Alternative splicing (AS) generates transcript and protein isoforms, but its evolutionary significance cannot be inferred from isoform number alone. This review uses a comparative evidence framework to separate splice-variant origin, functional consequence, and evolutionary retention in plants and animals. We synthesize evidence from long-read sequencing, single-cell isoform profiling, pangenomics, proteogenomics, and perturbation studies. Animal studies emphasize exon-level switches in neural, cardiac, immune, and germline programs, whereas plant studies highlight intron retention, exitron splicing, environmental responses, and polyploidy-associated regulation. These are tendencies shaped by biological and sampling differences, not exclusive properties of either kingdom. Three worked examples illustrate how reproducible regulation, conservation or population evidence, molecular consequences, and targeted perturbation support different levels of inference. Functional effects alone do not establish historical adaptation, and conservation alone does not identify the trait under selection. The framework helps prioritize isoforms for crop improvement, biomarkers, and RNA intervention. Clinical splice modulation and field-evaluated natural rice alleles demonstrate translational potential, while highlighting the distinction between validated outcomes and prospective engineering. Future studies should connect isoform-resolved measurements to causal perturbation and fitness or agronomic performance across relevant environments.

Conventional aptamer discovery by systematic evolution of ligands by exponential enrichment (SELEX) is often constrained by limited library capacity, PCR bias, and multi-round screening. The recent convergence of artificial intelligence with aptamer development is driving a paradigm shift from empirical screening toward rational, data-driven design. This Review presents a timely and comprehensive overview of AI integration across the entire aptamer development pipeline, with a particular emphasis on advances reported in 2024-2026. We begin by discussing bioinformatics databases and computational tools for high-throughput SELEX data analysis. We then review state-of-the-art strategies for secondary and tertiary structure prediction, including recent breakthroughs such as AlphaFold 3, RoseTTAFoldNA, and diffusion-based approaches. We also cover machine learning methods for aptamer-target interaction prediction, molecular docking, dynamics simulations, and free energy calculations. A major focus is placed on very recent AI-driven generative design, including diffusion models (AptaDiff), language model-based frameworks (InstructNA, GRAPE-LM), and reinforcement learning approaches (AiDTA) for de novo aptamer generation and optimization. Emerging applications of AI-designed aptamers in precision diagnostics, targeted therapy, and biosensing are also surveyed. Finally, we discuss current challenges and outline future directions toward closed-loop, interpretable AI frameworks for translational aptamer development. By synthesizing the latest computational and experimental advances, this Review aims to serve as a timely resource for researchers seeking to leverage AI for next-generation aptamer discovery.

Plant base editing enables targeted nucleotide substitutions and provides a powerful approach for functional genomics and crop improvement. The toolkit has expanded from canonical cytosine and adenine editors to dual-base, transversion, Cas12-based, and organelle-targeted systems, broadening the range of accessible mutations and genomic targets. These advances create new opportunities for generating useful alleles, modifying gene function, and enabling directed evolution. However, expanded base-editing capabilities do not necessarily ensure predictable outcomes or successful breeding applications. This review summarizes the development of plant base editors, computational tools for target design, and their applications in crop research. We examine editor architectures and compare editing efficiency, product purity, and specificity, with particular attention to transversion editing. We also highlight the intrinsic technical limitations of base editors and the bottlenecks encountered when moving from laboratory research to field applications. By connecting base editor development with practical constraints, this review provides guidance for editor selection and identifies priorities for applying base editing to crop improvement.

Microorganisms, endowed with extraordinary metabolic versatility and environmental adaptability, represent a transformative frontier for addressing global sustainability imperatives. Engineering microbial systems through regulatory network rewiring and synthetic biology-driven functional augmentation substantially amplifies their biotechnological utility. Herein, we review the convergence of synthetic biology, bio-electrocatalysis, and reactor engineering as an integrated framework for sustainable biohydrogen production. Comparative analysis with conventional hydrogen generation strategies underscores the superiority of bioelectrochemical systems (BESs), which simultaneously enable wastewater valorisation and clean hydrogen production through microbially driven electron transfer cascades. Moreover, the mechanistic electron generation-to-hydrogen conversion continuum highlights the indispensable dominance of biotic catalysis. Furthermore, CRISPR-Cas9-mediated multiplex genome editing is examined as a precision tool surpassing traditional mutagenesis approaches for pathway optimisation. Key electrogenic taxa, their electrochemical interfaces and influences within BESs architectures, prevailing technological bottlenecks in scalability, techno-economic feasibilitites and forward-looking strategies toward wholly biological, scalable hydrogen production systems are critically featured.

