Advancements in Genome Engineering Techniques

A new research carried by MIT researchers opens the door to a better gene editing method which may help treat a greater number of genetic diseases with precision and accuracy without making any double-stranded DNA cuts.   Funded by the Life Sciences Research Foundation, the National Institute of Biomedical Imaging and Bioengineering, the National Cancer Institute, and the Koch Institute Support (core) Grant from the National Cancer Institute, the scientists involved in the research were successful in significantly cutting down the error rate of prime editing, a genome editing method.   The research outcome improved error rate of prime editors from nearly one error in seven edits to one in 101 for the most-used editing mode, and from one error in 122 edits to one in 543 for a high-precision mode.   This new finding can potentially help treat many genetic illnesses by fixing defective genes without creating unintended changes which otherwise might be harmful.   As a participant of the research, Prof Phillip Sharp outlines that the prime editing method didn’t complicate the delivery system nor it led to additional steps, but it resulted in a much more precise editing with fewer unwanted mutations.   https://lnkd.in/gQD9tnHw

Synthetic biology is - quite literally - our future. A goundbreaking new biological foundation model Evo2 achieves state-of-the-art prediction of genetic variation impacts and generates coherent genome sequences, spanning all domains of life. A diverse team from leading research institutions including Arc Institute Stanford University NVIDIA University of California, Berkeley trained the model on 9.3 trillion DNA base pairs and has fully shared all code, parameters, and data. A few highlights from the paper (link in comments) 🔬 Zero-shot prediction achieves state-of-the-art accuracy in genetic variant interpretation. Evo 2 can predict the functional consequences of genetic mutations across all domains of life without specialized training. It surpasses existing models in assessing the pathogenicity of both coding and noncoding variants, including BRCA1 cancer-linked mutations. This generalist capability suggests Evo 2 could revolutionize genetic disease research, reducing reliance on expensive, manually curated datasets. 🛠 Genome-scale generation paves the way for synthetic life design. Evo 2 can generate full-length genome sequences with realistic structure and function, including mitochondrial genomes, bacterial chromosomes, and yeast DNA. Unlike prior models, Evo 2 ensures natural sequence coherence, improving synthetic biology applications like engineered microbes or artificial organelles. This sets the stage for programmable biology at an unprecedented scale. 🧬 Unprecedented long-context understanding revolutionizes genomic analysis. Evo 2 operates with a context window of up to 1 million nucleotides—far beyond the capabilities of previous models—allowing it to analyze genomic features across vast distances. This ability enables it to accurately identify regulatory elements, exon-intron boundaries, and structural components critical for understanding genome function. Its long-context recall is a major breakthrough for interpreting complex biological sequences. 🎛 Inference-time search enables controllable epigenomic design. Evo 2’s generative abilities extend beyond raw DNA sequence to epigenomic features, allowing researchers to design sequences with specific chromatin accessibility patterns. This approach successfully encoded Morse code messages into synthetic epigenomes, demonstrating a new method for controlling gene regulation via AI. This could lead to breakthroughs in gene therapy and epigenetic engineering. 🔮 Future potential: Toward AI-driven biological design and virtual cell modeling. Evo 2 represents a major leap toward AI-powered genomic engineering. Future iterations could integrate additional biological layers—such as transcriptomics and proteomics—to create virtual cell models that simulate complex cellular behaviors. This could revolutionize drug discovery, genetic therapy, and even synthetic life creation.

💡 A new gene editing technique derived from bacterial “jumping genes” can add, remove, recombine and invert DNA sequences, potentially overcoming some of the limitations of #CRISPR. It has been published in 2 recent #Nature papers, late June 2024.   🛑 There are limits to CRISPR’s utility: ❌ It can’t make edits without breaking both strands of DNA—a potential route of toxicity—and isn’t useful for inserting whole genes or even large chunks of DNA. ❌ It’s also not always as accurate as scientists would like.   👉 The new published technique is made possible by a molecule called bridge RNA. It is a lot like the guide RNA (gRNA) component of CRISPR-Cas9 systems. However, rather than recognizing one strand of DNA at a time as CRISPR gRNA does, bridge RNA recognizes two - the target DNA and the gene that will be inserted into it.  🤝 Once it’s bound to both, it brings in a DNA recombinase to do the editing.    💡 Bridge editing cuts and pastes DNA in a single-step mechanism that recombines and re-ligates the DNA, leaving it fully intact. This is very distinct from CRISPR editing, which creates exposed DNA breaks that require DNA repair and have been shown to create undesired DNA damage responses. ✔ By avoiding those, bridge editing could potentially lead to more precise or safer types of genome edits.   #GeneTherapy #Innovation   https://lnkd.in/eNGdr5RT

