Biology subjects

Fan, C.

Publications and source records attributed to Fan, C..

At least 19 recordsLinked to original sources

bioRxiv2026

A mosaic RBD-nanoparticle pan-sarbecovirus vaccine elicits nanobodies that target highly conserved epitopes

SARS-CoV-2, the virus that causes COVID-19, continually mutates, producing new variants of concern (VOCs). Substitutions in the SARS-CoV-2 spike protein receptor-binding domain (RBD) have resulted in reduced efficacies of vaccines and therapeutic monoclonal antibodies. Indeed, since the emergence of Omicron and earlier VOCs, most previously approved anti-RBD therapeutic monoclonal antibodies are no longer authorized as therapeutics. To address VOC substitutions effects on therapeutic antibody efficacy, we sought to identify single-domain nanobodies (VHHs) targeting RBD regions (class 4, class 1/4, portions of class 3 and class 5 RBD epitopes) that are more conserved across VOCs, reasoning that the smaller size of camelid nanobodies compared with conventional two-domain antibody combining sites would facilitate access to occluded RBD epitopes. We used a Beacon optofluidic platform to isolate and characterize single memory B cells from a llama immunized with a mosaic RBD-nanoparticle immunogen that elicits broad antibody responses across sarbecoviruses. Using multiplex binding assays, we identified nanobodies that bind to spike RBDs from diverse sarbecovirus strains. Competition ELISAs and crystal structures of VHH-RBD complexes showed that isolated nanobodies recognize broadly conserved epitopes, including those on the RBD that are inaccessible to analogous conventional two-domain antibodies. Promising VHH-Fcs neutralized SARS-CoV-2 variants of concern and zoonotic sarbecoviruses, demonstrating potential therapeutic utility for current and future outbreaks.

immunology↗

bioRxiv2026

Large Language Models Predict Human Social Behavior via Interpretable Mechanisms

The development of large language models (LLMs) offers promising opportunities for predicting human behavior across diverse contexts. However, most prior work has emphasized behavioral imitation, with limited attention to transparent or interpretable models of the cognitive mechanisms underlying human decisions. In this study, we introduce MindEvolve, an autonomous workflow designed to predict behavior in social interactions by generating interpretable symbolic models of cognition. We systematically evaluate the modeling capabilities of multiple LLMs across a battery of socioeconomic games covering four core domains of social cognition: economic preferences, social preferences, social reasoning (theory of mind), and recursive planning. The symbolic models proposed by the LLMs are assessed by expert human evaluators for interpretability and theoretical coherence. Our results show that while most LLMs robustly capture economic and social preferences in relatively simple strategic settings, and in some cases generate novel models that integrate broader knowledge than those proposed by human experts, their capacity to model more complex psychological processes remains limited. However, a subset of state-of-the-art models demonstrates promising performance in capturing higher-order reasoning processes such as theory of mind and recursive planning. Together, these findings highlight the emerging potential of LLMs not only to imitate behavior, but to generate interpretable, mechanistic accounts of human social decision-making. Our results provide a roadmap for advancing LLM-based cognitive modeling toward human-expert-level theory construction.

neuroscience↗

bioRxiv2026

DNA-Programmed Condensate-Membrane Wetting and Cellular Internalization

Deformable condensates offer dynamic interfaces for biomolecule delivery, yet membrane adhesion does not necessarily lead to cellular internalization. The physical transition that determines whether a membrane-bound soft material remains surface-anchored or proceeds through wetting towards productive uptake remains poorly understood, particularly at active living-cell membranes. Here, we engineer sequence-defined DNA condensates through liquid-liquid phase separation (LLPS) and program their interfacial behavior by tuning sticky-end valency and cholesterol organization. These molecular designs precisely regulate condensate fluidity, fusion dynamics and internal organization, generating distinct states of weak contact, persistent anchoring and rapid wetting. Increasing cholesterol-mediated affinity does not enhance uptake. Instead, productive internalization emerges from a balance between membrane adhesion and condensate fluidity and deformability. Native membrane composition further modulates condensate interfacial fate across mammalian cells and plant protoplasts. DNA condensates enrich and deliver CpG ODNs, mRNA ([~]2000 nt) and proteins, while cargo loading experiments reveal that preserving condensate architecture is essential for functional delivery. Our findings identify wetting competence as a design parameter for controlling soft material engagement and cellular entry.

