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Plastic waste poses an ecological challenge 1 – 3 and enzymatic degradation offers one, potentially green and scalable, route for polyesters waste recycling 4 . Poly(ethylene terephthalate) (PET) accounts for 12% of global solid waste 5 , and a circular carbon economy for PET is theoretically attainable through rapid enzymatic depolymerization followed by repolymerization or conversion/valorization into other products 6 – 10 . Application of PET hydrolases, however, has been hampered by their lack of robustness to pH and temperature ranges, slow reaction rates and inability to directly use untreated postconsumer plastics 11 . Here, we use a structure-based, machine learning algorithm to engineer a robust and active PET hydrolase. Our mutant and scaffold combination (FAST-PETase: functional, active, stable and tolerant PETase) contains five mutations compared to wild-type PETase (N233K/R224Q/S121E from prediction and D186H/R280A from scaffold) and shows superior PET-hydrolytic activity relative to both wild-type and engineered alternatives 12 between 30 and 50 °C and a range of pH levels. We demonstrate that untreated, postconsumer-PET from 51 different thermoformed products can all be almost completely degraded by FAST-PETase in 1 week. FAST-PETase can also depolymerize untreated, amorphous portions of a commercial water bottle and an entire thermally pretreated water bottle at 50 ºC. Finally, we demonstrate a closed-loop PET recycling process by using FAST-PETase and resynthesizing PET from the recovered monomers. Collectively, our results demonstrate a viable route for enzymatic plastic recycling at the industrial scale. Untreated, postconsumer-PET from 51 different thermoformed products can all be almost completely degraded by FAST-PETase in 1 week and PET can be resynthesized from the recovered monomers, demonstrating recycling at the industrial scale.

Genome-scale data and the development of novel statistical phylogenetic approaches have greatly aided the reconstruction of a broad sketch of the tree of life and resolved many of its branches. However, incongruence — the inference of conflicting evolutionary histories — remains pervasive in phylogenomic data, hampering our ability to reconstruct and interpret the tree of life. Biological factors, such as incomplete lineage sorting, horizontal gene transfer, hybridization, introgression, recombination and convergent molecular evolution, can lead to gene phylogenies that differ from the species tree. In addition, analytical factors, including stochastic, systematic and treatment errors, can drive incongruence. Here, we review these factors, discuss methodological advances to identify and handle incongruence, and highlight avenues for future research.Incongruence occurs when phylogenetic trees show conflicting evolutionary histories such as patterns of branching or relationships among taxa. This Review discusses the biological and analytical factors that lead to incongruence, methodological advances to identify and resolve incongruence, and avenues for future research.

Natural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could guide artificial evolution. Here we report that general protein language models can efficiently evolve human antibodies by suggesting mutations that are evolutionarily plausible, despite providing the model with no information about the target antigen, binding specificity or protein structure. We performed language-model-guided affinity maturation of seven antibodies, screening 20 or fewer variants of each antibody across only two rounds of laboratory evolution, and improved the binding affinities of four clinically relevant, highly mature antibodies up to sevenfold and three unmatured antibodies up to 160-fold, with many designs also demonstrating favorable thermostability and viral neutralization activity against Ebola and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudoviruses. The same models that improve antibody binding also guide efficient evolution across diverse protein families and selection pressures, including antibiotic resistance and enzyme activity, suggesting that these results generalize to many settings. A general protein language model guides protein evolution with 20 or fewer variants needed for testing.

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness 1 – 3 . Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces 4 – 6 . Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae , we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution. A framework for studying and engineering gene regulatory DNA sequences, based on deep neural sequence-to-expression models trained on large-scale libraries of random DNA, provides insight into the evolution, evolvability and fitness landscapes of regulatory DNA.

The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively 1 – 3 . In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function 4 . Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity. The genomic profile and early transmission dynamics of the Omicron strain of SARS-CoV-2.

High-quality and complete reference genome assemblies are fundamental for the application of genomics to biology, disease, and biodiversity conservation. However, such assemblies are available for only a few non-microbial species 1 – 4 . To address this issue, the international Genome 10K (G10K) consortium 5 , 6 has worked over a five-year period to evaluate and develop cost-effective methods for assembling highly accurate and nearly complete reference genomes. Here we present lessons learned from generating assemblies for 16 species that represent six major vertebrate lineages. We confirm that long-read sequencing technologies are essential for maximizing genome quality, and that unresolved complex repeats and haplotype heterozygosity are major sources of assembly error when not handled correctly. Our assemblies correct substantial errors, add missing sequence in some of the best historical reference genomes, and reveal biological discoveries. These include the identification of many false gene duplications, increases in gene sizes, chromosome rearrangements that are specific to lineages, a repeated independent chromosome breakpoint in bat genomes, and a canonical GC-rich pattern in protein-coding genes and their regulatory regions. Adopting these lessons, we have embarked on the Vertebrate Genomes Project (VGP), an international effort to generate high-quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences. The Vertebrate Genome Project has used an optimized pipeline to generate high-quality genome assemblies for sixteen species (representing all major vertebrate classes), which have led to new biological insights.

