Dengue, caused by four serotypes of dengue viruses (DENV-1 to DENV-4), is the most prevalent and widely mosquito-borne viral disease affecting humans. Dengue virus (DENV) infection has been reported in over 100 countries, and approximately half of the world's population is now at risk. The paucity of universally licensed DENV vaccines highlights the urgent need to address this public health concern. Action and attention to antibody-dependent enhancement increase the difficulty of vaccine development. With the worsening dengue fever epidemic, Dengvaxia^® (CYD-TDV) and Qdenga^® (TAK-003) have been approved for use in specific populations in affected areas. However, these vaccines do not provide a balanced immune response to all four DENV serotypes and the vaccination cannot cover all populations. There is still a need to develop a safe, broad-spectrum, and effective vaccine to address the increasing number of dengue cases worldwide. This review provides an overview of the existing DENV vaccines, as well as potential candidates for future studies on DENV vaccine development, and discusses the challenges and possible solutions in the field.

The escalating antibiotic resistance crisis poses a significant challenge to global public health, threatening the efficacy of current treatments and driving the emergence of multidrug-resistant pathogens. Among the various factors associated with bacterial antibiotic resistance, small regulatory RNAs (sRNAs) have emerged as pivotal post-transcriptional regulators which orchestrate bacterial adaptation to antibiotic pressure via diverse mechanisms. This review consolidates the current knowledge on sRNA-mediated mechanisms, focusing on drug uptake, drug efflux systems, lipopolysaccharides, cell wall modification, biofilm formation, and mutagenesis. Recent advances in transcriptomics and functional analyses have revealed novel sRNAs and their regulatory networks, expanding our understanding of resistance mechanisms. These findings highlight the potential of targeting sRNA-mediated pathways as an innovative therapeutic strategy to combat antibiotic resistance, and offer promising avenues for managing challenging bacterial infections.

The increase of sequence data in public nucleotide databases has made DNA sequence-based identification an indispensable tool for fungal identification. However, the large proportion of mislabeled sequence data in public databases leads to frequent misidentifications. Inaccurate identification is causing severe problems, especially for industrial and clinical fungi, and edible mushrooms. Existing species identification pipelines require separate validation of a dataset obtained from public databases containing mislabeled taxonomic identifications. To address this issue, we developed FunVIP, a fully automated phylogeny-based fungal validation and identification pipeline (https://github.com/Changwanseo/FunVIP). FunVIP employs phylogeny-based identification with validation, where the result is achievable only with a query, database, and a single command. FunVIP command comprises nine steps within a workflow: input management, sequence-set organization, alignment, trimming, concatenation, model selection, tree inference, tree interpretation, and report generation. Users may acquire identification results, phylogenetic tree evidence, and reports of conflicts and issues detected in multiple checkpoints during the analysis. The conflicting sample validation performance of FunVIP was demonstrated by re-iterating the manual revision of a fungal genus with a database with mislabeled sequences, Fuscoporia. We also compared the identification performance of FunVIP with BLAST and q2-feature-classifier with two mass double-revised fungal datasets, Sanghuangporus and Aspergillus section Terrei. Therefore, with its automatic validation ability and high identification performance, FunVIP proves to be a highly promising tool for achieving easy and accurate fungal identification.

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.

The increasing environmental concerns regarding conventional plastics have led to a growing demand for sustainable alternatives, such as biodegradable plastics. Yeast cell factories, specifically Saccharomyces cerevisiae and Yarrowia lipolytica, have emerged as promising platforms for bioplastic production due to their scalability, robustness, and ease of manipulation. This review highlights synthetic biology approaches aimed at developing yeast cell factories to produce key biodegradable plastics, including polylactic acid (PLA), polyhydroxyalkanoates (PHAs), and poly (butylene adipate-co-terephthalate) (PBAT). We explore recent advancements in engineered yeast strains that utilize various synthetic biology strategies, such as the incorporation of new genetic elements at the gene, pathway, and cellular system levels. The combined efforts of metabolic engineering, protein engineering, and adaptive evolution have enhanced strain efficiency and maximized product yields. Additionally, this review addresses the importance of integrating computational tools and machine learning into the Design-Build-Test-Learn cycle for strain development. This integration aims to facilitate strain development while minimizing effort and maximizing performance. However, challenges remain in improving strain robustness and scaling up industrial production processes. By combining advanced synthetic biology techniques with computational approaches, yeast cell factories hold significant potential for the sustainable and scalable production of bioplastics, thus contributing to a greener bioeconomy.

