The long-term and extensive use of conventional chemical pesticides has led to a series of problems, including pesticide resistance in plant pathogens and insect pests, environmental pollution, and risks to the safety of agricultural products. These challenges have created an urgent need for green, efficient, and sustainable alternative pest management strategies. As endogenous signaling molecules, phytohormones play crucial roles in regulating plant growth and development, enhancing stress tolerance, and inducing disease resistance. Owing to their environmental compatibility, target specificity, and biodegradability, phytohormones have emerged as promising candidates for the development of green agrochemicals. This review systematically summarizes the classification, biological functions, and biosynthetic pathways of the major phytohormones, including abscisic acid, gibberellins, jasmonic acid, salicylic acid, auxins, and others. Particular emphasis is placed on synthetic biology strategies for producing phytohormones using microbial cell factories, including promoter engineering, cofactor engineering, transporter engineering, dynamic regulation, cytochrome P450 engineering, subcellular compartmentalization, protein engineering and automation and artificial intelligence engineering. In addition, the major challenges associated with microbial production of phytohormones are discussed, such as the low activity of heterologously expressed cytochrome P450 and the complexity of subcellular compartmentalization. The review further highlights the potential applications of automation, machine learning, and artificial intelligence in accelerating the development of microbial cell factories and optimizing metabolic networks and fermentation processes. Finally, future directions for the industrial-scale production of phytohormones are proposed from three perspectives: efficient product recovery, the development of advanced synthetic biology tools, and AI-enabled biomanufacturing technologies.
Lignocellulose sugar-platform biorefinery is regarded as a promising alternative to fossil-based refining, with biomass providing a renewable carbon resource for bio-based products. Lignocellulosic pretreatment remains a major bottleneck because process outcomes are governed by the coupled effects of biomass heterogeneity, solvent chemistry, and operating conditions. Machine learning (ML) offers a practical framework for learning structure-process-outcome relationships from sparse, heterogeneous, and experimentally costly datasets. This review interprets pretreatment as a continuum from operating-condition-dominated systems to molecular-design-oriented solvent systems, and examines how process conditions, chemistry, and biomass can be represented and learned. Further, ML applications are explored for predicting pretreatment outputs such as sugar recovery, delignification, and lignin structural features; identifying operating windows within fixed chemistries; comparing chemically distinct solvent or reagent systems when appropriate descriptors are available; and prioritizing new experiments under limited data. ML-enabled lignin valorization is further discussed through structural decoding, mechanistic modeling, structure-property mapping, and inverse design. Its future utility will depend more on how well ML matches the dominant sources of variation in pretreatment systems than on algorithmic complexity or novelty. By organizing ML around pretreatment and valorization problems rather than algorithm categories, this review provides a biomass-centered framework for more reliable and actionable data-driven biorefinery research.
Natural fragrance compounds determine the sensory quality of food, cosmetics, and consumer goods. Driven by the limitations of traditional botanical extraction and petrochemical synthesis, sustainable biomanufacturing empowered by systems metabolic engineering and enzyme biocatalysis has emerged as a compelling alternative. This review systematically summarizes recent advances in the biosynthesis of high-value natural fragrances. First, we delineate the biosynthesis-driven classification of these volatile molecules and elucidate the structure-odor relationship, establishing a rational basis for target selection. Subsequently, we outline the core biosynthetic networks responsible for generating the four major lineages: terpenoids, phenylpropanoids and benzenoids, fatty acid derivatives, and amino acid derivatives. To provide systematic guidance for the biosynthesis of natural fragrance compounds, we comprehensively detail advanced engineering strategies across three progressive hierarchical tiers: the enzyme, metabolic, and cellular levels. Finally, we evaluate current challenges and future directions, with a specific emphasis on biosynthetic bottlenecks, bioprocess scale-up, and the rational design of complex multi-component aromas, thereby providing new insights into the sustainable bioproduction of these high-value natural fragrance compounds.
