Drugs for aging-related diseases may modulate aging itself, but standard clinical trial designs cannot detect such effects. Aging clocks could close this gap, but epigenetic models often yield inconsistent, hard-to-interpret results. In contrast, proteomic clocks, by tracking the immediate effectors of biological change, may excel in providing aging biomarkers or mechanistic insight. Here we compare six proteomic clocks (ProtAge, OrganAgemortality, OrganAgechrono, PAC, ipfP3GPT and PAOPAC) on serum proteomes from a published 12-week phase 2a trial of the candidate anti-fibrotic drug rentosertib in idiopathic pulmonary fibrosis. We measure the variance between the clocks and find that all six clocks consistently predicted lower biological age in treated arms. However, proteomic clocks alone cannot deconvolute aging- and disease-specific effects. We addressed this issue indirectly through pathway analyses that identified potential anti-aging shifts in senescence and metabolic processes alongside the anti-fibrotic activity of rentosertib. This work supports the goal of dual-purpose clinical trial designs that integrate aging endpoints into studies for specific disease indications. Six proteomic clocks are applied in a clinical trial to assess anti-aging effects.
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.
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.
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.
Reference materials should be adopted as a common calibrator for multiomics measurement and co‑profiled with study samples. Multiomics results should be reported as sample‑to‑reference ratios so that they are reproducible and suitable for artificial intelligence tools.
The degradation of cell membrane and extracellular proteins with lysosome-targeting chimeras (LYTACs) is limited by nonrecyclable, receptor-dependent mechanisms that shuttle proteins to lysosomes, restricting the broad use of this emerging technology. Here we developed a recyclable chimera composed of a polyzwitterion and protein of interest (POI) ligand for protein degradation. This chimera could interact with the cell membrane to trigger macropinocytosis together with the POI in a receptor-independent manner. Furthermore, it can dissociate from the POI in the acidic endocytic compartments and subsequently be exocytosed through the endoplasmic reticulum-Golgi transcytosis pathway. Ultimately, the exocytotic chimera initiates the next round of targeted protein degradation. These macropinocytosis-mediated recyclable LYTACs (McR-TACs) durably degrade the cell membrane protein (programmed cell death ligand 1) or the extracellular protein (macrophage migration inhibitory factor) in a triple-negative breast cancer mouse model, thereby inhibiting the tumor growth. Collectively, McR-TACs show the potential of leveraging natural transport pathways to create recyclable protein degraders with wide-ranging applications.
UniFrac measures phylogeny-aware differences between microbiome samples but scales poorly with modern dataset sizes. We introduce an algorithm, DartUniFrac, and a near-optimal implementation with graphics processing unit acceleration that is up to three orders of magnitude faster than UniFrac and scales to millions of samples (pairwise) and billions of taxa. DartUniFrac connects UniFrac with weighted Jaccard similarity and exploits sketching algorithms for fast computation.
Methods for precise genomic DNA insertion that avoid double-strand breaks (DSBs) are constrained by limited throughput or the need for multistep editing. Here we report donor-complementary prime editing (DoPE), which combines a 3'-overhang double-stranded DNA (odsDNA) donor with a pair of overhang-complementary prime editing guide RNAs (opegRNAs) and a PE2* prime editor to achieve precise insertion of DNA sequences up to 12.5 kilobases (kb). Using one opegRNA pair and donor pools constructed from synthesized single-stranded oligonucleotides, we demonstrate in situ saturation mutagenesis across a targeted EGFP region at both amino acid and nucleotide resolutions. DoPE employing short (approximately 30-nucleotide) overhangs supports various insertions ranging from small fragments to those exceeding 10 kb. Furthermore, we replace mutant exons of PRKCSH, either individually or simultaneously, to correct diverse mutations, establishing a mutation-agnostic approach that corrects distinct alleles uniformly in vitro. Our study demonstrates DoPE as a one-step, DSB-free and library-compatible method for precise insertion of large DNA fragments without requiring recombinases or transposases.
