Superintegrons (SIs) are chromosomal genetic elements containing assemblies of genes, each flanked by a recombination sequence (attC site) targeted by the integron integrase. SIs may contain hundreds of attC sites and intrinsic instability is anticipated; yet SIs are remarkably stable. This implies that either selective pressure maintains the genes or mechanisms exist which favour their persistence in the absence of selection. Toxin/antitoxin (TA) systems encode a stable toxin and a specific, unstable antitoxin. Once activated, the continued synthesis of the unstable antitoxin is necessary for cell survival. A bioinformatic search of accessible microbial genomes for SIs and TA systems revealed that large SIs harboured TA gene cassettes while smaller SIs did not. We demonstrated the function of TA loci in different genomic contexts where large-scale deletions can occur; in SIs and in a 165 kb dispensable region of the Escherichia coli genome. When devoid of TA loci, large-scale genome loss was evident in both environments. The inclusion of two TA loci, relBE1 and parDE1, which we identified in the Vibrio vulnificus SI rendered these environments refractory to gene loss. Thus, chromosomal TA loci can stabilize massive SI arrays and limit the extensive gene loss that is a hallmark of reductive evolution.
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J Transl Med
January 2025
Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, 3052, Australia.
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition's heterogeneity and the lack of validated biomarkers. The growing field of precision medicine offers a promising approach which focuses on the genetic and molecular underpinnings of individual patients.
View Article and Find Full Text PDFGenome Biol
January 2025
The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark.
Background: Streptomyces is a highly diverse genus known for the production of secondary or specialized metabolites with a wide range of applications in the medical and agricultural industries. Several thousand complete or nearly complete Streptomyces genome sequences are now available, affording the opportunity to deeply investigate the biosynthetic potential within these organisms and to advance natural product discovery initiatives.
Results: We perform pangenome analysis on 2371 Streptomyces genomes, including approximately 1200 complete assemblies.
BMC Genom Data
January 2025
Department of Management Information Systems, National Chung Hsing University, Taichung, 402, Taiwan.
Background: miRNAs (microRNAs) are endogenous RNAs with lengths of 18 to 24 nucleotides and play critical roles in gene regulation and disease progression. Although traditional wet-lab experiments provide direct evidence for miRNA-disease associations, they are often time-consuming and complicated to analyze by current bioinformatics tools. In recent years, machine learning (ML) and deep learning (DL) techniques are powerful tools to analyze large-scale biological data.
View Article and Find Full Text PDFSci China Life Sci
January 2025
State Key Laboratory of Protein and Plant Gene Research, School of Advanced Agricultural Sciences, Peking University, Beijing, 100871, China.
Epigenetic mechanisms are integral to plant growth, development, and adaptation to environmental stimuli. Over the past two decades, our comprehension of these complex regulatory processes has expanded remarkably, producing a substantial body of knowledge on both locus-specific mechanisms and genome-wide regulatory patterns. Studies initially grounded in the model plant Arabidopsis have been broadened to encompass a diverse array of crop species, revealing the multifaceted roles of epigenetics in physiological and agronomic traits.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, United States.
Single-cell technologies have enabled the high-dimensional characterization of cell populations at an unprecedented scale. The innate complexity and increasing volume of data pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e.
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