Biology subjects

Yang, F.

Publications and source records attributed to Yang, F..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Highly sensitive chemiluminescence imaging of misfolded proteins in neurodegenerative models

Visualizing misfolded proteins would greatly facilitate early diagnosis, etiology elucidation, and therapy monitoring of neurodegeneration. Although several probes have been reported, simple and versatile detection in vivo is still challenging. We demonstrated that both generic and precise detection of misfolded proteins could be achieved with a chemiluminescence probe, ADLumin-1. For the generic aspect, ADLumin-1 was highly sensitive to various misfolded proteins, showing up to 127.73-fold higher signal-to-noise ratio than Thioflavin T. ADLumin-1 could also non-invasively visualize misfolded proteins in mouse models of Parkinsons disease, Alzheimers disease and amyotrophic lateral sclerosis. Furthermore, ADLumin-1 displayed precise detection value for -synuclein by combining with PMCA in vitro and bioorthogonal ChRET imaging technology in vivo. ADLumin-1 can selectively detect -synuclein in CSF at the femtomolar level and enables in situ monitoring of misfolded -synuclein in vivo. Combining generality and precision, our findings could be widely applied in preclinical and clinical studies of neurodegenerative diseases. TeaserA highly sensitive chemiluminescence probe enables both generic and precise detection of various misfolded proteins

biochemistry↗

bioRxiv2025

ERK and PKA signaling drive the evolutionary expansion of the cortex

The human cerebral cortex, essential for intelligence, cognition, and language, evolved its uniqueness through mechanisms that drive neuronal expansion. Here, we demonstrate that ERK and PKA signaling cooperatively preserve the neurogenic capacity of cortical radial glia (RGs) by inhibiting gliogenic YAP/TAZ and SHH signaling. YAP/TAZ signaling drives cortical RGs toward ependymal fate, while SHH signaling facilitates the generation of cortical tripotential intermediate progenitor cells that produce cortical astrocytes, oligodendrocytes, and olfactory bulb interneurons. During evolution, cortical outer radial glia (oRGs) acquire dominant ERK/PKA signaling through a self-reinforcing feedback mechanism that suppresses both YAP and SHH pathways, thereby significantly enhancing oRG self-renewal capacity and extending the neurogenic period. These findings reveal that cortical neurogenesis, gliogenesis, and evolutionary expansion are coordinately regulated by an integrated ERK, PKA, YAP/TAZ, and SHH signaling, identifying a unifying principle of mammalian cortical development and evolution.

developmental biology↗

bioRxiv2025

Improving interpretability of transcription factor binding models with DNA shape features

Deep learning models in genomics that predict molecular phenotypes from DNA sequence traditionally focus on one-hot encoded nucleotide representations. Here, we develop a novel model that extends this approach by incorporating DNA structural attributes indicative of local DNA shape alongside canonical sequence inputs. This augmentation provides an additional axis for model interpretability and aids in identifying regulatory patterns not apparent from sequence alone. Applying this approach to prediction of transcription factor binding (ChIP-seq) demonstrates that combining sequence and structural DNA information can improve the identification of regulatory elements to provide a more nuanced understanding of genomic function and regulation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/646034v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@b648b6org.highwire.dtl.DTLVardef@15fa931org.highwire.dtl.DTLVardef@15d0d31org.highwire.dtl.DTLVardef@d212e_HPS_FORMAT_FIGEXP M_FIG C_FIG Schematic overview of the DeepShape model. One-hot encoded sequence, and five DNA shape attributes--minor groove width (MGW), helical twist (HelT), propeller twist (ProT), roll, and electrostatic potential (EP)--are input separately to sequence and shape branches of the model, each processed through two convolutional layers and subsequently concatenated for further processing through additional convolutional layers.

genomics↗

bioRxiv2025

Effects of amendments on remediation of heavy metal contaminated soil and accumulation of Cd and Pb in potato