Indian scientists from the CSIR-Institute of Genomics and Integrative Biology (IGIB) in New Delhi, in collaboration with the LV Prasad Eye Institute, have developed an advanced CRISPR gene-editing system using the FnCas9 enzyme. This new system is more precise and efficient than existing CRISPR-Cas9 technologies, significantly reducing unintended DNA damage. CRISPR technology allows researchers to edit genes by targeting specific DNA sequences. However, current systems, like SpCas9, often face issues with off-target effects and reduced efficiency. The IGIB team has engineered new versions of FnCas9 that show higher accuracy and lower off-target effects. The researchers demonstrated the efficacy of their system on induced pluripotent stem cells from individuals with the RPE65 mutation, associated with inherited blindness. The enhanced FnCas9 successfully corrected the mutation in these cells, showing potential for therapeutic applications. This breakthrough is poised to have significant implications for gene therapy, especially in treating genetic disorders such as inherited blindness. The team aims to patent their innovation, emphasizing its potential to offer cost-effective solutions for gene editing in countries like India. #CRISPR

Thrilled to share our review paper, out today in Nature Portfolio Reviews Genetics: "Harnessing artificial intelligence to advance CRISPR-based genome editing technologies" CRISPR has already changed medicine. AI is now changing CRISPR. We spent a long time mapping the full landscape of where machine learning and deep learning are having real, measurable impact across the genome editing workflow — and where the most exciting opportunities lie ahead. Here's what we cover: Guide RNA design — Deep learning models now predict on- and off-target activity for Cas9, Cas12, Cas13, and emerging systems like TnpB and IscB. We've gone from sequence heuristics to transformer-based models that generalize across organisms. Cell-type-specific generalization remains a frontier. Base and prime editing — ML models predict bystander effects, product purity, and editing efficiency from sequence context alone. For prime editing, tools like PRIDICT and DeepPE have made pegRNA design far more tractable at scale. Enzyme engineering — Protein language models (ESM, EVOLVEpro) are now guiding directed evolution of Cas proteins — expanding PAM compatibility, reducing immunogenicity, improving compactness — at a pace impossible through classical lab iteration alone. Novel enzyme discovery — Foundation models trained on metagenomics are uncovering entirely new CRISPR systems from microbial diversity: new Cas variants, TnpB systems, and eukaryotic Fanzor proteins. The search space is enormous; AI is how we navigate it. Virtual cell models — This is where I'm most excited. AI-powered virtual cells can, in principle, predict the functional consequences of any edit in any cell type — selecting targets, anticipating off-targets, modeling tissue-specific outcomes. But realizing this vision requires causally-rich, contextually diverse perturbation data. Scale of data matters as much as scale of model. Delivery — ML-guided LNP design is closing the last mile between an edit that works in a dish and one that works in a patient. Across all of this, one theme recurs: AI accelerates where data is abundant and well-structured. The field's next challenge is generating that data at the right diversity and scale. This paper was a true collaboration. Huge thanks to Tyler Thomson, Gen Li, Amy Strilchuk, Haotian Cui, and Bowen Li — you each brought something irreplaceable to this. Special shoutout to Bowen Li for his leaderhsip in this work! University Health Network University of Toronto Full paper : 🔗 https://lnkd.in/eMJU6UdJ #CRISPR #GenomeEditing #ArtificialIntelligence #MachineLearning #Genomics #ComputationalBiology #NatureReviews

#ScienceSaturday ❓ How can we turn T-cells into cancer-fighting “soldiers” without changing their DNA? ➡️ A new study in Nature Biotechnology introduces an all-RNA platform that can turn T-cell genes on or off using epigenetic CRISPR tools called CRISPRoff and CRISPRon. Instead of cutting or rewriting DNA, this method “reprograms” genes by adding or removing chemical marks, allowing researchers to boost or silence key immune functions safely. ➡️ The team showed this can enhance CAR T-cell therapies, helping immune cells better recognize and attack tumors while reducing potential side effects linked to traditional gene editing. 🌟 Congratulations to senior authors Alex Marson, Luke Gilbert, Brian Shy, and Justin Eyquem of UCSF, Arc Institute, and Gladstone Institutes for this exciting advance, a big step toward the next generation of precise, safer immunotherapies. University of California, San Francisco UCSF Helen Diller Family Comprehensive Cancer Center Gladstone Institutes 🔗 Read more in Nature Biotechnology: https://lnkd.in/eQ_EjEtT