Biology subjects

Xu, C.

Publications and source records attributed to Xu, C..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

circASbase：A Comprehensive Database of Alternative Splicing Events in circRNAs

Despite extensive studies highlight the critical roles of alternative splicing in generating mature circRNA isoforms and enhancing their function diversity, a significant gap remains in the availability of dedicated databases for circRNA alternative splicing events. To bridge this gap, we developed circASbase, a pioneering and comprehensive database that catalogues 452,129 alternative splicing events in 884,047 full-length circRNAs from 581 samples across 13 species, and provides rich annotations to facilitate understanding the splicing regulation of circRNAs. Our findings reveal substantial differences between circRNAs and linear transcripts regarding the distribution and occurrence of alternative splicing events, highlighting the unique regulatory landscape of circRNAs. These unique splicing events result in functional differences of circRNAs by affecting IRES sites, m6A sites, ORFs, protein features, miRNA targets, and more. In summary, circASbase not only covers the urgent need of the research community for data repositories, but also represents a significant advancement in our understanding of circRNA biology. With its user-friendly interfaces and web-based visualization tools, circASbase is poised to become an indispensable resource for researchers exploring the regulatory mechanisms and functional roles of alternative splicing events in circRNAs. This database will continuously drive new insights and discoveries in the field, setting the stage for further advancements in circRNA research. circASbase is available at http://reprod.njmu.edu.cn/cgi-bin/circASbase/

bioinformatics↗

bioRxiv2025

Tumor hypoxia adaptation depends on FLAD1 mediated mitochondrial metabolic reprogramming

Hypoxia, a hallmark of solid tumors, promotes the malignant progression and is challenging to target. Metabolic reprogramming and the resulting metabolic vulnerabilities provide a promising strategy to target tumor hypoxia. Here we systematically compared the metabolic network differences between hypoxic and non-hypoxic cells, and developed a deep learning model, "DepFormer", to predict the dependent metabolic genes in hypoxic tumor cells. The performance of DepFormer was validated using CRISPR screening dataset. Oxidative phosphorylation was identified as the most significantly hypoxia-dependent metabolic pathway, and FLAD1 was predicted to be one of the key hypoxia-dependent metabolic genes. FLAD1 locus is amplified, and FLAD1 expression is upregulated in various tumor types, especially in hypoxic tumors. FLAD1 depletion compromises tumors adaptation to hypoxia by disrupting mitochondrial complex II activity, leading to an imbalance between succinate and fumarate, and consequent failure to adapt to hypoxia. Subsequently, we identified a drug-like inhibitor of FLAD1, which selectively inhibits the growth of tumor cells under hypoxia. Our findings reveal FLAD1 as an innovative therapeutic target for hypoxic tumors.

cancer biology↗

bioRxiv2025

Repulsive interactions instruct synaptic partner matching in an olfactory circuit

Neurons exhibit extraordinary precision in selecting synaptic partners. Although cell-surface proteins (CSPs) mediating attractive interactions between developing axons and dendrites have been shown to instruct synaptic partner matching1,2, the degree to which repulsive interactions play a role is less clear. Here, using a genetic screen guided by single-cell transcriptomes3,4, we identified three CSP pairs--Toll2-Ptp10D, Fili-Kek1, and Hbs/Sns- Kirre--in mediating repulsive interactions between non-partner olfactory receptor neuron (ORN) axons and projection neuron (PN) dendrites in the developing Drosophila olfactory circuit. Each CSP pair exhibits inverse expression patterns in the select ORN-PN partners. Loss of each CSP in ORNs led to similar synaptic partner matching deficits as the loss of its partner CSP in PNs, and mistargeting phenotypes caused by overexpressing one CSP could be suppressed by loss of its partner CSP. All CSP pairs are also differentially expressed in other brain regions. Together, our data reveal that multiple repulsive CSP pairs work together to ensure precise synaptic partner matching during development by preventing neurons from forming connections with non-cognate partners.

neuroscience↗

bioRxiv2025

Rewiring an olfactory circuit by altering the combinatorial code of cell-surface proteins

