Biology subjects

Guo, X.

Publications and source records attributed to Guo, X..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Three-dimensional Virtual Adult Cardiomyocyte Transcriptomics

Obtaining transcriptomes of adult cardiomyocytes at single-cell resolution remains challenging due to their large size, elongated morphology, and frequent multinucleation. Although spatial transcriptomics preserves tissue architecture and captures gene expression in situ, current analytical frameworks largely rely on nuclear-based segmentation and are therefore poorly suited to adult cardiomyocytes. Furthermore, individual tissue sections capture only a fraction of a cardiomyocyte, preventing reconstruction of complete cell-level transcriptomes. Here we present three-dimensional virtual cardiomyocyte (3D-VirtualCM), a membrane-guided framework that integrates cell-contour similarity and optimal transport to reconstruct volumetric cardiomyocyte transcriptomes from consecutive spatial transcriptomic sections. Applying 3D-VirtualCM to infarcted adult mouse hearts, we generated a panoramic transcriptomic atlas spanning 100 m thickness at single-cell resolution. 3D-VirtualCM identified spatially and transcriptionally distinct cardiomyocyte populations within the infarct border zone, enabled high-throughput quantification of cardiomyocytes re-entering the cell cycle together with their associated molecular signatures, and revealed heterogeneous RNA distribution along the longitudinal axis of individual cardiomyocytes. By integrating three-dimensional cellular morphology with in situ transcriptomic data, 3D-VirtualCM provides a scalable approach for resolving cardiomyocyte states and spatial organization in physiological and pathological cardiac remodeling.

bioinformatics↗

bioRxiv2026

Structure-aware geometric graph learning for modeling protease-substrate specificity at scale

Protease-substrate specificity is central to cellular regulation and disease pathogenesis, and accurately modeling its structural determinants remains challenging. Substrate recognition is governed by spatial constraints and higher-order relationships that extend beyond local sequence motifs. Most computational approaches rely predominantly on motif-centric or sequence-based representations, limiting their ability to capture the geometric and relational structure underlying enzymatic specificity. Here, we introduce OmniCleave, a structure-aware geometric graph learning framework for modeling protease-substrate specificity at scale. OmniCleave is trained on 57,278 structure-informed protease-substrate pairs derived from 9,651 substrates spanning over 100 proteases across six distinct families. The framework integrates multi-scale structural graphs with higher-order protease relational topology, explicitly encoding spatial context and inter-protease dependencies within a unified geometric representation. This formulation moves beyond local pattern recognition and enables transferable modelling across six protease families. Across large-scale benchmarks, the framework consistently outperforms existing approaches and reveals interpretable geometric determinants underlying substrate recognition. Experimental validation confirms three novel caspase-3 substrates and 21 cleavage sites predicted by OmniCleave, supporting the biological relevance of the learned representations. Together, OmniCleave provides a scalable geometric framework for modeling protease-substrate specificity, with practical utility for systematic analysis of protease biology.

bioinformatics↗

bioRxiv2026

Continental-scale multi-omics reveals a distinct microbial-viral biome in sandy beach ecosystems structured by tidal zonation

Sandy beaches are dynamic coastal interfaces shaped by strong physical forcing and intense exchange between marine and terrestrial environments, yet their microbiomes remain poorly resolved at the genomic scale. Here we present a genome-resolved survey of microbial and viral communities across sandy beaches spanning a continental-scale latitudinal gradient along the Chinese coastline. By integrating cross-shore sampling, coastal geochemistry and large-scale multi-omics, we generated 978 metagenomes, 63 viromes and 72 metatranscriptomes, reconstructing 13,337 metagenome-assembled genomes and 38,255 viral populations. Sandy beach microbiomes exhibit exceptionally high genomic novelty, with more than 90% of species-level genomes representing previously undescribed taxa, suggesting that permeable coastal sediments constitute a distinct microbial and viral reservoir. Tidal zonation emerged as a dominant ecological driver structuring microbial diversity, metabolic strategies and virus-host interactions across cross-shore gradients. Genome-resolved analyses revealed systematic metabolic shifts from oxic heterotrophy in supratidal sediments toward increasingly chemolithotrophic and autotrophic pathways toward the low-intertidal and subtidal zone. Sandy beach microbiomes further encode broad potential for hydrocarbon and plastic transformation, together with diverse biosynthetic and antibiotic resistance repertoires that may mediate microbial chemical interactions. Together, these findings identify sandy beaches as a previously under-recognized microbial-viral biome shaped by tidal forcing, providing insight into microbiome evolution and coastal ecosystem resilience under increasing anthropogenic pressure.

