SEARCH · Search bioRxiv

Results for “Bioengineering”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28Linked to original sources

bioRxiv2019

Hair-bearing human skin generated entirely from pluripotent stem cells

The skin is important for regulating bodily fluid retention and temperature, guarding against external stresses, and mediating touch and pain sensation. The skin is also susceptible to damage from burns, diseases, or genetic defects, which affect nearly one billion people worldwide1,2. For the advancement of skin regenerative therapies, it remains challenging to construct new skin with hair follicles and nerves in tissue cultures and in bioengineered skin grafts3-8. Here, we report an organoid culture system that generates complex skin from human pluripotent stem cells. We use step-wise modulation of the TGF{beta} and FGF signalling pathways to co-induce cranial epithelial cells and neural crest cells within a spherical cell aggregate. During 4-5 months incubation, we observe the emergence of a cyst-like skin organoid composed of stratified epidermis, fat-rich dermis, and pigmented hair follicles equipped with sebaceous glands. A network of sensory neurons and Schwann cells form nerve-like bundles that target Merkel cells in organoid hair follicles, mimicking human touch circuitry. Single-cell RNA sequencing and direct comparison to foetal specimens suggest that skin organoids are equivalent to human facial skin in the second-trimester of development. Moreover, we show that skin organoids produce planar hair-bearing skin when grafted on nude mice. Together, our results demonstrate the self-assembly of nearly complete skin tissue in vitro that can be used to reconstitute skin in vivo. We anticipate that our skin organoid model will be foundational to future studies of human skin development, disease modelling, or reconstructive surgery.

developmental biology↗

bioRxiv2019

An aerobic link between lignin degradation and C1 metabolism: growth on methoxylated aromatic compounds by members of the genus Methylobacterium

Pink-pigmented facultative methylotrophs have long been studied for their ability to grow on reduced single-carbon (C1) compounds. The C1 groups that support methylotrophic growth may come from a variety of sources. Here, we describe a group of Methylobacterium strains that can engage in methoxydotrophy: they can metabolize the methoxy groups from several aromatic compounds that are commonly the product of lignin depolymerization. In addition, these organisms can utilize the full aromatic ring as a growth substrate, a phenotype that has rarely been described in Methylobacterium. We demonstrated growth on p-hydroxybenzoate, protocatechuate, vanillate, and ferulate in laboratory culture conditions. We also used comparative genomics to explore the evolutionary history of this trait, finding that the capacity for aromatic catabolism is likely ancestral to two clades of Methylobacterium, but has also been acquired horizontally by closely related organisms. In addition, we surveyed the published metagenome data to find that the most abundant group of aromatic-degrading Methylobacterium in the environment is likely the group related to M. nodulans, and they are especially common in soil and root environments. The demethoxylation of lignin-derived aromatic monomers in aerobic environments releases formaldehyde, a metabolite that is a potent cellular toxin but that is also a growth substrate for methylotrophs. We found that, whereas some known lignin-degrading organisms excrete formaldehyde as a byproduct during growth on vanillate, Methylobacterium do not. This observation is especially relevant to our understanding of the ecology and the bioengineering of lignin degradation.

microbiology↗

bioRxiv2019

Glucuronoxylomannan in the Cryptococcus species capsule as a target for CAR+ T-cell therapy