Terpenoids are among the most structurally diverse and commercially important natural products, with applications in pharmaceuticals, flavors, fragrances, and biofuels. Controllable supply of the universal precursors isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) remains a central limitation for microbial terpenoid production. The native mevalonate (MVA) and methylerythritol phosphate (MEP) pathways are deeply integrated with sterol homeostasis, central carbon metabolism and redox balance. Their distributed regulation, cofactor demands and scale-dependent bottlenecks complicate further intensification. The isopentenol utilization pathway (IUP) offers an orthogonal alternative: a compact, ATP-only, alcohol-fed bypass converting exogenous isoprenol and prenol to IPP and DMAPP in two kinase steps plus isomerization. This review treats the IUP as a configurable precursor module rather than a single pathway variant. Enzyme-level sections cover entry-kinase and isopentenyl phosphate kinase structure, kinetics, and engineering, including the kinetic imbalance that places most flux control in the first phosphorylation step in canonical C5 configurations, together with the feeding, energetic and downstream conditions under which control shifts elsewhere. Host-level sections examine expression hierarchies, construct design, host choice and compartmentalization, and the interaction between IUP flux, toxicity management, and native precursor pathways. Cell-free cascades and techno-economic analysis then identify the regimes, and the quantitative margins, within which an alcohol-fed bypass outperforms further MVA or MEP optimization.
Short-chain diols (C2-C6) are key platform chemicals widely used in polyesters and polyurethanes; however, their large-scale adoption remains limited by cost, scalability, and process robustness. This review summarizes recent advances in microbial production from an integrated perspective encompassing chassis engineering, pathway engineering, and process optimization. We compare model and non-model hosts, highlighting trade-offs between genetic tractability and native metabolic capacity, and analyze biosynthetic pathways with respect to carbon efficiency, redox balance, and pathway compatibility. Strategies to enhance microbial cell factories including enzyme engineering, metabolic flux regulation, cofactor engineering, and adaptive laboratory evolution are discussed to improve titer, yield, and productivity. We further evaluate the shift toward low-cost and sustainable feedstocks, such as crude glycerol, lignocellulosic biomass, and C1 substrates. To bridge microbial production with industrial implementation, key industrial constraints, including oxygen transfer, feedstock variability, downstream separation and biosafety, are examined. A second-hydroxyl-forming-unit conservation rule is proposed and integrated with process-level evidence to assess structural, performance, and process transferability across diol pathways. Techno-economic analysis (TEA) and life-cycle assessment (LCA) are discussed as essential tools for evaluating industrial feasibility. Finally, the downstream value of short-chain diols beyond their conventional uses is discussed, with emphasis on emerging opportunities for bioconversion and catalytic upgrading into high-value chemicals and functional materials. These analyses provide an evidence-based framework for product-specific pathway and process decisions in short-chain diol biomanufacturing.
Lignin is the largest renewable reservoir of aromatic carbon, but its microbial valorization is constrained by the chemical heterogeneity and changing composition of lignin-derived feedstocks. Although numerous aromatic-catabolic pathways and transcription factors have been characterized, their regulatory roles are usually discussed by protein family or individual pathway, obscuring the principles that govern pathway engagement under mixed-substrate conditions. Here, we present a mechanistic framework for bacterial lignin funneling organized around four axes: physiological effector identity, pathway sampling position, strength of regulatory evidence, and host carbon context. We distinguish regulators that sense extracellular aromatics from those responding to CoA-activated intermediates, downstream metabolites, or unidentified signals in authentic process streams, and examine how these sensing positions shape induction timing, basal expression, substrate discrimination, and metabolic commitment. Comparative analysis of MarR-, LysR-, IclR-, and σ54-dependent systems indicates that transcriptional output emerges from coordinated transport, intracellular effector formation, promoter architecture and occupancy, downstream sink capacity, and global carbon-control networks. To separate established mechanisms from indirect assignments, we evaluate evidence across ligand binding, regulator-DNA interaction, promoter occupancy, transcriptional response, genetic necessity, pathway-flux consequences, and host-fitness effects. This synthesis reframes aromatic-responsive regulators as components of integrated signal-to-flux modules and provides testable principles for selecting sensing positions, matching transport with catabolic and product-forming capacity, minimizing cross-induction and expression burden, and transferring regulatory circuits into engineered hosts. Linking regulatory output to metabolic flux and cellular fitness, predictive lignin valorization will require quantitative characterization under dynamic, mixed-substrate, and process-relevant conditions.