microbiology↗

bioRxiv2026

STDrug enables spatially informed personalized drug repurposing from spatial transcriptomics

Drug repurposing offers a scalable route to accelerate therapeutic discovery, yet existing approaches based on single-cell RNA sequencing (scRNA-seq) often overlook spatial tissue context, limiting their ability to capture microenvironment-dependent drug responses. Here we present STDrug, a spatially informed computational framework that integrates spatial transcriptomics, graph-based modeling, and multimodal learning to enable patient-specific therapeutic prioritization. STDrug identifies and aligns disease and control spatial domains using graph convolutional networks and coherent point drift, and prioritizes candidate drugs through an integrative scoring scheme combining tumor-reversible gene signatures, perturbation-based reversal scores, and knowledge-guided gene weighting within a machine learning framework. By modeling spatial domain interactions alongside predicted drug efficacy and toxicity, STDrug generates robust patient-level drug scores. Across hepatocellular carcinoma and prostate cancer datasets, STDrug outperforms existing single-cell and spatial transcriptomics-based drug repurposing methods, achieving signficantly improved predictive accuracy (AUCs=0.81-0.82) across patients. Validation using large-scale electronic health records and in vitro assays further supports the translational relevance of top-ranked candidates. Taking together, STDrug establishes a generalizable framework for incorporating spatial omics into therapeutic discovery, advancing spatially informed and personalized drug repurposing.

bioinformatics↗

bioRxiv2026

Transcriptional landscape of cardiac-specific Gpx4 deletion recapitulates human cardiomyopathy

Glutathione peroxidase 4 (GPX4) is an antioxidant enzyme important for the reduction of toxic lipid peroxide products. Previous studies revealed the importance of mouse Gpx4 in protecting cardiomyocytes from ferroptosis and, subsequently, the development of cardiovascular disease. In this paper, we investigate the transcriptional consequences of cardiac-specific deletion of Gpx4 in mice and compare this response with that observed in human cardiomyopathy. The findings in this study highlight the importance of GPX4 in maintaining both structural and functional stability of the heart and identify key pathway changes resulting from excessive ferroptosis in cardiac tissue. By overlapping common transcriptional programs perturbed in this animal model and human cardiomyopathy, our findings identify putative mechanisms through which ferroptosis contributes to the development and progression of heart disease. These studies may help guide future cardiovascular therapeutics targeting ferroptosis-dependent pathways.

genomics↗

bioRxiv2026

MAMGL: A memory-augmented meta-graph learning framework for adolescent major depression disorder diagnosis

BackgroundAdolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain development. Neuroimaging-based biomarkers derived from resting-state functional magnetic resonance imaging (rs-fMRI) hold promise for objective diagnosis; however, pronounced inter-individual variability and limited sample sizes pose major challenges for robust model development. MethodsWe propose a memory-augmented Meta-Graph Convolutional Network (BrainMetaGCN) to classify AMDD using rs-fMRI functional connectivity. Individual functional connectivity matrices were constructed by parcellating rs-fMRI time series into cortical regions of interest and computing pairwise correlations. A meta-graph generator dynamically learned subject-specific graph structures, which were processed by lightweight graph convolutional layers. A memory neural network was incorporated to encode population-level prototypical connectivity patterns and generate individualized representations via attention-based retrieval. Model performance was evaluated across multiple independent datasets and compared with state-of-the-art deep learning approaches. Additionally, network interpretability was examined through cortical hierarchy analysis and functional enrichment of discriminative network components. ResultsThe proposed BrainMetaGCN consistently outperformed baseline models, including convolutional and transformer-based approaches, achieving higher accuracy, area under the receiver operating characteristic curve, sensitivity, and specificity. Memory-module-derived functional networks exhibited clear modular organization and showed a significant positive correlation with cortical functional hierarchy, supporting their neurobiological validity. Functional enrichment analyses implicated synaptic transmission, axon guidance, receptor tyrosine kinase signaling, and immune-related pathways, suggesting neurodevelopmental and neuroimmune mechanisms underlying AMDD. Ablation analyses confirmed that memory augmentation and dynamic meta-graph construction were critical for robust performance under small-sample conditions. ConclusionsThis study introduces a robust and interpretable memory-augmented graph learning framework for AMDD classification. By effectively balancing individual specificity and population-level generalization, BrainMetaGCN advances neuroimaging-based precision diagnosis and provides new insights into the neural and biological mechanisms of adolescent depression.

