Nature biotechnology最新文献

Here we developed a DNA-centric strategy for optimizing site-specific recombination by rationally engineering chimeric attachment sites. The high-activity att variants enhance Bxb1-mediated integration efficiency in human cells and plants. Among these att variants, the engineered attB(V111) site achieved 51.9% integration efficiency in HEK293T cells (1.7-fold versus wild-type attB) and 35.6% in rice protoplasts (4.4-fold versus wild-type attB). When paired with an engineered single protein mutant in the Bxb1 catalytic domain, the optimized system achieved targeted integration efficiencies of 31% for a CD19 chimeric antigen receptor cassette and 25% for an ornithine transcarbamylase expression cassette in human cells. In rice, these engineered variants enabled integration of a 5.8 kb herbicide-resistance cassette at a targeted genomic locus, with stable integration detected in 24% of regenerated plants. Oxford Nanopore-based long-read sequencing of edited plants reveals complete and precise insertion with high specificity. Propagation of edited seedlings to T1 plants confirms heritable editing to future generations. This approach provides a safe, broadly applicable approach for recombinase-based genome editing.

Ultralarge virtual screenings (ULVSs) evaluate billions of molecules for drug discovery but face cost, flexibility and scalability limits. We introduce AdaptiveFlow, an open-source platform that makes ULVSs more accessible, scalable and efficient and supports artificial intelligence (AI) and machine learning (ML) method development. AdaptiveFlow provides a screening-ready version of the Enamine REAL Space, to our knowledge the largest library of ready-to-dock, drug-like molecules, comprising 69 billion compounds, also available in SELFIES format. An 18-dimensional grid of molecular properties prioritizes promising chemical subspaces, with optional active learning, reducing computational costs by orders of magnitude. AdaptiveFlow integrates >1,500 docking protocols, including GPU-accelerated and ML-based methods, and achieves near-linear scaling on up to 5.6 million CPUs in the Amazon Web Services cloud. We identified nanomolar inhibitors of two disease-relevant targets, ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1. Co-crystal structures provided mechanistic insights into FSP1 inhibition. AdaptiveFlow enables drug discovery at unprecedented scale and supports the development of AI-driven methods.

Knockdown efficiency of RNA-targeting CRISPR systems is commonly measured by quantitative polymerase chain reaction with reverse transcription (RT-qPCR). Here we discovered that guide RNAs copurify during RNA extraction and inhibit RT-qPCR for amplicons spanning or upstream of the guide RNA binding site, resulting in overestimation of knockdown efficiency across all CRISPR systems tested. We recommend using a processive reverse transcriptase with strong strand-displacing activity, with orthogonal methods, to ensure accurate quantification when using RT-qPCR.

Microbial symbiosis drives the functional and phylogenomic diversification of life on Earth yet remains underexplored because of culturing challenges. This study used machine learning (ML) to predict symbiotic lifestyles in more than a hundred thousand microbial genomes from diverse environmental metagenome samples and reference genomes. Predictions were performed using symclatron, an ML framework developed to identify genomic signatures of symbionts. Predictions were deposited in a catalog we established called Symbiont Genomes (SymGs). The results indicate that 15-23% of uncultivated microorganisms likely engage in symbiotic relationships with other organisms, categorized as host-associated or obligate intracellular lifestyles, and are present in half of all known bacterial and archaeal phyla. We also identify genomic signatures of symbiotic lifestyles, including the loss of certain metabolic functions and the differential presence of metabolic modules that may enable host-dependent living. The symclatron software and the SymGs catalog represent valuable resources for studying symbioses, potentially facilitating future mechanistic investigations and engineering of host-microorganism associations.