Neoantigen vaccines have rekindled interest in therapeutic cancer vaccination, yet their clinical efficacy remains constrained by imperfect antigen prioritization, incomplete modeling of immunogenicity, tumor heterogeneity, and immune evasion mechanisms. Current computational pipelines are dominated by discriminative models that rank pre-existing mutant peptides based on features related to HLA binding and antigen presentation. Although these approaches have improved candidate prioritization, their ability to optimize antigen selection and vaccine constructs across multiple determinants, including presentation, recognition potential, and translational feasibility, remains limited. Generative artificial intelligence offers a complementary, design-oriented framework that can explore and iteratively optimize biological sequence space under explicit constraints, rather than merely scoring predefined candidates. In this review, we discuss how generative AI may broaden the computational scope of tumor-anchored antigen discovery, support tumor-context-integrated optimization of neoantigen candidates, facilitate the engineering of multi-epitope and mRNA vaccine constructs, and incorporate tumor-specific constraints such as antigen-presentation defects, clonal architecture, and the state of the tumor immune microenvironment. Within this framework, generative models are considered components of an AI-assisted integrated workflow rather than substitutes for tumor-derived evidence, established prediction tools, or experimental validation. We further discuss the role of immunopeptidomics-guided calibration and iterative validation in improving biological realism and translational relevance. A longer-term frontier is immunopeptidomics-anchored synthetic immunogen design, in which validated tumor-presented immunogenic peptides may serve as templates for designing neoepitope mimetics or heteroclitic peptide analogs with improved HLA compatibility, pHLA stability and T-cell priming capacity. Finally, we examine current bottlenecks, including limited functionally validated immunogenicity datasets, uncertain generalizability, experimental validation burden, and the emerging regulatory demand for interpretability and traceability. At present, generative AI should be viewed as a promising design-enabling biotechnology platform whose clinical value remains to be established through prospective comparison with standard neoantigen pipelines.
Nanomaterial-bacterium interactions are commonly interpreted through antibacterial mechanisms such as reactive oxygen species generation, photothermal effects, membrane disruption and ion toxicity. However, under nonlethal or sublethal conditions, nano-bio interfaces can also regulate bacterial electron metabolism by reshaping extracellular electron dissipation, interfacial charge transfer, charge-transfer resistance (Rct), redox buffering and biofilm-associated electron networks. Here, we propose a functional framework that views bacterial metabolism as an integrated process of electron generation, interfacial transfer and electron dissipation. Within this framework, nanomaterials are classified by their positions in bacterial electron-flow networks as electron sinks, electron relays or electron buffers. We further distinguish beneficial coupling from electron hijacking by determining whether enhanced interfacial electron transfer is coupled to NADH/NAD+ balance, ATP production, membrane-potential maintenance and productive carbon-flux redistribution, or instead leads to futile electron loss, oxidative damage and energetic collapse. Extending this view from single cells to extracellular polymeric substances (EPS), electroactive biofilms and direct interspecies electron transfer (DIET), we discuss how nano-bio interfaces re-gate microbial electron flow at the community scale. This Review shifts the focus from how nanomaterials kill bacteria to how they reprogram microbial redox boundaries, providing a conceptual basis for antibacterial interface design, biofilm control, microbial sensing, biomanufacturing and microbiome engineering.
The rapid accumulation of food waste worldwide poses significant challenges to conventional waste management strategies. Recent studies have increasingly focused on the valorization of food waste for circular biorefineries. Among the multiple steps involved in biological conversion, e.g. anaerobic digestion, composting, and fermentation, hydrolysis is widely recognized as a critical rate-limiting step as it determines the release of soluble organic substrates and directly influences the efficiency of downstream biorefineries processes. Enzymatic hydrolysis offers advantages in terms of selectivity and mild reaction conditions, but its implementation is constrained by a core contradiction, i.e. food waste complexity requires adaptive, multi-enzyme systems, whereas current reliance on externally supplied commercial enzymes imposes substantial economic limitations. This mismatch has emerged as a central bottleneck in food waste biorefining. Thus, this review summarizes recent advances in improving enzymatic hydrolysis of food waste, and particular attention is given to the development of in situ enzyme cocktail production approaches, in which food waste is utilized as a fermentation substrate for generating compound enzyme systems. Such strategies enable the integration of enzyme production and substrate conversion, thereby reducing costs and improving process flexibility under heterogeneous feedstock conditions. The review further elucidates how enzyme-driven hydrolysis unlocks multi-pathway valorization, including enhanced anaerobic digestion, denitrification via functional carbon sources, biofertilizer generation, and the synthesis of high-value biochemicals from carbohydrates, lipids, and proteins. Engineering translation is addressed through process retrofitting, co-digestion strategies, and scale-up considerations. Finally, future directions are outlined toward adaptive, data-driven biorefineries, integrating machine learning, reactor innovation, and life-cycle assessment. Overall, a comprehensive framework for advancing food waste valorization toward sustainable, closed-loop systems was provided in this review, which is essential to reposition food waste management from an end-of-pipe solution to a cornerstone of circular, low-carbon biorefineries. By linking in situ enzyme cocktail production, enzymatic hydrolysis, product valorization, and engineering-scale retrofitting, this review highlights enzyme cocktails as enabling interfaces between food waste and downstream high-value bioconversion.