Outer membrane vesicles (OMVs) are spherical, nano-sized proteoliposomes naturally secreted by Gram-negative bacteria. By incorporating parental membrane components and encapsulating diverse cellular cargos, OMVs function as potent mediators of biological communication. Despite inherent challenges, including low production yields, heterogeneous composition, and stochastic cargo loading, OMVs hold immense promise across broad biotechnological landscapes. In this review, we examine current advancements in OMV chassis engineering and functional diversification, providing a comprehensive synthetic biology perspective on their role as modular and programmable systems. We discuss five established strategies for inducing hyper-vesiculation alongside two complementary frameworks for constructing synthetic vesicles. These engineered OMVs facilitate transformative applications spanning biomedical and biotechnological domains, notably as high-fidelity platforms for studying membrane proteins within their native lipid environments. As synthetic biology increasingly converge with materials science, future innovations such as stimuli-responsive vesicles, self-assembling architectures, and hybrid bio-synthetic materials are poised to emerge. Such advancements position synthetic OMVs at a critical intersection to address complex challenges in medicine, environmental remediation, and advanced materials science, offering unprecedented opportunities for both fundamental discovery and industrial applications.

The hydroprocessed esters and fatty acids (HEFA) pathway is a technically mature and commercially deployable process for producing sustainable aviation fuel (SAF) from lipid feedstocks. Nevertheless, the current supply of sustainable lipids is insufficient to meet the projected demand for SAF, emphasizing the need to identify alternative lipid sources. This review comprehensively evaluates the potential of insects as biological conversion platforms for transforming low-lipid organic wastes into lipid-rich biomass suitable for HEFA-based SAF production. The lipid contents and compositions of three representative insect species (black soldier fly larvae (BSFL), housefly larvae (HFL), and mealworms (MW)) are compiled and compared. The assessment indicates that fatty acid saturation and chain-length distributions are key determinants of the suitability of insect lipids for HEFA processing, influencing hydrogen demand and upgrading requirements. Among these species, BSFL-derived lipids show potential because of their high proportion of saturated fatty acids, especially lauric acid, which may reduce hydrogenation requirements and provide hydrocarbon chain lengths close to the jet-fuel range. Integration of insect-mediated waste conversion with existing HEFA infrastructure offers a potential route to expand the supply of waste-derived lipid feedstocks. However, the integrated waste-to-insect lipid-to-SAF pathway has not yet been demonstrated, and its environmental benefits and commercial feasibility require whole-pathway evaluation. Consequently, this review proposes insect-mediated lipid production as a complementary feedstock strategy for HEFA-based SAF and identifies feedstock consistency, contaminant control, experimental validation of catalytic upgrading and fuel properties, and integrated life-cycle and techno-economic assessments as priorities for practical implementation.

Tryptophan-drived neuroactive compounds, including serotonin, melatonin, 5-hydroxytryptophan (5-HTP), psilocybin, and related indole derivatives, represent an important class of therapeutics for depression and other neuropsychiatric disorders. However, conventional production through chemical synthesis or natural extraction remains limited by low sustainability, poor selectivity, and high manufacturing costs. Advances in synthetic biology, metabolic engineering, and enzyme engineering have enabled the development of microbial cell factories for the efficient biosynthesis of these high-value compounds. This Review highlights recent progress in engineering microbial platforms for antidepressant tryptophan derivatives, focusing on tryptophan overproduction, hydroxylation biocatalysts, downstream biosynthetic pathways, and cofactor engineering. We further discuss the emerging roles of bacterial heme-dependent tryptophan hydroxylases and AI-assisted enzyme and pathway engineering in expanding the biosynthetic toolbox. Finally, we outline current challenges and future opportunities for integrating intelligent strain design with advanced bioprocess engineering to advance the biomanufacturing of tryptophan-derived neuroactives.

Directed evolution (DE) represents a pivotal interdisciplinary paradigm that synergistically integrates biotechnology, computational methodologies, and precision analytical instrumentation. It enables accelerated, controllable in vitro laboratory evolution of diverse biomolecules toward predefined functional targets. This review comprehensively summarizes fundamental principles and cutting-edge progress underlying core DE frameworks, encompassing mutagenesis engineering, biosensing, high-throughput screening (HTS), cell-free and display techniques, and integrated computational approaches. We systematically elaborate on their intrinsic features, comparative merits, inherent bottlenecks, applicable scopes, and prospective optimization trajectories. Furthermore, we emphasize the transformative capacity of these technologies for the development of organisms, enzymes, antibodies, and diverse functional biomolecules with tailored functional properties, spanning industrial, clinical, and fundamental research applications. Collectively, this work aims to provide a holistic mechanistic framework of contemporary DE platforms and inspire novel conceptual and methodological directions for future research.