Proper brain function requires the precise assembly of neural circuits during development. Despite the identification of many cell-surface proteins (CSPs) that help guide axons to their targets1,2, it remains largely unknown how multiple CSPs work together to assemble a functional circuit. Here, we used synaptic partner matching in the Drosophila olfactory circuit3,4 to address this question. By systematically altering the combination of differentially expressed CSPs in a single olfactory receptor neuron (ORN) type, which senses a male pheromone that inhibits male-male courtship, we switched its connection from its endogenous postsynaptic projection neuron (PN) type nearly completely to a new PN type that promotes courtship. From this switch, we deduced a combinatorial code including CSPs that mediate both attraction between synaptic partners and repulsion between non-partners5,6. The anatomical switch changed the odor response of the new PN partner and markedly increased male-male courtship. We generalized three manipulation strategies from this rewiring-- increasing repulsion with the old partner, decreasing repulsion with the new partner, and matching attraction with the new partner--to successfully rewire a second ORN type to multiple distinct PN types. This work demonstrates that manipulating a small set of CSPs is sufficient to respecify synaptic connections, paving ways to explore how neural systems evolve through changes of circuit connectivity.

neuroscience↗

bioRxiv2025

Intraguild parasitism promotes the persistence of facultative hyperparasitoids by extending temporal host availability

Intraguild predation (IGP) is pervasive in natural food webs, although theoretical models predict restricted parameter space for the coexistence of IG-prey and IG-predators. One potential mechanism is that IGP persistence might be increased when IG-prey extend resource availability for IG-predators. Here we test this hypothesis using manipulative experiments in combination with mathematical modeling. Our experiment system includes Drosophila flies (Drosophila melanogaster, basal resources), primary larval parasitoids (Asobara leveri, IG-prey) and facultative pupal hyperparasitoid (Pachycrepoideus vindemiae, IG-predator). We measured the performance of facultative pupal parasitoids with and without the primary larval parasitoid. Our study revealed that larval parasitoids significantly increased the number of pupal parasitoids emerged from limited host resources, due to the extended resource availability to facultative hyperparasitoid. Specifically, immature primary parasitoids within Drosophila pupae can be explored by facultative hyperparasitoids for up to 14 days, while the healthy fly pupae are available to hyperparasitoids for only 5 days. Despite a significant reduction in offspring fitness of the facultative hyperparasitoids when reared on larval parasitoids versus healthy Drosophila pupae, adult female facultative hyperparasitoids showed no significant preference for healthy fly pupae over immature primary parasitoids. Our theoretical models demonstrated that an extended development period of the primary parasitoid can promote the persistence of the facultative hyperparasitoids and allow their coexistence with the primary parasitoid, although too prolonged development could drive the extinction of the primary parasitoid. As IG-prey often take longer time to develop after consuming the shared resources, extension of temporal resource availability could be a general mechanism contribute to the persistence of intraguild predators. Open research statementData are not yet available, but all of the original experimental data has been displayed in figures 2[~]4. The data will be made publicly available upon manuscript acceptance and archived in the Dryad Digital Repository.

ecology↗

bioRxiv2025

Persistent fatty acid catabolism during plant oil synthesis

Plant lipids are an essential energy source for diets and are a sustainable alternative to petroleum-based fuels and feed stocks. Fatty acid breakdown during seed germination is crucial for seedling establishment but unexpected during seed filling. Here, we demonstrate that the simultaneous biosynthesis and degradation of fatty acids begins early and continues across all phases of oil filling and throughout the photoperiod. Tests in camelina, rapeseed and engineered high-oil tobacco line confirmed that concomitant synthesis and breakdown in oil-producing tissues over development is the rule rather than the exception. We show that transgenics, designed for significantly higher fatty acid biosynthesis, fail to achieve a proportional increase in storage lipid levels due to increasing degradation, potentially explaining the underperformance of engineered lines compared to expectations.