Replacing large-scale fragments in human cells remains a substantial challenge. Here, we present a programmable gene replacement tool, named prime assembly (PA), which adapts prime editors to produce one or two pairs of 3'-flaps on both the genome and donor DNA. These 3'-flaps anneal to each other precisely, similar to Gibson assembly in DNA oligonucleotides, allowing megabase-scale genomic excision and/or kilobase-scale donor insertion at the gene of interest. PA accepts DNA plasmids and linear double-stranded DNA as donors, ranging from 1.0 to 6.5 kb in size. We demonstrate an efficiency of up to 57.8% in replacing endogenous sequences with a 2.9-kb donor DNA fragment in HEK293T cells, with an accuracy of >90% for integrated PA fragments. Furthermore, PA enables site-specific chimeric antigen receptor integration with up to 28.1% efficiency in primary human T cells. When PA containing a GFP donor is delivered to mice by hydrodynamic injection, an average integration efficiency of 4.3% is measured in GFP-positive hepatocytes.
Silicate mineral weathering (dissolution) is a scalable strategy for capture and storage of CO2 but is too slow for industrial deployment. Bacteria can accelerate mineral dissolution by secreting siderophores, molecules that solubilize iron released from the mineral. Here we investigate how to deploy siderophore-producing bacteria at scale to continuously enhance dissolution of the mineral olivine. We demonstrate that natural genetic regulation precludes continuous siderophore production in mineral bioreactors. To overcome this limitation, we engineer the marine bacterium Alteromonas macleodii for enhanced siderophore production, conferring a 2.6-fold increase in the rate of olivine dissolution. Life-cycle analysis indicated that renewable feedstocks and minimal replenishment of modified cells are critical to achieve net CO2 removal at scale. With these guidelines, we constructed pilot-scale continuous mineral bioreactors that use unprocessed seawater and a renewable acetate feedstock to weather 4 kg of olivine. In reactors with engineered cells, we directly measured removal of 0.50 g CO2 per day from the air through alkalinity generation.
Each year, Nature Biotechnology highlights companies that received sizeable early-stage funding in the previous year. Dispatch Bio delivers antigens to solid tumors for CAR T immunotherapy.
An analysis of FDA-approved drugs reveals uneven timing and concentration of revenue life cycles across modalities and therapeutic areas, offering evidence to guide innovation strategy and policy design in a post-Inflation Reduction Act world.
Primary human myeloid cells hold promise for immunotherapies, yet efficient, scalable technologies for engineering and screening in these cells remain limited. Here we present a virus-like particle (VLP)-based toolkit that delivers diverse CRISPR editing modalities to human monocytes, macrophages and dendritic cells with high efficiency while preserving viability and innate immune responsiveness. VLP-mediated delivery of ribonucleoproteins supports gene knockout, base editing and epigenetic silencing. Combined with adeno-associated virus-mediated donor delivery, this approach enables site-specific integration of large DNA sequences by homology-directed repair. We developed SLICeVLP, which pairs sgRNA delivery by VPX-lentivirus with Cas9 protein delivery by engineered VLPs, and used it for pooled loss-of-function and Perturb-seq screens in human macrophages. We uncovered regulators of tumor necrosis factor (TNF) and CD80 expression, converging on TNFAIP3 as a central regulator of inflammatory polarization. TNFAIP3 ablation drove a proinflammatory state resistant to suppressive repolarization and enhanced cytotoxicity in chimeric antigen receptor macrophages. This system enables unbiased functional genomics in primary human myeloid cells, with implications for myeloid cell therapy design.
Molecular glue degraders (MGDs), such as pomalidomide, induce degradation of non-native substrates by the cullin-RING E3 ligase 4 (CRL4) through its substrate receptor cereblon (CRBN). Here, to explore CRBN programmability, we tested whether reported CRBN-MGD substrates are part of a network of latent CRBN interactors, proteins capable of MGD-induced CRBN binding without detectable degradation. Leveraging a highly parallel protein complementation assay (GluePCA) to measure MGD-induced interaction between CRBN and zinc fingers, we identified ~210 zinc fingers bound to CRBN-pomalidomide, where top binders are already reported as degraded by dedicated MGDs. To map latent CRBN-MGD interactions proteome-wide and define the accessible CRBN interaction space, we combined artificial intelligence-derived protein surface queries (MaSIF-mimicry) with GluePCA. This pipeline identified 6 known and 43 novel CRBN-pomalidomide binders, including orthogonally validated hits. We find that these binders provide privileged starting points for MGD development. We expect this binding-focused workflow to be applicable to other MGD-E3 ligase systems, potentially extending the scope of this emerging drug class.