neuroscience↗

bioRxiv2026

Knockdown of the long isoform of the prolactin receptor selectively targets pathogenic immune cells and averts lupus nephritis

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by chronic inflammation in multiple organ systems. While a clinical association between elevated levels of the hormone/cytokine, prolactin (PRL), and exacerbation of SLE has been recognized for some time, little is known about the mechanisms through which PRL affects the course of this disease. Here, we show that immune cells in SLE have aberrant splicing of the prolactin receptor (PRLR) such that the ratio of the long to short splice variants is increased. To determine whether the change in PRLR isoform expression was causal in this disease, we used a splice-modulating oligomer (SMO) that knocked down expression of the long splice variant (LFPRLR). Using patient samples ex vivo, SLE-prone mice in vivo, high-dimensional flow cytometry and single-cell RNA-sequencing, we demonstrate that the aberrant PRLR isoform expression in SLE both directly and indirectly drives production of autoreactive immune cell phenotypes. Thus, LFPRLR knockdown decreased the expression of type I interferon signaling/response genes known to be biomarkers and hub genes for SLE, reduced immunoglobulins with signatures considered autoreactive, and averted glomerular kidney damage in SLE-prone mice. Importantly, LFPRLR knockdown reduced pathogenic B cells and other immune subsets that drive B-cell activation, without negative impact on healthy donor counterparts. Current treatments for SLE adversely affect healthy cells and do not concurrently eradicate multiple pathogenic immune subsets. Since LFPRLR SMO does not share these disadvantages, knockdown of the LFPRLR represents a potential treatment strategy for SLE that merits further investigation. Graphical AbstractThe LFPRLR represents an attractive therapeutic target in SLE. (Left) In addition to increased pituitary/circulating PRL, individuals with SLE exhibit aberrant increases in the production of autocrine/paracrine PRL by immune cells, and in their expression of specifically the long isoform (LF) of the PRLR. (Middle) Expression of the LFPRLR specifically enhances autoreactive immunophenotypes and promotes lupus nephritis. (Right) A splice modulating oligomer (SMO), that prevents synthesis of only the LFPRLR but not the short PRLR isoforms, reduces pathogenic immunophenotypes without affecting the normal counterparts of immune cells.

immunology↗

bioRxiv2026

Telomere-to-telomere assembly and haplotype analysis of tetraploid Dendrobium officinale illuminate Orchidaceae polyploid evolution and mycorrhizal symbiosis genes

Dendrobium officinale is a typical epiphytic orchid. We report the telomere-to-telomere (T2T) genome assembly for D. officinale, representing the first T2T reference genome within the Orchidaceae family. The assembly is anchored to 19 chromosomes and contains 38 complete telomeres and 15 characterized centromeres. We further generated haplotype-resolved assemblies of the autotetraploid genome, identifying 12,761 sets of tetra-allelic genes. Based on synonymous substitution analysis, we inferred that the autotetraploidization event occurred approximately 0.86 million years ago. A systematic analysis of the SWEET gene family across the genus Dendrobium revealed that the gene family size is shaped primarily by epiphytic types and environmental factors. In D. officinale from Langshan, eight SWEET genes were specifically expressed in roots, suggesting they may play specialized roles in the root mycorrhizal system, potentially contributing to the D. officinales ability to recruit and maintain fungal partners. Together, these resources provide valuable foundations for studies of orchid evolution, functional genomics, and molecular breeding.

bioinformatics↗

bioRxiv2026

Minimal biophysical rules are sufficient for the emergence of computational intelligence at the neuronal scale

