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Cas12a RNA-guided endonucleases are promising tools for multiplexed genetic perturbations because they can process multiple guide RNAs expressed as a single transcript, and subsequently cleave target DNA. However, their widespread adoption has lagged behind Cas9-based strategies due to low activity and the lack of a well-validated pooled screening toolkit. In the present study, we describe the optimization of enhanced Cas12a from Acidaminococcus (enAsCas12a) for pooled, combinatorial genetic screens in human cells. By assaying the activity of thousands of guides, we refine on-target design rules and develop a comprehensive set of off-target rules to predict and exclude promiscuous guides. We also identify 38 direct repeat variants that can substitute for the wild-type sequence. We validate our optimized AsCas12a toolkit by screening for synthetic lethalities in OVCAR8 and A375 cancer cells, discovering an interaction between MARCH5 and WSB2. Finally, we show that enAsCas12a delivers similar performance to Cas9 in genome-wide dropout screens but at greatly reduced library size, which will facilitate screens in challenging models.
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http://dx.doi.org/10.1038/s41587-020-0600-6 | DOI Listing |
Nat Biomed Eng
September 2025
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Phenotype-driven approaches identify disease-counteracting compounds by analysing the phenotypic signatures that distinguish diseased from healthy states. Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem and predicts the perturbagens needed to achieve a desired response by embedding disease cell states into networks, learning a latent representation of these states, and identifying optimal combinatorial perturbations.
View Article and Find Full Text PDFPLoS Biol
September 2025
National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India.
Morphogenetic information arises from a combination of genetically encoded cellular properties and emergent cellular behaviors. The spatio-temporal implementation of this information is critical to ensure robust, reproducible tissue shapes, yet the principles underlying its organization remain unknown. We investigated this principle using the mouse auditory epithelium, the organ of Corti (OC).
View Article and Find Full Text PDFAdv Pharm Bull
July 2025
Department of Biotechnology and Bioinformatics, North-Eastern Hill University, Shillong, India 793022.
One of the major reason of deaths due to cancer globally is caused by lung cancer of which the two main types include non-small cell and small cell lung cancer. The onset of treatment-resistance in cancer cells offers a serious obstacle to the therapeutic effect despite that primary conventional treatments have provided significant benefits and cures. Cancer immunotherapy offers a compelling alternative in patients by utilizing their immune system to enhance its ability to fight against tumors.
View Article and Find Full Text PDFAust N Z J Psychiatry
September 2025
Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University, Melbourne, VIC, Australia.
Psychotropic pharmacogenetics (PGx) offers significant potential advancements in psychiatric care by optimising medication selection and dosing based on genetic factors. This perspective article highlights the clinical utility, health economic implications and implementation challenges of psychotropic PGx, proposing that its broader implementation could enhance patient outcomes and reduce healthcare costs. Landmark studies show that PGx-guided care results in fewer adverse drug reactions and improved medication efficacy, with substantial cost savings compared to traditional prescribing methods.
View Article and Find Full Text PDFTheor Appl Genet
September 2025
Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, Australia.
Stacking desirable haplotypes across the genome to develop superior genotypes has been implemented in several crop species. A major challenge in Optimal Haplotype Selection is identifying a set of parents that collectively contain all desirable haplotypes, a complex combinatorial problem with countless possibilities. In this study, we evaluated the performance of metaheuristic search algorithms (MSAs)-genetic algorithm (GA), differential evolution (DE), particle swarm optimisation (PSO), and simulated annealing (SA) for optimising parent selection under two genotype building (GB) objectives: Optimal Haplotype Selection (OHS) and Optimal Population Value (OPV).
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