Patrick David Hsu

All Publications

• Genome modelling and design across all domains of life with Evo 2. Nature Brixi, G., Durrant, M. G., Ku, J., Naghipourfar, M., Poli, M., Sun, G., Brockman, G., Chang, D., Fanton, A., Gonzalez, G. A., King, S. H., Li, D. B., Merchant, A. T., Nguyen, E., Ricci-Tam, C., Romero, D. W., Schmok, J. C., Taghibakhshi, A., Vorontsov, A., Yang, B., Deng, M., Gorton, L., Nguyen, N., Wang, N. K., Pearce, M. T., Simon, E., Adams, E., Amador, Z. J., Ashley, E. A., Baccus, S. A., Dai, H., Dillmann, S., Ermon, S., Guo, D., Herschl, M. H., Ilango, R., Janik, K., Lu, A. X., Mehta, R., Mofrad, M. R., Ng, M. Y., Pannu, J., Ré, C., St John, J., Sullivan, J., Tey, J., Viggiano, B., Zhu, K., Zynda, G., Balsam, D., Collison, P., Costa, A. B., Hernandez-Boussard, T., Ho, E., Liu, M. Y., McGrath, T., Powell, K., Pinglay, S., Burke, D. P., Goodarzi, H., Hsu, P. D., Hie, B. L. 2026

  Abstract

  All of life encodes information with DNA. Although tools for genome sequencing, synthesis and editing have transformed biological research, we still lack sufficient understanding of the immense complexity encoded by genomes to predict the effects of many classes of genomic changes or to intelligently compose new biological systems. Artificial intelligence models that learn information from genomic sequences across diverse organisms have increasingly advanced prediction and design capabilities1,2. Here we introduce Evo 2, a biological foundation model trained on 9 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life to have a 1 million token context window with single-nucleotide resolution. Evo 2 learns to accurately predict the functional impacts of genetic variation-from noncoding pathogenic mutations to clinically significant BRCA1 variants-without task-specific fine-tuning. Mechanistic interpretability analyses reveal that Evo 2 learns representations associated with biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements and prophage genomic regions. The generative abilities of Evo 2 produce mitochondrial, prokaryotic and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Evo 2 also generates experimentally validated chromatin accessibility patterns when guided by predictive models3,4 and inference-time search. We have made Evo 2 fully open, including model parameters, training code5, inference code and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

  View details for DOI 10.1038/s41586-026-10176-5

  View details for PubMedID 41781614

  View details for PubMedCentralID 12057570

• Rapid directed evolution guided by protein language models and epistatic interactions. Science (New York, N.Y.) Tran, V. Q., Nemeth, M., Bartie, L. J., Chandrasekaran, S. S., Fanton, A., Moon, H. C., Hie, B. L., Konermann, S., Hsu, P. D. 2026: eaea1820

  Abstract

  Protein engineering is limited by the inefficient search through a high-dimensional sequence space to find combinations of synergistic mutations. Traditional approaches use stepwise mutation stacking, whereas machine learning methods require extensive datasets or multiple experimental rounds and are bottlenecked by costly, length-limited gene synthesis. We present MULTI-evolve, a rapid evolution framework that systematically engineers multimutants. Our approach combines protein language models or existing functional data with epistatic modelling to predict synergistic combinations. Proposed multimutants are built through MULTI-assembly, a mutagenesis method enabling high-efficiency assembly across multikilobase sequences. Applying MULTI-evolve to three proteins achieved up to 10-fold improvements with a single round of machine learning-guided directed evolution. MULTI-evolve provides a streamlined approach for end-to-end, multimutant engineering for a broad range of protein types and functions.

