CRISPR-Cas technologies have emerged as powerful and versatile tools in gene therapy. In addition to the widely used SpCas9 system, alternative platforms including modified amino acid sequences, size-optimized variants, and other Cas enzymes from diverse bacterial species have been developed to apply this technology in various genetic contexts. In addition, base editors and prime editors for precise gene editing, the Cas13 system targeting RNA, and CRISPRa/i systems have enabled diverse and adaptable approaches for genome and RNA editing, as well as for regulating gene expression. Typically, CRISPR-Cas components are transported to the target in the form of DNA, RNA, or ribonucleoprotein complexes using various delivery methods, such as electroporation, adeno-associated viruses, and lipid nanoparticles. To amplify therapeutic efficiency, continued developments in targeted delivery technologies are required, with increased safety and stability of therapeutic biomolecules. CRISPR-based therapeutics hold an inexhaustible potential for the treatment of many diseases, including rare congenital diseases, by making permanent corrections at the genomic DNA level. In this review, we present various CRISPR-based tools, their delivery systems, and clinical progress in the CRISPR-Cas technology, highlighting its innovative prospects for gene therapy.

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.

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.

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.

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.

Actinobacteria, a phylum of Gram-positive bacteria, are renowned for their remarkable ability to produce antibacterial natural products. The National Institute of Biological Resources (NIBR) of Korea maintains a collection of Korean native actinobacteria. In this study, we explored the phylogenetic and biosynthetic diversity of the NIBR actinobacteria collection to assess its potential as a source of new antibacterial natural products. A 16S rDNA-based phylogenetic analysis revealed a high level of genetic diversity within the collection, with a predominance of Streptomyces, along with rare actinobacterial genera such as Kitasatospora and Micromonospora. Additionally, genetic network analysis of biosynthetic gene clusters (BGCs) from 15 sequenced NIBR actinobacterial strains demonstrated extensive BGC diversity, with many clusters identified as cryptic. Screening of culture extracts for antibacterial activity, followed by dereplication of active extracts, suggested the presence of potentially novel antibacterial natural products. Activity-guided isolation and whole-genome sequencing of the active strain KU57 led to the isolation of one new and three known svetamycin congeners along with their BGC. Overall, our findings highlight the NIBR actinobacteria collection as a valuable source for the discovery of new antibacterial natural products.

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.

Microbial biosynthesis using yeast species offers numerous advantages to produce industrially relevant biofuels and biochemicals. Conventional metabolic engineering approaches in yeast focus on biosynthetic pathways in the cytoplasm, but these approaches are disturbed by various undesired factors including metabolic crosstalk, competing pathways and insufficient precursors. Given that eukaryotic cells contain subcellular organelles with distinct physicochemical properties, an emerging strategy to overcome cytosolic pathway engineering bottlenecks is through repurposing these organelles as specialized microbial cell factories for enhanced production of valuable chemicals. Here, we review recent progress and significant outcomes of harnessing organelle engineering for biofuels and biochemicals production in both conventional and non-conventional yeasts. We highlight key engineering strategies for the compartmentalization of biosynthetic pathways within specific organelles such as mitochondria, peroxisomes, and endoplasmic reticulum; involved in engineering of signal peptide, cofactor and energy enhancement, organelle biogenesis and dual subcellular engineering. Finally, we discuss the potential and challenges of organelle engineering for future studies and propose an automated pipeline to fully exploit this approach.

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.

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.