The genus Cryptococcus comprises two major fungal species that cause clinical infections in humans: C. gattii and C. neoformans. To establish invasive human disease, inhaled Cryptococci must penetrate the lung tissue and reproduce. Each year, about 1 million cases of Cryptococcus infection are reported worldwide, and the infections mortality rate ranges from 20% to 70%. HIV+/AIDS patients are highly susceptible to Cryptococcus infection. Therefore, we hypothesized that CD8+ T cells could be redirected to target glucuronoxylomannan (GXM), a sugar present in the Cryptococcus species capsule, via expression of a GXM-specific chimeric antigen receptor (GXMR-CAR) for treatment of cryptococcosis. GXMR-CAR has an anti-GXM single-chain variable fragment followed by an IgG4 stalk, a CD28 transmembrane domain, and CD3-{varsigma} and CD28 signaling domains. After lentiviral transduction of human T cells with the GXMR-CAR construct, flow cytometry demonstrated that 82.4% of the cells expressed GXMR-CAR on their surface. To determine whether the GXMR-CAR+ T cells exhibited GXM-specific recognition, these cells were incubated with GXM for 24 h and examined using bright-field microscopy. Large clusters of proliferating GXMR-CAR+ T cells were observed, while no clusters were present in the control cells. Moreover, the interaction of GXM with GXMR-CAR+ T cells was detected via flow cytometry using a GXM-specific antibody. The ability of GXMR-CAR T cells to bind to the yeast form of C. neoformans was detected by fluorescent microscopy, but no binding was detected with NoDNA T cells. Furthermore, when GXMR-CAR+ T cells were administered to immunocompromised NSG mice infected with C. neoformans their C. neoformans burden was significantly lower than mock-transduced control T cell treated mice as shown via immunofluorescence using an anti-GXM antibody and Gomori methenamine-silver (GMS) staining of Titan cells in lung tissue. Thus, these findings demonstrated the effectiveness of GXMR-CAR+ T-cell therapy for cryptococcosis in a murine model.\n\nAuthor summaryCryptococcus gattii infects both immunocompetent and immunodeficient patients such as those with HIV/AIDS, while C. neoformans usually infects only immunocompromised patients. Every year, almost one million HIV/AIDS patients suffer from Cryptococcus fungal co-infection. At present, no curative treatment is available to treat cryptococcosis in chronic HIV/AIDS patients. The objective of this research was to develop novel \"Bioengineered\" Cryptococcus specific chimeric antigen receptor (CAR) CD8+ T-cells to target and kill Cryptococcus. By using a culture model, we demonstrated that the Cryptococcus specific CAR T cells were able to bind to the yeast form of C. neoformans. Using a mouse model of Cryptococcus, the Cryptococcus specific CAR treated group showed a significant reduction of fungal burden in lung tissue when compared to the control group. This gives new hope to HIV/AIDS patients suffering from cryptococcal infections.

immunology↗

bioRxiv2019

Hot-spots and their contribution to the self-assembly of the viral capsid: in-vitro validation

The viral capsid is a macromolecular complex formed by self-assembled proteins (CPs) which, in many cases, are biopolymers with an identical amino acid sequence. Specific CP-CP interactions drive the capsid self-assembly process. However, it is believed that only a small set of protein-protein interface residues significantly contribute to the formation of the capsid; the so-called "hot-spots". Here, we investigate the effect of in-vitro point-mutations on the Bromoviridae family structure-conserved interface residues of the icosahedral Cowpea Chlorotic Mottle Virus, previously hypothesized as hot-spots. We study the self-assembly of those mutated recombinant CPs for the formation of capsids by Thermal Shift Assay (TSA). We show that the TSA biophysical technique is a reliable way to characterize capsid assembly. Our results show that point-mutations on non-conserved interface residues produce capsids indistinguishable from the wild-type. In contrast, a single mutation on structure-conserved residues E176 or V189 prevents the formation of the capsid while maintaining the tertiary fold of the CP. Our findings provide experimental evidence of the in-silico conservation-based hot-spot prediction accuracy. As a whole, our methodology provides a framework that could aid in the rational development of molecules to inhibit virus formation, or advance capsid bioengineering to design for their stability, function and applications.

biophysics↗

bioRxiv2019

Structural elements that modulate the substrate specificity of plant purple acid phosphatases: avenues for improved phosphorus acquisition in crops