plant biology↗

bioRxiv2025

scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data

Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in two and three- dimensional space. However, sparse sequencing capacity and growing spatial resolution in spatial omics present significant computational challenges in identifying biologically meaningful molecules that exhibit variable spatial distributions across different omics. We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in high-resolution spatial omics data. scBSP leverages sparse matrix operation to significantly increase computational efficiency in both computational time and memory usage. In diverse spatial sequencing data and simulations, scBSP consistently and rapidly identifies spatially variable genes and spatially variable peaks across various sequencing techniques and spatial resolutions, handling two- and three-dimensional data with up to millions of cells. It can process high-definition spatial transcriptomics data for 19,950 genes across 181,367 spots within 10 seconds on a typical desktop computer, making it the fastest tool available for handling such high-resolution, sparse spatial omics data while maintaining high accuracy. In a case study of kidney disease using 10x Xenium data, scBSP identified spatially variable genes representative of critical pathological mechanisms associated with histology.

bioinformatics↗

bioRxiv2025

High-speed mapping of whole-mouse peripheral nerves at subcellular resolution

In contrast to the rapid advancements in mesoscale connectomic mapping of the mammalian brain, similar mapping the peripheral nervous system has remained challenging due to the bodys size and complexity. Here, we present a high-speed blockface volumetric imaging system with an optimized workflow of whole-body clearing, capable of imaging the entire adult mouse at micrometer resolution within 40 hours. Three-dimensional reconstruction of individual spinal fibers in Thy1-EGFP mice reveals distinct morphological features of sensory and motor projections along the ventral and dorsal rami. Immunostaining facilitates body-wide mapping of sympathetic nerves and their branches, highlighting their perivascular patterns in limb muscles, bones, and most visceral organs. Viral tracing elucidates the fine architecture of vagus nerves and individual vagal fibers, revealing unexpected projection routes to various organs. Our approach offers an effective means to achieve a holistic understanding of cellular-level interactions among different systems that underlie body physiology and disease. HighlightsO_LIImaging of whole adult mouse at uniform subcellular resolution within 40 hours C_LIO_LIReconstruction of 191 thoracic spinal neuron projections for Thy1-EGFP mice C_LIO_LIMapping of immunolabeled sympathetic projections and their perivascular patterns C_LIO_LIResolving virus-labeled architecture of vagus nerves and complex single-fiber routes C_LI

neuroscience↗

bioRxiv2025

Micromorphology and ammonium transmembrane transport characteristics in roots of rice seedlings

During the seedling stage, rice root exhibit rapid growth and absorb significant quantities of nutrients. This study involved cultivating rice seedlings in two different seedling-raising mediums characterized by high ammonium concentration (HN, NH4+ 1.2 mg g-1) and low ammonium concentration (LN, NH4+ 0.006 mg g-1). The morphology of distinct root zones (root cap, meristematic zone, elongation zone, and maturation zone) was observed under a microscope throughout root development, and variations in transmembrane flux rates of NH4+ in different root zones were assessed using Non-invasive micro-test technology (NMT). Results showed that the root caps of both seminal and adventitious roots became separated from the root tip between 9 and 12 days after root emergence. During this time, the root tip transformed from a sharp to a round shape, and the distance from the maturation zone to the root tip progressively decreased from over 900 m on 6 days after sowing (DAS) to under 500 m on DAS12. The meristematic zone was the primary site for NH4+ absorption in new root and proved to be particularly sensitive to environmental NH4+ concentration. At DAS 6, the net NH4+ flux rate was highest in the meristematic zone under both HN and LN treatments. Additionally, the external NH4+ concentration influenced the direction of NH4+ flux in the meristematic zone at DAS9, with HN being a net NH4+ influx and LN being an NH4+ efflux, and the net NH4+ influx in the meristematic zone persisted for 3 days longer in the HN treatment compared to the LN treatment. In mature roots, the root hair zone emerged as the primary site of NH4+ uptake, exhibiting an infNH4+ influx rate of 40-140 pmol cm-1 s-1, and this low rate of uptake could be sustained for up to 12 days after root emergence. By DAS15, a net NH4+ efflux was observed in the entire seminal root, signaling the loss of NH4+ absorption function 15 days post-emergence. A similar trend was noted in adventitious roots, where NH4+ uptake remained functional for 12-15 days after root emergence. As rice seedlings continued to grow, new adventitious roots replaced old ones, facilitating ongoing NH4+ uptake.

plant biology↗

bioRxiv2025

Cell-death induced immune response and coagulopathy promote cachexia in Drosophila