Immune cell-based therapies have transformed immuno-oncology by converting living cells into therapeutic agents able to recognize, infiltrate, and eliminate diseased tissues. While T cell, NK cell, and dendritic cell platforms have delivered meaningful clinical advances, in solid tumors and chronically inflamed tissues, poor trafficking and retention, and functional suppression within hostile microenvironments, often constrain their efficacy. Macrophages provide a complementary therapeutic platform because they are abundant tissue-resident sentinels, professional phagocytes, and key coordinators of local inflammation and adaptive immunity. However, successful macrophage immunotherapy requires solving two central in vivo determinants: preserving a therapeutically favorable functional state despite suppressive cues, and achieving robust, disease-selective localization and engagement. These determinants map onto two defining macrophage properties (i.e., plasticity and homing), which serve as central engineering levers. Here, we review recent strategies that (1) reprogram and stabilize engineered macrophage phenotypes within the disease environment, and (2) enhance targeting, retention, and contact-dependent functions through engineered recognition modules. We further highlight emerging combinatorial designs that integrate phenotype maintenance with improved tissue targeting and discuss design considerations to translate both genetic and non-genetic macrophage engineering approaches into effective therapy in complex disease microenvironments.
Artificial enzymes represent transformative biocatalysts that overcome the inherent limitations of natural enzymes in substrate scope and activity specificity, enabling challenging new-to-nature reactions. Despite remarkable advances in designing diverse artificial enzymes through synthetic cofactor or non-canonical amino acid incorporation, their widespread industrial application remains hindered by the high costs and operational complexity of in vitro systems. Whole-cell biocatalysis emerges as a promising alternative by capitalizing on the cellular environment, including cofactor regeneration, multi-enzyme cascades, and enhanced enzyme stability. This review provides a comprehensive introduction to recent progress in artificial enzyme design and their successful applications in whole-cell biocatalysis. We summarize innovative protein engineering tools for creating artificial enzymes, assembly strategies to enhance in vivo catalytic performance, and representative applications of artificial enzymes-containing whole-cell systems in non-natural transformations. We also discuss the challenges and prospects of whole-cell artificial enzyme catalysis in advancing sustainable and scalable biomanufacturing.
Long non-coding RNAs (lncRNAs) are nominated at scale as disease biomarkers and as targets for antisense and RNA-targeting therapeutics, yet many fail to replicate — a translational liability: an artefactual target wastes preclinical investment. The reflex explanation is that lncRNA biology is intrinsically hard to measure. This Review argues instead that a large, addressable fraction of the irreproducibility is the signature of three pervasive measurement confounds: low transcript abundance, cell-type composition shifts, and steady-state snapshotting. Abundance is the most recurrent axis across them, but not a universal root cause: composition is causally distinct, driven by cell-type restriction, and snapshotting is a separate identifiability problem. Drawing on the primary literature, I show how apparent class-specific phenomena (noisier half-lives, inflated network centrality, detection deficits) largely dissolve once transcripts are compared at matched abundance, and how composition shifts can manufacture, invert or mask a bulk disease association independently of within-cell regulation. All three are preceded by a transcript-definition problem: lncRNA isoforms can carry opposing functions, and roughly half of lncRNAs are estimated to lack a poly(A) tail, so isoform identity and library chemistry determine what is measured at all. I then distil a portable, confound-aware workflow — a transcript-definition step followed by four analytical gates: abundance-matched nulls, composition adjustment, reliability and leakage flags, and orthogonal or temporal validation — and map it onto the biotechnology pipeline as a low-cost risk-reduction filter for biomarker discovery, drug-target selection, diagnostic design and predictive modelling. Trustworthy measurement is the foundation on which trustworthy lncRNA biotechnology is built.