  View details for DOI 10.1126/science.aea1820

  View details for PubMedID 41712694

• Megabase-scale human genome rearrangement with programmable bridge recombinases. Science (New York, N.Y.) Perry, N. T., Bartie, L. J., Katrekar, D., Gonzalez, G. A., Durrant, M. G., Pai, J. J., Fanton, A., Martins, J. Q., Hiraizumi, M., Ricci-Tam, C., Nishimasu, H., Konermann, S., Hsu, P. D. 2025: eadz0276

  Abstract

  Bridge recombinases are naturally occurring RNA-guided DNA recombinases that we previously demonstrated can programmably insert, excise, and invert DNA in vitro and in Escherichia coli. In this study, we report the discovery and engineering of the bridge recombinase ortholog ISCro4 for universal rearrangements of the human genome. We defined strategies for the optimal application of bridge systems, leveraging mechanistic insights to improve their targeting specificity. Through rational engineering of the ISCro4 bridge RNA and deep mutational scanning of its recombinase, we achieved up to 20% insertion efficiency into the human genome and genome-wide specificity as high as 82%. We further demonstrated intrachromosomal inversion and excision, mobilizing up to 0.93 megabases of DNA. Lastly, we provided proof-of-concept for plasmid-based excision of disease-relevant gene regulatory regions or repeat expansions.

  View details for DOI 10.1126/science.adz0276

  View details for PubMedID 40997214

• Sequence modeling and design from molecular to genome scale with Evo. Science (New York, N.Y.) Nguyen, E., Poli, M., Durrant, M. G., Kang, B., Katrekar, D., Li, D. B., Bartie, L. J., Thomas, A. W., King, S. H., Brixi, G., Sullivan, J., Ng, M. Y., Lewis, A., Lou, A., Ermon, S., Baccus, S. A., Hernandez-Boussard, T., Re, C., Hsu, P. D., Hie, B. L. 2024; 386 (6723): eado9336

  Abstract

  The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

  View details for DOI 10.1126/science.ado9336

  View details for PubMedID 39541441

• Bridge RNAs direct programmable recombination of target and donor DNA. Nature Durrant, M. G., Perry, N. T., Pai, J. J., Jangid, A. R., Athukoralage, J. S., Hiraizumi, M., McSpedon, J. P., Pawluk, A., Nishimasu, H., Konermann, S., Hsu, P. D. 2024; 630 (8018): 984-993

  Abstract

  Genomic rearrangements, encompassing mutational changes in the genome such as insertions, deletions or inversions, are essential for genetic diversity. These rearrangements are typically orchestrated by enzymes that are involved in fundamental DNA repair processes, such as homologous recombination, or in the transposition of foreign genetic material by viruses and mobile genetic elements1,2. Here we report that IS110 insertion sequences, a family of minimal and autonomous mobile genetic elements, express a structured non-coding RNA that binds specifically to their encoded recombinase. This bridge RNA contains two internal loops encoding nucleotide stretches that base-pair with the target DNA and the donor DNA, which is the IS110 element itself. We demonstrate that the target-binding and donor-binding loops can be independently reprogrammed to direct sequence-specific recombination between two DNA molecules. This modularity enables the insertion of DNA into genomic target sites, as well as programmable DNA excision and inversion. The IS110 bridge recombination system expands the diversity of nucleic-acid-guided systems beyond CRISPR and RNA interference, offering a unified mechanism for the three fundamental DNA rearrangements-insertion, excision and inversion-that are required for genome design.

  View details for DOI 10.1038/s41586-024-07552-4

  View details for PubMedID 38926615

  View details for PubMedCentralID PMC11208160

• A combinatorial domain screening platform reveals epigenetic effector interactions for transcriptional perturbation. Nature communications Moon, H. C., Herschl, M. H., Sclip, A., Tran, V. Q., Pawluk, A., Konermann, S., Hsu, P. D. 2026

  Abstract

  Epigenetic regulation involves the coordinated interplay of diverse proteins. To systematically explore these combinations, we present COMBINE (combinatorial interaction exploration), a high-throughput platform that tests over 50,000 pairs of epigenetic effector domains up to 2,094 amino acids in length for their ability to modulate endogenous human gene transcription. COMBINE reveals diverse synergistic interactions between epigenetic protein domains, including a potent KRAB-L3MBTL3 fusion that increases the effective targeting window, enhances gene silencing in dose-limited conditions, and enables robust dual-directional CRISPR perturbation. Inducible screening shows DNA methylation modifiers are essential for epigenetic memory, with distinct combinations driving long-term repression and activation. This systematic analysis of pairwise domain interactions advances our understanding of epigenetic crosstalks and the development of next-generation epigenome editing tools. More broadly, COMBINE offers a generalizable platform to functionally characterize combinatorial biological processes at scale.

