Recently, floating membrane filter cultivation was adopted to simulate solid surface and enrich surface-adapted soil ammonia-oxidizing archaea (AOA) communities from agricultural soil, as opposed to the conventional liquid medium. Here, we conducted metagenomic sequencing to recover nitrifier bins from the floating membrane filter cultures and reveal their genomic properties. Phylogenomic analysis showed that AOA bins recovered from this study, designated FF_bin01 and FF_bin02, are affiliated with the Nitrososphaeraceae family, while the third bin, FF_bin03, is a nitrite-oxidizing bacterium affiliated with the Nitrospiraceae family. Based on the ANI/AAI analysis, FF_bin01 and FF_bin02 are identified as novel species within the genera “Candidatus Nitrosocosmicus” and Nitrososphaera, respectively, while FF_bin03 represents a novel species within the genus Nitrospira. The pan and core genome analysis for the 29 AOA genomes considered in this study revealed 5,784 orthologous clusters, out of which 653 were core orthologous clusters. Additionally, 90 unique orthologous clusters were conserved among the Nitrososphaeraceae family, suggesting their potential role in enhancing culturability and adaptation to diverse environmental conditions. Intriguingly, FF_bin01 and FF_bin02 harbor a gene encoding manganese catalase and FF_bin03 also possesses a heme catalase gene, which might enhance their growth on the floating membrane filter. Overall, the floating membrane filter cultivation has proven to be a promising approach for isolating distinct soil AOA, and further modifications to this technique could stimulate the growth of a broader range of uncultivated nitrifiers from diverse soil environments.

The anti-cancer effects of Cladonia borealis (an Arctic lichen) methanol extract (CBME) on human colon carcinoma HCT116 cells were investigated for the first time. The proliferation of the HCT116 cells treated with CBME significantly decreased in a dose- and time-dependent manner. Flow cytometry results indicated that treatment with CBME resulted in significant apoptosis in the HCT116 cells. Furthermore, immunoblotting and qRT-PCR results revealed the expression of apoptosis-related marker genes and indicated a significant downregulation of the apoptosis regulator B-cell lymphoma expression and upregulation of the cleaved form of poly (ADP-ribose) polymerase as DNA repair and apoptosis regulators and central tumor suppressor p53. Therefore, CBME significantly inhibited cell proliferation by inducing apoptosis via the mitochondrial apoptotic pathway in colon carcinoma cells. Collectively, these data suggested that CBME contained one or more compounds with anti-cancer effects and could be a potential therapeutic agent. Further studies are required to identify candidate compounds and understand the mechanism of action of CBME.

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.

The application of genetic code expansion has enabled the incorporation of non-canonical amino acids (ncAAs) into proteins, introducing novel functional groups and significantly broadening the scope of protein engineering. Over the past decade, this approach has extended beyond ncAAs to include non-proteinogenic monomers (npMs), such as β-amino acids and hydroxy acids. In vivo incorporation of these monomers requires maintaining orthogonality between endogenous and engineered aminoacyl-tRNA synthetase (aaRS)/tRNA pairs while optimizing the use of the translational machinery. This review introduces the fundamental principles of genetic code expansion and highlights the development of orthogonal aaRS/tRNA pairs and ribosomal engineering to incorporate npMs. Despite these advancements, challenges remain in engineering aaRS/tRNA pairs to accommodate npMs, especially monomers that differ significantly from L-α-amino acids due to their incompatibility with existing translational machinery. This review also introduces recent methodologies that allow aaRSs to recognize and aminoacylate npMs without reliance on the ribosomal translation system, thereby unlocking new possibilities for synthesizing biopolymers with chemically diverse monomers.

