Soil salinization poses a major challenge to global food security, affecting over one billion hectares of arable land and severely constraining crop productivity. As the primary interface between plants and soil, roots play a pivotal role in sensing and adapting to salinity stress through remarkable structural and functional plasticity. This review integrates recent advances in root system architecture (RSA) dynamics, suberin biosynthesis, hormonal regulation, and microbiome interactions to elucidate how plants achieve salinity resilience. We discuss key genes and regulatory modules controlling primary root elongation, lateral root patterning, and barrier formation, emphasizing transcriptional networks involving MYB, NAC, and WRKY families and their coordination with ABA, auxin, and ethylene signaling. Special attention is given to the biosynthesis and deposition of suberin as a dynamic ion-selective barrier governed by hormonal crosstalk and lipid metabolism. We further highlight how beneficial microbes such as Azospirillum, Bacillus, and arbuscular mycorrhizal fungi enhance salt tolerance by modulating phytohormones, antioxidant systems, and ionic homeostasis. Integrating multi-omics and CRISPR-based tools with microbiome engineering offers new avenues to design salt-resilient root ideotypes. We propose a conceptual framework linking molecular regulation, hormonal dynamics, and rhizosphere ecology to root system plasticity, providing a blueprint for engineering next-generation crops capable of maintaining growth and productivity in saline environments.

Hemoglobin is a functional protein with heme as a cofactor, playing a crucial role in transporting oxygen and maintaining nitric oxide metabolic balance. Besides its physiological functions, hemoglobin has broad potential applications in medicine and biotechnology. However, the widespread use of hemoglobin is constrained by limited natural sources, challenges in heterologous synthesis, and functional restrictions that hinder efficient application. In this review, we discuss the key challenges and solutions associated with microbial synthesis of hemoglobin. We systematically elucidate the engineering strategies to improve the stability, autoxidation rate, heme-binding capacity, oxygen transport efficiency, and nitric oxide scavenging rate of hemoglobin, with particular emphasis on the use of artificial intelligence algorithms to customize the function modification of hemoglobin. Also, we provide a comprehensive overview of the various applications of hemoglobin, including artificial oxygen carriers, medical treatments requiring enhanced oxygen supply, synthesis of high-value products, biocatalysis, artificial foods, agriculture, functional substance testing, and bioactive peptide production, with a special focus on the potential of hemoglobin mutants and derivatives in expanding its use across various fields. Finally, we explore the prospects for accelerating the resolution of hemoglobin synthesis and overcoming the application challenges by integrating Pareto-optimal and iterative bioengineering frameworks, deep learning, synthetic biology, and other advanced technologies.

Obligate parasites pose a significant threat to animal, human, and plant health by affecting host gene expression through mechanisms that are poorly understood. Spatial transcriptomic technologies are revolutionizing our understanding of animal-parasite interactions, revealing tissue reorganization, cellular responses, and infection dynamics at a microscopic scale. These technologies also accelerate the identification of potential targets for treating animal parasite infections. Despite their potential, the application of spatial transcriptomic technologies to plant-parasite interactions is limited. This review highlights key challenges in applying spatial transcriptomics to plants. By drawing parallels with advances in animal systems, we discuss how spatial transcriptomics could contribute to localize and identify effectors, uncover the molecular mechanisms of plant-parasite infections, and find novel disease control targets. This cross-disciplinary perspective provides a roadmap for future research in plant and animal parasitology.

Data are crucial for machine learning (ML) applications, yet acquiring large datasets can be costly and timeconsuming, especially in complex, resource-intensive fields like biopharmaceuticals. A key process in this industry is upstream bioprocessing, where living cells are cultivated and optimised to produce therapeutic proteins and biologics. The intricate nature of these processes, combined with high resource demands, often limits data collection, resulting in smaller datasets. This comprehensive review explores ML methods designed to address the challenges posed by small data and classifies them into a taxonomy to guide practical applications. Furthermore, each method in the taxonomy was thoroughly analysed, with a detailed discussion of its core concepts and an evaluation of its effectiveness in tackling small data challenges, as demonstrated by application results in the upstream bioprocessing and other related domains. By analyzing how these methods tackle small data challenges from different perspectives, this review provides actionable insights, identifies current research gaps, and offers guidance for leveraging ML in data-constrained environments.

