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.

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 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.

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.

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.

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.

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.

Precise and tunable gene expression is crucial for various biotechnological applications, including protein overexpression, fine-tuned metabolic pathway engineering, and dynamic gene regulation. Untranslated regions (UTRs) of mRNAs have emerged as key regulatory elements that modulate transcription and translation. In this review, we explore recent advances in UTR engineering strategies for bacterial gene expression optimization. We discuss approaches for enhancing protein expression through AU-rich elements, RG4 structures, and synthetic dual UTRs, as well as ProQC systems that improve translation fidelity. Additionally, we examine strategies for fine-tuning gene expression using UTR libraries and synthetic terminators that balance metabolic flux. Finally, we highlight riboswitches and toehold switches, which enable dynamic gene regulation in response to environmental or metabolic cues. The integration of these UTR-based regulatory tools provides a versatile and modular framework for optimizing bacterial gene expression, enhancing metabolic engineering, and advancing synthetic biology applications.

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.

Evolution has been systematically exploited to engineer biological systems to obtain improved or novel functionalities by selecting beneficial mutations. Recent innovations in continuous targeted mutagenesis within living cells have emerged to generate large sequence diversities without requiring multiple steps. This review comprehensively introduces recent advancements in this field, categorizing them into three approaches depending on methods to create mutations: orthogonal error-prone DNA polymerases, site-specific base editors, and homologous recombination of mutagenic DNA fragments. Combined with high-throughput screening methods, these advances expedited evolution processes with significant reduction of labor and time. These approaches promise broader industrial and research applications, including enzyme improvement, metabolic engineering, and drug resistance studies.

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.

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.

Existing microbial engineering strategies—encompassing metabolic engineering, systems biology, and systems metabolic engineering—have significantly enhanced the potential of microbial cell factories as sustainable alternatives to the petrochemical industry by optimizing metabolic pathways. Recently, systems metabolic engineering, which integrates tools from synthetic biology, enzyme engineering, omics technology, and evolutionary engineering, has been successfully developed. By leveraging modern engineering strategies within the Design-Build-Test-Learn (DBTL) cycle framework, these advancements have revolutionized the biosynthesis of valuable compounds. This review highlights recent progress in the metabolic engineering of Corynebacterium glutamicum, a versatile microbial platform, achieved through various approaches from traditional metabolic engineering to advanced systems metabolic engineering, all within the DBTL cycle. A particular focus is placed C5 platform chemicals derived from L-lysine, one of the key amino acid production pathways of C. glutamicum. The development of DBTL cycle-based metabolic engineering strategies for this process is discussed.

Candida albicans (C. albicans) is a common opportunistic fungal pathogen that can cause infections ranging from superficial to severe systemic diseases. This study investigates the antifungal effects of metformin on C. albicans and explores its underlying mechanisms. Growth inhibition was assessed via XTT assays, and hyphal formation and morphological changes were observed by light microscope and scanning electron microscopy (SEM). Mitochondrial membrane potential (MMP) and reactive oxygen species (ROS) levels were measured with JC-1 and DCFH-DA probes, respectively. Gene expression related to ROS and autophagy was quantified by RT-qPCR, and autophagosomes were visualized using transmission electron microscopy (TEM). Metformin significantly inhibited C. albicans growth and hyphal formation, altered cell morphology, reduced MMP, and increased ROS levels. It activated autophagy in planktonic C. albicans but suppressed it in biofilm forms. Additionally, metformin exhibited synergistic effects with amphotericin B against planktonic C. albicans and with caspofungin against biofilms. The findings suggest that metformin exerts antifungal activity by modulating MMP, ROS levels, and autophagy-related pathways, and enhances the efficacy of specific antifungal drugs.