Microalgal strains are engineered and benchmarked under fixed conditions, yet industrial cultures experience light, temperature, CO2, salinity and nutrient regimes that shift from seconds to seasons. Programmable microalgae, cells that sense environmental change, integrate signals and adjust their physiology (Sense-Decide-Adapt, SDA), could close this gap. Native nutrient-sensing, photoprotective, carbon-concentrating and osmotic programs already approximate SDA architectures, and genome editing, biosensors and single-cell screening make them tractable to rewire, although direct evidence that altering response dynamics raises productivity remains sparse. Here, we introduce the Environmental Performance Envelope (EPE), a framework defining the region of environmental space in which a strain retains a defined fraction of its reference productivity under realistic trajectories. The EPE turns a design question into an experiment: does engineering expand the operating space or merely relocate the optimum? A staged roadmap, including CO2-fluctuation trials and evolutionary stability testing, shifts engineering targets from static traits to response kinetics, recovery, heterogeneity and stability, and couples cellular control to bioprocess design, pointing toward more robust algal biomanufacturing. Because environmental variability constrains any living system deployed outside the laboratory, the same logic may extend to other microbial cell factories, crops and engineered organisms.

Fungal-microbial interactions drive key ecosystem functions and offer vast, untapped potential for the bio-economy. However, translating these natural consortia into controllable applications is hindered by the structural and metabolic complexity of the interplay. This review navigates the complexity landscape of these interactions, examining the technological trajectory from the mechanistic foundation of 1:1 dialogue to the rational design of synthetic consortia. We discuss the transition from static cultivation toward advanced microfluidic ("fungi-on-a-chip") and biomimetic platforms that resolve the spatial ecology across diverse environmental gradients. Beyond extracellular associations, we highlight the paradigm shift toward synthetic endosymbiosis, where precise interaction engineering enables the creation of novel endosymbiotic chassis. Furthermore, we examine how integrating multi-omics with artificial intelligence-driven phenotyping and genome-scale metabolic modeling can bridge the gap between mechanistic discovery and predictive design through integrative systems biology and design-build-test-learn cycles, enabling the rational engineering of fungal-microbial consortia. These approaches offer opportunities to iteratively optimize community composition, metabolic exchange, and functional performance, thereby connecting laboratory-scale discovery with ecosystem-level complexity. Despite these advances, further development and integration of these approaches are needed to improve the reproducibility, scalability, and controllability of fungal-microbial consortia for agriculture, environmental, and industrial applications.

Anaerobic digestion (AD) is increasingly recognized as a foundational process in the global shift toward sustainable, circular, and low-carbon systems. Long established for its capacity to convert organic waste into biogas and nutrient-rich digestate, AD now plays a pivotal role in climate-smart production, decentralized energy generation, and resource recovery. Yet its full potential extends far beyond these traditional outputs. Advances in microbial ecology and process engineering are transforming AD into a modular biomanufacturing platform capable of producing high-value compounds. These biomolecules offer renewable pathways for the synthesis of biofuels, single-cell protein (SCP), methanol, microalgal products, and other speciality chemicals, positioning AD at the heart of emerging bioeconomies. This dual capacity, addressing today's urgent need for sustainability and enabling tomorrow's bio-based production system, positions AD as an important technology in future biorefineries. This article appeals to a broad readership seeking to understand AD, which is one of the most mature yet dynamically evolving processes for biological resource recovery. Increasingly positioned as a central platform for future circular bioeconomies and sustainable biomanufacturing, this review specifically focuses on pivotal aspects of next-generation biorefineries, including biomethanation, SCP and methanol production, microalgal-based products, and integrated resource recovery strategies.