Intracellular optogenetics represents a rapidly advancing biotechnology that enables precise, reversible control of protein activity, signaling dynamics, and cellular behaviours using genetically encoded, light-responsive systems. Originally pioneered in neuroscience through channelrhodopsins to manipulate neuronal excitability, the field has since expanded into diverse intracellular applications with broad implications for medicine, agriculture, and biomanufacturing. Key to these advances are photoreceptors such as cryptochrome 2 (CRY2), light-oxygen-voltage (LOV) domains, and phytochromes, which undergo conformational changes upon illumination to trigger conditional protein-protein interactions, localization shifts, or phase transitions. Recent engineering breakthroughs-including the creation of red-light responsive systems such as MagRed that exploit endogenous biliverdin-have enhanced tissue penetration, minimized phototoxicity, and expanded applicability to complex biological systems. This review provides an overarching synthesis of the molecular principles underlying intracellular optogenetic actuators, including the photophysical basis of light-induced conformational changes, oligomerization, and signaling control. We highlight strategies that employ domain fusions, rational mutagenesis, and synthetic circuits to extend their utility across biological and industrial contexts. We also critically assess current limitations, such as chromophore dependence, light delivery challenges, and safety considerations, so as to frame realistic paths towards translation. Looking ahead, future opportunities include multi-colour and multiplexed systems, integration with high-throughput omics and artificial intelligence, and development of non-invasive modalities suited for in vivo and industrial applications. Intracellular optogenetics is thus emerging as a versatile platform technology, with the potential to reshape how we interrogate biology and engineer cells for therapeutic, agricultural, and environmental solutions.

The projected expansion of the global market for cell manufacturing, which contributes to regenerative medicine and cell therapies, warrants the designing and development of scalable cryopreservation processes for cell-based products (CBPs) for use in both standard and personalized therapies. However, the change in scale causes variations in process parameters, which affects the stability of the CBP quality. Therefore, the cryopreservation process for CBPs needs to be designed based on the concept of cell manufacturability and consideration of both engineering and biological aspects. In this review, we discussed strategies to enhance the quality stability of CBPs during cryopreservation, focusing primarily on four key processes: dispensing, freezing, storage, and thawing. Additionally, we discussed the application of simulation technologies because they aid in constructing digital twins for the designing and development of the cryopreservation process and facilitate efficiency with limited time and resources.

Coenzyme A thioester derivatives, particularly acetyl-CoA, malonyl-CoA and fatty acyl-CoA, are essential central metabolites in microorganisms. These compounds play pivotal roles in numerous metabolic pathways and serve as key precursors in the biosynthesis of various high-value compounds, including fatty acids, polyketides, and flavonoids. The spatiotemporal distribution of CoA thioester derivatives is variable and tightly regulated, making real-time monitoring worthwhile. Biosensors have emerged as valuable tools for rapid and immediate detection because of their respond to changes of inducers. This has facilitated the development of efficient metabolic engineering strategies, including dynamic regulation and high-throughput screening. In this context, the review offers a comprehensive overview of the current progress, optimization, applications and limitations of biosensors for acetyl-CoA, malonyl-CoA, fatty acyl-CoA and other CoA thioester derivatives. Based on these limitations, it also outlines prospects for further development and discusses potential biosensor elements for CoA thioester derivatives.

Natural products (NPs) are increasingly applied in food, medicine, and biotechnology. However, their biosynthesis remains constrained by low titers and yields. Transcription factor-based biosensors (TFBs) can convert biological signals into measurable outputs, enabling real-time monitoring and dynamic regulation of biosynthetic pathways, thereby facilitating the overcoming of these limitations. This review highlights recent advances in applying TFBs to NP production, with a focus on high-throughput screening, adaptive evolution, and dynamic control. We further discuss innovative engineering approaches aimed at optimizing TFB performance, including in silico TF identification, protein engineering, and fine-tuning of regulatory elements. Finally, we examine the challenges associated with using TFBs for microbial NP production and explore their potential in emerging platforms such as cell-free systems and non-model microorganisms. These insights offer valuable perspectives on overcoming the current limitations of biosensing technologies and advancing the scalable production of NPs.