biophysics↗

bioRxiv2026

Neural decoding of speech using deep neural ensembles

Speech brain-computer interfaces (BCIs) can restore rapid communication to people with paralysis, but decoding errors still limit performance. In recent brain-to-text decoding competitions, deep ensemble methods, which combine predictions from multiple independently trained decoders, have delivered striking accuracy improvements and account for the largest gains over baseline approaches. However, these methods have not previously been tested in real-time, require substantial computational resources, and their performance under various clinically relevant constraints remains poorly understood. Here, we present the first closed-loop test of deep ensembles in a participant with bilateral intracortical microelectrode arrays, demonstrating a reduction in word error rate from 33.7% to 26.0% on a large-vocabulary task. Using additional data from three participants, we then assess how these gains depend on baseline error rate, training dataset size, and ensemble size, including the resource-accuracy tradeoffs most relevant for real-world deployment. Finally, we introduce a computationally efficient pseudoensembling approach based on test-time augmentation that improves decoding accuracy while requiring only a single base decoder, greatly reducing the computational burden of ensembling. Together, these results show that the benefits of deep ensembling can be realized in real time and under practical resource constraints, bringing speech BCIs closer to broader clinical adoption.

neuroscience↗

bioRxiv2026

Hydrophobic and lipid-mediated gating mechanism revealed by low-conductance MthK mutants

Large-conductance calcium (Ca2+)-activated potassium (K+) channels are involved in several essential cellular processes. This requires the ability to alternate between conductive and non-conductive states, yet the molecular mechanism of their gating remains incompletely understood. Bundle-crossing, selectivity filter, and hydrophobic gating are some of the mechanisms previously proposed for these channels, including the archaeal Ca2+-activated K+ channel MthK. Here, we took advantage of two MthK mutants that show dramatically-reduced conductance to gain insights into how the channels gate at the molecular level by combining cryo-electron microscopy, functional assays, and extensive molecular dynamics simulations. Functional and computational analyses reveal distinct mechanisms of reduced conductance between the two mutants. One mutant, where a glutamate residing at the intracellular mouth of the pore was replaced by an alanine (E92A) reduced ion density in the cavity and dramatically accelerated spontaneous channel closure. The second mutant, where an alanine residing in the channels aqueous cavity below the selectivity filter was replaced by a phenylalanine (A88F), introduced a hydrophobic constriction that partially dehydrates the cavity, imposing a barrier to ion permeation. The cryo-EM structures of these mutants, virtually identical to those of WT MthK, were used as models to investigate gating using molecular dynamics (MD) simulations. Long-timescale simulations captured complete open-to-closed transitions in both mutants, uncovering a dehydrated intermediate state and a tightly coupled lipid-entry mechanism that drove final pore closure. Our results show that hydrophobicity and lipid interactions are central determinants of MthK gating.

biophysics↗

bioRxiv2026

Imaging Intrinsic Stochastic Magnetic Fluctuations in Living Cells

SignificanceThis work establishes a probabilistic magnetometry framework for detecting weak stochastic magnetic fluctuations at the nanoscale, which provides the first quantitative access to intrinsic magnetic activity in living cells. Weak stochastic magnetic fluctuations at nanoscale are difficult to quantify. In living cells, ionic transport and molecular currents generate electromagnetic activity whose magnetic component is likewise nanoscale, weak, stochastic, and rapidly varying and has therefore remained experimentally inaccessible. Here we introduce Bio-Spin Probabilistic Inference (BISPIN), a digital statistical framework that can quantify weak, stochastic magnetic fluctuations at the nanoscale. Using threshold-resolved signals from enhanced nitrogen-vacancy quantum sensors, BISPIN converts unstable analog magnetic readouts into statistically convergent digital observables and infers fluctuation strength through probabilistic modeling, enabling robust quantification under random sensor orientations and biological heterogeneity within the experimental bandwidth. Applied to living cells, this approach distinguishes live from fixed cells, resolves agonist-induced activation, and maps subcellular variations in magnetic fluctuation strength. By providing the first quantitative access to intrinsic stochastic magnetic fluctuations in living cells, this work establishes a probabilistic magnetometry framework for cellular electrodynamics and opens a new magnetic dimension of cellular phenotyping for bio-spin omics.

biophysics↗

bioRxiv2026

MRN-ATM Pathway Activation in CD4 T-Cell Senescence during Chronic Hepatitis B Virus Infection