  View details for DOI 10.1038/s41467-026-72227-9

  View details for PubMedID 42031729

• Generalist biological artificial intelligence in modeling the language of life. Nature biotechnology Rao, V. M., Zhang, S., Plosky, B. S., Hsu, P. D., Wang, B., Zou, J., Zitnik, M., Topol, E. J., Rajpurkar, P. 2026

  Abstract

  Generalist biological artificial intelligence (GBAI) represents a transformative approach to modeling the 'language of life'-the flow of information from DNA to cellular function. This Review synthesizes rapid advances in biological AI to interpret and generate DNA, RNA, proteins and cellular systems. We chart a course toward comprehensive systems that can concurrently process and predict across these domains, performing several critical biological tasks simultaneously. Substantial opportunities lie in synergizing language and structural AI, leveraging specialized models and improving AI agents for autonomous discovery. After addressing challenges in data, biological complexity, scalability and experimental validation, GBAI has the potential to deepen our understanding of disease pathways and biomarkers, advance automated therapeutic design and evaluation, and integrate within virtual cells to meaningfully simulate biological activity.

  View details for DOI 10.1038/s41587-026-03064-w

  View details for PubMedID 41862602

  View details for PubMedCentralID 11168924

• Rewriting endogenous human transcripts with dual CRISPR-guided 3' trans-splicing. Cell systems Chandrasekaran, S. S., Tau, C., Fu, B. X., Nemeth, M., Bartie, L., Pawluk, A., Konermann, S., Hsu, P. D. 2026: 101487

  Abstract

  Unlike genome editing, RNA editing offers the ability to transiently alter cells with minimal risk from off-target effects. While exon-skipping technologies can influence splice site selection, many desired perturbations to the transcriptome require replacement or addition of exogenous exons to target mRNAs, such as replacing disease-causing exons, repairing truncated proteins, or engineering protein fusions. Here, we report the development of RNA-guided trans-splicing with Cas editor (RESPLICE). RESPLICE uses two orthogonal RNA-targeting CRISPR effectors to co-localize a trans-splicing pre-mRNA and to inhibit the cis-splicing reaction, respectively. We demonstrate efficient, specific, and programmable trans-splicing of RNA cargo (up to 2.1 kb) into 11 endogenous transcripts across 3 cell types, achieving up to 45% trans-splicing efficiency in bulk or 90% when sorting for high effector expression. Our results present RESPLICE as a mode of RNA editing that could provide fine-tuned and transient control of cellular programs.

  View details for DOI 10.1016/j.cels.2025.101487

  View details for PubMedID 41653914

• Site-specific DNA insertion into the human genome with engineered recombinases. Nature biotechnology Fanton, A., Bartie, L. J., Martins, J. Q., Tran, V. Q., Goudy, L., Kernick, C., Durrant, M. G., Wei, J., Armour-Garb, Z., Pawluk, A., Konermann, S., Marson, A., Gilbert, L. A., Roth, T. L., Hsu, P. D. 2025

  Abstract

  Insertions of large DNA sequences into the genome are broadly enabling for research and therapeutic applications. Large serine recombinases (LSRs) can mediate direct, site-specific genomic integration of multi-kilobase DNA sequences without a pre-installed landing pad, albeit with low insertion rates and high off-target activity. Here we present an engineering roadmap for jointly optimizing their DNA recombination efficiency and specificity. We combine directed evolution, structural analysis and computational models to rapidly identify additive mutational combinations. We further enhance performance through donor DNA optimization and dCas9 fusions, enabling simultaneous target and donor recruitment. Our top engineered LSR variants, superDn29-dCas9, goldDn29-dCas9 and hifiDn29-dCas9, achieve up to 53% integration efficiency and 97% genome-wide specificity at an endogenous human locus and effectively integrate large DNA cargoes up to 12 kb for stable expression in non-dividing cells, stem cells and primary human T cells. Rational engineering of DNA recombinases enables precise and efficient single-step genome insertion for diverse applications across gene and cell therapies.