Cell viability assays (CVAs) are widely used in cell biology, biomedical research, drug development, and biotechnology to assess cell health, proliferation, cytotoxicity, and functional activity under various conditions. Key applications span from everyday cell culture monitoring to drug screening and toxicology studies, immunology, vaccine development, and stem cell and regenerative medicine. Despite the long history and widespread use of CVAs, selecting the right assay remains a challenge for researchers. The increasing number of available assay options has led to confusion and inefficiencies, as scientists struggle to navigate the differences, trade-offs, and technical limitations of each method. Many researchers continue using the assays they were trained with, rather than exploring newer, more sensitive, or more appropriate techniques. Lab protocols are often passed down without reassessment, and new projects frequently adopt assays based on convenience (e.g., reagent availability or existing equipment) rather than rational selection criteria. Some groups deliberately choose less sensitive assays under the assumption that they produce "better-distributed" data. However, this incorrect justification arises because assays with a high limit of detection (LOD) fail to capture small variations, creating the misleading perception of clean and well-distributed data. Ignoring small variations does not improve accuracy - it simply reduces sensitivity, potentially leading to incorrect conclusions. Hence, the purpose of this review is to provide a comprehensive overview of contemporary CVAs by categorizing detection methods and summarizing their concepts, applications, benefits, and limitations, while also highlighting the potential need for novel approaches in this field. To assist researchers in selecting the most appropriate assay for their experimental goals, we also present a visual decision tree that integrates mechanistic insights with practical considerations.

By mimicking natural photosynthesis, the photo-enzyme coupling catalysis (PECC) for carbon dioxide conversion integrates the advantages of photocatalysis and enzymatic catalysis, offering an effective and innovative pathway for capture and utilization of greenhouse gas. This review provides a comprehensive overview of recent advancements in this technology, covering the fundamental principles, key components, synergistic mechanisms, compatibility, and future perspectives. A photo-enzyme coupling system (PECS) can be categorized into cofactor-dependent or cofactor-independent system based on the requirement for cofactor mediation. Its main components include photocatalyst and enzyme, which demonstrates unique advantage in the synergism of energy transfer and substrate activation. In order to improve the compatibility of PECS, the strategies including compartmentalized immobilization and process optimization are employed. By developing highly efficient photocatalyst, strengthening interfacial interaction, and optimizing enzyme engineering, PECC holds great promise for transitioning from laboratory research to industrial application, providing robust support for mitigating global climate change and addressing energy crisis.

Engineering microbial computers has been a longstanding endeavor in synthetic biology. Like other unconventional computing disciplines, the goal is to bring computation into real world scenarios. Several potential applications in bioproduction, bioremediation, and biomedicine highlight the promise of this discipline. The first biocomputers were bottom-up predictable circuits that relied on a monoculture-based digital logic and were able to emulate simple logic gates. Drawing from computer theory and extending the analogy with conventional hardware has enabled the engineering of more complex circuits. However this abstraction soon reached its limits and introduced a semantic gap, which, alongside the constraints imposed by the monoculture paradigm, led to significant scalability limitations such as metabolic burden, orthogonality issues and noisy expression. This review outlines the strategies developed to overcome these issues and engineer more complex biodevices : (i) mitigation strategies that focus on the optimization of the circuits, (ii) multicellular computing that distributes the metabolic load across a consortium and (iii) the implementation of more energy-efficient computing frameworks, such as analog and neuromorphic architectures. While these bottom-up strategies have yielded significant progress, they remain insufficient to emulate the computational complexity of the cellular signal-processing system. In this review, we additionally introduce a new perspective on biocomputing with a top-down approach named reservoir computing. This framework leverages the inherent dynamical computational capabilities and functionalities of biosystems to solve more complex and diverse tasks, thus offering a promising new path for engineering the next generation of microbial computers.

