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Bacteriophages impose a strong evolutionary pressure on microbes for the development of mechanisms of survival. Multiple new mechanisms of innate defense have been described recently, with the molecular mechanism of most of them remaining uncharacterized. Here, we show that a Class 1 DISARM (defense island system associated with restriction-modification) system from Serratia sp. provides broad protection from double-stranded DNA phages, and drives a population of single-stranded phages to extinction. We identify that protection is not abolished by deletion of individual DISARM genes and that the absence of methylase genes drmMI and drmMII does not result in autoimmunity. In addition to antiphage activity we also observe that DISARM limits conjugation, and this activity is linked to the number of methylase cognate sites in the plasmid. Overall, we show that Class 1 DISARM provides robust anti-phage and anti-plasmid protection mediated primarily by drmA and drmB, which provide resistance to invading nucleic acids using a mechanism enhanced by the recognition of unmethylated cognate sites of the two methylases drmMI and drmMII.

We are in the midst of a golden age of uncovering defense systems against bacteriophages. Apart from the fundamental interest in these defense systems, and revolutionary applications that have been derived from them (e.g. CRISPR-Cas9 and restriction endonucleases), it is unknown how defense systems contribute to resistance formation against bacteriophages in clinical settings. Bacteriophages are now being reconsidered as therapeutic agents against bacterial infections due the emergence of multidrug resistance. However, bacteriophage resistance through defense systems and other means could hinder the development of successful phage-based therapies. Here, we review the current state of the field of bacteriophage defense, highlight the relevance of bacteriophage defense for potential clinical use of bacteriophages as therapeutic agents and suggest new directions of research.

The last decade has witnessed a remarkable increase in our ability to measure genetic information. Advancements of sequencing technologies are challenging the existing methods of data storage and analysis. While methods to cope with the data deluge are progressing, many biologists have lagged behind due to the fast pace of computational advancements and tools available to address their scientific questions. Future generations of biologists must be more computationally aware and capable. This means they should be trained to give them the computational skills to keep pace with technological developments. Here, we propose a model that bridges experimental and bioinformatics concepts using the Oxford Nanopore Technologies (ONT) sequencing platform. We provide both a guide to begin to empower the new generation of educators, scientists, and students in performing long-read assembly of bacterial and bacteriophage genomes and a standalone virtual machine containing all the required software and learning materials for the course., Author summary Genomes contain all the information required for an organism to function. Understanding the genome sequence is often the key to answer important biological questions. For example, the sequences of human genomes are used for diagnosis of genetic disorders or for the development of personalized treatments, while the sequences of microbes may inform about their mechanisms of infection and guide the development of novel drugs. Today, our capacity to generate genome sequencing data is tremendous. However, our capacity to process this information is insufficient. This is partially due to limitations of current methods for data analysis but is mostly caused by lack of training for most biologists to leverage high-throughput sequencing data and use their full potential. It is urgent that we train the new generations of biologists to become computationally aware and able to keep pace with technological developments in the field. In this manuscript, we illustrate our efforts in adopting an integrated teaching model that bridges experimental and bioinformatics works. Our course integrates data generation in the lab with bioinformatics work to illustrate the interlinking of lab practices and downstream effects. In our demonstration course, we used nanopore sequencing to train nanobiology students, but the model is easily customizable to suit students of different educational backgrounds or alternative technologies. The tools we provide help not only science educators but also biologists to address many relevant questions in biology.