Nature biotechnology最新文献

Treatment of diseases of oxidative stress through activation of the antioxidant nuclear factor E2-related factor 2 (NRF2) is limited by systemic side effects. We chemically functionalize the NRF2 activator monomethyl fumarate to require Baeyer–Villiger oxidation for release of the active drug at sites of oxidative stress. This prodrug reverses chronic pain in mice with reduced side effects and could be applied to other disorders of oxidative stress.

Science is frequently subject to the Gartner hype cycle¹: emergent technologies spark intense initial enthusiasm with the recruitment of dedicated scientists. As limitations are recognized, disillusionment often sets in; some scientists turn away, disappointed in the inability of the new technology to deliver on initial promise, while others persevere and further develop the technology. Although the value (or not) of a new technology usually becomes clear with time, appropriate benchmarks can be invaluable in highlighting strengths and areas for improvement, substantially speeding up technology maturation. A particular challenge in computational engineering and artificial intelligence (AI)/machine learning (ML) is that benchmarks and best practices are uncommon, so it is particularly hard for non-experts to assess the impact and performance of these methods. Although multiple papers have highlighted best practices and evaluation guidelines^2,3,4, the true test for such methods is ultimately prospective performance, which requires experimental testing.

The copy number of a plasmid is linked to its functionality, yet there have been few attempts to optimize higher-copy-number mutants for use across diverse origins of replication in different hosts. We use a high-throughput growth-coupled selection assay and a directed evolution approach to rapidly identify origin of replication mutations that influence copy number and screen for mutants that improve Agrobacterium-mediated transformation (AMT) efficiency. By introducing these mutations into binary vectors within the plasmid backbone used for AMT, we observe improved transient transformation of Nicotiana benthamiana in four diverse tested origins (pVS1, RK2, pSa and BBR1). For the best-performing origin, pVS1, we isolate higher-copy-number variants that increase stable transformation efficiencies by 60–100% in Arabidopsis thaliana and 390% in the oleaginous yeast Rhodosporidium toruloides. Our work provides an easily deployable framework to generate plasmid copy number variants that will enable greater precision in prokaryotic genetic engineering, in addition to improving AMT efficiency.

Transcriptional effectors are protein domains known to activate or repress gene expression; however, a systematic understanding of which effector domains regulate transcription across genomic, cell type and DNA-binding domain (DBD) contexts is lacking. Here we develop dCas9-mediated high-throughput recruitment (HT-recruit), a pooled screening method for quantifying effector function at endogenous target genes and test effector function for a library containing 5,092 nuclear protein Pfam domains across varied contexts. We also map context dependencies of effectors drawn from unannotated protein regions using a larger library tiling chromatin regulators and transcription factors. We find that many effectors depend on target and DBD contexts, such as HLH domains that can act as either activators or repressors. To enable efficient perturbations, we select context-robust domains, including ZNF705 KRAB, that improve CRISPRi tools to silence promoters and enhancers. We engineer a compact human activator called NFZ, by combining NCOA3, FOXO3 and ZNF473 domains, which enables efficient CRISPRa with better viral delivery and inducible control of chimeric antigen receptor T cells.

The properties of Cas12a nucleases constrict the range of accessible targets and their applications. In this study, we applied ancestral sequence reconstruction (ASR) to a set of Cas12a orthologs from hydrobacteria to reconstruct a common ancestor, ReChb, characterized by near-PAMless targeting and the recognition of diverse nucleic acid activators and collateral substrates. ReChb shares 53% sequence identity with the closest Cas12a ortholog but no longer requires a T-rich PAM and can achieve genome editing in human cells at sites inaccessible to the natural FnCas12a or the engineered and PAM-flexible enAsCas12a. Furthermore, ReChb can be triggered not only by double-stranded DNA but also by single-stranded RNA and DNA targets, leading to non-specific collateral cleavage of all three nucleic acid substrates with similar efficiencies. Finally, tertiary and quaternary structures of ReChb obtained by cryogenic electron microscopy reveal the molecular details underlying its expanded biophysical activities. Overall, ReChb expands the application space of Cas12a nucleases and underscores the potential of ASR for enhancing CRISPR technologies.

Research and medical genomics require comprehensive, scalable methods for the discovery of novel disease targets, evolutionary drivers and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size or location. Here we present DRAGEN, which uses multigenome mapping with pangenome references, hardware acceleration and machine learning-based variant detection to provide insights into individual genomes, with ~30 min of computation time from raw reads to variant detection. DRAGEN outperforms current state-of-the-art methods in speed and accuracy across all variant types (single-nucleotide variations, insertions or deletions, short tandem repeats, structural variations and copy number variations) and incorporates specialized methods for analysis of medically relevant genes. We demonstrate the performance of DRAGEN across 3,202 whole-genome sequencing datasets by generating fully genotyped multisample variant call format files and demonstrate its scalability, accuracy and innovation to further advance the integration of comprehensive genomics. Overall, DRAGEN marks a major milestone in sequencing data analysis and will provide insights across various diseases, including Mendelian and rare diseases, with a highly comprehensive and scalable platform.