Search bioRxiv⌕ Search

bioRxiv · 10.1101/546275

Optimality of the spontaneous prophage induction rate

Abstract

Lysogens are bacterial cells that have survived after being infected by bacterial viruses called bacteriophages. Instead of being killed by the virus, the infected cell survives by integrating the viral DNA into its own genome. This is only possible with "temperate" bacteriophages which do not always lyse their host to reproduce, but sometimes replicate passively using the lysogenic pathway. After an infection resulting in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome which is now referred to as a prophage. However, the prophage can have an impact on the hosts phenotype and overall fitness in certain environments. This makes competition between the lysogen and its nonlysogen counterpart possible because both cells have different genomes and potentially different growth rates. Additionally, the prophages within the lysogens are capable of spontaneously reverting back to the lytic pathway via spontaneous prophage induction (SPI), causing death of the lysogen and the release of new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it directly causes cell death but also attacks nonlysogenic competitiors, either lysing or lysogenizing them. In this work we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on the rate of SPI resulting in lysogens displacing nonlysogens. We show that there exists an optimal SPI rate, and apply the model to bacteriophage{lambda} . We find that the model can explain why the experimentally measured SPI rate for phage{lambda} is so low. We also investigate the impact of stochasticity and conclude that even at low copy numbers the SPI rate can still be fairly low while still providing an advantage to the lysogens.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cortes, M. G., Krog, J., Balazsi, G.. 2019-02-11. Optimality of the spontaneous prophage induction rate. https://doi.org/10.1101/546275

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Ocean current models predict spatial structure for coastal biodiversity

Genomic diversity provides the raw material through which all evolutionary processes occur. Accurately describing how environmental features affect patterns of diversity shows promise in predicting the resilience of populations and entire communities to dynamic environments and novel stressors. Identifying the contribution of purely neutral responses to environmental effects can be difficult, especially across large spatial ranges with many covarying environmental features. Using an individual-based model of larvae carried by ocean currents, we predict patterns of neutral genomic diversity in a passively dispersed coastal species, suggesting large geographic regions of relative homogeneity with ancestry determined by the nearest "upstream" population with limited larval input. The genomic identity that dominates each region sharply transitions at the next such interruption, generating hierarchical structure and violating assumptions of isolation-by-distance. We call these upstream populations "lineage source regions" (LSRs), and we find strong concordance between LSR model predictions and an empirical population genomic survey of a keystone salt marsh mussel (Geukensia demissa). We quantitatively validate model performance and goodness-of-fit, updating baseline predictions of genomic diversity patterns from the classic isolation-by-distance model to this LSR framework. This spatially explicit coastal ancestry model has potential for generalization across passive dispersing species.

evolutionary biology↗

Shared landscape, divergent genomes: Comparative population genomics of co-occurring flora in an isolated Australian mountain range

Understanding how landscape heterogeneity shapes genomic variation is key to conserving biodiversity and evolutionary potential. Yet conservation genomics is typically conducted at the single species level, limiting our ability to identify shared genomic patterns or understand how co-occurring taxa differ in their response to common environmental gradients. We assessed spatial patterns of genomic variation and their associations with geographic and environmental features among populations of nine co-existing plant species with contrasting life-histories and ecological traits across Gariwerd, a threatened biodiversity hotspot in south-eastern Australia. All species exhibited weak overall genetic structure, although the magnitude and spatial distribution of differentiation varied among species. Despite this variation, broadly consistent spatial trends were apparent, with higher genomic divergence largely concentrated in central zones and higher genomic diversity localised in southern regions of the landscape. Significant associations between genetic differentiation and geographic distance were observed in three species, while another species showed associations with environmental distance. These findings demonstrate that coexisting species can show overlapping spatial patterns in genomic variation while differing markedly in their associations with geographic and environmental variation across the same landscape. This comparative approach provides a basis for spatial prioritisation and conservation planning, while highlighting the complex eco-evolutionary processes operating across heterogeneous landscapes.

evolutionary biology↗

BryoCAD: Extending Plant DNA Foundation Models Beyond Flowering Plants

Bryophytes exhibit diverse genome architectures and gene repertoires, yet their functional interpretation remains constrained by incomplete genomic annotations and limited experimental resources. Although DNA foundation models offer promising solutions, their applicability to evolutionarily divergent bryophytes remains largely unexplored. Here, we introduce BryoCAD, a DNA language model developed through continued pre-training of the angiosperm-specific PlantCAD2 model on 17 bryophyte genomes spanning hornworts, liverworts, and mosses. Across three representative bryophyte genomes and four gene-boundary motif recovery tasks, BryoCAD-Small improved mean zero-shot accuracy from 45.7% to 57.0%, a 24.8% relative increase over PlantCAD2-Small, while the over 200-fold larger Evo2-20B achieved a mean accuracy of only 39.3%. Remarkably, BryoCAD-Small outperformed PlantCAD2-Large in 11 of 12 motif-recovery comparisons across the three bryophytes, highlighting the benefits of lineage-specific adaptation over model scaling alone. These improvements extended to all six tested algal species, increasing mean accuracy from 26.3% to 37.5%, although performance varied across ferns and gymnosperms. BryoCAD also enhanced gene structure annotation: replacing PlantCAD2-Small with BryoCAD-Small increased transcript-level F1 scores from 0.578 to 0.625 in Marchantia polymorpha and from 0.485 to 0.530 in Physcomitrium patens. Together, our findings demonstrate that lineage-specific continued pre-training enhances genomic sequence interpretation and gene annotation across bryophytes, providing a transferable framework for extending DNA foundation models to evolutionarily diverse, understudied plant lineages.

evolutionary biology↗