Condensin plays a central role in mitotic chromosome organization and segregation by mediating long-range chromatin interactions. However, the extent to which cellular metabolic status influences condensin function remains unclear. To gain insights into the relationship of metal ion homeostasis and the function of condensin, we conducted genome-wide chromatin immunoprecipitation sequencing (ChIP-seq) using Schizosaccharomyces pombe under iron- or zinc-deficient conditions. Under iron- or zinc-deficient conditions, ChIP-seq results revealed a selective reduction in condensin binding at high-affinity target loci, particularly genes regulated by Ace2 and Ams2, while cohesin binding remained largely unaffected. Hi-C analysis showed that iron depletion weakened chromatin interactions at these condensin targets and centromeres, without disrupting global genome architecture. DNA fluorescence in situ hybridization (FISH) confirmed that iron deficiency impaired long-range associations between centromeres and Ace2 target loci at the single-cell level. Notably, iron deficiency led to chromosome segregation defects during mitosis, suggesting that diminished condensin occupancy compromised genome stability. These changes occurred without significant alterations in condensin protein levels or global transcription, indicating a direct effect of metal ion availability on condensin activity. Collectively, our findings revealed a previously unrecognized regulatory axis in which cellular metal ion homeostasis modulated condensin-dependent chromatin organization and mitotic chromosome segregation, offering new insights into the integration of metabolic state with genome maintenance.

CRISPR-Cas9-based gene editing enables precise genetic modifications. However, its application to human cytomegalovirus (HCMV) remains challenging due to the large size of the viral genome and the essential roles of key regulatory genes. Here, we establish an optimized CRISPR-Cas9 system for precise labeling and functional analysis of HCMV immediate early (IE) genes. By integrating a multifunctional cassette encoding an auxin-inducible degron (AID), a self-cleaving peptide (P2A), and GFP into the viral genome via homology-directed repair (HDR), we achieved efficient knock-ins without reliance on bacterial artificial chromosome (BAC) cloning, a labor-intensive and time-consuming approach. We optimized delivery strategies, donor template designs, and component ratios to enhance HDR efficiency, significantly improving knock-in success rates. This system enables real-time fluorescent tracking and inducible protein degradation, allowing temporal control of essential viral proteins through auxin-mediated depletion. Our approach provides a powerful tool for dissecting the dynamic roles of viral proteins throughout the HCMV life cycle, facilitating a deeper understanding of viral pathogenesis and potential therapeutic targets.

Two Gram-stain-negative, obligately aerobic, non-motile, short rod-shaped bacteria, designated IMCC43871^T and IMCC45206^T, were isolated from coastal surface seawater collected from the Yellow Sea and the South Sea of Korea, respectively. The two strains shared 99.2% 16S rRNA gene sequence similarity with each other and exhibited ≤ 98.4% similarity to three described Rubrivirga species. Average nucleotide identity (ANI) and digital DNA–DNA hybridization (dDDH) values between IMCC43871^T and IMCC45206^T were 88.5% and 36.3%, respectively, confirming that they represent two distinct species. Their ANI (≤ 77.7%) and dDDH (≤ 21.4%) values relative to the type strains of the genus Rubrivirga further supported the recognition of strains IMCC43871^T and IMCC45206^T as two novel species within the genus. The complete genomes of IMCC43871^T (4.17 Mb, 71.8% G + C content) and IMCC45206^T (4.17 Mb, 72.8% G + C content) fall within the known genomic range of the genus. Cellular fatty acid, quinone, and polar lipid profiles were consistent with the chemotaxonomic features of the genus Rubrivirga, supporting their affiliation with the genus. Based on phylogenetic, genomic, and phenotypic evidence, strains IMCC43871^T and IMCC45206^T are proposed as two novel species, Rubrivirga aquatilis sp. nov. and Rubrivirga halophila sp. nov., respectively. The type strains are IMCC43871^T (= KCTC 102072^T = NBRC 116463^T) and IMCC45206^T (= KCTC 92925^T = NBRC 116172^T = CCTCC AB 2023136^T).

Streptomyces are a crucial source of bioactive secondary metabolites with significant clinical applications. Recent studies of bacterial and metagenome-assembled genomes have revealed that Streptomyces harbors a substantial number of uncharacterized silent secondary metabolite biosynthetic gene clusters (BGCs). These BGCs represent a vast diversity of biosynthetic pathways for natural product synthesis, indicating significant untapped potential for discovering new metabolites. To exploit this potential, genome mining using comprehensive strategies that leverage extensive genomic databases can be conducted. By linking BGCs to their encoded products and integrating genetic manipulation techniques, researchers can greatly enhance the identification of new secondary metabolites with therapeutic relevance. In this context, we present a step-by-step guide for using the antiSMASH pipeline to identify secondary metabolite-coding BGCs within the complete genome of a novel Streptomyces strain. This protocol also outlines gene manipulation methods that can be applied to Streptomyces to activate cryptic clusters of interest and validate the functions of biosynthetic genes. By following these guidelines, researchers can pave the way for discovering and characterizing valuable natural products.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technologies have emerged as powerful tools for precise genome editing, leading to a revolution in genetic research and biotechnology across diverse organisms including microalgae. Since the 1950s, microalgal production has evolved from initial cultivation under controlled conditions to advanced metabolic engineering to meet industrial demands. However, effective genetic modification in microalgae has faced significant challenges, including issues with transformation efficiency, limited target selection, and genetic differences between species, as interspecies genetic variation limits the use of genetic tools from one species to another. This review summarized recent advancements in CRISPR systems applied to microalgae, with a focus on improving gene editing precision and efficiency, while addressing organism-specific challenges. We also discuss notable successes in utilizing the class 2 CRISPR-associated (Cas) proteins, including Cas9 and Cas12a, as well as emerging CRISPR-based approaches tailored to overcome microalgal cellular barriers. Additionally, we propose future perspectives for utilizing CRISPR/Cas strategies in microalgal biotechnology.