Genome-scale metabolic models (GEMs) have emerged as powerful tools for optimizing microbial fermentation. This review traces the application of GEMs from model reconstruction through to industrial implementation. Automated reconstruction tools including CarveMe, ModelSEED, and RAVEN are discussed alongside quality-control frameworks such as MEMOTE. Biological validation of these models ultimately depends on comparing predicted phenotypes against experimental data. The review then focuses on how GEMs guide strain engineering and process optimization, enabling rational target identification, improved substrate utilization, and enhanced fermentation performance. The predictive accuracy and experimental validation of these applications vary considerably across microbial hosts and production contexts. Recent methodological advances, including machine learning-assisted model refinement, synthetic pathway design, and community-scale metabolic modeling, have extended the scope and predictive power of GEMs still further. Persistent challenges in model accuracy, data integration, and scale-up validation remain, yet continued progress in computational and experimental approaches is expected to solidify the role of GEMs in fermentation research and bioprocess development.

The oxidation of n-alkanes into higher-value products, such as diols and diacids, is facilitated by various wild-type and engineered microorganisms. Depending on the substrate chain length, several distinct enzymatic systems are involved in achieving highly selective diterminal oxidation. In this review, we comprehensively summarise the enzymatic pathways responsible for the conversion of alkanes to α,ω-diols and α,ω-diacids and present strategies in the field of enzyme engineering, metabolic engineering and process optimisation aiming to increase the efficiency of these bioconversions. Furthermore, we examine integrated strategies for the conversion of diacids into diols and highlight recent advancements in this dynamic field. Finally, we discuss future research directions and key bottlenecks that must be addressed to achieve efficient n-alkane valorisation, whether derived from petroleum or sustainable alternatives such as chemically recycled plastics and plant-based renewable resources.

Antigen selection is the earliest and most consequential decision in vaccine design, yet it remains fragmented across disciplines and disease areas. Here we present a comprehensive, disease-agnostic pipeline intended as a practical resource for prioritizing and selecting candidate antigens, the foundational step on which downstream efficacy, safety, and manufacturability ultimately depend. Rather than categorization delivery platforms, we develop a mechanism-driven decision framework that aligns each candidate with the correlate of protection and the effector program its biological context demands. We first distinguish upstream public-health prioritization from downstream molecular selection, then apply a shared set of criteria, biological essentiality, evolutionary conservation, structural accessibility, natural processing and presentation, host-population coverage, and safety relative to the self-proteome, across a broad range of indications: pathogen-driven infectious diseases, zoonoses within a One Health framework, infection-associated and non-infection-derived cancers, autoimmune disorders, severe allergy, and transplantation. For each setting we examine the condition-specific considerations that separate antibody- and cell-mediated protection, cytotoxic T-cell-based tumor control, and deletion-, anergy-, or regulatory-based tolerance, while foregrounding the principles they hold in common. Throughout, biological rationale is coupled to developability, showing how conformational stabilization, modality-specific constraints, and manufacturability decide whether computational candidates become viable products. Finally, we consider how deep learning, generative artificial intelligence, and digital-twin modeling can forecast immune escape and accelerate preparedness against emerging pathogens ("Disease X"). The result is a unifying, translationally grounded guide to rational antigen selection applicable regardless of the target disease.

Microbiologically influenced corrosion (MIC) caused by microbial biofilms poses significant challenges to industrial systems. MIC and MIC mitigation cost billions of dollars each year in the US alone. Although most microbes flourish under mild conditions, increasing evidence demonstrates that in extreme environments such as high temperature, pressure, salinity, and low pH, extremophiles including thermophiles, barophiles, halophiles, and acidophiles can still form resilient biofilms and contribute to corrosion and biofouling. This review summarizes recent advances in understanding the formation, adaptation, and persistence of microbial biofilms in extreme environments and their roles in MIC. Key corrosion mechanisms such as extracellular electron transfer-MIC (EET-MIC) and metabolite-MIC (M-MIC) are discussed with environmental stressors and microbial physiology. Existing studies reveal that extreme environmental conditions do not necessarily suppress MIC, as extremophiles can adapt to environmental stresses and maintain corrosive activities. Different environmental stressors affect MIC through various pathways such as microbial metabolism and corrosion kinetics. Representative case studies highlight MIC behaviors under deep-sea, geothermal, hypersaline, and acidic conditions that are relevant to industrial operations. Current detection and monitoring approaches are evaluated along with mitigation strategies. The major limitation is that they are mostly developed and validated under mild conditions. Future work should focus on testing their reliability under extreme conditions. Emerging approaches including advanced corrosion-resistant materials, bifunctional chemical inhibitors, and AI-assisted data integration are discussed as promising strategies for closing this gap as well as improving MIC prediction and mitigation under extreme conditions.