With the development of agriculture and industry, a large amount of heavy metals are loaded into the soil, posing a high ecological and public health risk. Many methods have been proposed to repair heavy metal contaminated soil, such as electroremediation, chemical washing, phytoremediation and in situ fixation. In the above methods, the application of passivators to in-situ immobilization of heavy metals is an environmentally friendly and cost-effective choice. The effects of different amendments on the improvement of heavy metal contaminated soil and the accumulation of Cd and Pb in potato plants were investigated by using attapulgite, diatomite and humic acid as materials.The results showed that soil pH, organic matter content and cation exchange capacity increased after the application of amendments. Under the treatment of attapulgite+humic acid, the soil pH, organic matter content and cation exchange capacity were increased by 0.57units, 10.0 mg/kg and 7.28cmol/kg, respectively, compared with the control. The content of soil hydrolyzable nitrogen increased, and the content of available phosphorus decreased. The application of amendments increased soil enzyme activity; effectively reduce the bioavailability of Cd and Pb in soil ; under the treatment of attapulgite+humic acid, the contents of Cd and Pb in potato plants decreased by 85.50%and 55.54%respectively, and the contents of Cd and Pb in potato tubers decreased to 0.08 mg/kg and 1.41 mg/kg respectively. The application of amendments affected the accumulation and transport of heavy metals Cd and Pb in potato plants to a certain extent and promoted the transformation of Cd and Pb from acid-soluble and reduced states to oxidized and residual states.

ecology↗

bioRxiv2025

Cortactin regulates metastatic dormancy of circulating tumor cells by suppressing mTOR-dependent senescence

Metastatic dormancy often refers to the stable cell cycle arrest of disseminated tumor cells (DTCs) at distant sites. However, whether circulating tumor cells (CTCs) in the blood microenvironment can enter a dormant state prior to extravasation and becoming DTCs remains unclear. Using patient-derived melanoma CTC lines and animal explant models (CDX), we identified a previously unrecognized role of the cytoskeletal regulator cortactin (encoded by CTTN) in controlling mTOR/p53-dependent senescence and metastatic dormancy. Cortactin was localized to Rab7-postive endosomes and engaged in late endosomal tethering and homeostasis. The depletion of cortactin resulted in the accumulation of aberrantly enlarged late endosomal aggregates that were positive for Rab7 and mTOR. The mTOR protein complex was accumulated and activated within these abnormal vesicular structures, leading to robust p53 activation through phosphorylation at serine (S) 15 and S33 sites. Consequently, melanoma CTCs underwent G0/G1 cell cycle arrest and entered cellular senescence. This unusual oncogene-induced senescence (OIS) mechanism was characterized by SASP upregulation, {beta}-galactosidase activity, depletion of Ki-67 and Lamin B1, and elevated mitochondrial ROS (mtROS) levels. Notably, a positive feedback loop between p53 and mtROS was essential for maintaining stable senescence in CTCs. In preclinical CDX mouse models, we developed a sequential therapeutic strategy combining cortactin depletion with anti-Bcl-xL senolytic drugs. Such "One-two punch" treatment strategy effectively eliminated viable CTCs and suppressed metastatic tumor growth in vivo. Thus, targeting cortactin to induce CTC senescence, followed by senolytic therapy, may represent a promising strategy to block CTC-mediated metastatic progression.

cancer biology↗

bioRxiv2025

NeoAtlas and NeoBert: A Database and A Predictive Model for Canonical and Noncanonical Tumor Neoantigens

Neoantigens are classified into canonical and noncanonical types. Noncanonical neoantigens include those derived from noncoding regions, transposable elements (TE), and intron retention events, and they have recently gained considerable attention in cancer immunity. We curated 35,574 non-redundant neoantigen-HLA pairs from 14 immunopeptidomes studies, by analyzing unique features and differences across various sources of neoantigens. This knowledge enabled us to develop machine learning models for the prediction of different types of neoantigens. Our data and models are available at a public portal (https://ngdc.cncb.ac.cn/neoatlas) to facilitate broad access and future research. This resource offers advanced functionalities, including integration with epigenome browsers which allow easy navigation of epigenomic datasets to support and confirm the expression of neoantigens. We further demonstrate that combining our database with mass spectrometry analysis can identify noncanonical neoantigens. The resource we constructed holds significant value and promise for the development of neoantigen-based vaccines.