The modification of key enzymes for chemical production plays a crucial role in enhancing the yield of targeted products. However, manipulating key nodes in specific signalling pathways remains constrained by traditional gene overexpression or knockout strategies. Discovering and designing optogenetic tools enable us to regulate enzymatic activity or gene expression at key nodes in a spatiotemporal manner, rather than relying solely on chemical induction throughout production processes. In this review, we discuss the recent applications of optogenetic tools in the regulation of microbial metabolites, plant sciences and disease therapies. We categorize optogenetic tools into five classes based on their distinct applications. First, light-induced gene expression schedules can balance the trade-off between chemical production and cell growth phases. Second, light-triggered liquid-liquid phase separation (LLPS) modules provide opportunities to co-localize and condense key enzymes for enhancing catalytic efficiency. Third, light-induced subcellular localized photoreceptors enable the relocation of protein of interest across various subcellular compartments, allowing for the investigation of their dynamic regulatory processes. Fourth, light-regulated enzymes can dynamically regulate production of cyclic nucleotides or investigate endogenous components similar with conditional depletion or recovery function of protein of interest. Fifth, light-gated ion channels and pumps can be utilized to investigate dynamic ion signalling cascades in both animals and plants, or to boost ATP accumulation for enhancing biomass or bioproduct yields in microorganisms. Overall, this review aims to provide a comprehensive overview of optogenetic strategies that have the potential to advance both basic research and bioindustry within the field of synthetic biology.

Integrating biological systems with artificial optoelectronic materials for efficient solar energy conversion has emerged as a cutting-edge and promising research direction in the pursuit of sustainable energy solutions. Natural photosynthesis, through intricate biological mechanisms, converts solar energy into chemical energy, serving as an inspiration for human innovation; concurrently, photovoltaic technologies utilize semiconductor materials to directly transform solar radiation into electricity. Recent interdisciplinary research efforts have led to the development of bio-abiotic hybrid interfaces, combining the regenerative capabilities of biological systems with the tunable optoelectronic properties of artificial materials, aiming to enhance solar energy conversion efficiency. This review focuses on the latest advancements in artificial photosynthesis, bio-photoelectrochemical systems, and bio-photovoltaic systems, emphasizing their potential to improve solar energy conversion efficiency. We explore the design principles, operational mechanisms, and performance metrics of these hybrid devices, and conduct an in-depth analysis of technical challenges such as interface stability and electron transfer efficiency. Furthermore, we propose future research directions to optimize these systems for practical applications in sustainable energy production. By integrating knowledge from biology, materials science, and energy engineering, we aim to provide new perspectives and strategies for the development of solar energy conversion technologies, advancing toward more efficient and sustainable energy solutions.

Lignin biosynthesis and plant cell wall engineering are central to plant structural integrity and biomass utility. Recent advances in molecular and synthetic biology have opened opportunities to tailor lignin contents, composition, and polymer structure for renewable bioenergy and sustainable biomaterial applications. This review provides an integrative perspective on biosynthesis, regulation, and engineering of lignin. It summarizes the current progress in understanding the genetic, transcriptional, epigenetic, and metabolic networks that control lignin formation, with a focus on emerging tools such as CRISPR/Cas genome editing, synthetic promoters, and metabolic rewiring. Beyond cataloguing current knowledge, it critically analyzes the trade-offs involved in lignin modification for biomaterials, addressing unresolved challenges such as monolignol transport, metabolic flux control, and species-specific regulatory divergence. Engineered lignin and modified plant cell walls hold significant potential for biorefineries, advanced polymers, pharmaceuticals, and carbon sequestration, yet their translation from the laboratory to the field remains limited. Engineered lignin offers real-world applications across diverse industries, including bioenergy, bioplastics, carbon fiber composites, pharmaceuticals, and sustainable construction materials, thereby reinforcing its pivotal role in advancing a circular bioeconomy. The review further proposes future research directions that integrate multi-omics, single-cell technologies, machine learning, and field-based validation to enable precision lignin engineering. Strategic advances in this field will support next-generation bioenergy systems, advanced biomaterials, and the transition to a circular bioeconomy.