T-cell senescence is a hallmark of immune dysfunction in persistent viral infections, characterized by DNA damage accumulation and telomere erosion. However, the mechanisms driving CD4 T-cell senescence in the context of chronic hepatitis B virus (HBV) infection remain poorly defined. In this study, we demonstrated that people with chronic HBV infection exhibited CD4 T-cell senescence, marked by elevated KLRG1, along with increased DNA damage and telomere shortening, compared to HS. Notably, activation of the MRN-ATM (MRE11/RAD50/NBS1-Ataxia Telangiectasia Mutated Protein) pathway was prominent in CD4 T cells from HBV patients. Importantly, suppression of MRN attenuated ATM phosphorylation and its downstream signaling molecules, and inhibition of ATM reduced the production of proinflammatory cytokines in CD4 T cells derived from both HBV patients and HS. These results suggest that in chronic HBV infection, the virus induced CD4 T-cell senescence, telomere erosion, and DNA damage, while concurrent activation of the MRN-ATM pathway may serve as a compensatory mechanism to preserve CD4 T-cell function. Elucidating this relationship between T-cell senescence and DNA damage repair helps to understanding the mechanisms underlying HBV persistence and providing potential therapeutic targets against chronic HBV infection.

immunology↗

bioRxiv2026

Neuronal expression of Retinoid-Related Orphan Receptor Gamma (RORγ) and revisiting its role in the Central Nervous System

Retinoid-related orphan receptor gamma (ROR{gamma}) is a lineage-defining transcription factor for T helper 17 (Th17) cells and has long been considered selectively expressed within the immune system. However, accumulating evidence has implicated Th17 cells and other ROR{gamma}-expressing immune populations in embryonic brain development and central nervous system (CNS) function. Notably, ROR{gamma}-Cre-mediated deletion of the tuberous sclerosis complex (TSC) has been reported to cause spontaneous seizures and premature lethality, leading to the conclusion that ROR{gamma}-expressing immune cells directly regulate CNS physiology. Using multiple ROR{gamma}-based reporter mouse lines, we identified unexpected and widespread neuronal labeling throughout the forebrain and cerebellum. ROR{gamma}-Cre:GFPf/ mice exhibited robust GFP expression in neurons despite the absence of detectable T cells in the brain. Neuronal recombination was evident prenatally and independently validated using the mT/mG reporter line, indicating transient ROR{gamma} expression in embryonic neurons. In contrast, a ROR{gamma}-GFP reporter showed no detectable ROR{gamma} expression in the postnatal brain, either at baseline or following pilocarpine-induced status epilepticus. These findings demonstrate that ROR{gamma} expression is not restricted to the immune lineage and reveal previously unrecognized developmental expression in the embryonic brain. Consequently, seizures observed following ROR{gamma}-Cre-mediated deletion of TSC1 are unlikely to arise from ROR{gamma}-expressing immune cell-dependent mechanisms but instead reflect direct neuronal loss of TSC1. Our results call for careful reinterpretation of prior studies attributing CNS phenotypes to ROR{gamma}-expressing immune cells based on ROR{gamma}-Cre-driven genetic models. SignificanceThis work identifies transient neuronal ROR{gamma} expression as a critical confounder in immune-targeted genetic studies and reveals a previously unrecognized embryonic expression of ROR{gamma} in neurons that may contribute to neurodevelopment. Our findings further raise the possibility that embryonic neurons represent unintended off-targets when deploying therapeutic strategies involving ROR{gamma} ligands.

neuroscience↗

bioRxiv2026

Sex-specific Genetic Regulatory Effects in Chickens

Sexual dimorphism is a defining vertebrate feature, yet its sex-specific molecular architecture remains poorly understood. Here we established a sex-balanced, uniformly reared chicken cohort to map this landscape, integrating individual whole-genome sequencing with 7,969 bulk and 779,380 single nucleus transcriptomes across 32 tissues from 280 birds. We identified 495,098 independent expression quantitative trait loci for 20,194 genes, including 10,937 loci modulated by cell-type composition. Notably, 340 genes were regulated by 449 loci in a sex-dependent manner, significantly enrichment in endocrine tissues like adipose and the adrenal gland. Furthermore, we fine-mapped 1,219 structural variants, demonstrating their unique roles to tissue- and sex-specific expression beyond SNPs. Ultimately, we showed the utility of these regulatory effects in elucidating the molecular basis of metabolism and complex traits in both chickens and humans. This comprehensive atlas of regulatory effects provides profound insights into the genomic and molecular basis of sexual dimorphism in vertebrates.

genomics↗

bioRxiv2026

A reconfigurable DNA memory architecture for hierarchical data management via programmable phase transitions