Methane gas is recognized as a promising carbon substrate for the biosynthesis of value-added products due to its abundance and low price. Methanotrophs utilized methane as their sole source of carbon and energy, thus they can serve as efficient biocatalysts for methane bioconversion. Methanotrophs-catalyzed microbial bioconversion offer numerous advantages, compared to chemical processes. Current indirect chemical conversions of methane suffer from their energy-intensive processes and high capital expenditure. Methanotrophs can be cell factories capable of synthesizing various value-added products from methane such as methanol, organic acids, ectoine, polyhydroxyalkanoates, etc. However, the large-scale commercial implementation using methanotrophs remains a formidable challenge, primarily due to limitations in gas-liquid mass transfer and low metabolic capacity. This review explores recent advancements in methanotroph research, providing insights into their potential for enabling methane bioconversion.

Bacteria-free reverse genetics techniques are crucial for the efficient generation of recombinant viruses, bypassing the need for labor-intensive bacterial cloning. These methods are particularly relevant for studying the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19. This study compared the efficiency of three bacteria-free approaches—circular polymerase extension reaction (CPER) with and without nick sealing and infectious sub-genomic amplicons (ISA)—to bacterial artificial chromosome (BAC)-based technology for rescuing SARS-CoV-2. Significant differences in viral titers following transfection were observed between methods. CPER with nick sealing generated virus titers comparable to those of the BAC-based method and 10 times higher than those of the standard CPER. In contrast, ISA demonstrated extremely low efficiency, as cytopathic effects were detected only after two passages. All rescued viruses exhibited replication kinetics consistent with those of the original strain, with no significant deviation in replication capacity. Furthermore, the utility of CPER and ISA in genetically modifying SARS-CoV-2 was demonstrated by successfully inserting the gene encoding green fluorescent protein into the genome. Overall, this study underscores the potential of bacteria-free methods, such as CPER and ISA, in advancing SARS-CoV-2 research while highlighting their significant differences in efficiency.

Salmonella enterica is a clinically significant oro-fecal pathogen that causes a wide variety of illnesses and can lead to epidemics. S. enterica expresses a lot of virulence factors that enhance its pathogenesis in host. For instance, S. enterica employs a type three secretion system (T3SS) to translocate a wide array of effector proteins that could change the surrounding niche ensuring suitable conditions for the thrive of Salmonella infection. Many antimicrobials have been recently introduced to overcome the annoying bacterial resistance to antibiotics. Enoxacin is member of the second-generation quinolones that possesses a considerable activity against S. enterica. The present study aimed to evaluate the effect of enoxacin at sub-minimum inhibitory concentration (sub-MIC) on S. enterica virulence capability and pathogenesis in host. Enoxacin at sub-MIC significantly diminished both Salmonella invasion and intracellular replication within the host cells. The observed inhibitory effect of enoxacin on S. enterica internalization could be attributed to its ability to interfere with translocation of the T3SS effector proteins. These results were further confirmed by the finding that enoxacin at sub-MIC down-regulated the expression of the genes encoding for T3SS-type II (T3SS-II). Moreover, enoxacin at sub-MIC lessened bacterial adhesion to abiotic surface and biofilm formation which indicates a potential anti-virulence activity. Importantly, in vivo results showed a significant ability of enoxacin to protect mice against S. enterica infection and decreased bacterial colonization within animal tissues. In nutshell, current findings shed light on an additional mechanism of enoxacin at sub-MIC by interfering with Salmonella intracellular replication. The outcomes presented herein could be further invested in conquering bacterial resistance and open the door for additional effective clinical applications.

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/.

Protein solubility is a critical factor in the production of recombinant proteins, which are widely used in various industries, including pharmaceuticals, diagnostics, and biotechnology. Predicting protein solubility remains a challenging task due to the complexity of protein structures and the multitude of factors influencing solubility. Recent advances in computational methods, particularly those based on machine learning, have provided powerful tools for predicting protein solubility, thereby reducing the need for extensive experimental trials. This review provides an overview of current computational approaches to predict protein solubility. We discuss the datasets, features, and algorithms employed in these models. The review aims to bridge the gap between computational predictions and experimental validations, fostering the development of more accurate and reliable solubility prediction models that can significantly enhance recombinant protein production.