  View details for DOI 10.1038/s41587-025-02895-3

  View details for PubMedID 41199024

  View details for PubMedCentralID 4786924

• Virtual Cell Challenge: Toward a Turing test for the virtual cell. Cell Roohani, Y. H., Hua, T. J., Tung, P. Y., Bounds, L. R., Yu, F. B., Dobin, A., Teyssier, N., Adduri, A., Woodrow, A., Plosky, B. S., Mehta, R., Hsu, B., Sullivan, J., Ricci-Tam, C., Li, N., Kazaks, J., Gilbert, L. A., Konermann, S., Hsu, P. D., Goodarzi, H., Burke, D. P. 2025; 188 (13): 3370-3374

  Abstract

  Virtual cells are an emerging frontier at the intersection of artificial intelligence and biology. A key goal of these cell state models is predicting cellular responses to perturbations. The Virtual Cell Challenge is being established to catalyze progress toward this goal. This recurring and open benchmark competition from the Arc Institute will provide an evaluation framework, purpose-built datasets, and a venue for accelerating model development.

  View details for DOI 10.1016/j.cell.2025.06.008

  View details for PubMedID 40578317

• How to build the virtual cell with artificial intelligence: Priorities and opportunities. Cell Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., Califano, A., Cool, J., Dernburg, A. F., Ewing, K., Fox, E. B., Haury, M., Herr, A. E., Horvitz, E., Hsu, P. D., Jain, V., Johnson, G. R., Kalil, T., Kelley, D. R., Kelley, S. O., Kreshuk, A., Mitchison, T., Otte, S., Shendure, J., Sofroniew, N. J., Theis, F., Theodoris, C. V., Upadhyayula, S., Valer, M., Wang, B., Xing, E., Yeung-Levy, S., Zitnik, M., Karaletsos, T., Regev, A., Lundberg, E., Leskovec, J., Quake, S. R. 2024; 187 (25): 7045-7063

  Abstract

  Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior. Advances in AI and omics offer groundbreaking opportunities to create an AI virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent and simulate the behavior of molecules, cells, and tissues across diverse states. This Perspective provides a vision on their design and how collaborative efforts to build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, and guiding experimental studies, offering new opportunities for understanding cellular functions and fostering interdisciplinary collaborations in open science.

  View details for DOI 10.1016/j.cell.2024.11.015

  View details for PubMedID 39672099

• How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities. ArXiv Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., Califano, A., Cool, J., Dernburg, A. F., Ewing, K., Fox, E. B., Haury, M., Herr, A. E., Horvitz, E., Hsu, P. D., Jain, V., Johnson, G. R., Kalil, T., Kelley, D. R., Kelley, S. O., Kreshuk, A., Mitchison, T., Otte, S., Shendure, J., Sofroniew, N. J., Theis, F., Theodoris, C. V., Upadhyayula, S., Valer, M., Wang, B., Xing, E., Yeung-Levy, S., Zitnik, M., Karaletsos, T., Regev, A., Lundberg, E., Leskovec, J., Quake, S. R. 2024

  Abstract

  The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

  View details for PubMedID 39398201

  View details for PubMedCentralID PMC11468656

• Structural mechanism of bridge RNA-guided recombination. Nature Hiraizumi, M., Perry, N. T., Durrant, M. G., Soma, T., Nagahata, N., Okazaki, S., Athukoralage, J. S., Isayama, Y., Pai, J. J., Pawluk, A., Konermann, S., Yamashita, K., Hsu, P. D., Nishimasu, H. 2024; 630 (8018): 994-1002