How intelligence emerges from the brains complex microscopic physical system is a central question for neuroscience and artificial intelligence. Constrained by the genomic bottleneck that precludes synapse-by-synapse specification, we propose and validate a microscopic structure-function concise-constraint sufficiency hypothesis. We develop the Neuro-Informed Generative Connectome (NIGC) framework, and show that connectomes generated under a concise set of biophysical constraints (geometric embedding, node propensity modulation, a global energy budget and maximum-entropy selection) closely match the structural statistics of a measured mouse V1 microcircuit (similarities, 0.997). In parallel, using the generated connectome as the fixed reservoir of an echo state network (ESN), training only a linear readout achieves 90% accuracy on an auditory multi-classification task. Moreover, multidimensional biologically consistent functional phenotypes, such as hierarchical transmission delays and low-dimensional spatiotemporal trajectories, are obtained without fitting functional matrices or time courses. Further, by combining single-constraint ablations, pathological perturbations and cross-modal validation, we clarify how specific structural constraints map onto functional consequences. Together, these results delineate sufficient conditions for computational intelligence emergence at the microscopic scale, and provide an auditable benchmark for first-principles understanding of brain construction.

neuroscience↗

bioRxiv2026

MolX: A Geometric Foundation Model for Protein-Ligand Modelling

Understanding how small molecules interact with protein binding pockets is central to structure-based drug discovery. Accurately modelling these interactions requires capturing the 3D geometry and physicochemical complementarity of binding interfaces, yet existing computational approaches encode proteins and ligands separately or rely on simplified structural representations that do not explicitly model cross-entity spatial relationships. Such decoupled representations restrict their capacity to capture interface-level geometric constraints that arise from protein-ligand coorganisation. Here we present MolX, a Graph Transformer foundation model that jointly learns geometric and chemical representations of protein pockets and ligands from large-scale 3D structural data. Integrating over 3 million protein pockets and 5 million molecules, MolX represents both entities as E(3)-equivariant graphs to preserve spatial geometry and chemical context. The architecture employs dual E(3)-equivariant graph Transformer encoders to model pocket and ligand embeddings, ensuring representations remain invariant to rotation, translation, and reflection. MolX is pretrained using a hybrid learning paradigm that combines supervised biochemical objectives, logP and energy-gap regression, with self-supervised geometric objectives, coordinate re-construction, and atom-type prediction, fostering generalisable molecular understanding. Across eight downstream benchmarks, including antibody-drug conjugates (ADC), proteolysis-targeting chimeras (PROTAC), molecular glue, and PCBA activity prediction, as well as binding affinity and physicochemical property regression, MolX achieves consistent state-of-the-art performance and strong cross-domain generalisation. Furthermore, MolX incorporates a sparse autoencoder module to decompose latent representations into interpretable biological components, thereby revealing the pocket-ligand interactions that drive prediction outcomes. Together, MolX establishes a scalable and interpretable foundation model for molecular representation learning, providing a unified framework for predicting and interpreting complex small-molecule-protein interactions.

bioinformatics↗

bioRxiv2026

The Bacteriocin Sublancin Restores Vancomycin efficacy against vancomycin-resistant enterococci in vitro and in vivo

Vancomycin-resistant enterococci (VRE) is one of the serious threat to global public health, with the diminishing effectiveness of antibiotics. There is an urgent need for novel strategies to control this multidrug-resistant bacterial infections. Here, our findings demonstrate that sublancin, a bacteriocin produced by Bacillus subtilis, exhibits intrinsic antibacterial activity and, more importantly, potentiates vancomycin, therapy restoring its efficacy against VRE. Using a series of in vitro assays, including fractional inhibitory concentration index, time-killing analysis, and resistance development assays, we show that sublancin significantly enhances the bactericidal effectiveness of vancomycin. In vivo, the sublancin-vancomycin combination therapy markedly reduced bacterial loads, improved svrvival rates in a Galleria mellonella model and enhanced bacterial clearance rates in a mouse model. Mechanistic studies using RT-PCR revealed that sublancin down-regulates the expression of the vanA resistance gene cluster. All in all, these findings make vancomycin and this antibiotic peptide combination as a promising candidatese to enhance vancomycin efficacy and overcome VRE infections, potentially through inteference with resistance gene expression.