In recent years, self-assembled antimicrobial peptides (AMPs) have shown promise in biomedical applications due to their multifunctional properties and high therapeutic potential. This stable nanostructure significantly addresses the clinical limitations associated with peptides, such as protease degradation, limited in vivo efficacy, and short half-life during systemic or oral administration. In this review, we provide a comprehensive overview of the recent advancements in the innovative design of self-assembled nano AMPs and their therapeutic effects in vivo. We categorize design strategies for nanopeptides based on intermolecular forces between amino acids, including hydrophobic interactions, hydrogen bonding, π-π stacking, and cation-anion-π interactions. Additionally, we discuss novel chemical modifications, including hydrophobic modification of peptide chains, glycosylation, and amphiphilic polymerization. Finally, we explore the potential applications of nanopeptides in drug delivery. We hope to provide guidance on promising directions in the field, stimulate broad scientific interest and provide new, effective and selective solutions to the limitations of self-assembled AMPs in clinical applications.
Polyhydroxyalkanoate (PHA) production from plastic-derived substrates offers a promising route to mitigate plastic pollution while reducing dependence on conventional PHA feedstocks. Plastic waste represents an abundant carbon source for microbial fermentation, but efficient conversion remains limited by incomplete deconstruction, inhibitory intermediates, low carbon recovery, and challenges in process integration. Plastic-derived streams contain diverse compounds, including fatty acids, hydrocarbons, fatty alcohols, aldehydes, esters, and aromatic compounds generated during depolymerization. These intermediates can be metabolized by selected microorganisms, particularly Pseudomonas species with versatile fatty-acid and hydrocarbon pathways, as well as Cupriavidus necator and mixed microbial cultures. Unlike reviews that address plastic upcycling or PHA biosynthesis separately, this review focuses on the deconstruction-fermentation interface that governs plastic-to-PHA conversion. It consolidates current progress in plastic deconstruction, substrate conditioning, microbial metabolism, fermentation control, polymer recovery, and techno-economic and life-cycle considerations. By emphasizing substrate composition, biological compatibility, plastic‑carbon recovery, and final polymer quality, the review identifies priorities for scalable and environmentally sustainable PHA production from plastic-derived substrates.
Base editors (BEs) are transformative genome engineering tools that enable precise nucleotide substitutions without inducing double-strand breaks (DSBs) or requiring donor DNA templates. Since the first cytosine base editor (CBE) was developed in 2016, the field has advanced rapidly, with the creation of diverse BE variants that incorporate distinct deaminases and glycosylases. These engineered editors have significantly expanded the scope of genome editing by generating deaminated bases or apurinic/apyrimidinic (AP) site lesions, thereby harnessing endogenous DNA repair or replication mechanisms to produce base transitions and transversions. Among these endogenous pathways, trans-lesion synthesis (TLS) plays a particularly critical role in converting AP sites into specific base substitutions. TLS polymerases insert nucleotides opposite AP lesions, and the final editing outcome is dictated by the unique nucleotide preferences of individual TLS polymerases. This review focuses on the action mode of different base editors, highlights their interplays with the TLS, summarizes their potential therapeutic applications and discusses perspective strategies to improve precision and expand targeting scope.