Phosphate acquisition by plants is an essential process that is directly implicated in the optimization of crop yields. Purple acid phosphatases (PAPs) are ubiquitous metalloenzymes, which catalyze the hydrolysis of a wide range of phosphate esters and anhydrides. While some plant PAPs display a preference for ATP as the substrate, others are efficient in hydrolyzing phytate or 2-phosphoenolpyruvate (PEP). PAP from red kidney bean (rkbPAP) is an efficient ATP- and ADPase, but has no activity towards phytate. The crystal structure of this enzyme in complex with an ATP analogue (to 2.20 [A] resolution) provides insight into the amino acid residues that play an essential role in binding this substrate. Homology modelling was used to generate three-dimensional structures for the active sites of PAPs from tobacco (NtPAP) and Arabidopsis thaliana (AtPAP12 and AtPAP26) that are efficient in hydrolyzing phytate and PEP as substrates, respectively. In combination with substrate docking simulations and a phylogenetic analysis of 49 plant PAP sequences (including the first PAP sequences reported from Eucalyptus), several active site residues were identified that are important in defining the substrate specificities of plant PAPs. These results may inform bioengineering studies aimed at identifying and incorporating suitable plant PAP genes into crops to improve phosphorus use efficiency. Organic phosphorus sources increasingly supplement or replace inorganic fertilizer, and efficient phosphorus use of crops will lower the environmental footprint of agriculture while enhancing food production.

biochemistry↗

bioRxiv2019

A Bayesian Non-parametric Mixed-Effects Model of MicrobialPhenotypes

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

systems biology↗

bioRxiv2019

Improved phenotypic predictions of metabolic models by integrating regulatory constraints

Genome-scale metabolic networks (GSMs) are mathematic representation of a set of stoichiometrically balanced reactions. However, such static GSMs do not reflect or incorporate functional organization of genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; and downstream metabolites often dynamically regulate the gene expression of their reactions via feedback. Here, we present a method which reconstructs GSMs with locally coupled reactions and transcriptional regulation of metabolism by key metabolites. The proposed method has outstanding performance in phenotype prediction of wild-type and mutants in Escherichia coli (E. coli), Saccharomyces cerevisiae (S. cerevisiae) and Bacillus subtilis (B. subtilis) growing in various conditions, outperforming existing methods. The predicted growth rate and metabolic fluxes are highly correlated with those experimentally measured. More importantly, our method can also explain the observed growth rates by capturing the real (experimentally measured) changes in flux between the wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology and microbial pathology.

systems biology↗

bioRxiv2019

Simulating the human tumor microenvironment in colorectal cancer organoids in vitro and in vivo

The tumor microenvironment (TME) plays a significant role in cancer growth and metastasis. Bioengineered models of the TME will advance our understanding of cancer progression and facilitate identification of novel anti-cancer therapeutics that target TME components such as extracellular matrix (ECM) and stromal cells. However, most current in vitro models fail to recapitulate the extensive features of the human tumor stroma, especially ECM architecture, and are not exposed to intact body physiology. On the other hand, in vivo animal models do not accurately capture human tumor architecture. Using the features of biopsied colorectal cancer (CRC) tissue as a guide, we address these deficiencies by creating human organoids containing a colonic stromal ECM layer and CRC spheroids. Organoids were studied in vitro and upon implantation in mice for 28 days. We show that the stromal ECM micro-architecture, generated in vitro, was maintained in vivo for at least 28 days. Furthermore, comparisons with biopsied CRC tumors revealed that organoids with orderly structured TMEs induce an epithelial phenotype in CRC cells, similar to low-grade tumors, compared to a mesenchymal phenotype observed in disordered TMEs, similar to high-grade tumors. Altogether, these results are the first demonstration of replicating the human tumor ECM architecture in biofabricated tumor organoids under ex vivo and in vivo conditions.

cancer biology↗

bioRxiv2019

Controlling the speed and trajectory of evolution with counterdiabatic driving

The pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer, understanding how species respond to environmental perturbations like climate change, and developing artificial selection approaches for agriculture. Great progress has been made in quantitative modeling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and the distributions of these trajectories remains elusive. We propose an approach to achieve this using ideas originally developed in a completely different context - counterdiabatic driving to control the behavior of quantum states for applications like quantum computing and manipulating ultra-cold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (i.e. varying drug concentrations / types, temperature, nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval. This level of control, allowing empirical optimization of evolutionary speed and trajectories, has myriad potential applications, from enhancing adaptive therapies for diseases, to the development of thermotolerant crops in preparation for climate change, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules.