pharmacology and toxicology↗

bioRxiv2026

Graph-based RNA structural representation reveals determinants of subcellular localization

RNA subcellular localization is a key determinant of RNA function and regulation, yet existing computational approaches rely primarily on sequence or simplified structural descriptors, limiting their scalability to long transcripts, their ability to model inter-label dependencies, and their applicability across RNA types. Here, we present GRASP, a unified graph neural network framework for predicting RNA subcellular localization using a heterogeneous graph representation that is RNA substructure-aware. GRASP presents each RNA as a multi-scale graph comprising nucleotide nodes and secondary-structure-derived substructure nodes, connected by relational edges, enabling joint modeling of base-level interactions and regional structural context. The model further incorporates multi-label dependency learning to capture co-localization patterns across cellular compartments within a unified framework. Across multiple benchmark datasets and RNA types, GRASP consistently outperforms state-of-the-art sequence-based and structure-informed methods, achieving substantial improvements in accuracy, F1 score, and AUC while maintaining strong scalability to long transcripts. In addition, the graph-based representation provides biologically interpretable insights into structural determinants of RNA localization. The source code and data are available at https://github.com/ABILiLab/GRASP, and the web server is accessible at http://grasp.biotools.bio.

bioinformatics↗

bioRxiv2026

CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction

Graph foundation models have emerged as powerful tools for drug repurposing by enabling the prediction of novel drug-disease indications from large biomedical knowledge graphs. A representative example is TxGNN, which was previously developed and trained on PrimeKG, a comprehensive biomedical knowledge graph covering over 17,000 diseases. While TxGNN demonstrates strong performance, existing biomedical knowledge graphs largely lack fine-grained, cell-type-specific expression context. This limits their ability to capture disease mechanisms driven by dysregulated cellular programs, such as immune cell-specific pathways in autoimmune diseases. Moreover, prior evaluations typically test only randomly selected subsets of diseases, leaving many diseases unexamined and limiting conclusions about model performance across the full disease spectrum. To address these limitations, we first update PrimeKG to PrimeKG-U by incorporating expanded and curated biomedical knowledge and then develop TxGNN-U as a stronger graph-based baseline. Building on this foundation, we introduce CellAwareGNN, a graph foundation model that integrates single-cell genomics into PrimeKG-U. We construct a single-cell-enhanced knowledge graph, scPrimeKG, by incorporating cell-type-specific gene expression signatures from the OneK1K dataset, expanding PrimeKG from approximately 8.1 million edges and 129k nodes to over 14 million edges and 147k nodes. CellAwareGNN is pre-trained on all relation types in scPrimeKG and evaluated on drug indication prediction with explicit coverage of all diseases in the knowledge graph. CellAwareGNN consistently outperforms TxGNN and TxGNN-U. For drug indication prediction, CellAwareGNN achieves an AUPRC of 0.826, representing a 1.2% improvement over TxGNN-U (0.816) and a 3.4% improvement over TxGNN (0.799). Notably, for autoimmune diseases, CellAwareGNN attains an AUPRC of 0.864, improving by 2.0% over TxGNN-U (0.847) and 6.0% over TxGNN (0.815). Importantly, CellAwareGNN prioritizes promising repurposing candidates, including Ocrelizumab for Pemphigus via CD20-expressing B cells, Methotrexate for Pemphigus through DHFR and ATIC activity in T and B cells, and Rosiglitazone for Rheumatoid Arthritis through PPAR-{gamma} activation. These results demonstrate the value of incorporating cell-type-specific expression context to improve both predictive performance and biological interpretability in graph-based drug repurposing. CCS ConceptsO_LIApplied computing [->] Health informatics; Bioinformatics; C_LIO_LIComputing methodologies [->] Knowledge representation and reasoning; Neural networks. C_LI ACM Reference FormatXinmeng Zhang, Eugene Jeong, Chao Yan, Yubo Feng, Linshuoshuo Lyu, Xingyi Guo, and You Chen. 2026. CellAwareGNN: Single-Cell Enhanced Knowledge Graph Foundation Model for Drug Indication Prediction. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD 2026), August 9-13, 2026, Jeju Island, Republic of Korea. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3770855.3819012