Tumors can exert a far-reaching influence on the body, triggering systemic responses that contribute to debilitating conditions like cancer cachexia. To characterize the mechanisms underlying tumor-host interactions, we utilized a BioID-based proximity labeling method to identify proteins secreted by Ykiact adult Drosophila gut tumors into the bloodstream/hemolymph. Among the major proteins identified are coagulation and immune-responsive factors that contribute to the systemic wasting phenotypes associated with Ykiact tumors. The effect of innate immunity factors is mediated by NF{kappa}B transcription factors Relish, dorsal, and Dif, which in turn upregulate the expression of the cachectic factors Pvf1, Impl2, and Upd3. In addition, Ykiact tumors secrete Eiger, a TNF-alpha homolog, which activates the JNK signaling pathway in neighboring non-tumor cells, leading to cell death. The release of damage-associated molecular patterns (DAMPs) from these dying cells presumably amplifies the inflammatory response, exacerbating systemic wasting. Targeting the inflammatory response, the JNK pathway, or the production of cachectic factors could potentially alleviate the debilitating effects of cancer cachexia.

genetics↗

bioRxiv2025

AAVone: A Cost-Effective, Single-Plasmid Solution for Efficient AAV Production with Reduced DNA Impurities

Currently, the most common approach for manufacturing GMP-grade adeno-associated virus (AAV) vectors involves transiently transfecting mammalian cells with three plasmids that carry the essential components for production. The requirement for all three plasmids to be transfected into a single cell and the necessity for high quantities of input plasmid DNA, limits AAV production efficiency, introduces variability between production batches, and increases time and labor costs. Here, we developed an all-in-one, single-plasmid AAV production system, called AAVone. In this system, the adenovirus helper genes (E2A, E4orf6, and VA RNA), packaging genes (rep and cap), and the vector transgene cassette are consolidated into a single compact plasmid with a 13-kb backbone. The AAVone system achieves a two- to four-fold increase in yields compared to the traditional triple-plasmid system. Furthermore, the AAVone system exhibits low batch-to-batch variation and eliminates the need for fine-tuning the ratios of the three plasmids, simplifying the production process. In terms of vector quality, AAVs generated by the AAVone system show similar in vitro and in vivo transduction efficiency, but a substantial reduction in sequences attributed to plasmid backbones and a marked reduction in non-functional snap-back genomes. In Summary, the AAVone platform is a straightforward, cost-effective, and highly consistent AAV production system - making it particularly suitable for GMP-grade AAV vectors.

bioengineering↗

bioRxiv2024

T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction With Uncertainty-Aware Self-Learning for Protein-Specific Alignment

There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the rapid identification of candidate inhibitors and facilitates the optimization of on-target potency. In this work, we present T-ALPHA, a novel deep learning model that enhances protein-ligand binding affinity prediction by integrating multimodal feature representations within a hierarchical transformer framework to capture information critical to accurately predicting binding affinity. T-ALPHA outperforms all existing models reported in the literature on multiple benchmarks designed to evaluate protein-ligand binding affinity scoring functions. Remarkably, T-ALPHA maintains state-of-the-art performance when utilizing predicted structures rather than crystal structures, a powerful capability in real-world drug discovery applications where experimentally determined structures are often unavailable or incomplete. Additionally, we present an uncertainty-aware self-learning method for protein-specific alignment that does not require additional experimental data, and demonstrate that it improves T-ALPHAs ability to rank compounds by binding affinity to biologically significant targets such as the SARS-CoV-2 main protease and the epidermal growth factor receptor. To facilitate implementation of T-ALPHA and reproducibility of all results presented in this paper, we have made all of our software available at https://github.com/gregory-kyro/T-ALPHA.