systems biology↗

bioRxiv2025

Progressively reduced cerebral oxygen metabolism and elevated plasma NfL levels in the zQ175DN mouse model of Huntington disease

Huntingtons disease (HD) is a progressive neurodegenerative disorder caused by a CAG-repeat expansion in exon-1 of the huntingtin gene. Currently, no disease-modifying therapies are available, with a significant challenge in evaluating therapeutic efficacy before clinical symptoms emerge. This highlights the need for early biomarkers and intervention strategies. Therefore, it is essential to develop and characterize accurate mouse models and identify early biomarkers for preclinical therapeutic development. In this study, we characterized the pathological progression of the heterozygous zQ175 neo-deleted knock in (zQ175DN) mouse model across four age groups: 3, 6, 10, and 16 months to identify human translatable outcome measures. T2-relaxation-under-spin-tagging (TRUST) MRI was used to assess global CMRO2, while T2-weighted MRI was used to analyze brain volumes. Significant brain volume loss was detected as early as 6 months of age, worsening progressively with age in the zQ175 DN mice, resembling HD brain volumetric changes. A decline in CMRO2 was observed in 6-month-old zQ175 DN mice, with significant and progressive reductions in 10- and 16-months old HD mice. Additionally, PHP1-positive mutant huntingtin (mHTT) aggregates were present in the striatum of zQ175 DN mice at all four age groups, with intranuclear localization prior to 6 months, transitioning to both intranuclear and neuropil aggregates in older zQ175 DN mice, suggesting that the localization of mHTT aggregates may reflect the severity of HD pathogenesis. Interestingly, plasma neurofilament light chain (NfL) protein concentrations were significantly elevated at 6 months of age and older zQ175DN mice. These findings provide valuable insights for selecting outcome measures in preclinical evaluations of HD therapies using the zQ175 DN mouse model.

neuroscience↗

bioRxiv2025

Reduced processing and toxin binding associated with resistance to Vip3Aa in a resistant strain of fall armyworm (Spodoptera frugiperda) from Louisiana

BackgroundTransgenic crops expressing Cry and Vip3Aa insecticidal proteins from the bacterium Bacillus thuringiensis are a primary tool for controlling fall armyworm (Spodoptera frugiperda) populations. The evolution of resistance to Cry proteins in the native range of the fall armyworm has increased reliance and intensified the selection of resistance to Vip3Aa. In this study, we identified mechanisms of resistance to Vip3Aa in the LA-RR strain of S. frugiperda originating from Louisiana (USA). ResultsMidgut epithelial damage in susceptible larvae was evidenced by a significant drop in midgut pH after feeding on either Vip3Aa protoxin or activated toxin. In contrast, this midgut pH drop was only detected for activated Vip3Aa toxin in LA-RR larvae. Midgut fluids from LA-RR larvae displayed delayed processing of Vip3Aa protoxin when compared to fluids from susceptible larvae, and this slower processing was associated with reduced activity and expression of trypsin and chymotrypsin enzyme genes in the LA-RR strain. In bioassays, LA-RR larvae were significantly more susceptible to Vip3Aa protoxin pre-processed by midgut fluids from susceptible than from LA-RR larvae. In addition, midgut brush border membrane vesicles from LA-RR larvae exhibited lower specific Vip3Aa toxin binding than vesicles from the susceptible strain. ConclusionThe results of this study support that both slower proteolytic processing and reduced specific binding are associated with resistance to Vip3Aa in an S. frugiperda strain from the Western hemisphere, the native range of this pest. This information increases our understanding of resistance to Vip3Aa and advances monitoring and fall armyworm management.