This study aimed to provide a taxonomic description of two bacterial strains, NKC19-3^T and NKC19-16^T, isolated from commercially produced kimchi obtained from various regions within the Republic of Korea. Both strains were rod-shaped, gram-stain-positive, facultatively anaerobic, and displayed positive reactions for oxidase and catalase. Additionally, these bacteria were motile, halophilic (salt-tolerant), and proliferated under alkaline conditions. Genetically, both strains showed 98.0% similarity in their 16S rRNA gene sequences and were most closely related to Virgibacillus natechei FarD^T, with 96.5 and 96.8% sequence similarity, respectively. ANI values indicated that the two novel strains were distinct from V. natechei FarD^T, as they were below the species demarcation threshold. The ANI value between strains NKC19-3ᵀ and NKC19-16ᵀ was 84.64–84.75%, and the values between these strains and other related strains did not exceed 80.0%, further supporting their classification as novel species. Phylogenetic analysis revealed that strains NKC19-3^T and NKC19-16^T formed a distinct branch within the genus Virgibacillus, clearly distinguishing them from other species in the same genus. Regarding genomic characteristics, the GC content was 38.9% for strain NKC19-3^T and 39.5% for strain NKC19-16^T. The genome of strain NKC19-3^T had a size of approximately 4.1 Mb and contained 3,785 protein-coding genes (CDSs). Strain NKC19-16^T had a slightly smaller genome, approximately 3.9 Mb in size and harbored 3,726 CDSs. The polar lipid profiles of strains NKC19-3ᵀ and NKC19-16ᵀ included diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), glycolipids (GL), and an unidentified lipid (L). The predominant fatty acids of both strains were anteiso-C_15:0 and anteiso-C_17:0. Considering the comprehensive analysis encompassing phenotypic, genomic, phylogenetic, and chemotaxonomic data, strains NKC19-3^T and NKC19-16^T are proposed to represent two novel species within the genus Virgibacillus. The suggested names for these species are Virgibacillus saliphilus sp. nov. (type strain NKC19-3^T, also referred to as KACC 22326^T and DSM 112707^T) and Virgibacillus salidurans sp. nov. (type strain NKC19-16^T, also referred to as KACC 22327^T and DSM 112708^T).

This review explores current advancements in microbiome functional analysis enabled by next-generation sequencing technologies, which have transformed our understanding of microbial communities from mere taxonomic composition to their functional potential. We examine approaches that move beyond species identification to characterize microbial activities, interactions, and their roles in host health and disease. Genome-scale metabolic models allow for in-depth simulations of metabolic networks, enabling researchers to predict microbial metabolism, growth, and interspecies interactions in diverse environments. Additionally, computational methods for predicting metabolite profiles offer indirect insights into microbial metabolic outputs, which is crucial for identifying biomarkers and potential therapeutic targets. Functional pathway analysis tools further reveal microbial contributions to metabolic pathways, highlighting alterations in response to environmental changes and disease states. Together, these methods offer a powerful framework for understanding the complex metabolic interactions within microbial communities and their impact on host physiology. While significant progress has been made, challenges remain in the accuracy of predictive models and the completeness of reference databases, which limit the applicability of these methods in under-characterized ecosystems. The integration of these computational tools with multi-omic data holds promise for personalized approaches in precision medicine, allowing for targeted interventions that modulate the microbiome to improve health outcomes. This review highlights recent advances in microbiome functional analysis, providing a roadmap for future research and translational applications in human health and environmental microbiology.

With the advent of whole-genome sequencing, opportunities to investigate the population structure, transmission patterns, antimicrobial resistance profiles, and virulence determinants of Streptococcus pneumoniae at high resolution have been increasingly expanding. Consequently, a user-friendly bioinformatics tool is needed to automate the analysis of Streptococcus pneumoniae whole-genome sequencing data, summarize clinically relevant genomic features, and further guide treatment options. Here, we developed PneusPage, a web-based tool that integrates functions for species prediction, molecular typing, drug resistance determination, and data visualization of Streptococcus pneumoniae. To evaluate the performance of PneusPage, we analyzed 80 pneumococcal genomes with different serotypes from the Global Pneumococcal Sequencing Project and compared the results with those from another platform, PathogenWatch. We observed a high concordance between the two platforms in terms of serotypes (100% concordance rate), multilocus sequence typing (100% concordance rate), penicillin-binding protein typing (88.8% concordance rate), and the Global Pneumococcal Sequencing Clusters (98.8% concordance rate). In addition, PneusPage offers integrated analysis functions for the detection of virulence and mobile genetic elements that are not provided by previous platforms. By automating the analysis pipeline, PneusPage makes whole-genome sequencing data more accessible to non-specialist users, including microbiologists, epidemiologists, and clinicians, thereby enhancing the utility of whole-genome sequencing in both research and clinical settings. PneusPage is available at https://pneuspage.minholee.net/.