Counteraction of antibiotic production and degradation stabilizes microbial communities.

A major challenge in theoretical ecology is understanding how natural microbial communities support species diversity, and in particular how antibiotic-producing, -sensitive and -resistant species coexist. While cyclic 'rock­paper­scissors' interactions can stabilize communities in spatial environments, coexistence in unstructured environments remains unexplained. Here, using simulations and analytical models, we show that the opposing actions of antibiotic production and degradation enable coexistence even in well-mixed environments. Coexistence depends on three-way interactions in which an antibiotic-degrading species attenuates the inhibitory interactions between two other species. These interactions enable coexistence that is robust to substantial differences in inherent species growth rates and to invasion by 'cheating' species that cease to produce or degrade antibiotics. At least two antibiotics are required for stability, with greater numbers of antibiotics enabling more complex communities and diverse dynamic behaviours ranging from stable fixed points to limit cycles and chaos. Together, these results show how multi-species antibiotic interactions can generate ecological stability in both spatially structured and mixed microbial communities, suggesting strategies for engineering synthetic ecosystems and highlighting the importance of toxin production and degradation for microbial biodiversity.

Overcoming Immunological Challenges Limiting Capsid-Mediated Gene Therapy With Machine Learning.

A key hurdle to making adeno-associated virus (AAV) capsid mediated gene therapy broadly beneficial to all patients is overcoming pre-existing and therapy-induced immune responses to these vectors. Recent advances in high-throughput DNA synthesis, multiplexing and sequencing technologies have accelerated engineering of improved capsid properties such as production yield, packaging efficiency, biodistribution and transduction efficiency. Here we outline how machine learning, advances in viral immunology, and high-throughput measurements can enable engineering of a new generation of de-immunized capsids beyond the antigenic landscape of natural AAVs, towards expanding the therapeutic reach of gene therapy.

Deep diversification of an AAV capsid protein by machine learning.

Modern experimental technologies can assay large numbers of biological sequences, but engineered protein libraries rarely exceed the sequence diversity of natural protein families. Machine learning (ML) models trained directly on experimental data without biophysical modeling provide one route to accessing the full potential diversity of engineered proteins. Here we apply deep learning to design highly diverse adeno-associated virus 2 (AAV2) capsid protein variants that remain viable for packaging of a DNA payload. Focusing on a 28-amino acid segment, we generated 201,426 variants of the AAV2 wild-type (WT) sequence yielding 110,689 viable engineered capsids, 57,348 of which surpass the average diversity of natural AAV serotype sequences, with 12-29 mutations across this region. Even when trained on limited data, deep neural network models accurately predict capsid viability across diverse variants. This approach unlocks vast areas of functional but previously unreachable sequence space, with many potential applications for the generation of improved viral vectors and protein therapeutics.

Escape mutations circumvent a tradeoff between resistance to a beta-lactam and resistance to a beta-lactamase inhibitor.

Beta-lactamase inhibitors are increasingly used to counteract antibiotic resistance mediated by beta-lactamase enzymes. These inhibitors compete with the beta-lactam antibiotic for the same binding site on the beta-lactamase, thus generating an evolutionary tradeoff: mutations that increase the enzyme's beta-lactamase activity tend to increase also its susceptibility to the inhibitor. Here, we investigate how common and accessible are mutants that escape this adaptive tradeoff. Screening a deep mutant library of the blaampC beta-lactamase gene of Escherichia coli, we identified mutations that allow growth at beta-lactam concentrations far exceeding those inhibiting growth of the wildtype strain, even in the presence of the enzyme inhibitor (avibactam). These escape mutations are rare and drug-specific, and some combinations of avibactam with beta-lactam drugs appear to prevent such escape phenotypes. Our results, showing differential adaptive potential of blaampC to combinations of avibactam and different beta-lactam antibiotics, suggest that it may be possible to identify treatments that are more resilient to evolution of resistance.

Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design.

Adeno-associated virus (AAV) capsids can deliver transformative gene therapies, but our understanding of AAV biology remains incomplete. We generated the complete first-order AAV2 capsid fitness landscape, characterizing all single-codon substitutions, insertions, and deletions across multiple functions relevant for in vivo delivery. We discovered a frameshifted gene in the VP1 region that expresses a membrane-associated accessory protein that limits AAV production through competitive exclusion. Mutant biodistribution revealed the importance of both surface-exposed and buried residues, with a few phenotypic profiles characterizing most variants. Finally, we algorithmically designed and experimentally verified a diverse in vivo targeted capsid library with viability far exceeding random mutagenesis approaches. These results demonstrate the power of systematic mutagenesis for deciphering complex genomes and the potential of empirical machine-guided protein engineering.

High-throughput creation and functional profiling of DNA sequence variant libraries using CRISPR-Cas9 in yeast.

Construction and characterization of large genetic variant libraries is essential for understanding genome function, but remains challenging. Here, we introduce a Cas9-based approach for generating pools of mutants with defined genetic alterations (deletions, substitutions, and insertions) with an efficiency of 80-100% in yeast, along with methods for tracking their fitness en masse. We demonstrate the utility of our approach by characterizing the DNA helicase SGS1 with small tiling deletion mutants that span the length of the protein and a series of point mutations against highly conserved residues in the protein. In addition, we created a genome-wide library targeting 315 poorly characterized small open reading frames (smORFs, <100 amino acids in length) scattered throughout the yeast genome, and assessed which are vital for growth under various environmental conditions. Our strategy allows fundamental biological questions to be investigated in a high-throughput manner with precision.

High-order species interactions shape ecosystem diversity.

Classical theory shows that large communities are destabilized by random interactions among species pairs, creating an upper bound on ecosystem diversity. However, species interactions often occur in high-order combinations, whereby the interaction between two species is modulated by one or more other species. Here, by simulating the dynamics of communities with random interactions, we find that the classical relationship between diversity and stability is inverted for high-order interactions. More specifically, while a community becomes more sensitive to pairwise interactions as its number of species increases, its sensitivity to three-way interactions remains unchanged, and its sensitivity to four-way interactions actually decreases. Therefore, while pairwise interactions lead to sensitivity to the addition of species, four-way interactions lead to sensitivity to species removal, and their combination creates both a lower and an upper bound on the number of species. These findings highlight the importance of high-order species interactions in determining the diversity of natural ecosystems.

RNA Structural Determinants of Optimal Codons Revealed by MAGE-Seq.

Synonymous codon choices at the beginning of genes optimize 5' RNA structures for enhanced translation initiation, but less is known about mechanisms that drive codon optimization downstream within the gene. To understand what determines codon choices across a gene, we generated 12,726 in situ codon mutants in the Escherichia coli essential gene infA and measured their fitness by combining multiplex automated genome engineering mutagenesis with amplicon deep sequencing (MAGE-seq). Correlating predicted 5' RNA structure with fitness revealed that codons even far from the start of the gene are deleterious if they disrupt the native 5' RNA conformation. These long-range structural interactions generate context-dependent rules that constrain codon choices beyond intrinsic codon preferences. Genome-wide RNA folding predictions confirm that natural codon choices far from the start codon are optimized in part to prevent disruption of native structures near the 5' UTR. Our results shed light on natural codon distributions and should improve engineering of gene expression for synthetic biology applications.

Spatiotemporal microbial evolution on antibiotic landscapes.

A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front. While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front, we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behind more sensitive lineages. The MEGA-plate provides a versatile platform for studying microbial adaption and directly visualizing evolutionary dynamics.