plant biology↗

bioRxiv2025

BRCA1-A and LIG4 complexes mediate ecDNA biogenesis and cancer drug resistance

Extrachromosomal circular DNA (ecDNA) are commonly produced within the nucleus to drive genome dynamics and heterogeneity, enabling cancer cell evolution and adaptation. However, the mechanisms underlying ecDNA biogenesis remain poorly understood. Here using genome-wide CRISPR screening in human cells, we identified the BRCA1-A and the LIG4 complexes mediate ecDNA production. Following DNA fragmentation, the upstream BRCA1-A complex protects DNA ends from excessive resection, promoting end-joining for circularization. Conversely, the MRN complex, which mediates end resection and thus antagonizes the BRCA1-A complex, suppresses ecDNA formation. Downstream, LIG4 conservatively catalyzes ecDNA production in Drosophila and mammals, with patient tumor ecDNA harboring junctions marked by LIG4 activity. Notably, disrupting LIG4 or BRCA1-A in cancer cells impairs ecDNA-mediated adaptation, hindering resistance to both chemotherapy and targeted therapies. Together, our study reveals the roles of the LIG4 and BRCA1-A complexes in ecDNA biogenesis, and uncovers new therapeutic targets to block ecDNA-mediated adaptation for cancer treatment.

cancer biology↗

bioRxiv2025

Integrative Analysis of Single-cell and Bulk RNA Sequencing Reveals Macrophage Heterogeneity in Lyme Arthritis

IntroductionLyme arthritis (LA) is a common late-stage manifestation of Lyme disease caused by Borrelia burgdorferi (Bb) infection. Macrophages play a crucial role in LA pathogenesis, yet their heterogeneity and functional states within the arthritic microenvironment remain poorly understood. MethodsWe integrated single-cell RNA sequencing data from ankle joint tissues of Bb-infected C57BL/6 mice with bulk RNA sequencing data from both infected ankle joints and bone marrow-derived macrophages. We developed a LA mouse model and validated key findings through histopathological examination and RT-qPCR analysis. ResultsWe identified nine distinct cell types in the joint microenvironment with significant compositional shifts during disease progression. Analysis of 16,617 Macrophages/Monocytes revealed seven functionally distinct subpopulations with differential responses to infection. Pseudo-time trajectory analysis demonstrated three principal differentiation pathways from monocyte-derived macrophages toward anti-inflammatory, TNF+, and IL1b+ inflammatory phenotypes. Cell-cell communication analysis revealed significantly altered TGF-{beta} signaling networks in LA, with the Tgfb1-(Tglbr1+TgIbr2) axis playing a critical role. GSVA and pathway analyses showed metabolic reprogramming from oxidative phosphorylation toward glycolysis in macrophages during infection. Through integrated analysis and LASSO regression, we identified four characteristic genes (SIRPB1C, FABP5, MMP14, and EGR1) as potential LA biomarkers and developed a diagnostic nomogram with high predictive accuracy. ConclusionOur integrative analysis provides comprehensive insights into macrophage heterogeneity and functional plasticity in LA, identifying characteristic biomarkers and potential therapeutic targets. The metabolic reprogramming and altered TGF-{beta} signaling networks we identified may contribute to disease pathogenesis and offer new avenues for intervention strategies.

genomics↗

bioRxiv2025

DNA G-quadruplex Profiling Reveals Functional and Mechanistic Role of G-quadruplexes in Skeletal Muscle Stem Cells