evolutionary biology↗

bioRxiv2025

Alphaviral capsid proteins inhibit stress granule assembly via competitive RNA binding with G3BP1

Viral infection is one of the conditions that induces stress granule (SG) formation, a cellular defense mechanism that exerts antiviral effects. To counteract this host response, viruses have evolved a broad spectrum of strategies to inhibit SG formation. However, the molecular mechanisms underlying SG inhibition remain poorly understood. The nucleocapsid proteins play a critical role in virus replication and host interaction. Here, using Semliki Forest Virus (SFV) as a model, we uncover the function of the alphavirus nucleocapsid in SG inhibition. This inhibitory function depends on oligomerization mediated by an N-terminal -helix and with a positively charged intrinsically disordered region (IDR). We show that SFV capsid directly competes with G3BP1 for RNA binding, thereby disrupting G3BP1-RNA liquid-liquid phase separation (LLPS) in vitro and SG assembly in cells. This mechanism is conserved across the alphavirus family but is not shared by the nucleocapsid of SARS-CoV-2 or other endemic viruses examined. Notably, expression of a peptide from SFV capsid is sufficient to inhibit SG formation induced by Amyotrophic Lateral Sclerosis (ALS)-associated mutations, suggesting potential therapeutic applications. Our findings reveal mechanistic insight into SG modulation by the viral capsid protein and provide a possible bioengineering tool for probing SG dynamics in health and disease.

cell biology↗

bioRxiv2025

End-to-end single-stranded DNA sequence design with all-atom structure reconstruction

Designing biological sequences that fold into predefined conformations is a central challenge in bioengineering. Although deep learning has enabled significant advances in protein and RNA sequence design, progress in single-stranded DNA (ssDNA) design has been constrained by the limited availability of structural data. To address this challenge, we introduce InvDNA, a deep learning-based method that designs ssDNA sequences directly from backbone atomic coordinates. This end-to-end formulation avoids the loss of structural information during backbone-to-feature conversion and further accommodates flexible backbone representations, dynamic sequence masking, and structural reconstruction objectives. These strategies bolster InvDNAs ability to generalize across diverse ssDNA structural contexts while enabling additional functionalities, including generating diverse sequences for a given backbone, reconstructing nucleotide conformations from backbone and preserving functional sites. In benchmarks using experimentally determined ssDNA structures, InvDNA demonstrates more than a twofold improvement in sequence recovery compared with existing ssDNA and RNA sequence design approaches. Further computational validation using AlphaFold3 shows that 44.4% of InvDNA-designed sequences successfully fold into their predefined conformations. Notably, this success rate increases when backbone coordinates are perturbed to diversify the InvDNA-designed sequences. Collectively, these results establish InvDNA as a robust framework for rational ssDNA engineering.

molecular biology↗

bioRxiv2025

Dopamine biosynthesis in plants

Dopamine, an essential human neurotransmitter, also plays important roles in plant-microbe interactions and abiotic stress tolerance. While dopamine biosynthesis is well-characterized in mammals, it remains largely unclear in plants. Through metabolite-based genome-wide association studies, we identified two putative polyphenol oxidase genes for dopamine biosynthesis in Brachypodium distachyon, located in the same chromosomal region as two tyrosine decarboxylase genes. Biochemical and knockout mutant analyses demonstrated that dopamine biosynthesis proceeds primarily via tyramine as an intermediate and contributes to plant tissue browning. Homology searches and functional assays indicate that this dopamine biosynthetic pathway is conserved across diverse plant species. This discovery provides valuable insights into plant dopamine biosynthesis with potential applications in reducing food browning, improving stress resilience, and bioengineering valuable compounds.

plant biology↗

bioRxiv2026

Hyaluronan underlies the emergence of form, fate, and function in human cardioids