Geminiviruses, the largest plant DNA virus family, cause devastating diseases in crops worldwide. These viruses possess distinctive features, such as the stem-loop structure and replication protein (Rep), which enable the creation of functional geminiviral replicons (GVRs) in plants. Over three decades, geminiviruses have been developed into vectors for virus-induced gene silencing (VIGS), high-level protein expression, and genome editing. This review introduces the genomic structure, Rep protein domains and functions, as well as the historical applications of geminiviruses, then highlights their prominent roles in VIGS and synthetic biology. As VIGS vectors, bipartite geminiviruses utilize AV1 gene replacement, while monopartite species rely on satellite DNAs to insert target sequences, enabling gene silencing in diverse plants. In synthetic biology, GVRs facilitate high-level protein expression through autonomous replication and enhance CRISPR/Cas genome editing efficiency in crops. Additionally, gene regulatory elements, including tissue-specific promoters and gene expression enhancement sequences from geminiviral genomes or satellite DNA expand their utility in genetic engineering. Finally, this review provides an outlook on the future development of geminivirus vectors. GVRs can work as plasmid-like DNAs for supporting diverse and creative designs in plant synthetic biology. The stem-loop structure and Rep are not unique to geminiviruses, a fact that suggests potential cross-kingdom applications of GVRs beyond plants. Vast viral resources enable further acceleration of GVR applications through resource mining and optimization. Moreover, attenuated or engineered geminiviral strains hold promise as "plant vaccines" via cross-protection. Collectively, geminivirus vectors bridge fundamental viral research with practical innovations in crop improvement, biomanufacturing, and synthetic biology.

Mycotoxins, among the most extensively studied biological toxins, pose significant health risks to humans and animals, causing substantial economic losses in the agricultural sector. Numerous conventional enzymes isolated from microorganisms have been reported to detoxify mycotoxin, but their stability is questionable for detoxifying mycotoxin and the direct industrial production of enzymes. Currently, few commercial enzymes are available for the detoxification of mycotoxins. Enhancing enzyme stability is essential to ensure effective detoxification under feed-appropriate temperature and pH conditions. To overcome this challenge, the amalgamation of numerous fields, such as bioinformatics and protein engineering, is crucial for improving the enzyme for industrial production. Computational tools are crucial for determining the nucleotides of the sequence for modification using site-directed mutagenesis (SDM) for the existing conventional enzyme. The SDM technique offers a promising approach for modifying conventional enzymes for commercial purposes. Therefore, focusing on identifying, modifying, and producing enzymes that effectively detoxify mycotoxins is crucial for mitigating their effects on animals and preventing economic losses. Also, a fusion of modified enzymes involved in the cascade detoxification of mycotoxin and its derivatives should be focused on. This review provides an overview of the computational tools and protein engineering approaches, focusing on SDM and cascade catalysis for enhanced mycotoxin detoxification. We also discuss the future directions for incorporating these engineered enzyme systems on a commercial scale.

Chorismate serves not only as a critical branch point for aromatic amino acids biosynthesis, but also as an essential precursor for various high-value aromatic compounds. Currently, the sustainable production of chorismate derivatives is attracting escalating interest due to their pivotal roles as vital components in pharmaceuticals, cosmetics, and specialty chemical intermediates. However, these compounds are commonly obtained through plant extraction or chemical synthesis, which suffer from complex extraction processes, low yields, and environmental burdens, often leading to supply constraints. Therefore, to overcome these challenges, an increasing number of studies are focusing on exploring more production routes for chorismate derivatives to achieve green and efficient synthesis. With the advancement of systems metabolic engineering and contemporary synthetic biology, genetically redesigned Escherichia coli has become pivotal microbial platforms for chorismate derivative biosynthesis. This review outlines the significance of chorismate derivatives and their de novo biosynthetic pathways in E. coli, while reviewing the latest metabolic engineering and synthetic biology strategies employed to boost production of these compounds. Additionally, the persisting challenges and emerging opportunities in leveraging engineered E. coli platforms for industrial-scale biosynthesis of these high-value compounds are discussed.