DNA has emerged as a promising medium for the post-silicon era of information storage due to its ultrahigh density and longevity. However, current systems are bifurcated, with solid-state systems providing robust cold archival but lacking accessibility, while fluidic molecular computing systems offer dynamic processing but suffer from low density and instability. This mutual exclusivity has hindered the development of hierarchical memory, a standard in modern computing, within molecular storage systems. Here, we bridge this gap by engineering a reconfigurable DNA memory architecture driven by programmable liquid-liquid phase separation (LLPS). Our system leverages sequence-based encoding to achieve an ultrahigh storage density of 7x1010 GB/g, approaching the theoretical limits of DNA accessibility. In its fluidic hot state, DNA droplets enable rapid data loading ([~]83.8% in 5 min) and function as an in-memory editing platform supporting versatile, addressable bit-level operations including selective erasure ([~]65.1%) and high-efficiency rewriting and replacement (>99%) via programmable strand displacement. Importantly, to resolve the stability trade-off, we engineered a programmable phase transition whereby the triggered assembly of a rigid tetrahedral DNA framework (TDF) armor transforms liquid condensates into robust armored droplets. This cold state confers exceptional resistance to enzymatic and physical degradation, projecting multi-millennial data stability. By enabling reversible transitions between a editable, high-density computing mode and a stabilized archival mode, this work establishes the architectural foundation for scalable molecular information storage capable of hierarchical data management.

synthetic biology↗

bioRxiv2025

Representation, Alignment, and Generation: A Comprehensive Survey of Foundation Models for Non-Invasive Brain Decoding

The ability to decode human thoughts, intentions, and perceptions directly from non-invasive brain recordings holds transformative potential for healthcare, communication, and human-computer interaction. However, translating the safety and scalability of methods like Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Magnetoencephalography (MEG) into real-world utility has traditionally been hindered by low signal-to-noise ratios, limited spatial-temporal resolution, and the difficulty to collect large-scale high-quality data from an individual user. Recently, the emergence of Foundation Models (FMs)--large-scale, pre-trained architectures--has expanded the feasible operating region of non-invasive brain decoding under controlled protocols, primarily through representation learning, neuro-semantic alignment, and strong generative priors. However, evidence for robust cross-subject and real-world deployment remains uneven, as many results are still demonstrated on limited cohorts or highly controlled settings. This survey provides a comprehensive overview of how FMs are redefining the boundaries of non-invasive brain decoding. We propose a unified methodological framework that synthesizes recent advancements into a coherent process: extracting robust, transferable representations from noisy neural signals; aligning these signals with the rich semantic spaces of pre-trained vision and language models; and leveraging powerful conditional generative priors to reconstruct high-fidelity outputs. We systematically review state-of-the-art applications across three key domains: visual reconstruction, language and speech decoding, and auditory processing. Furthermore, we critically examine the persisting challenges of computational efficiency, cross-subject generalization, and privacy governance. By mapping the current landscape and identifying key gaps, this work outlines a strategic research agenda aimed at transitioning FM-driven neurotechnology from laboratory proofs-of-concept to reliable, real-world applications.

neuroscience↗

bioRxiv2025

DNA-barcoded microneedle array patch enabling the profiling of in vivo spatiotemporal transcriptomics

Recent years have witnessed profound advances in spatial transcriptomics (ST). However, current ST strategies are limited to tissue sections, unable to directly profile spatiotemporal transcriptome landscapes of individual living animals. To overcome this limitation, we present acupuncture-extracted in vivo spatiotemporal transcriptomics (aivST). It is mainly based on engineering clinical-grade acupuncture gold microneedles as a spatial-barcoding array patch with high-performance DNA recognition interfaces. These interfaces first utilize nanoparticles surface deposition to increase the density of DNA probes. A freeze-thawing method is further developed to regulate interfaced DNA probes with laterally uniform distribution and vertically upright conformation, mainly by loose stretching and local concentration of these DNA probes. It increased mRNA capture with 250% and speeded up reaction (from 60 min to 15 min), compared to that without freeze-thawing surface engineering. Using this method, we explored in vivo spatiotemporal transcriptome dynamics during skin wound healing of individual mice. We found four distinct expression changes of transcripts, including continuous upregulation, continuous downregulation, initial upregulation then downregulation, and initial downregulation then upregulation. Our aivST method advances the field from sparse tissue section sampling toward in vivo measurements of biological stereotypy and variability in individual living animals.

