To explore the diversity of mobile genetic elements (MGE) associated with archaea of the phylum Thaumarchaeota, we exploited the property of most MGE to integrate into the genomes of their hosts. Integrated MGE (iMGE) were identified in 20 thaumarchaeal genomes amounting to 2 Mbp of mobile thaumarchaeal DNA. These iMGE group into five major classes: (i) proviruses, (ii) casposons, (iii) insertion sequence-like transposons, (iv) integrative-conjugative elements and (v) cryptic integrated elements. The majority of the iMGE belong to the latter category and might represent novel families of viruses or plasmids. The identified proviruses are related to tailed viruses of the order Caudovirales and to tailless icosahedral viruses with the double jelly-roll capsid proteins. The thaumarchaeal iMGE are all connected within a gene sharing network, highlighting pervasive gene exchange between MGE occupying the same ecological niche. The thaumarchaeal mobilome carries multiple auxiliary metabolic genes, including multicopper oxidases and ammonia monooxygenase subunit C (AmoC), and stress response genes, such as those for universal stress response proteins (UspA). Thus, iMGE might make important contributions to the fitness and adaptation of their hosts. We identified several iMGE carrying type I-B CRISPR-Cas systems and spacers matching other thaumarchaeal iMGE, suggesting antagonistic interactions between coexisting MGE and symbiotic relationships with the ir archaeal hosts.
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http://dx.doi.org/10.1111/1462-2920.14564 | DOI Listing |
Front Immunol
January 2025
Postdoctoral Workstation, Liaocheng People's Hospital, Liaocheng, China.
Background: This study aims to identify the hub genes and immune-related pathways in acute myeloid leukemia (AML) to provide new theories for immunotherapy.
Methods: We use bioinformatics methods to find and verify the hub gene. At the same time, we use the results of GSEA enrichment analysis to find immune-related mediators.
Many sharks, rays and skates are highly threatened and vulnerable to overexploitation, as such reliable monitoring of elasmobranchs is key to effective management and conservation. The mobile and elusive nature of these species makes monitoring challenging, particularly in temperate waters with low visibility. Environmental DNA (eDNA) methods present an opportunity to study these species in the absence of visual identification or invasive techniques.
View Article and Find Full Text PDFBMC Microbiol
January 2025
Engineering Research Center of Health Emergency, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China.
Background: Wastewater systems are usually considered antibiotic resistance hubs connecting human society and the natural environment. Antibiotic usage can increase the abundance of both ARGs (antibiotic resistance genes) and MGEs (mobile gene elements). Understanding the transcriptomic profiles of ARGs and MGEs remains a major research goal.
View Article and Find Full Text PDFISME J
January 2025
Universidad Pablo de Olavide, Centro Andaluz de Biología del Desarrollo/ Consejo Superior de Investigaciones Científicas/ Junta de Andalucía, Seville, Spain.
Genomic reorganisation between species and horizontal gene transfer have been considered the most important mechanism of biological adaptation under selective pressure. Still, the impact of mobile genes in microbial ecology is far from being completely understood. Here we present the collection and characterisation of microbial consortia enriched from environments contaminated with emerging pollutants, such as non-steroidal anti-inflammatory drugs.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Product & Systems Design Engineering, University of the Aegean, 84100 Syros, Greece.
This paper addresses the complex problem of multi-goal robot navigation, framed as an NP-hard traveling salesman problem (TSP), in environments with both static and dynamic obstacles. The proposed approach integrates a novel path planning algorithm based on the Bump-Surface concept to optimize the shortest collision-free path among static obstacles, while a Genetic Algorithm (GA) is employed to determine the optimal sequence of goal points. To manage static or dynamic obstacles, two fuzzy controllers are developed: one for real-time path tracking and another for dynamic obstacle avoidance.
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