DNA G-quadruplexes (G4s) are non-canonical secondary structures formed in guanine-rich DNA sequences and play important roles in modulating biological processes through a variety of gene regulatory mechanisms. Emerging G4 profiling permits global mapping of endogenous G4 formation. Here in this study, we map the G4 landscapes in adult skeletal muscle stem cells (MuSCs) which are essential for injury induced muscle regeneration. Throughout the myogenic lineage progression of MuSCs from quiescent to activated and further differentiated cells, we uncover dynamic endogenous G4 formation with a pronounced G4 induction when MuSCs become activated and proliferating. We further demonstrate that the G4 induction promotes MuSC activation thus the regeneration process. Mechanistically, we found that promoter associated G4s regulate gene transcription through facilitating chromatin looping. Furthermore, we found that G4 sites are enriched for transcription factor (TF) binding events in activated MuSCs; MAX binds to G4 structures to synergistically facilitate chromatin looping and gene transcription thus promoting MuSC activation and regeneration. The above uncovered global regulatory functions/mechanisms are further dissected on the paradigm of Ccne1 promoter, demonstrating Ccne1 is a bona fide G4/MAX regulatory target in activated MuSCs. Altogether, our findings for the first time demonstrate the prevalent and dynamic formation of G4s in adult MuSCs and the mechanistic role of G4s in modulating gene expression and MuSC activation/proliferation.

molecular biology↗

bioRxiv2025

Unifying Genetic and Chemical Perturbagen Representation through a Hybrid Deep Learning Framework

Artificial Intelligence virtual cell (AIVC) holds transformative potential for biomedical research. Central to this vision is the systematic modeling of genetic and chemical perturbation phenotypes to accurately predict cellular dynamic states from diverse interventions. However, disparities in screening agents, library scales, experimental technologies, and data production efficiency hinder the integration, modeling, and analysis of the cross-data. Here we present UniPert-G2CP, a two-phase deep learning approach comprising i) UniPert, a multimodal molecular representation model that bridges genetic and chemical domains, and ii) G2CP (Genetic-to-Chemical Perturbation transfer learning), which systematically transforms CRISPR screen-based genetic insights into chemical perturbation modeling for cost-effective in silico drug screening. UniPert not only encodes multimodal perturbagens into a unified functionally interpretable sematic embedding space, but also improves phenotypic effect prediction for previously unseen gene perturbations and drug treatments. Building upon UniPert, G2CP successfully modeled large-scale cellular post-perturbation states spanning 4,994 gene and 7,821 compound perturbagens, while reducing modeling data costs by over 60%. We demonstrate that UniPert-G2CP enables efficient, generalizable simulations of multicellular, multi-domain perturbation cause-effect spaces, revealing differential cellular biological causality and informing mechanism-driven therapy. UniPert-G2CP opens new avenues for biological causal foundation model building, AIVC creation, and AI-powered precision medicine.

bioinformatics↗

bioRxiv2025

Cellular and synaptic organization of the Octopus vertical lobe

Understanding memory formation and its influence on behavior is a central challenge in neuroscience. Associative learning networks, including the mushroom body in insects, the cerebellum in mammals, and the vertical lobe (VL) in cephalopods, typically exhibit a 3-layered architecture, characterized by divergence (fan-out) followed by convergence (fan-in), facilitating sparse sensory coding (Babadi and Sompolinsky, 2014; Lin et al., 2014; Litwin-Kumar et al., 2017; Turchetti-Maia et al., 2017). Previously, using volumetric electron microscopy, we showed that the VL uniquely comprises 22 million simple amacrine (SAM) interneurons, each receiving a singular input subject to activity-dependent long-term potentiation, contrasting with typical middle-layer interneurons (Bidel, Meirovitch et al., 2023). We also demonstrated that these SAMs provide excitatory feedforward input to the output cell layer, balanced by approximately 400,000 inhibitory complex amacrines (CAM), which are morphologically diverse and integrate numerous inputs (Bidel, Meirovitch et al., 2023). Here, we leverage the same digital tissue to explore the CAMs morphological diversity, identifying correlations between structure, postsynaptic site density, and synaptic input proportions, which led to the classification of CAMs into distinct groups. Further analysis of the input layer in the VL revealed a meticulous structural and synaptic compartmentalization, with distinct synaptic bouton types forming three zones that integrate different inputs towards CAMs. Additionally, we identify the potential presence of a neurogenic niche in the VL, hinting at parallels with neurogenic processes in other species and warranting further investigation, particularly in the context of learning and memory. This study deepens our understanding of the VLs cellular and synaptic architecture, revealing both shared and unique features compared to other associative networks, and highlighting the intricate interplay of structural and functional elements in memory formation. Highlights (85 characters including space)O_LIThe VL both aligns with and deviates from standard associative network features C_LIO_LIThe VL input layer presents a unique compartmentalization by axonal bouton type C_LIO_LIThe minority inhibitory interneurons differentiate into unique functional subtypes C_LIO_LIMirroring the mammalian hippocampus, the VL may contain an adult neurogenic niche C_LI