  Abstract

  Insertion sequence (IS) elements are the simplest autonomous transposable elements found in prokaryotic genomes1. We recently discovered that IS110 family elements encode a recombinase and a non-coding bridge RNA (bRNA) that confers modular specificity for target DNA and donor DNA through two programmable loops2. Here we report the cryo-electron microscopy structures of the IS110 recombinase in complex with its bRNA, target DNA and donor DNA in three different stages of the recombination reaction cycle. The IS110 synaptic complex comprises two recombinase dimers, one of which houses the target-binding loop of the bRNA and binds to target DNA, whereas the other coordinates the bRNA donor-binding loop and donor DNA. We uncovered the formation of a composite RuvC-Tnp active site that spans the two dimers, positioning the catalytic serine residues adjacent to the recombination sites in both target and donor DNA. A comparison of the three structures revealed that (1) the top strands of target and donor DNA are cleaved at the composite active sites to form covalent 5'-phosphoserine intermediates, (2) the cleaved DNA strands are exchanged and religated to create a Holliday junction intermediate, and (3) this intermediate is subsequently resolved by cleavage of the bottom strands. Overall, this study reveals the mechanism by which a bispecific RNA confers target and donor DNA specificity to IS110 recombinases for programmable DNA recombination.

  View details for DOI 10.1038/s41586-024-07570-2

  View details for PubMedID 38926616

  View details for PubMedCentralID PMC11208158

• Bridge RNAs direct modular and programmable recombination of target and donor DNA. bioRxiv : the preprint server for biology Durrant, M. G., Perry, N. T., Pai, J. J., Jangid, A. R., Athukoralage, J. S., Hiraizumi, M., McSpedon, J. P., Pawluk, A., Nishimasu, H., Konermann, S., Hsu, P. D. 2024

  Abstract

  Genomic rearrangements, encompassing mutational changes in the genome such as insertions, deletions, or inversions, are essential for genetic diversity. These rearrangements are typically orchestrated by enzymes involved in fundamental DNA repair processes such as homologous recombination or in the transposition of foreign genetic material by viruses and mobile genetic elements (MGEs). We report that IS110 insertion sequences, a family of minimal and autonomous MGEs, express a structured non-coding RNA that binds specifically to their encoded recombinase. This bridge RNA contains two internal loops encoding nucleotide stretches that base-pair with the target DNA and donor DNA, which is the IS110 element itself. We demonstrate that the target-binding and donor-binding loops can be independently reprogrammed to direct sequence-specific recombination between two DNA molecules. This modularity enables DNA insertion into genomic target sites as well as programmable DNA excision and inversion. The IS110 bridge system expands the diversity of nucleic acid-guided systems beyond CRISPR and RNA interference, offering a unified mechanism for the three fundamental DNA rearrangements required for genome design.

  View details for DOI 10.1101/2024.01.24.577089

  View details for PubMedID 38328150

  View details for PubMedCentralID PMC10849738

• Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting. Cell systems Wei, J., Lotfy, P., Faizi, K., Baungaard, S., Gibson, E., Wang, E., Slabodkin, H., Kinnaman, E., Chandrasekaran, S., Kitano, H., Durrant, M. G., Duffy, C. V., Pawluk, A., Hsu, P. D., Konermann, S. 2023

  Abstract

  Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides. We next identified and screened 46 novel Cas13d orthologs, finding that DjCas13d achieves low cellular toxicity and high specificity-even when targeting abundant transcripts in sensitive cell types, including stem cells and neurons. Our Cas13d guide efficiency model was successfully generalized to DjCas13d, illustrating the power of combining machine learning with ortholog discovery to advance RNA targeting in human cells.