Influenza A viruses have caused more documented global pandemics in human history than any other pathogen 1 , 2 . High pathogenicity avian influenza viruses belonging to the H5N1 subtype are a leading pandemic risk. Two decades after H5N1 ‘bird flu’ became established in poultry in Southeast Asia, its descendants have resurged 3 , setting off a H5N1 panzootic in wild birds that is fuelled by: (1) rapid intercontinental spread, reaching South America and Antarctica for the first time 4 , 5 ; (2) fast evolution via genomic reassortment 6 ; and (3) frequent spillover into terrestrial 7 , 8 and marine mammals 9 . The virus has sustained mammal-to-mammal transmission in multiple settings, including European fur farms 10 , 11 , South American marine mammals 12 , 13 , 14 – 15 and US dairy cattle 16 , 17 , 18 – 19 , raising questions about whether humans are next. Historically, swine are considered optimal intermediary hosts that help avian influenza viruses adapt to mammals before jumping to humans 20 . However, the altered ecology of H5N1 has opened the door to new evolutionary pathways. Dairy cattle, farmed mink or South American sea lions may have the potential to serve as new mammalian gateways for transmission of avian influenza viruses to humans. In this Perspective, we explore the molecular and ecological factors driving the sudden expansion in H5N1 host range and assess the likelihood of different zoonotic pathways leading to an H5N1 pandemic. This Perspective reviews the molecular and ecological factors that have driven the expansion in geographical distribution and host species range of H5N1 avian influenza viruses, leading to the current panzootic.

The number and diversity of known CRISPR–Cas systems have substantially increased in recent years. Here, we provide an updated evolutionary classification of CRISPR–Cas systems and cas genes, with an emphasis on the major developments that have occurred since the publication of the latest classification, in 2015. The new classification includes 2 classes, 6 types and 33 subtypes, compared with 5 types and 16 subtypes in 2015. A key development is the ongoing discovery of multiple, novel class 2 CRISPR–Cas systems, which now include 3 types and 17 subtypes. A second major novelty is the discovery of numerous derived CRISPR–Cas variants, often associated with mobile genetic elements that lack the nucleases required for interference. Some of these variants are involved in RNA-guided transposition, whereas others are predicted to perform functions distinct from adaptive immunity that remain to be characterized experimentally. The third highlight is the discovery of numerous families of ancillary CRISPR-linked genes, often implicated in signal transduction. Together, these findings substantially clarify the functional diversity and evolutionary history of CRISPR–Cas.The number and diversity of known CRISPR–Cas systems have substantially increased in recent years. In this Review, Koonin and colleagues provide an updated evolutionary classification of CRISPR–Cas systems and cas genes, with an emphasis on major developments, and outline a complete scenario for the origins and evolution of CRISPR–Cas systems.

The mobile resistance gene bla NDM encodes the NDM enzyme which hydrolyses carbapenems, a class of antibiotics used to treat some of the most severe bacterial infections. The bla NDM gene is globally distributed across a variety of Gram-negative bacteria on multiple plasmids, typically located within highly recombining and transposon-rich genomic regions, which leads to the dynamics underlying the global dissemination of bla NDM to remain poorly resolved. Here, we compile a dataset of over 6000 bacterial genomes harbouring the bla NDM gene, including 104 newly generated PacBio hybrid assemblies from clinical and livestock-associated isolates across China. We develop a computational approach to track structural variants surrounding bla NDM , which allows us to identify prevalent genomic contexts, mobile genetic elements, and likely events in the gene’s global spread. We estimate that bla NDM emerged on a Tn 125 transposon before 1985, but only reached global prevalence around a decade after its first recorded observation in 2005. The Tn125 transposon seems to have played an important role in early plasmid-mediated jumps of bla NDM , but was overtaken in recent years by other elements including IS26-flanked pseudo-composite transposons and Tn3000. We found a strong association between bla NDM -carrying plasmid backbones and the sampling location of isolates. This observation suggests that the global dissemination of the bla NDM gene was primarily driven by successive between-plasmid transposon jumps, with far more restricted subsequent plasmid exchange, possibly due to adaptation of plasmids to their specific bacterial hosts. Gene bla NDM , conferring resistance to carbapenem antibiotics, is globally distributed across Gram-negative bacteria on multiple plasmids. Here, Acman et al. study the dynamics underlying bla NDM dissemination across over 6000 bacterial genomes, and identify mobile genetic elements and specific mobilisation events likely involved in the gene’s global spread.