activity of these systems is controlled in situ has remained largely unknown. Here we characterize the MerR-type transcriptional regulator Alr3614 that is encoded by one of the CAST (AnCAST) system genes in the genome of cyanobacterium Anabaena sp. PCC 7120. We identify a number of Alr3614 homologs across cyanobacteria and suggest naming these regulators CvkR for Cas V-K repressors. Alr3614/CvkR is translated from leaderless mRNA and represses the AnCAST core modules cas12k and tnsB directly, and indirectly the abundance of the tracr-CRISPR RNA. We identify a widely conserved CvkR binding motif 5'-AnnACATnATGTnnT-3'. Crystal structure of CvkR at 1.6 Å resolution reveals that it comprises distinct dimerization and potential effector-binding domains and that it assembles into a homodimer, representing a discrete structural subfamily of MerR regulators. CvkR repressors are at the core of a widely conserved regulatory mechanism that controls type V-K CAST systems.

ARA: a flexible pipeline for automated exploration of NCBI SRA datasets

BACKGROUND

One of the most effective and useful methods to explore the content of biological databases is searching with nucleotide or protein sequences as a query. However, especially in the case of nucleic acids, due to the large volume of data generated by the next-generation sequencing (NGS) technologies, this approach is often not available. The hierarchical organization of the NGS records is primarily designed for browsing or text-based searches of the information provided in metadata-related keywords, limiting the efficiency of database exploration.

FINDINGS

We developed an automated pipeline that incorporates the well-established NGS data-processing tools and procedures to allow easy and effective sampling of the NCBI SRA database records. Given a file with query nucleotide sequences, our tool estimates the matching content of SRA accessions by probing only a user-defined fraction of a record's sequences. Based on the selected parameters, it allows performing a full mapping experiment with records that meet the required criteria. The pipeline is designed to be easy to operate-it offers a fully automatic setup procedure and is fixed on tested supporting tools. The modular design and implemented usage modes allow a user to scale up the analyses into complex computational infrastructure.

CONCLUSIONS

We present an easy-to-operate and automated tool that expands the way a user can access and explore the information contained within the records deposited in the NCBI SRA database.

CRISPR/CAS9: A promising approach for the research and treatment of cardiovascular diseases

The development of gene-editing technology has been one of the biggest advances in biomedicine over the past two decades. Not only can it be used as a research tool to build a variety of disease models for the exploration of disease pathogenesis at the genetic level, it can also be used for prevention and treatment. This is done by intervening with the expression of target genes and carrying out precise molecular targeted therapy for diseases. The simple and flexible clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 gene-editing technology overcomes the limitations of zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs). For this reason, it has rapidly become a preferred method for gene editing. As a new gene intervention method, CRISPR/Cas9 has been widely used in the clinical treatment of tumours and rare diseases; however, its application in the field of cardiovascular diseases is currently limited. This article reviews the application of the CRISPR/Cas9 editing technology in cardiovascular disease research and treatment, and discusses the limitations and prospects of this technology.