  View details for DOI 10.1016/j.cels.2023.11.006

  View details for PubMedID 38091991

• The KDM6A-KMT2D-p300 axis regulates susceptibility to diverse coronaviruses by mediating viral receptor expression. PLoS pathogens Wei, J., Alfajaro, M. M., Cai, W. L., Graziano, V. R., Strine, M. S., Filler, R. B., Biering, S. B., Sarnik, S. A., Patel, S., Menasche, B. L., Compton, S. R., Konermann, S., Hsu, P. D., Orchard, R. C., Yan, Q., Wilen, C. B. 2023; 19 (7): e1011351

  Abstract

  Identification of host determinants of coronavirus infection informs mechanisms of pathogenesis and may provide novel therapeutic targets. Here, we demonstrate that the histone demethylase KDM6A promotes infection of diverse coronaviruses, including SARS-CoV, SARS-CoV-2, MERS-CoV and mouse hepatitis virus (MHV) in a demethylase activity-independent manner. Mechanistic studies reveal that KDM6A promotes viral entry by regulating expression of multiple coronavirus receptors, including ACE2, DPP4 and Ceacam1. Importantly, the TPR domain of KDM6A is required for recruitment of the histone methyltransferase KMT2D and histone deacetylase p300. Together this KDM6A-KMT2D-p300 complex localizes to the proximal and distal enhancers of ACE2 and regulates receptor expression. Notably, small molecule inhibition of p300 catalytic activity abrogates ACE2 and DPP4 expression and confers resistance to all major SARS-CoV-2 variants and MERS-CoV in primary human airway and intestinal epithelial cells. These data highlight the role for KDM6A-KMT2D-p300 complex activities in conferring diverse coronaviruses susceptibility and reveal a potential pan-coronavirus therapeutic target to combat current and emerging coronaviruses. One Sentence Summary: The KDM6A/KMT2D/EP300 axis promotes expression of multiple viral receptors and represents a potential drug target for diverse coronaviruses.

  View details for DOI 10.1371/journal.ppat.1011351

  View details for PubMedID 37410700

  View details for PubMedCentralID PMC10325096

• DYRK1A promotes viral entry of highly pathogenic human coronaviruses in a kinase-independent manner. PLoS biology Strine, M. S., Cai, W. L., Wei, J., Alfajaro, M. M., Filler, R. B., Biering, S. B., Sarnik, S., Chow, R. D., Patil, A., Cervantes, K. S., Collings, C. K., DeWeirdt, P. C., Hanna, R. E., Schofield, K., Hulme, C., Konermann, S., Doench, J. G., Hsu, P. D., Kadoch, C., Yan, Q., Wilen, C. B. 2023; 21 (6): e3002097

  Abstract

  Identifying host genes essential for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has the potential to reveal novel drug targets and further our understanding of Coronavirus Disease 2019 (COVID-19). We previously performed a genome-wide CRISPR/Cas9 screen to identify proviral host factors for highly pathogenic human coronaviruses. Few host factors were required by diverse coronaviruses across multiple cell types, but DYRK1A was one such exception. Although its role in coronavirus infection was previously undescribed, DYRK1A encodes Dual Specificity Tyrosine Phosphorylation Regulated Kinase 1A and is known to regulate cell proliferation and neuronal development. Here, we demonstrate that DYRK1A regulates ACE2 and DPP4 transcription independent of its catalytic kinase function to support SARS-CoV, SARS-CoV-2, and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) entry. We show that DYRK1A promotes DNA accessibility at the ACE2 promoter and a putative distal enhancer, facilitating transcription and gene expression. Finally, we validate that the proviral activity of DYRK1A is conserved across species using cells of nonhuman primate and human origin. In summary, we report that DYRK1A is a novel regulator of ACE2 and DPP4 expression that may dictate susceptibility to multiple highly pathogenic human coronaviruses.