Antarctic fungi can effectively adapt to extreme environments, which leads to the production of unique bioactive compounds. Studies on the discovery of fungi in the diverse environments of Antarctica and their potential applications are increasing, yet remain limited. In this study, fungi were isolated from various substrates on the Fildes Peninsula in Antarctica and screened for their antibiosis activity against two significant plant pathogenic fungi, Botrytis cinerea and Fusarium culmorum. Phylogenetic analysis using multiple genetic markers revealed that the isolated Antarctic fungal strains are diverse, some of which are novel, emphasizing the underexplored biodiversity of Antarctic fungi. These findings suggest that these fungi have potential for the development of new antifungal agents that can be applied in agriculture to manage fungal plant pathogens. Furthermore, the antibiosis activities of the isolated Antarctic fungi were evaluated using a dual-culture assay. The results indicated that several strains from the genera Cyathicula, Penicillium, and Pseudeurotium significantly inhibited pathogen growth, with Penicillium pancosmium showing the highest inhibitory activity against Botrytis cinerea. Similarly, Aspergillus and Tolypocladium strains exhibited strong antagonistic effects against Fusarium culmorum. This study enhances our understanding of Antarctic fungal diversity and highlights its potential for biotechnological applications.

Antimicrobial resistance (AMR) poses a serious threat to public health, with the emergence of extended-spectrum beta-lactamases (ESBLs) in Enterobacteriaceae, particularly Escherichia coli, raising significant concerns. This study aims to elucidate the drivers of antimicrobial resistance, and the global spread of cefotaxime-resistant E. coli (CREC) strains. Whole-genome sequencing (WGS) was performed to explore genome-level characteristics, and phylogenetic analysis was conducted to compare twenty CREC strains from this study, which were isolated from broiler chicken farms in Bangladesh, with a global collection (n = 456) of CREC strains from multiple countries and hosts. The MIC analysis showed over 70% of strains isolated from broiler chickens exhibiting MIC values ≥ 256 mg/L for cefotaxime. Notably, 85% of the studied farms (17/20) tested positive for CREC by the end of the production cycle, with CREC counts increasing from 0.83 ± 1.75 log10 CFU/g feces on day 1 to 5.24 ± 0.72 log10 CFU/g feces by day 28. WGS revealed the presence of multiple resistance genes, including bla_CTX-M, which was found in 30% of the strains. Phylogenetic comparison showed that the Bangladeshi strains were closely related to strains from diverse geographical regions and host species. This study provides a comprehensive understanding of the molecular epidemiology of CREC. The close phylogenetic relationships between Bangladeshi and global strains demonstrate the widespread presence of cefotaxime-resistant bacteria and emphasize the importance of monitoring AMR in food-producing animals to mitigate the spread of resistant strains.

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.

The widespread use of antibiotics in aquaculture has led to the emergence of multidrug-resistant pathogens and environmental concerns, highlighting the need for sustainable, eco-friendly alternatives. In this study, we isolated and characterized three novel bacteriophages from aquaculture effluents in Korean shrimp farms that target the key Vibrio pathogens, Vibrio harveyi, and Vibrio parahaemolyticus. Bacteriophages were isolated through environmental enrichment and serial purification using double-layer agar assays. Transmission electron microscopy revealed that the phages infecting V. harveyi, designated as vB_VhaS-MS01 and vB_VhaS-MS03, exhibited typical Siphoviridae morphology with long contractile tails and icosahedral heads, whereas the phage isolated from V. parahaemolyticus (vB_VpaP-MS02) displayed Podoviridae characteristics with an icosahedral head and short tail.

Whole-genome sequencing produced complete, circularized genomes of 81,710 bp for vB_VhaS-MS01, 81,874 bp for vB_VhaS-MS03, and 76,865 bp for vB_VpaP-MS02, each showing a modular genome organization typical of Caudoviricetes. Genomic and phylogenetic analyses based on the terminase large subunit gene revealed that although vB_VhaS-MS01 and vB_VhaS-MS03 were closely related, vB_VpaP-MS02 exhibited a distinct genomic architecture that reflects its unique morphology and host specificity. Collectively, these comparative analyses demonstrated that all three phages possess genetic sequences markedly different from those of previously reported bacteriophages, thereby establishing their novelty. One-step growth and multiplicity of infection (MOI) experiments demonstrated significant differences in replication kinetics, such as burst size and lytic efficiency, among the phages, with vB_VhaS-MS03 maintaining the most effective bacterial control, even at an MOI of 0.01. Additionally, host range assays showed that vB_VhaS-MS03 possessed a broader spectrum of activity, supporting its potential use as a stand-alone agent or key component of phage cocktails. These findings highlight the potential of region-specific phage therapy as a targeted and sustainable alternative to antibiotics for controlling Vibrio infections in aquaculture.