neuroscience↗

bioRxiv2025

The metabolic enzyme GYS1 condenses with NONO/p54nrb in the nucleus to spatiotemporally regulate glycogenesis and myogenic differentiation

Accumulating evidence indicates that metabolic enzymes can directly couple metabolic signals to transcriptional adaptation and cell differentiation. Glycogen synthase 1 (GYS1), the key metabolic enzyme for glycogenesis, is a nucleocytoplasmic shuttling protein compartmentalized in the cytosol and nucleus. However, the spatiotemporal regulation and biological function of nuclear GYS1 (nGYS1) microcompartments remain unclear. Here, we show that GYS1 dynamically reorganizes into nuclear condensates under conditions of glycogen depletion or transcription inhibition. nGYS1 complexes with the transcription factor NONO/p54nrb and undergoes liquid-liquid phase separation to form biomolecular condensates, leading to its nuclear retention and inhibition of glycogen biosynthesis. Compared to their wild-type littermates, Nono-deficient mice exhibit exercise intolerance, higher muscle glycogen content, and smaller myofibers. Additionally, Gys1 or Nono deficiency prevents C2C12 differentiation and cardiotoxin-induced muscle regeneration in mice. Mechanistically, nGYS1 and NONO co-condense with the myogenic transcription factor MyoD and preinitiation complex (PIC) proteins to form transcriptional condensates, driving myogenic gene expression during myoblast differentiation. These results reveal the spatiotemporal regulation and subcellular function of nuclear GYS1 condensates in glycogenesis and myogenesis, providing mechanistic insights into glycogenoses and muscular dystrophy.

cell biology↗

bioRxiv2025

Recessive but damaging alleles of muscle-specific ribosomal protein gene RPL3L drive neonatal dilated cardiomyopathy

The heart employs a specialized ribosome in its muscle cells to translate genetic information into proteins, a fundamental adaptation with an elusive physiological role1-3. Its significance is underscored by the discovery of neonatal patients suffering from often fatal heart failure caused by severe dilated cardiomyopathy when both copies of the gene RPL3L are mutated4-9. RPL3L is a muscle-specific paralog1-3 of the ubiquitous ribosomal protein L3 (RPL3), which makes the closest contact of any protein to the ribosomes RNA-based catalytic center10. RPL3L-linked heart failure represents the only known human disease associated with tissue-specific ribosomes, yet the underlying pathogenetic mechanisms remain poorly understood. Intriguingly, disease is linked to a large number of mostly missense variants in RPL3L, and RPL3L-knockout resulted in no severe heart defect in either human or mice3, 11-13, challenging the prevailing view that autosomal recessive diseases are caused by loss-of-function mutations. Here, we report three new cases of RPL3L-linked severe neonatal heart failure and present a unifying pathogenetic mechanism by which a large number of variants in the muscle-specific ribosome led to disease. Specifically, affected families often carry one of two recurrent toxic gain-of-function variants alongside a family-specific putative loss-of-function variant. While the non-recurrent variants often trigger partial compensation of RPL3 similar to Rpl3l-knockout mice, both recurrent variants exhibit increased affinity for the RPL3/RPL3L chaperone GRWD114-16 and 60S biogenesis factors, sequester 28S rRNA in the nucleus, disrupt ribosome biogenesis, and trigger severe cellular toxicity that extends beyond the loss of ribosomes. These findings provide critical insights for genetic screening and therapeutic development of neonatal heart failure. Our results suggest that gain-of-toxicity mechanisms may be more prevalent in autosomal recessive diseases, and a combination of gain-of-toxicity and loss-of-function mechanisms could underlie many diseases involving genes with paralogs.