bioinformatics↗

bioRxiv2024

Benchmarking Inverse Folding Models for Antibody CDR Sequence Design

Antibody-based therapies are at the forefront of modern medicine, addressing diverse challenges across oncology, autoimmune diseases, infectious diseases, and beyond. The ability to design antibodies with enhanced functionality and specificity is critical for advancing next-generation therapeutics. Recent advances in artificial intelligence (AI) have propelled the field of antibody engineering, particularly through inverse folding models for Complementarity-Determining Region (CDR) sequence design. These models aim to generate novel antibody sequences that fold into desired structures with high antigen-binding affinity. However, current evaluation metrics, such as amino acid recovery rates, are limited in their ability to assess the structural and functional accuracy of designed sequences. This study benchmarks state-of-the-art inverse folding models--ProteinMPNN, ESM-IF, LM-Design, and AntiFold--using comprehensive datasets and alternative evaluation metrics like sequence similarity. By systematically analyzing recovery rates, mutation prediction capabilities, and amino acid composition biases, we identify strengths and limitations across models. AntiFold exhibits superior performance in Fab antibody design, particularly in variable regions like CDRH3, whereas LM-Design demonstrates adaptability across diverse antibody types, including VHH antibodies. In contrast, models trained on general protein datasets (e.g., ProteinMPNN and ESM-IF) struggle with antibody-specific nuances. Key insights include the models varying reliance on antigen structure and their distinct capabilities in capturing critical residues for antigen binding. Our findings highlight the need for enhanced training datasets, integration of functional data, and refined evaluation metrics to advance antibody design tools. By addressing these challenges, future models can unlock the full potential of AI-driven antibody engineering, paving the way for innovative therapeutic applications. Author SummaryAntibodies play a vital role in modern medicine, offering targeted therapies for diseases ranging from cancer to infectious diseases. Designing new antibodies with specific and enhanced functionalities remains a key challenge in advancing therapeutic applications. In this study, we benchmarked cutting-edge artificial intelligence models for antibody sequence design, focusing on their ability to generate sequences for the critical antigen-binding regions of antibodies, known as Complementarity-Determining Regions (CDRs). Our findings reveal that specialized models like AntiFold excel in designing human antibody fragments, particularly in complex regions, while other models such as LM-Design demonstrate versatility across different antibody types. Importantly, we identified the limitations of models trained on general protein datasets, highlighting the need for antibody-specific training data to capture the unique features critical for therapeutic effectiveness. By evaluating these models against robust datasets and diverse metrics, our work underscores the importance of improving training data and evaluation methods to advance AI-driven antibody design. These insights pave the way for more accurate and effective tools, ultimately supporting the development of next-generation antibody-based therapeutics.

bioengineering↗

bioRxiv2024

Preferential Release of microRNAs via Extracellular Vesicles is Associated with Ductal Carcinoma In Situ to Invasive Breast Cancer Progression

Ductal carcinoma in situ (DCIS) is a benign "pre-cancer" that increases the risk of invasive breast cancer (IBC). Not all DCIS progress to IBC, and the primary factors driving progression remain unclear. Small extracellular vesicles (sEVs) or exosomes are known to play a role in advanced cancers, but their involvement in DCIS is poorly understood. This study examined the role of sEVs and their RNA content in DCIS progression. Rab27A, which regulates exosome release, is elevated in DCIS and IBC tissues compared with normal breast tissues. Inhibition of sEV release via Rab27A knockdown alters pro-invasive pathways and reduces invasion in a DCIS mouse model. Using the isogenic MCF10 breast cancer progression series, we found a significant increase in microRNAs (miRNAs) in sEVs from normal to malignant states, with the highest number of differentially expressed miRNAs in IBC sEVs compared with DCIS sEVs. In vivo, DCIS invasive progression elevated circulating sEV miRNA levels, which decreased upon Rab27A knockdown. Re-expression of miR-205, preferentially loaded into IBC sEVs, reduced proliferation, invasion, and EMT marker expression in DCIS cells. Combined Rab27A knockdown and miR-205 expression repressed TGF-{beta} signaling, activated p38, and induced cell cycle arrest and cell death. These findings illustrate that sEVs and their miRNAs promote DCIS progression, and the reintroduction of miR-205 in DCIS cells can inhibit invasive progression.

cancer biology↗

bioRxiv2024

DDI-GPT: Explainable Prediction of Drug-Drug Interactions using Large Language Models enhanced with Knowledge Graphs