Parasitic helminth infections impose major burdens on human and animal health, and increasing anthelmintic resistance reinforces the need for new drugs. Early-stage discovery remains constrained by the scarcity of novel, potent, selective and chemically tractable starting points, the limited predictive value of some experimental models, mechanistic uncertainty and insufficient capacity to sustain promising series through medicinal chemistry, safety and exposure studies. Discovery can begin through complementary routes, including target-agnostic whole-organism screening, screening of known-pharmacology and repurposing collections, target- or pathway-led approaches and structure-guided or computational selection. Whole-organism models are valuable because they capture compound access and integrated parasite responses, but do not establish mechanism; conversely, target-based approaches require validation in the intact parasite. This review presents an iterative framework, connecting these entry routes with chemical diversity, hit confirmation and triage, early structure-activity relationship analysis, fit-for-purpose mechanistic investigation and translational assessment. Nematode models provide scalable platforms, while selected trematode and cestode examples illustrate broader applicability. Chemoproteomics, functional genomics and multi-omics can link phenotypes to candidate targets and pathways, while artificial intelligence can support compound and hypothesis prioritisation. Their value lies in informing experimental decisions rather than replacing validation. Quantitative examples show that measurable activity can be identified, but development-quality hit series remain rare. Scientific integration is therefore necessary but insufficient: sustained investment, cross-sector expertise, development infrastructure and continuity of project ownership are also required. Together, these principles define a balanced, experimentally grounded and mechanism-informed approach to early-stage anthelmintic discovery.
Animal-parasitic nematodes are pathogens of medical and veterinary importance, yet their cellular biology remains poorly resolved because complex life cycles, host-dependent development and limited access to particular stages constrain investigation. This conceptual review traces nematode cellular biotechnology from efforts to isolate and maintain nematode-derived cells to the definition of cellular identity and its integration with biological function. Early culture studies showed that cells from free-living and parasitic nematodes could be isolated, maintained and sometimes propagated, but their identity was inferred indirectly from morphology, biochemical activity and immunological characteristics. Single-cell and single-nucleus transcriptomics transformed this landscape, with Caenorhabditis elegans providing foundational methods and atlases for resolving cellular diversity, developmental trajectories and tissue organisation. Advances in plant-parasitic nematodes and parasitic trematodes provide methodological insights. In animal-parasitic nematodes, these approaches now include whole-parasite dissociation, nuclei isolation, tissue-specific profiling and spatial transcriptomics. Organoid and co-culture systems provide access to host-parasite interfaces, while functional-genetic platforms enable experimental perturbation. The emerging landscape is heterogeneous: some systems provide cellular or spatial maps, whereas others offer greater tractability through culture, host-derived models or genetic manipulation. No single system yet provides life-cycle-wide cellular coverage, spatially resolved tissue organisation, sustained culture of parasite-derived cells or tissues and reliable functional perturbation. We identify priorities for linking cellular identity to developmental dynamics, anatomical context, culture authentication and experimentally testable function. Collectively, these advances are moving the field from maintaining cells and defining their identity towards understanding and manipulating cellular function, thereby supporting approaches to the treatment and control of animal-parasitic nematode infections.
The M13 filamentous bacteriophage has transcended its origins as a model for viral replication to become a premier programmable bio-scaffold at the intersection of nanotechnology, synthetic biology and clinical medicine. While famously known for the Nobel Prize-winning development of phage display, M13's utility now extends far beyond peptide libraries. Its unique anisotropic structure, genetic plasticity and chemically addressable coat proteins enable the precise bottom-up assembly of functional nanomaterials, ranging from high-performance energy storage to targeted theranostic agents. This review provides a definitive account of the M13 platform, synthesising foundational structural biology and infection dynamics with state-of-the-art engineering strategies. We detail the physical and genetic architecture of the virion, provide a critical evaluation of production and purification methodologies, and explore the chemical-genetic toolboxes used to functionalise its surface. By bridging the gap between fundamental virology and applied materials science, this synthesis identifies the current bottlenecks in clinical and industrial translation and offers a roadmap for the next generation of M13-based biotechnologies.
Methanogenic archaea (methanogens) are key drivers of global carbon cycling and biomethane production, thriving in diverse anaerobic environments through specialized metabolic and transport systems. While methanogenesis is well understood, transport mechanisms underlying nutrient uptake, ion homeostasis, and macromolecule translocation remain poorly understood. This review compiles current knowledge on transporter proteins and substrate uptake in methanogens. It also highlights fundamental knowledge gaps and outlines experimental procedures for identifying new transporters experimentally and bioinformatic strategies to identify transporter-encoding genes. These approaches enable the investigation of cultured and uncultured methanogens, providing a broader view of transporter diversity and global distribution. Advancing transporter research will enhance insights into archaeal physiology and supports further developing biotechnological applications of methanogens for biofuels and chemical production, and sustainable energy systems.
