Alison Fanton

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 7 kb. 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.

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. Evo 2 is an artificial intelligence-based biological foundation model trained on 9 trillion DNA base pairs spanning all domains of life that predicts functional properties from genomic sequences and provides a rich generative model for researchers in biology.

Rapid nucleic acid testing is a critical component of a robust infrastructure for increased disease surveillance. Here, we report a microfluidic platform for point-of-care, CRISPR-based molecular diagnostics. We first developed a nucleic acid test which pairs distinct mechanisms of DNA and RNA amplification optimized for high sensitivity and rapid kinetics, linked to Cas13 detection for specificity. We combined this workflow with an extraction-free sample lysis protocol using shelf-stable reagents that are widely available at low cost, and a multiplexed human gene control for calling negative test results. As a proof-of-concept, we demonstrate sensitivity down to 40 copies/μL of SARS-CoV-2 in unextracted saliva within 35 minutes, and validated the test on total RNA extracted from patient nasal swabs with a range of qPCR Ct values from 13-35. To enable sample-to-answer testing, we integrated this diagnostic reaction with a single-use, gravity-driven microfluidic cartridge followed by real-time fluorescent detection in a compact companion instrument. We envision this approach for Diagnostics with Coronavirus Enzymatic Reporting (DISCoVER) will incentivize frequent, fast, and easy testing.

Author(s): Crawford, Emily D; Acosta, Irene; Ahyong, Vida; Anderson, Erika C; Arevalo, Shaun; Asarnow, Daniel; Axelrod, Shannon; Ayscue, Patrick; Azimi, Camillia S; Azumaya, Caleigh M; Bachl, Stefanie; Bachmutsky, Iris; Bhaduri, Aparna; Brown, Jeremy Bancroft; Batson, Joshua; Behnert, Astrid; Boileau, Ryan M; Bollam, Saumya R; Bonny, Alain R; Booth, David; Borja, Michael Jerico B; Brown, David; Buie, Bryan; Burnett, Cassandra E; Byrnes, Lauren E; Cabral, Katelyn A; Cabrera, Joana P; Caldera, Saharai; Canales, Gabriela; Castaneda, Gloria R; Chan, Agnes Protacio; Chang, Christopher R; Charles-Orszag, Arthur; Cheung, Carly; Chio, Unseng; Chow, Eric D; Citron, Y Rose; Cohen, Allison; Cohn, Lillian B; Chiu, Charles; Cole, Mitchel A; Conrad, Daniel N; Constantino, Angela; Cote, Andrew; Crayton-Hall, Tre'Jon; Darmanis, Spyros; Detweiler, Angela M; Dial, Rebekah L; Dong, Shen; Duarte, Elias M; Dynerman, David; Egger, Rebecca; Fanton, Alison; Frumm, Stacey M; Fu, Becky Xu Hua; Garcia, Valentina E; Garcia, Julie; Gladkova, Christina; Goldman, Miriam; Gomez-Sjoberg, Rafael; Gordon, M Grace; Grove, James CR; Gupta, Shweta; Haddjeri-Hopkins, Alexis; Hadley, Pierce; Haliburton, John; Hao, Samantha L; Hartoularos, George; Herrera, Nadia; Hilberg, Melissa; Ho, Kit Ying E; Hoppe, Nicholas; Hosseinzadeh, Shayan; Howard, Conor J; Hussmann, Jeffrey A; Hwang, Elizabeth; Ingebrigtsen, Danielle; Jackson, Julia R; Jowhar, Ziad M; Kain, Danielle; Kim, James YS; Kistler, Amy; Kreutzfeld, Oriana; Kulsuptrakul, Jessie; Kung, Andrew F