Identifying potential drug-drug interactions (DDIs) before clinical use is essential for patient safety yet remains a significant challenge in drug development. We presented DDI-GPT, a deep learning framework that predicts DDIs by combining knowledge graphs (KGs) and pre-trained large language models (LLMs), enabling early detection of potential drug interactions. We demonstrated that DDI-GPT outperforms current state-of-the-art methods by capturing contextual dependencies between biomedical entities to infer potential DDIs. Through feature attribution methods, we show that our explainable deep learning (DL) models enhance the quality of explanations on the pathways and interactome networks. Using TwoSIDES as our benchmark dataset, DDI-GPT achieved the best performance of 0.964 in AUROC compared with existing DL methods. We also applied DDI-GPT to perform zero-shot prediction on 9,480 DDI records, encompassing 442 distinct drugs from the FDA Adverse Event Reporting System. DDI-GPT can attain a high accuracy of in 0.84 AUROC, with an improvement of 14% compared to the best previously published method. We explored model interpretations on predicted DDIs involving Brutons tyrosine kinase (BTK) inhibitors and uncovered CYP3A-enriched signals underlying the contaminant use of BTK inhibitors with other drugs leading to toxicity. Altogether, DDI-GPT, implemented as both a web server platform and a software package, identifies DDI events and offers a deep learning tool for drug safety use with expandable features.

bioinformatics↗

bioRxiv2024

Vgamma1+ gammadelta T cell-derived IL-4 initiates CD8 T cell immunity

Dendritic cells (DC) are pivotal for initiating adaptive immunity, a process triggered by the activation of DC via pathogen products or damage. Here, we describe an additional layer to this process, essential when pathogen-derived signals alone cannot directly achieve full DC activation. Immunisation with sporozoites from Plasmodium leads to CD8 T cell priming in a complex response that is initiated by a collaboration between conventional type 1 DC (cDC1) and {gamma}{delta} T cells. We unveil a pivotal initiating role for V{gamma}1+ {gamma}{delta} T cells, as they directly supply IL-4 to DC and CD8 T cells. IL-4 synergises with a CD4 T cell-derived CD40L signal to induce IL-12 production by cDC1. Both IL-12 and IL-4 then directly signal CD8 T cells, with synergy between these cytokines driving enhanced IL-12 receptor expression and expansion of responding CD8 T cells. This study reveals a key role for V{gamma}l+ {gamma}{delta} T cells in initiating CD8 T cell immunity to Plasmodium. More broadly, it shows that responses to some pathogens require help from innate-like T cells to pass an initiation threshold and further amplify the response in a process underscored by IL-4 production.

immunology↗

bioRxiv2024

The sparse driver system for in vivo single-cell labeling and manipulation in Drosophila

In this protocol, we introduce a sparse driver system for cell-type specific single-cell labeling and manipulation in Drosophila, enabling complete and simultaneous expression of multiple transgenes in the same cells. The system precisely controls expression probability and sparsity via mutant FRT sites with reduced recombination efficiency and tunable FLP levels adjusted by heat-shock durations. We demonstrate that this generalizable toolkit enables tunable sparsity, multi-color staining, single-cell trans-synaptic tracing, single-cell manipulation, and in vivo analysis of cell-autonomous gene function. For details on the use and execution of this protocol, please refer to Xu et al. 2024. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/626507v1_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@1a53b9org.highwire.dtl.DTLVardef@6eaa3aorg.highwire.dtl.DTLVardef@169cb2forg.highwire.dtl.DTLVardef@9aafb3_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

In situ Membrane Protein Expression by Efficient Recruitment of mRNA to the Membranes of Synthetic Cells

The synthesis of artificial cells is crucial for understanding the origins of life. In synthetic cells, however, the absence of membrane-bound organelles and auxiliary proteins severely limits the efficient expression and precise localization of membrane proteins using cell-free expression systems. Here we introduce a robust method that significantly enhances membrane protein synthesis by recruiting mRNA to phospholipid membranes. This is achieved through the use of cholesterol-modified, single-stranded DNA that anchors to the membrane and pairs with the mRNAs untranslated region. This strategic placement facilitates the assembly of protein expression machinery, promoting direct co-translational folding at the membrane. Our approach not only ensures correct protein topology and functionality but also demonstrates broad applicability for the in situ expression of various membrane proteins. It effectively addresses the challenges of membrane protein localization and assembly in synthetic cells, showcasing its versatility for synthesizing a wide array of membrane proteins.

synthetic biology↗