Introduction: the secret lives of microbial mobile genetic elements

Systematic identification of cargo-mobilizing genetic elements reveals new dimensions of eukaryotic diversity

Cargo-mobilizing mobile elements (CMEs) are genetic entities that faithfully transpose diverse protein coding sequences. Although common in bacteria, we know little about eukaryotic CMEs because no appropriate tools exist for their annotation. For example, Starships are giant fungal CMEs whose functions are largely unknown because they require time-intensive manual curation. To address this knowledge gap, we developed starfish, a computational workflow for high-throughput eukaryotic CME annotation. We applied starfish to 2 899 genomes of 1 649 fungal species and found that starfish recovers known Starships with 95% combined precision and recall while expanding the number of annotated elements ten-fold. Extant Starship diversity is partitioned into 11 families that differ in their enrichment patterns across fungal classes. Starship cargo changes rapidly such that elements from the same family differ substantially in their functional repertoires, which are predicted to contribute to diverse biological processes such as metabolism. Many elements have convergently evolved to insert into 5S rDNA and AT-rich sequence while others integrate into random locations, revealing both specialist and generalist strategies for persistence. Our work establishes a framework for advancing mobile element biology and provides the means to investigate an emerging dimension of eukaryotic genetic diversity, that of genomes within genomes.

Geographical distribution of mobile genetic elements in microbial communities along the Yucatan coast

Horizontal gene transfer (HGT) is a well-documented strategy used by bacteria to enhance their adaptability to challenging environmental conditions. Through HGT, a group of conserved genetic elements known as mobile genetic elements (MGEs) is disseminated within bacterial communities. MGEs offer numerous advantages to the host, increasing its fitness by acquiring new functions that help bacteria contend with adverse conditions, including exposure to heavy metal and antibiotics. This study explores MGEs within microbial communities along the Yucatan coast using a metatranscriptomics approach. Prior to this research, nothing was known about the coastal Yucatan's microbial environmental mobilome and HGT processes between these bacterial communities. This study reveals a positive correlation between MGEs and antibiotic resistance genes (ARGs) along the Yucatan coast, with higher MGEs abundance in more contaminated sites. The Proteobacteria and Firmicutes groups exhibited the highest number of MGEs. It's important to highlight that the most abundant classes of MGEs might not be the ones most strongly linked to ARGs, as observed for the recombination/repair class. This work presents the first geographical distribution of the environmental mobilome in Yucatan Peninsula mangroves.

Origins and evolution of biological novelty

Understanding the origins and impacts of novel traits has been a perennial interest in many realms of ecology and evolutionary biology. Here, we build on previous evolutionary and philosophical treatments of this subject to encompass novelties across biological scales and eco-evolutionary perspectives. By defining novelties as new features at one biological scale that have emergent effects at other biological scales, we incorporate many forms of novelty that have previously been treated in isolation (such as novelty from genetic mutations, new developmental pathways, new morphological features, and new species). Our perspective is based on the fundamental idea that the emergence of a novelty, at any biological scale, depends on its environmental and genetic context. Through this lens, we outline a broad array of generative mechanisms underlying novelty and highlight how genomic tools are transforming our understanding of the origins of novelty. Lastly, we present several case studies to illustrate how novelties across biological scales and systems can be understood based on common mechanisms of change and their environmental and genetic contexts. Specifically, we highlight how gene duplication contributes to the evolution of new complex structures in visual systems; how genetic exchange in symbiosis alters functions of both host and symbiont, resulting in a novel organism; and how hybridisation between species can generate new species with new niches.

PLASMe: a tool to identify PLASMid contigs from short-read assemblies using transformer

Plasmids are mobile genetic elements that carry important accessory genes. Cataloging plasmids is a fundamental step to elucidate their roles in promoting horizontal gene transfer between bacteria. Next generation sequencing (NGS) is the main source for discovering new plasmids today. However, NGS assembly programs tend to return contigs, making plasmid detection difficult. This problem is particularly grave for metagenomic assemblies, which contain short contigs of heterogeneous origins. Available tools for plasmid contig detection still suffer from some limitations. In particular, alignment-based tools tend to miss diverged plasmids while learning-based tools often have lower precision. In this work, we develop a plasmid detection tool PLASMe that capitalizes on the strength of alignment and learning-based methods. Closely related plasmids can be easily identified using the alignment component in PLASMe while diverged plasmids can be predicted using order-specific Transformer models. By encoding plasmid sequences as a language defined on the protein cluster-based token set, Transformer can learn the importance of proteins and their correlation through positionally token embedding and the attention mechanism. We compared PLASMe and other tools on detecting complete plasmids, plasmid contigs, and contigs assembled from CAMI2 simulated data. PLASMe achieved the highest F1-score. After validating PLASMe on data with known labels, we also tested it on real metagenomic and plasmidome data. The examination of some commonly used marker genes shows that PLASMe exhibits more reliable performance than other tools.

