bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.07.18.500453

An integrase clade that repeatedly targets prophage late genes, yielding helper-embedded satellites

Abstract

Satellites are mobile genetic elements that rely on helper phages for their mobilization. The many known satellite-helper interactions are trans-regulatory, with gene products from one partner modulating the nucleic acid or protein activities of the other. We discovered a satellite type with a more intimate cis-regulatory configuration: integrated within, and co-oriented with, a late gene of its lambdoid helper prophage. This helper-embedded satellite (HES) configuration would delay expression of the interrupted helper late gene until the satellite excises; it also offers potential passive components to both HES replication and late transcription, driven by the helper. Induction of a helper-satellite composite was monitored; precise excision of the entire composite was observed, followed by its replication, and the excision of the satellite from it. We mapped 491 HESs to one of 14 sites in cognates of phage lambda late genes A, B, C, E, V, T, H, L and J. The associated integrases form a single phylogenetic clade with subclades respecting the 14 site groups, while the attP attachment site regions contained a new doubled DNA sequence motif. This clade thus exhibits a repeated tropism for prophage late genes as it develops new integration sites. HESs bear close genomic similarities to gram-negative phage-induced chromosomal islands (PICIs, of which we found many more integrated into fis and hpt genes). We describe four ordered zones in a general HES/PICI genome organization: an integration zone encoding integrase and AlpA, a Bro zone encoding members of the Bro-N network of domain-swapping DNA-interactive proteins and immunity repressor RNAs, a replication zone, and a late zone in which clusters as large as 18 consecutive helper late genes have been captured. Like the late zone, the Bro zone is dynamic, perhaps due to activity of the Bro proteins themselves.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tommasini, D., Mageeney, C. M., Williams, K. P.. 2022-07-19. An integrase clade that repeatedly targets prophage late genes, yielding helper-embedded satellites. https://doi.org/10.1101/2022.07.18.500453

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

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