genomics↗

bioRxiv2025

Inhibition mechanism of pancreatic KATP channels by centipede toxins

The pancreatic ATP-sensitive potassium (KATP) channel acts as a crucial metabolic sensor by regulating insulin secretion to maintain whole-body energy homeostasis. Gain-of-functional mutations in this channel lead to neonatal diabetes mellitus, a rare disorder in which certain mutants demonstrate resistance to standard sulfonylurea therapy. Recent studies have identified a centipede toxin, SpTx1, as a potent inhibitor of both human pancreatic KATP channels and their gain-of-functional mutants. This toxin stimulates insulin secretion, offering a promising therapeutic strategy for neonatal diabetes. Nevertheless, the molecular mechanism by which SpTx1 inhibits the KATP channel has remained elusive. Here, we report the crystal structure of SpTx1 and the cryo-electron microscopy (cryo-EM) structure of the KATP channel in complex with SpTx1. Our results demonstrate that SpTx1 binds to the extracellular surface of the Kir6.2 pore-forming subunit of the KATP channel. Multiple interactions at the SpTx1-Kir6.2 interface underpin the high affinity and specificity of SpTx1 for human Kir6.2. We show that SpTx1 inhibits potassium currents by physically blocking the ion conduction pore. Furthermore, a structure-guided search identified another centipede toxin, Sm3a, as a novel KATP channel blocker. Together, these findings provide key insights into the inhibitory mechanism of centipede toxins against the human Kir6.2-containing KATP channel and establish a foundation for developing these toxins into potential therapeutics targeting KATP channel-related diseases.

biophysics↗

bioRxiv2025

p53 activity licenses transcriptional regulation by YAP/TAZ-TEAD to shape expression landscapes during tumorigenesis

The tumor suppressor p53 is the most frequently disabled gene in cancer, where its inactivation promotes the transition between transcriptionally distinct pre-malignant and malignant disease. How p53 action shapes these stage-dependent expression landscapes is poorly understood. Using a mouse model where p53 function is restored in advanced pancreatic cancer, we demonstrate that p53 dependent gene expression is determined by regulation of YAP/TAZ-TEAD transcriptional complexes at multiple levels of molecular control. Restoration of p53 activity in pancreatic cancer cells expands transcriptional regulation by reorganizing TEAD binding at newly accessible enhancer landscapes, resulting in distinct TEAD-driven gene regulatory networks when p53 is inactivated versus engaged. Furthermore, p53 potentiates expression of newly licensed TEAD targets by increasing TAZ levels in response to the actin remodeling associated with cell cycle arrest and senescence. Thus, p53 activity controls the qualitative and quantitative output of YAP/TAZ-TEAD, including secretory programs involved in tumor-immune communication and remodeling of the microenvironment. Our work nominates regulation of YAP/TAZ-TEAD via p53 as a mechanism that determines stage dependent transcriptional landscapes during stepwise tumorigenesis.

cancer biology↗

bioRxiv2025

Genome dynamics across the radiation of a mega-diverse genus

Understanding the drivers of species diversity and rapid radiations is a major goal in evolutionary biology. Begonia is one of the most species-rich angiosperm genera with 2,164 species currently identified. This genus exhibits considerable variation in chromosome number and a wide range of genome sizes, allowing us to associate genome dynamics with divergence and speciation at a range of temporal scales. We investigate all main radiations within the Begoniaceae family using five previously published Begonia genomes and seven new genome assemblies. We show that Begonia species show more complex, repetitive and dynamic genomes overall than their close relative, the monotypic Hillebrandia sandwicensis. We identify families of repetitive elements that have recently expanded in species from two different highly speciose Southeast Asian sections and two large Neotropical radiations. Detailed characterisation of genomes from species belonging to two parallel radiations, one in Southeast Asia (Begonia section Coelocentrum) and the other in the Neotropics (Begonia section Gireoudia), revealed recent expansion in LTR retrotransposons (LTR-RTs) and satellite DNA, in contrast to more species-poor closely related clades. We further investigate variation in repetitive elements within species, finding that accessions from a population of the widespread Begonia heracleifolia with unusually large genomes show a markedly higher satellite repeat and Ty3/Gypsy LTR-RT content associated with the expansion of a few abundant repeat lineages. We find that accessions derived from this population show lower seed viability in crosses with other conspecific populations, and thus identify a direct link between expansions of repetitive DNA and the process of genetic isolation. These results show how genome dynamics may promote speciation in one of the most diverse flowering plant genera.

genomics↗

bioRxiv2025

A Modular Fusion Neural Network Approach to Efficiently Predict Multi-Metal Binding Sites in Protein Sequences