  View details for DOI 10.1371/journal.pbio.3002097

  View details for PubMedID 37310920

• Systematic discovery of recombinases for efficient integration of large DNA sequences into the human genome. Nature biotechnology Durrant, M. G., Fanton, A., Tycko, J., Hinks, M., Chandrasekaran, S. S., Perry, N. T., Schaepe, J., Du, P. P., Lotfy, P., Bassik, M. C., Bintu, L., Bhatt, A. S., Hsu, P. D. 2022

  Abstract

  Large serine recombinases (LSRs) are DNA integrases that facilitate the site-specific integration of mobile genetic elements into bacterial genomes. Only a few LSRs, such as Bxb1 and PhiC31, have been characterized to date, with limited efficiency as tools for DNA integration in human cells. In this study, we developed a computational approach to identify thousands of LSRs and their DNA attachment sites, expanding known LSR diversity by >100-fold and enabling the prediction of their insertion site specificities. We tested their recombination activity in human cells, classifying them as landing pad, genome-targeting or multi-targeting LSRs. Overall, we achieved up to seven-fold higher recombination than Bxb1 and genome integration efficiencies of 40-75% with cargo sizes over 7kb. We also demonstrate virus-free, direct integration of plasmid or amplicon libraries for improved functional genomics applications. This systematic discovery of recombinases directly from microbial sequencing data provides a resource of over 60 LSRs experimentally characterized in human cells for large-payload genome insertion without exposed DNA double-stranded breaks.

  View details for DOI 10.1038/s41587-022-01494-w

  View details for PubMedID 36217031

• What are the current bottlenecks in developing and applying CRISPR technologies? CELL SYSTEMS Kellogg, E. H., Gootenberg, J., Abudayyeh, O., Wong, A. S. L., Dahlman, J. E., Lapinaite, A., Myhrvoid, C., Liu, C. C., Hsu, P. D., Mali, P., Qi, L. 2022; 13 (8): 589-593

  View details for Web of Science ID 000944344800001

  View details for PubMedID 35981511

• Genome-wide bidirectional CRISPR screens identify mucins as host factors modulating SARS-CoV-2 infection. Nature genetics Biering, S. B., Sarnik, S. A., Wang, E., Zengel, J. R., Leist, S. R., Schafer, A., Sathyan, V., Hawkins, P., Okuda, K., Tau, C., Jangid, A. R., Duffy, C. V., Wei, J., Gilmore, R. C., Alfajaro, M. M., Strine, M. S., Nguyenla, X., Van Dis, E., Catamura, C., Yamashiro, L. H., Belk, J. A., Begeman, A., Stark, J. C., Shon, D. J., Fox, D. M., Ezzatpour, S., Huang, E., Olegario, N., Rustagi, A., Volmer, A. S., Livraghi-Butrico, A., Wehri, E., Behringer, R. R., Cheon, D., Schaletzky, J., Aguilar, H. C., Puschnik, A. S., Button, B., Pinsky, B. A., Blish, C. A., Baric, R. S., O'Neal, W. K., Bertozzi, C. R., Wilen, C. B., Boucher, R. C., Carette, J. E., Stanley, S. A., Harris, E., Konermann, S., Hsu, P. D. 2022

  Abstract

  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a range of symptoms in infected individuals, from mild respiratory illness to acute respiratory distress syndrome. A systematic understanding of host factors influencing viral infection is critical to elucidate SARS-CoV-2-host interactions and the progression of Coronavirus disease 2019 (COVID-19). Here, we conducted genome-wide CRISPR knockout and activation screens in human lung epithelial cells with endogenous expression of the SARS-CoV-2 entry factors ACE2 and TMPRSS2. We uncovered proviral and antiviral factors across highly interconnected host pathways, including clathrin transport, inflammatory signaling, cell-cycle regulation, and transcriptional and epigenetic regulation. We further identified mucins, a family of high molecular weight glycoproteins, as a prominent viral restriction network that inhibits SARS-CoV-2 infection in vitro and in murine models. These mucins also inhibit infection of diverse respiratory viruses. This functional landscape of SARS-CoV-2 host factors provides a physiologically relevant starting point for new host-directed therapeutics and highlights airway mucins as a host defense mechanism.

