Measuring prion propagation in single bacteria elucidates a mechanism of loss.

Prions are self-propagating protein aggregates formed by specific proteins that can adopt alternative folds. Prions were discovered as the cause of the fatal transmissible spongiform encephalopathies in mammals, but prions can also constitute nontoxic protein-based elements of inheritance in fungi and other species. Prion propagation has recently been shown to occur in bacteria for more than a hundred cell divisions, yet a fraction of cells in these lineages lost the prion through an unknown mechanism. Here, we investigate prion propagation in single bacterial cells as they divide using microfluidics and fluorescence microscopy. We show that the propagation occurs in two distinct modes. In a fraction of the population, cells had multiple small visible aggregates and lost the prion through random partitioning of aggregates to one of the two daughter cells at division. In the other subpopulation, cells had a stable large aggregate localized to the pole; upon division the mother cell retained this polar aggregate and a daughter cell was generated that contained small aggregates. Extending our findings to prion domains from two orthologous proteins, we observe similar propagation and loss properties. Our findings also provide support for the suggestion that bacterial prions can form more than one self-propagating state. We implement a stochastic version of the molecular model of prion propagation from yeast and mammals that recapitulates all the observed single-cell properties. This model highlights challenges for prion propagation that are unique to prokaryotes and illustrates the conservation of fundamental characteristics of prion propagation.

Phylogenomic fingerprinting of tempo and functions of horizontal gene transfer within ochrophytes.

Horizontal gene transfer (HGT) is an important source of novelty in eukaryotic genomes. This is particularly true for the ochrophytes, a diverse and important group of algae. Previous studies have shown that ochrophytes possess a mosaic of genes derived from bacteria and eukaryotic algae, acquired through chloroplast endosymbiosis and from HGTs, although understanding of the time points and mechanisms underpinning these transfers has been restricted by the depth of taxonomic sampling possible. We harness an expanded set of ochrophyte sequence libraries, alongside automated and manual phylogenetic annotation, in silico modeling, and experimental techniques, to assess the frequency and functions of HGT across this lineage. Through manual annotation of thousands of single-gene trees, we identify continuous bacterial HGT as the predominant source of recently arrived genes in the model diatom Phaeodactylum tricornutum Using a large-scale automated dataset, a multigene ochrophyte reference tree, and mathematical reconciliation of gene trees, we note a probable elevation of bacterial HGTs at foundational points in diatom evolution, following their divergence from other ochrophytes. Finally, we demonstrate that throughout ochrophyte evolutionary history, bacterial HGTs have been enriched in genes encoding secreted proteins. Our study provides insights into the sources and frequency of HGTs, and functional contributions that HGT has made to algal evolution.

The gut microbiota and its biogeography.

Biogeography is the study of species distribution and diversity within an ecosystem and is at the core of how we understand ecosystem dynamics and interactions at the macroscale. In gut microbial communities, a historical reliance on bulk sequencing to probe community composition and dynamics has overlooked critical processes whereby microscale interactions affect systems-level microbiota function and the relationship with the host. In recent years, higher-resolution sequencing and novel single-cell level data have uncovered an incredible heterogeneity in microbial composition and have enabled a more nuanced spatial understanding of the gut microbiota. In an era when spatial transcriptomics and single-cell imaging and analysis have become key tools in mammalian cell and tissue biology, many of these techniques are now being applied to the microbiota. This fresh approach to intestinal biogeography has given important insights that span temporal and spatial scales, from the discovery of mucus encapsulation of the microbiota to the quantification of bacterial species throughout the gut. In this Review, we highlight emerging knowledge surrounding gut biogeography enabled by the observation and quantification of heterogeneity across multiple scales.

In Vitro Enzyme Self-Selection Using Molecular Programs.