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.

Minicells, which are anucleate cells generated by irregular cell division, are emerging as promising drug delivery systems owing to advances in synthetic biology. However, their development is largely limited to a few model bacteria, highlighting the need to explore minicell platforms in alternative hosts. Lactiplantibacillus plantarum (L. plantarum), a probiotic bacterium classified as Generally Recognized as Safe, is an ideal candidate for such exploration. Minicell-producing L. plantarum was engineered by deleting the putative minD gene via plasmid-mediated homologous recombination, which inactivates cell division to form spherical minicells. Anucleate cells were isolated through differential centrifugation and filtration, followed by additional drug treatment to completely eliminate progenitor cells. Microscopy and flow cytometry analyses of the purified sample confirmed the absence of progenitor cells by DAPI staining. This protocol effectively produces bacterial minicells from L. plantarum for use in various biotechnological applications, including therapeutic agent delivery.

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.

Gout is an inflammatory arthritis resulting from the deposition of monosodium urate crystals. Urate-lowering therapies for gout have limitations, including side effects and limited efficacy, highlighting the need for novel therapeutic approaches to improve patient outcomes. In this context, our research team conducted a microbiome analysis of fecal samples from healthy individuals and gout patients, identifying Bifidobacterium as a key biomarker. Subsequently, we isolated and identified this strain, B. longum PMC72, and demonstrated its efficacy in a gout mouse model. In potassium oxonate (PO)-induced hyperuricemia mice, PMC72 significantly alleviated nausea, gait disturbances, ankle inflammation, and improved renal health. These effects were associated with marked reductions in oxidative stress markers, including serum uric acid, blood urea nitrogen, hepatic xanthine oxidase, and malondialdehyde (MDA) levels in serum, liver, and joint samples, as well as the downregulation of inflammation and uric acid transport-related gene expression in kidney samples. These benefits were comparable to those treated with Febuxostat, a standard urate-lowering therapy for gout. Furthermore, gut microbiome analysis revealed that PMC72 restored dysbiosis induced by hyperuricemia, contrasting with the reduced microbial diversity observed with febuxostat alone, and showed a complete recovery to eubiosis when combined with Febuxostat. These findings position PMC72 as a promising microbial therapeutic candidate for gout management, demonstrating significant development potential and serving as a benchmark for reverse translational microbiome-based therapeutic research.

Two novel, Gram-stain-negative, anaerobic, and non-motile bacterial strains, designated KFT8^T and CG01^T, were isolated from the feces of healthy individuals without diagnosed diseases and characterized using a polyphasic approach. Phylogenetic analysis revealed that both strains belong to the genus Bacteroides, with < 99.0% similarity in their 16S rRNA gene sequences to B. facilis NSJ-77^T and B. nordii JCM 12987^T. Within the genus Bacteroides, strain KFT8^T exhibited the highest Orthologous Average Nucleotide Identity value of 94.7% and a digital DNA-DNA hybridization value of 63.7% with B. ovatus ATCC 8483^T, whereas strain CG01^T showed the highest values of 95.3% and 63.3%, respectively, with B. nordii JCM 12987^T. The values between the two novel strains were 74.8% and 21.4%, respectively, which are below the species delineation thresholds, supporting their classification as novel species. The major fatty acid of strain KFT8^T was C_18:1 ω9c, whereas strain CG01^T predominantly contained summed feature 11 (comprising iso-C_17:0 3OH and/or C_18:2 DMA). The only respiratory quinone was MK-11, the major polar lipid was phosphatidylethanolamine. Both strains produced succinic acid and acetic acid as common metabolic end-products of fermentation, while lactic acid and formic acid were detected individually in each strain. Based on polyphasic characterization, strains KFT8^T (= KCTC 15614^T = JCM 36011^T) and CG01^T (= KCTC 15613^T = JCM 36010^T) represent two novel species within the genus Bacteroides, for which the names Bacteroides celer sp. nov. and Bacteroides mucinivorans sp. nov. are proposed, respectively. Additionally, genome-based analyses and phenotypic comparisons revealed that B. koreensis and B. kribbi represent the same strain, showing genomic relatedness to B. ovatus that exceeds the threshold for species delineation. Consequently, we propose the reclassification of B. koreensis Shin et al. 2017 and B. kribbi Shin et al. 2017 as later heterotypic synonyms of B. ovatus Eggerth and Gagnon 1933 (Approved Lists 1980).

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.