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.
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This study aimed to provide a taxonomic description of two bacterial strains, NKC19-3T and NKC19-16T, 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 FarDT, with 96.5 and 96.8% sequence similarity, respectively. ANI values indicated that the two novel strains were distinct from V. natechei FarDT, 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 NKC19-3T and NKC19-16T 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 NKC19-3T and 39.5% for strain NKC19-16T. The genome of strain NKC19-3T had a size of approximately 4.1 Mb and contained 3,785 protein-coding genes (CDSs). Strain NKC19-16T 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-C15:0 and anteiso-C17:0. Considering the comprehensive analysis encompassing phenotypic, genomic, phylogenetic, and chemotaxonomic data, strains NKC19-3T and NKC19-16T are proposed to represent two novel species within the genus Virgibacillus. The suggested names for these species are Virgibacillus saliphilus sp. nov. (type strain NKC19-3T, also referred to as KACC 22326T and DSM 112707T) and Virgibacillus salidurans sp. nov. (type strain NKC19-16T, also referred to as KACC 22327T and DSM 112708T).
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
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