The extracellular matrix (ECM) is crucial for organ development and disease. Yet, the interplay among cells, function, and the ECM during human cardiogenesis remains obscure. Using human cardioids, we discovered that cardiac mesoderm-synthesized hyaluronan (HA) underlies early cardiac functional development. HA drives cardioid cavity formation through hydrogel swelling and bioscaffolding, critical functions of the cardiac jelly in the early vertebrate heart. During an early developmental window, HA is essential for establishing cardiac cell identity, while at later stages, HA-generated forces promote beating function through mechanosensitive channels. Chamber-specific differences in mechanical sensitivity ensure robust contractions in multi-chambered tissues. Our findings reveal how a single endogenous ECM component orchestrates the co-emergence of form, fate, and function during human organogenesis, opening new avenues for bioengineering physiologically relevant organ models.

developmental biology↗

bioRxiv2026

Rectifying Stress into Structural Memory Drives Specialization in Cells and Adaptive Computational Systems

Functional specialization in continuous systems requires balancing adaptation to environmental stress with the preservation of encoded information. Yet, the physical constraints governing how living systems reconcile selective information retention with energetic structural reorganization remain unclear-a trade-off that may offer transferable design rules for adaptive computing. Here we present a unified framework that connects cellular specialization to adaptive machine learning through principles of nonequilibrium optimization. We find that asymmetric fluctuations in key parameters act as tunable drives that, when phase-aligned, are rectified into durable functional specialization. Biologically, we show that in adipose mesenchymal cells, transient nutrient deficits trigger cytosolic pH fluctuations. These oscillations, regulated by the ATP12A proton pump, modulate nuclear compartment dynamics to coordinate mitochondrial specialization and lock in epigenetic memory for phenotype persistence. Notably, modulating ATP12A amplifies these drives, enhancing functional specialization and metabolic homeostasis in disease models. Computationally, we formalize analogous principles using a compositional framework based on operadic algebra, where structural updates act as a topological ratchet converting drive fluctuations into persistent architectures. This reveals a learning regime where gains in predictive information are accompanied by auditable residual costs for non-predictive states and reorganization. As a proof of concept, we deploy a self-organizing machine learning system that discovers latent hierarchy and reconstructs data through adaptive compartment dynamics. We show that internal organization fluctuations increase with residuals, an information-processing ledger guiding adaptive computation. Across both domains, local organization gains are directionally linked to dissipation proxies (pH variance in vivo and residuals in silico), consistent with operation under shared constraints. Overall, this work identifies asymmetric-drive rectification as a fundamental mechanism of adaptive specialization, providing thermodynamically informed design rules for both bioengineering and adaptive AI. One-Sentence SummaryAsymmetric-drive rectification is a fundamental mechanism by which cells and adaptive computational systems convert internal stress into structural memory, encoding functional specialization

biophysics↗

bioRxiv2026

Computational insights on the competition between electrotaxis and durotaxis

Understanding how cells migrate in response to external cues has important implications for biology, medicine, and bioengineering. Chemical, mechanical, and electrical signals are the primary drivers of directed cell migration, and each has been extensively studied over the past decades. Among them, chemical cues were the first to be investigated and remain the most widely studied due to their undeniable role in in vivo guidance. Mechanical signals--particularly substrate stiffness gradients--have gained prominence for their ubiquity across cell types and their potential to direct migration. More recently, growing evidence suggests that electrotaxis offers a highly precise and programmable means to guide cell movement. Despite this, these cues are often studied in isolation, whereas in vivo they typically coexist and interact. Using wellestablished biophysical models, we investigate how mechanical and electrical signals cooperate and how they can be engineered to compete for control over cell migration. We demonstrate that an electric field can override and even reverse durotaxis, with outcomes that depend strongly on the specific cell type. To address this large variability in controlling cell migration, we propose particular steps toward further exploration. To support such future research, we provide a freely available platform for predicting electro-mechanical interactions in cell migration, based on a given cells sensing and signaling characteristics, which could tailor the mechanical and electrical signals that arise naturally during organ development, cancer invasion, or tissue regeneration.