CRISPR-Cas-Based Antimicrobials: Design, Challenges, and Bacterial Mechanisms of Resistance

The emergence of antibiotic-resistant bacterial strains is a source of public health concern across the globe. As the discovery of new conventional antibiotics has stalled significantly over the past decade, there is an urgency to develop novel approaches to address drug resistance in infectious diseases. The use of a CRISPR-Cas-based system for the precise elimination of targeted bacterial populations holds promise as an innovative approach for new antimicrobial agent design. The CRISPR-Cas targeting system is celebrated for its high versatility and specificity, offering an excellent opportunity to fight antibiotic resistance in pathogens by selectively inactivating genes involved in antibiotic resistance, biofilm formation, pathogenicity, virulence, or bacterial viability. The CRISPR-Cas strategy can enact antimicrobial effects by two approaches: inactivation of chromosomal genes or curing of plasmids encoding antibiotic resistance. In this Review, we provide an overview of the main CRISPR-Cas systems utilized for the creation of these antimicrobials, as well as highlighting promising studies in the field. We also offer a detailed discussion about the most commonly used mechanisms for CRISPR-Cas delivery: bacteriophages, nanoparticles, and conjugative plasmids. Lastly, we address possible mechanisms of interference that should be considered during the intelligent design of these novel approaches.

A mathematician's guide to plasmids: an introduction to plasmid biology for modellers

Plasmids, extrachromosomal DNA molecules commonly found in bacterial and archaeal cells, play an important role in bacterial genetics and evolution. Our understanding of plasmid biology has been furthered greatly by the development of mathematical models, and there are many questions about plasmids that models would be useful in answering. In this review, we present an introductory, yet comprehensive, overview of the biology of plasmids suitable for modellers unfamiliar with plasmids who want to get up to speed and to begin working on plasmid-related models. In addition to reviewing the diversity of plasmids and the genes they carry, their key physiological functions, and interactions between plasmid and host, we also highlight selected plasmid topics that may be of particular interest to modellers and areas where there is a particular need for theoretical development. The world of plasmids holds a great variety of subjects that will interest mathematical biologists, and introducing new modellers to the subject will help to expand the existing body of plasmid theory.

Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings

The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment - including animals, plants, soil, and water - are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.

Estimating the transfer rates of bacterial plasmids with an adapted Luria–Delbrück fluctuation analysis

To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need reliable estimates of their rate of transfer between bacterial cells. Current assays to measure transfer rate are based on deterministic modeling frameworks. However, some cell numbers in these assays can be very small, making estimates that rely on these numbers prone to noise. Here, we take a different approach to estimate plasmid transfer rate, which explicitly embraces this noise. Inspired by the classic fluctuation analysis of Luria and Delbrück, our method is grounded in a stochastic modeling framework. In addition to capturing the random nature of plasmid conjugation, our new methodology, the Luria–Delbrück method (“LDM”), can be used on a diverse set of bacterial systems, including cases for which current approaches are inaccurate. A notable example involves plasmid transfer between different strains or species where the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias or constrain current transfer estimates, thereby limiting our capabilities for estimating transfer in microbial communities. In contrast, the LDM overcomes obstacles of traditional methods by avoiding restrictive assumptions about growth and transfer rates for each population within the assay. Using stochastic simulations and experiments, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to the most widely used methods, which can produce estimates that differ from the LDM estimate by orders of magnitude.

Plasmid transfer can often spread resistance between important clinical pathogens. This study shows that widely used methods can lead to biased estimates of plasmid transfer rate by several orders of magnitude, and presents a new approach, inspired by the classic Luria-Delbrück approach, for accurately assessing this fundamental rate parameter