  View details for DOI 10.1038/s41588-022-01131-x

  View details for PubMedID 35879412

• GENOME-WIDE, BIDIRECTIONAL CRISPR SCREENS IDENTIFY MUCINS AS CRITICAL MODULATORS OF SARS-COV-2 INFECTION Biering, S. B., Sarnik, S., Wang, E., Sathyan, V., Nguyenla, X., Zengel, J., Van Dis, E., Yamashiro, L., Kim, J., Fox, D., Carette, J., Stanley, S., Konermann, S., Harris, E., Hsu, P. AMER SOC TROP MED & HYGIENE. 2021: 151

  View details for Web of Science ID 000778105602160

• Pairwise library screen systematically interrogates Staphylococcus aureus Cas9 specificity in human cells NATURE COMMUNICATIONS Tycko, J., Barrera, L. A., Huston, N. C., Friedland, A. E., Wu, X., Gootenberg, J. S., Abudayyeh, O. O., Myer, V. E., Wilson, C. J., Hsu, P. D. 2018; 9: 2962

  Abstract

  Therapeutic genome editing with Staphylococcus aureus Cas9 (SaCas9) requires a rigorous understanding of its potential off-target activity in the human genome. Here we report a high-throughput screening approach to measure SaCas9 genome editing variation in human cells across a large repertoire of 88,692 single guide RNAs (sgRNAs) paired with matched or mismatched target sites in a synthetic cassette. We incorporate randomized barcodes that enable whitelisting of correctly synthesized molecules for further downstream analysis, in order to circumvent the limitation of oligonucleotide synthesis errors. We find SaCas9 sgRNAs with 21-mer or 22-mer spacer sequences are generally more active, although high efficiency 20-mer spacers are markedly less tolerant of mismatches. Using this dataset, we developed an SaCas9 specificity model that performs robustly in ranking off-target sites. The barcoded pairwise library screen enabled high-fidelity recovery of guide-target relationships, providing a scalable framework for the investigation of CRISPR enzyme properties and general nucleic acid interactions.

  View details for PubMedID 30054474

• Twinfilin 2 Regulates Actin Filament Lengths in Cochlear Stereocilia JOURNAL OF NEUROSCIENCE Peng, A. W., Belyantseva, I. A., Hsu, P. D., Friedman, T. B., Heller, S. 2009; 29 (48): 15083-15088

  Abstract

  Inner ear sensory hair cells convert mechanical stimuli into electrical signals. This conversion happens in the exquisitely mechanosensitive hair bundle that protrudes from the cell's apical surface. In mammals, cochlear hair bundles are composed of 50-100 actin-filled stereocilia, which are organized in three rows in a staircase manner. Stereocilia actin filaments are uniformly oriented with their barbed ends toward stereocilia tips. During development, the actin core of each stereocilium undergoes elongation due to addition of actin monomers to the barbed ends of the filaments. Here we show that in the mouse cochlea the barbed end capping protein twinfilin 2 is present at the tips of middle and short rows of stereocilia from postnatal day 5 (P5) onward, which correlates with a time period when these rows stop growing. The tall stereocilia rows, which do not display twinfilin 2 at their tips, continue to elongate between P5 and P15. When we expressed twinfilin 2 in LLC/PK1-CL4 (CL4) cells, we observed a reduction of espin-induced microvilli length, pointing to a potent function of twinfilin 2 in suppressing the elongation of actin filaments. Overexpression of twinfilin 2 in cochlear inner hair cells resulted in a significant reduction of stereocilia length. Our results suggest that twinfilin 2 plays a role in the regulation of stereocilia elongation by restricting excessive elongation of the shorter row stereocilia thereby maintaining the mature staircase architecture of cochlear hair bundles.

  View details for DOI 10.1523/JNEUROSCI.2782-09.2009

  View details for Web of Science ID 000272361700007

  View details for PubMedID 19955359

  View details for PubMedCentralID PMC2823077