biophysics↗

bioRxiv2026

Next generation protein-corrole bio-assemblies provide effective tumoricidal treatment in a metastatic triple-negative breast cancer model

Assemblies that combine chemotherapeutics with tumor-targeting proteins are promising agents for treating resistant cancers but require full biochemical characterization before therapeutic deployment. We developed and optimized a HER3-targeting capsomere, HPK2.0, which forms stable nanoscale assemblies with cytotoxic corroles via electrostatic neutralization and shape complementarity. These nanocomplexes exhibit durable serum stability, HER3-dependent tumor invasion, and efficient endosomal escape, resulting in potent and selective cytotoxicity in triple-negative breast cancer (TNBC) cells. In an orthotopic metastatic TNBC model, systemic treatment with HPK2.0-corrole assemblies achieved 67-83% tumor regression, near-complete suppression of spontaneous lung metastasis, and a [~]2-fold improvement in survival relative to mock treatment, with minimal off-target toxicity. By integrating tumor specificity with therapeutic potency, this next-generation protein-corrole platform establishes a clinically scalable strategy for treating metastatic HER3-positive TNBC. SignificanceTriple-negative breast cancer (TNBC) is an aggressive disease with high rates of metastasis and mortality, largely because it lacks molecular targets for precision therapy. As a result, patients rely primarily on chemotherapy, which causes systemic toxicity and frequently fails to control metastatic spread. Here, we introduce a targeted therapeutic strategy in which a bioengineered protein selectively recognizes a receptor highly expressed in metastatic TNBC and delivers a potent cytotoxic payload directly into tumor cells. In mouse models, this approach produced robust tumor regression, markedly reduced lung metastases, extended survival, and showed minimal off-target toxicity. These findings establish a versatile platform for targeted treatment of TNBC and highlight a strategy that may be broadly applicable to other HER3-expressing cancers.

cancer biology↗

bioRxiv2026

Deconstructing empirical fitness seascapes across scales of granularity

The fitness landscape metaphor remains resonant in evolutionary theory and has facilitated the birth of newer concepts--like the fitness seascape--that consider the role of environmental context in shaping the dynamics of evolution. Since the emergence of the fitness seascape, it has appeared in several studies that examine how different and fluctuating environments shape evolutionary outcomes. Despite a growing interest in these topics, we lack comprehensive examinations of the role of environmental context in shaping features of fitness seascapes. In this study, we address this gap by deconstructing empirical fitness seascapes across scales of granularity: mutational steps, loci, locus interactions, alleles, trajectories, and entire seascapes. For each, we examine how environmental context influences qualitative and quantitative aspects of seascapes, and find that they change appreciably, with patterns that are specific to individual systems of study. In summary, we reflect on the implications of the seascape metaphor with respect to the incorporation of environmental effects into theoretical population genetics, for understanding how the environment shapes evolution in disease systems, and for contemporary bioengineering excursions.

evolutionary biology↗

bioRxiv2026

Engineering auronidin production in Marchantia polymorpha enables a new class of plant-derived textile pigments

Plant-derived pigments offer sustainable alternatives to synthetic colorants, yet their practical deployment in textiles is limited by restricted chemical diversity and low abundance. Liverworts represent a source of diverse chemical compounds, and the model liverworts Marchantia polymorpha is an emerging as chassis for bioengineering and synthetic biology. Here, we report the biotechnological application of auronidins, a rare class of flavonoid pigments, as textile dyes. Using the Marchantia, we engineered enhanced auronidin production through controlled expression of the R2R3-MYB transcription factor MpMYB14. We systematically benchmarked constitutive and inducible gene expression systems, including heat-shock, glucocorticoid receptor, and {beta}-estradiol (XVE) circuits, identifying inducible strategies that decouple biomass accumulation from secondary metabolite production while achieving high pigment yields. Extracted auronidins were used to dye cotton yarn directly, demonstrating the feasibility of auronidins for textile dyeing. Our results establish Marchantia as a versatile plant chassis for programmable secondary metabolite production and introduce auronidins as a promising natural pigment platform for sustainable textile biotechnology.

synthetic biology↗