Accurate identification of metal-binding residues is essential for the study of metalloproteins, like zinc finger proteins, haemoglobin, and DNA polymerase. Due to the high cost and time demands of experimental methods, computational prediction has been applied. However, computational complexity and inextensibility of rigid frameworks impeded the application. This work presents a two-stage, sequence-based deep learning framework, which can predict zinc, iron, and magnesium binding amino acids of proteins. In the first stage, tokenized sequences are processed by independent one-dimensional convolutional neural networks (CNNs) to generate single-residue probability maps. In the second stage, a lightweight fusion network integrates these maps to model inter-metal dependencies and refine predictions. The framework employs an imbalance-aware loss function and the ensemble evaluation to improve robustness. Structural agnostic and modular design enable efficient training and inference, making it suitable for the annotation of large-scale proteome. CCS CONCEPTS* Applied computing; * Life and medical sciences; * Bioinformatics

bioinformatics↗

bioRxiv2025

Bivalent mRNA booster encoding virus-like particles elicits potent polyclass RBD antibodies in pre-vaccinated mice

mRNA vaccines emerged as a leading vaccine technology during the COVID-19 pandemic. However, their sustained protective efficacies were limited by relatively short-lived antibody responses and the emergence of SARS-CoV-2 variants, necessitating frequent and variant-updated boosters. We recently developed the ESCRT- and ALIX-binding region (EABR) mRNA vaccine platform, which encodes engineered immunogens that induce budding of enveloped virus-like particles (eVLPs) from the plasma membrane, thereby resulting in presentation of immunogens on cell surfaces and eVLPs. Prior studies showed that spike (S)-EABR mRNA-LNP immunizations elicited enhanced neutralizing antibody responses against ancestral and variant SARS-CoV-2 compared with conventional S mRNA-LNP in naive mice, but the effectiveness of S-EABR mRNA-LNP boosters in the context of pre-existing immunity has not been investigated. Here, we evaluated monovalent Wuhan-Hu-1 (Wu1) and bivalent (Wu1/BA.5) S-EABR mRNA-LNP boosters in mice pre-vaccinated with conventional Wu1 S mRNA-LNP. Compared to conventional S mRNA-LNP boosters, the EABR approach enhanced monovalent and bivalent mRNA-LNP booster-induced neutralizing responses against Omicron subvariants BA.1, BA.5, BQ.1.1, and XBB.1, with bivalent S-EABR mRNA-LNP consistently eliciting the highest titers. Epitope mapping of polyclonal antisera by deep mutational scanning revealed that bivalent S-EABR mRNA-LNP boosted diverse "polyclass" anti-receptor-binding domain (RBD) responses, suggesting balanced targeting of multiple RBD epitope classes. In contrast, monovalent S, bivalent S, and monovalent S-EABR mRNA-LNP boosters elicited less diverse polyclonal serum responses primarily targeting immunodominant RBD epitopes. Cryo-EM structures demonstrated that bivalent mRNA immunizations promote S heterotrimer formation, potentially enhancing bivalent S-EABR mRNA-LNP booster-induced antibody breadth and polyclass epitope targeting by activating cross-reactive B cells through intra-S crosslinking. These findings support the future design of bivalent or multivalent S-EABR mRNA-LNP boosters as a promising strategy to confer broader, and therefore potentially more durable, protection against emerging SARS-CoV-2 variants and other rapidly evolving viruses.

immunology↗

bioRxiv2025

Expression of nano-engineered RNA organelles in bacteria

Designing synthetic biomolecular condensates, or membrane-less organelles, offers insights on the functions of their natural counterparts, and is equally valuable for cellular and metabolic engineering. Choosing E. coli for its biotechnological relevance, we deploy RNA nanotechnology to design and express non-natural membrane-less organelles in vivo. The designer condensates assemble co-transcriptionally from branched RNA motifs interacting via base-pairing. Exploiting binding selectivity we express orthogonal, non-mixing condensates, and by embedding a protein-binding aptamer we achieve selective protein recruitment. Condensates can be made to dissolve and re-assemble upon thermal cycling, thereby reversibly releasing and re-capturing protein clients. The synthetic organelles are expressed robustly across the cell population and remain stable despite enzymatic RNA processing. Compared with existing solutions based on peptide building blocks or repetitive RNA sequences, these nanostructured RNA motifs enable algorithmic control over interactions, affinity for clients, and condensate microstructure, opening new directions in synthetic biology and biotechnology.

synthetic biology↗