molecular biology↗

bioRxiv2024

Geometry-based BERT: an experimentally validated deep learning model for molecular property prediction in drug discovery

Various deep learning based methods have significantly impacted the realm of drug discovery. The development of deep learning methods for identifying novel structural types of active compounds has become an urgent challenge. In this paper, we introduce a self-supervised representation learning framework, i.e., GEO-BERT. GEO-BERT considers the information of atoms and chemical bonds in chemical structures as the input, and integrates the positional information of the three-dimensional conformation of the molecule for training. Specifically, GEO-BERT enhances its ability to characterize molecular structures by introducing three different positional relationships: atom-atom, bond-bond, and atom-bond. By benchmarking study, GEO-BERT has demonstrated optimal performance on multiple benchmarks. We also performed prospective study to validate the GEO-BERT model, with screening for DYRK1A inhibitors as a case. Two potent and novel DYRK1A inhibitors (IC50: <1 M) were ultimately discovered at a hit rate of 10%. Taken together, we have developed the Geometry-based BERT model for molecular property prediction and proved its practical utility in early-stage drug discovery. Graphical Abstract GEO-BERT is a model pretrained on large-scale drug molecule data, while improving the accuracy of property prediction by using three-dimensional structural information within the molecule. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/630211v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1e9bc54org.highwire.dtl.DTLVardef@4b6832org.highwire.dtl.DTLVardef@a5d8eeorg.highwire.dtl.DTLVardef@44f7be_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2024

Accurate de novo design of a voltage-gated anion channel

Ion channels gated by environmental cues play key roles in fundamental biological processes. Designing ion channels with tailored gating mechanisms remains a significant challenge due to the complexities involved in designing conformational changes in proteins. Here we report the accurate de novo design of a voltage-gated anion channel, namely VGAC. VGAC adopts a 15-helix pentameric structure featuring a constriction composed of five arginine residues within the transmembrane span. In patch-clamp experiments, VGAC showed strictly voltage-dependent currents and demonstrated selectivity for chloride anions over iodide anions. Our data suggests that the arginine constriction undergoes voltage-induced conformational changes, serving as both the voltage sensor and selectivity filter. A 2.9-[A]-resolution cryo-electron microscopy structure of VGAC closely aligns with the design model. The ability to design ion channels with custom-designed conformational changes provides new insights into our understanding of the fundamental principles of membrane biophysics and unveils a wide range of potential applications.

synthetic biology↗

bioRxiv2024

The pseudoknot structure of a viral RNA reveals a conserved mechanism for programmed exoribonuclease resistance

Exoribonuclease-resistant RNAs (xrRNAs) are viral RNA structures that block degradation by cellular 5'-3' exoribonucleases to produce subgenomic viral RNAs during infection. Initially discovered in flaviviruses, xrRNAs have since been identified in wide range of RNA viruses, including those that infect plants. High sequence variability among viral xrRNAs raises questions about the shared molecular features that characterize this functional RNA class. Here, we present the first structure of a plant-virus xrRNA in its active exoribonuclease-resistant conformation. The xrRNA forms a 9 base pair pseudoknot that creates a knot-like topology similar to that of flavivirus xrRNAs, despite lacking sequence similarity. Biophysical assays confirm a compact pseudoknot structure in solution, and functional studies validate its relevance both in vitro and during infection. Our study reveals how viral RNAs achieve a common functional outcome through highly divergent sequences and identifies the knot-like topology as a defining feature of xrRNAs.

biochemistry↗