Accurately characterizing metagenome-assembled genomes remains a substantial challenge due to the presence of sequencing errors, incomplete assembly and contamination. Here, we present MetaSBT, a tool for organizing, indexing and characterizing microbial reference genomes and metagenome-assembled genomes, demonstrated in this study using viruses. MetaSBT identifies clusters of genomes across all seven taxonomic levels using the Sequence Bloom Tree data structure, which relies on Bloom filters to index large amounts of genomes based on their k-mer composition. We built an initial set of databases composed of over 190,000 viral genomes from public sources, grouped into sequence-consistent clusters at different taxonomic levels. We defined over 40,000 candidate species, ~80% of which, to our knowledge, do not match viral species in reference databases to date. Furthermore, we showed that our databases are useful to existing quantitative metagenomic profilers to unlock the detection of unknown microbes and the estimation of their abundance in metagenomic samples. The open-source framework and databases are fully integrated into the Galaxy platform.
Simultaneous mapping of chromatin states and transcriptomes in rare cell populations is challenging, as most methods require thousands of cells and are limited in their ability to capture multiple molecular layers accurately within the same cell. Here we introduce OneCell CUT&Tag, a method that provides matched high-resolution epigenome, full-transcriptome and surface marker quantification from every cell, with input as low as one cell, without relying on computational aggregation into metacells. Using this approach, we uncover epigenomic priming of basal cells in the mammary gland and capture the dynamics of basal-to-luminal transdifferentiation, suggesting that epigenomic and transcriptional remodeling do not occur in complete synchrony during cell-fate conversion. Adaptable to diverse samples and tissues, this method also reveals the role of H3K27me3 in shaping zygotic expression programs. By matching multiple layers of molecular information within individual cells, OneCell CUT&Tag reveals how complementary regulatory layers shape cellular identity and state, enabling the study of rare biological samples in development and disease.
Prime editing (PE) can make specific local changes to genomic DNA in living systems but its efficient application currently requires extensive optimization of PE guide RNA (pegRNA) sequences. Here we present OptiPrime, a machine learning model of PE efficiency based on current understanding of PE mechanisms. OptiPrime achieves state-of-the-art accuracy on PE efficiency prediction and enables prediction of nicking guide RNA (PE3) and dual pegRNA (twinPE) outcomes. We validate that OptiPrime has learned the determinants of mammalian mismatch repair (MMR) and is well suited for nominating MMR-evasive silent edits that improve PE efficiency. We demonstrate the use of OptiPrime in a variety of prospective therapeutic contexts in primary human and mouse cells. Lastly, we show that OptiPrime can be used to achieve streamlined and efficient in vivo correction of a pathogenic mutation in the brain of a mouse model of KIF1A-associated neurological disorder. We provide a webserver for OptiPrime ( https://optipri.me/ ) as a community resource.
Cell surface display (CSD) elements are a major class of bioengineering modules, but systematic rules linking CSD sequence to functional potency are lacking. Here we develop DeepSCan, a suite of deep learning and artificial intelligence models to systematically map CSD sequence-function relationships to reliably capture potent elements' conserved features and iteratively design and develop potent de novo CSD modules for mRNA antigen display. We experimentally quantify surface expression across >570 chimeric antigens, derive cell surface translocation strength labels for ~310 CSD elements and compile ~45 independent training datasets. We train three generations of DeepSCan models and evaluate their performance. Guided by these models, we computationally design 3,700 and experimentally validate approximately 120 generative CSDs, identifying 7 generative CSDs that match or exceed the cell surface translocation strength of the most potent naturally occurring CSDs. Enhanced surface displays are validated across multiple cell types and are functional in an antigen-specific CAR-T cytotoxicity assay.
