Mobile genetic elements (MGEs) and the interactions between them are a major source of evolutionary innovation. Insertion sequences, the simplest MGEs usually encoding only the necessary genes for transposition and maintenance, are widespread in bacterial genomes, and are particularly common in plasmids. Plasmids, self-replicating extrachromosomal DNA elements, often exist in multiple copies imparting a stochastic barrier to the fixation of an insertion sequence by limiting the proportion of the plasmid population harboring the IS. In this work we demonstrate that to overcome this, the IS200/605 family of insertion sequences utilizes programmable RNA guided nucleases as gene drive to spread the IS through the plasmid population. TnpB, the likely ancestor of Cas12, records the specific insertion site of the IS in its RNA guide to prevent loss of the IS during transposition. When introduced to a plasmid TnpB will be reprogrammed to target and cleave IS- plasmids, resulting in biased replication of IS+ plasmids. Furthermore, the gene drive activity is critical for the IS to invade high copy plasmid populations. Because TnpB can only be mobilized between microbes on other mobile genetic elements, this advantage to fixing in plasmids may help explain the prevalence of TnpB across the tree of life. More generally, the unique pressures arising from movement between genetic contexts with different multiplicities shapes the evolution of strategies for MGE spread.
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http://dx.doi.org/10.1101/2025.02.20.638934 | DOI Listing |
Brief Bioinform
March 2025
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 633 Third Avenue, New York, NY 10017, United States.
Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate classification accuracy without undue resource allocation. Current sample size calculation methods rely on assumptions and algorithms that may not align with supervised machine learning techniques for sample classification.
View Article and Find Full Text PDFDrug Deliv Transl Res
March 2025
Regenerative Medicine & Cellular Therapies Division, School of Pharmacy, The University of Nottingham Biodiscovery Institute (BDI), University of Nottingham, Nottingham, NG7 2RD, UK.
Topically applied therapies must not only be effective at the molecular level but also efficiently access the target site which can be on milli/centimetre-scales. This bottleneck is particularly inhibitory for peptide and nucleic acid macromolecule drug delivery strategies, especially when aiming to target wounded, infected, and poorly perfused tissues of significant volume and geometry. Methods to drive fluid-flow or to enhance physical distribution of such formulations after local administration in accessible tissues (skin, eye, intestine) would be transformative in realizing the potential of such therapeutics.
View Article and Find Full Text PDFAppl Environ Microbiol
March 2025
School of Environmental and Life Sciences, The University of Newcastle, Callaghan, New South Wales, Australia.
Unlabelled: This study investigated the prevalence and co-occurrence of antimicrobial (AMR) and metal resistance (MR) in aquatic environments with different human impacts. Metagenomes from pristine, rural and urban sites in Australia were analyzed with AMR ++ and customized binning pipelines. AMR was present in all environments, while MR was mainly in rural and urban samples.
View Article and Find Full Text PDFJ Pathol
March 2025
Translational Cancer Medicine Program, University of Helsinki, Helsinki, Finland.
Anim Microbiome
March 2025
Center for Quantitative Genetics and Genomics, Aarhus University, CF Møllers Allé 3, 8000, Aarhus, Denmark.
Background: Methane emissions from livestock, particularly from dairy cattle, represent a significant source of greenhouse gas, contributing to the global climate crisis. Understanding the complex interactions within the rumen microbiota that influence methane emissions is crucial for developing effective mitigation strategies.
Results: This study employed Weighted Gene Co-expression Network Analysis to investigate the complex interactions within the rumen microbiota that influence methane emissions.
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