Amino acids and their derivatives play pivotal roles across diverse fields including biotechnology, pharmaceuticals, agriculture, and industrial manufacturing. The development of high-throughput screening methods for strains producing amino acids and their derivatives is crucial for both mining key enzymes and screening overproducers. This review systematically evaluates six classes of direct biosensors employed in the metabolic engineering of amino acid- or derivative-producing strains. These include biosensors based on transcription factors, riboswitches, Förster resonance energy transfer, circularly permuted fluorescent proteins, compound-inducible putative promoter regions, and protein translation elements. Their operational principles and recent advances in rational design, performance optimization, and practical implementation are critically analyzed. In addition, a systematic analysis of four categories of indirect biosensing strategies for the screening or regulation of amino acid- or derivative-producing strains is provided. These strategies target universal metabolic precursors, pathway-specific precursors, enzymatically transformed downstream metabolites, or competitive intermediates in branched pathways. Then, the design strategies, performance optimization methods, and practical implementation challenges of the existing biosensors are compared, which are accompanied by the discussion of the key parameters that are optimal for the biosensors applied in metabolic engineering. This work will facilitate the development of biosensors for metabolites that currently lack biosensing systems, and promote the innovation of the existing biosensors. These developments are expected to support efficient and sustainable production of amino acid-related compounds and other high-value metabolites.

Bacteria exhibit remarkable precision in controlling the stoichiometry of protein subunits within metabolic pathways and macromolecular complexes-a requirement for optimal function and fitness. This review explores the RNA-level mechanisms that enable bacteria to maintain precise subunit ratios, moving beyond canonical transcriptional regulation to highlight the role of post-transcriptional fine-tuning. We discuss how internal transcriptional terminators serve as tunable attenuators, creating expression gradients within polycistronic operons, and how selective RNA processing and stabilization (SRPS) systems generate differential mRNA stability to shape proteomic stoichiometry. Furthermore, we outline how these native strategies have inspired the design of synthetic genetic circuits-including promoter libraries, engineered terminators, and RNase-based processing modules-that allow programmable control of gene expression levels. By leveraging modular and layered regulatory elements, synthetic biologists can now construct robust systems with user-defined stoichiometric outputs, facilitating the engineering of complex metabolic pathways and protein assemblies for biotechnological and biomedical applications.

Peroxisome engineering in yeast has emerged as a promising strategy for biomanufacturing, as it enables the compartmentalization of biosynthetic pathways and thus alleviates key bottlenecks in natural product biosynthesis. By sequestering specific metabolic pathways within peroxisomes, this strategy effectively reduces product cytotoxicity, enhances intracellular product storage, and allows precise redirection of metabolic fluxes. Nevertheless, its broader application remains limited by several unresolved challenges, including the insufficient understanding of peroxisomal membrane permeability, inadequate cofactor supply, and glucose-mediated repression of peroxisomal capacity. To overcome these obstacles, a range of conventional and emerging approaches-such as engineering peroxisomal targeting signal type 1 (PTS1), regulation of peroxisome proliferation, development of orthogonal artificial peroxisomal protein transport systems, and applying machine learning to predict gene overexpression for optimizing peroxisomal functional capacity-have expanded the toolkit for peroxisome engineering in yeast. This review summarizes recent advances in peroxisomal surface display engineering, peroxisomal matrix engineering, and multi-organelle spatial combination coordination, highlighting the importance of peroxisome engineering in optimizing yeast-based cell factories for natural product biosynthesis. Moreover, it critically evaluates current limitations, along with a comprehensive discussion of both conventional and emerging approaches aimed at further optimizing peroxisome engineering. In the future, integrating peroxisome engineering with advanced machine learning will be crucial for addressing remaining challenges and fully realizing the potential of sustainable and scalable yeast-based biomanufacturing.

The CRISPR/Cas9 system has emerged as a revolutionary tool for gene editing, widely used in the biomedical field due to its simplicity, efficiency, and cost-effectiveness. However, evidence suggests that CRISPR/Cas9 can induce off-target effects, leading to unintended mutations that may compromise the precision of gene modifications. Consequently, predicting，detecting and evaluating these off-target effects is crucial for optimizing the accuracy and reliability of CRISPR/Cas9 system. This paper provides an overview of the various methodologies and strategies, used or to be used for identifying off-target effects in CRISPR/Cas9-based genome editing, offering insights to improve the precision and safety of CRISPR applications in research and therapeutics.