Directed evolution provides a powerful route for in vitro enzyme engineering. State-of-the-art techniques functionally screen up to millions of enzyme variants using high throughput microfluidic sorters, whose operation remains technically challenging. Alternatively, in vitro self-selection methods, analogous to in vivo complementation strategies, open the way to even higher throughputs, but have been demonstrated only for a few specific activities. Here, we leverage synthetic molecular networks to generalize in vitro compartmentalized self-selection processes. We introduce a programmable circuit architecture that can link an arbitrary target enzymatic activity to the replication of its encoding gene. Microencapsulation of a bacterial expression library with this autonomous selection circuit results in the single-step and screening-free enrichment of genetic sequences coding for programmed enzymatic phenotypes. We demonstrate the potential of this approach for the nicking enzyme Nt.BstNBI (NBI). We applied autonomous selection conditions to enrich for thermostability or catalytic efficiency, manipulating up to 107 microcompartments and 5 × 105 variants at once. Full gene reads of the libraries using nanopore sequencing revealed detailed mutational activity landscapes, suggesting a key role of electrostatic interactions with DNA in the enzyme's turnover. The most beneficial mutations, identified after a single round of self-selection, provided variants with, respectively, 20 times and 3 °C increased activity and thermostability. Based on a modular molecular programming architecture, this approach does not require complex instrumentation and can be repurposed for other enzymes, including those that are not related to DNA chemistry.

Measuring prion propagation in single bacteria elucidates mechanism of loss.

Prions are self-propagating protein aggregates formed by specific proteins that can adopt alternative folds. Prions were discovered as the cause of the fatal transmissible spongiform encephalopathies in mammals, but prions can also constitute non-toxic protein-based elements of inheritance in fungi and other species. Prion propagation has recently been shown to occur in bacteria for more than a hundred cell divisions, yet a fraction of cells in these lineages lost the prion through an unknown mechanism. Here, we investigate prion propagation in single bacterial cells as they divide using microfluidics and fluorescence microscopy. We show that the propagation occurs in two distinct modes with distinct stability and inheritance characteristics. We find that the prion is lost through random partitioning of aggregates to one of the two daughter cells at division. Extending our findings to prion domains from two orthologous proteins, we observe similar propagation and loss properties. Our findings also provide support for the suggestion that bacterial prions can form more than one self-propagating state. We implement a stochastic version of the molecular model of prion propagation from yeast and mammals that recapitulates all the observed single-cell properties. This model highlights challenges for prion propagation that are unique to prokaryotes and illustrates the conservation of fundamental characteristics of prion propagation across domains of life.

Using Models to (Re-)Design Synthetic Circuits.

Mathematical models play an important role in the design of synthetic gene circuits, by guiding the choice of biological components and their assembly into novel gene networks. Here, we present a guide for biologists to build and utilize models of gene networks (synthetic or natural) to analyze dynamical properties of these networks while considering the low numbers of molecules inside cells that results in stochastic gene expression. We start by describing how to write down a model and discussing the level of details to include. We then briefly demonstrate how to simulate a network's dynamics using deterministic differential equations that assume high numbers of molecules. To consider the role of stochastic gene expression in single cells, we provide a detailed tutorial on running stochastic Gillespie simulations of a network, including instructions on coding the Gillespie algorithm with example code. Finally, we illustrate how using a combination of quantitative experimental characterization of a synthetic circuit and mathematical modeling can guide the iterative redesign of a synthetic circuit to achieve the desired properties. This is shown using a classic synthetic oscillator, the repressilator, which we recently redesigned into the most precise and robust synthetic oscillator to date. We thus provide a toolkit for synthetic biologists to build more precise and robust synthetic circuits, which should lead to a deeper understanding of the dynamics of gene regulatory networks.

Chimeric origins of ochrophytes and haptophytes revealed through an ancient plastid proteome.

Plastids are supported by a wide range of proteins encoded within the nucleus and imported from the cytoplasm. These plastid-targeted proteins may originate from the endosymbiont, the host, or other sources entirely. Here, we identify and characterise 770 plastid-targeted proteins that are conserved across the ochrophytes, a major group of algae including diatoms, pelagophytes and kelps, that possess plastids derived from red algae. We show that the ancestral ochrophyte plastid proteome was an evolutionary chimera, with 25% of its phylogenetically tractable nucleus-encoded proteins deriving from green algae. We additionally show that functional mixing of host and plastid proteomes, such as through dual-targeting, is an ancestral feature of plastid evolution. Finally, we detect a clear phylogenetic signal from one ochrophyte subgroup, the lineage containing pelagophytes and dictyochophytes, in plastid-targeted proteins from another major algal lineage, the haptophytes. This may represent a possible serial endosymbiosis event deep in eukaryotic evolutionary history.