bioinformatics↗

bioRxiv2026

Elimination of intramuscular immunoglobulin accumulation alleviates Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a devastating neuromuscular disorder due to loss of dystrophin, a cytoskeletal protein critical for muscle integrity and functionality. Despite recent therapeutic advances, there remains a significant unmet need for more effective and accessible therapeutics. Here, we discovered an early accumulation of immunoglobulin G (IgG) in the sarcolemma, which exacerbates tissue inflammation and disease progression. The IgG accumulation primarily resulted from ectopic localization of Fc{gamma}R1, a high-affinity IgG Fc receptor, on dystrophin-deficient myofibers. In two independent murine models, eliminating IgG accumulation via B cell depletion provided sustained benefit to alleviate DMD progression. Our findings uncover a novel disease accelerator in DMD and demonstrate the potential to target this mechanism as therapeutics for broader population of patients with DMD.

immunology↗

bioRxiv2026

Stenotrophomonas oleivorans sp. nov. A polycyclic aromatic hydrocarbon-degrading strain isolated from crude oil contaminated soil

ASS1 was isolated as a motile Stenotrophomonas strain from crude oil-contaminated soils in Tabasco, Mexico. We characterized this strain using physiological and biochemical traits. ASS1 grew at temperature 25-37 (optimally at 37 {degrees} C) and at pH 6 to 8 (optimally at pH 7 to 8). The assembled genome has a total length of 4.56MB with a G + C content of 66.6%. The 16S rRNA gene sequence analysis confirmed that this strain belongs to the genus Stenotrophomonas. Based on the 16S rRNA analysis, Stenotrophomonas geniculata ATCC 19374 is the closest species, and it shares 99.86% similarity with ASS1. Similarly, a phylogenomic tree based on core genome sequence revealed that the closest species to ASS1 is Stenotrophomonas geniculata ATCC 19374. The major fatty acids in ASS1 are C16:0, antesio C15:0, iso C12:0, iso C15:0, iso C17:0 and C18:0. The genome of ASS1 consists of 4,373,402 bp. The Average Nucleotide Identity (ANI) values for ASS1 which it shared with its closest phylogenetic neighbors, are Stenotrophomonas geniculata ATCC 19374 = JCM 13324 [T] 92.66 %, Stenotrophomonas maltophilia 13637[T] 92.15%, Stenotrophomonas maltophilia K279a 92.13% Stenotrophomonas maltophilia R551-3 92.15% Stenotrophomonas maltophilia MTCC 434 [T] 92.08% and Pseudomonas hibisicicola ATCC [T] 91.66%. ASS1 possesses genes that are essential for the degradation of Polycyclic Aromatic hydrocarbon. Genes such as 1, 2 dihydroxyl 1, 2 dihydronaphthalene dehydrogenase; MG068 17425, homologous to 2 hydroxyl chromene 2 carboxylate isomerases; MG 18055, homologous to salicylaldehyde dehydrogenase and MG068 20095, homologous to naphthalene 1, 2 dioxygenases were identified in ASS1. The dDDH value between ASS1 and its closest neighbor Stenotrophomonas geniculata ATCC 19374 = JCM 13324 [T] is 50%, which is the highest for all the typed species and as such we proposed that ASS1 is a novel species with the name Stenotrophomonas oleivorans sp. nov. sp. nov. and ASS1T as the typed strain

microbiology↗

bioRxiv2026

Poly(A)-tail-length-dependent surveillance of viral RNA by PABPC1 orchestrates broad-spectrum antiviral defense

RNA processing and modification are critical for virus replication and pathogenesis, yet how the host exploits viral RNA features--particularly the poly(A) tail--for antiviral defense remains unclear. Through multi-omics integration and systematic functional screening, we identify the poly(A)-binding protein PABPC1 as a broad-spectrum restriction factor against multiple coronaviruses. We demonstrate that PABPC1 preferentially binds viral RNAs bearing short poly(A) tails--distinct from the longer and more heterogeneous host poly(A) tails--and, in a poly(A)-length-dependent manner, recruits the mitochondrial exonuclease EXD2 to assemble a degradative RNA-protein complex. Both the recognition and subsequent degradation of viral RNA strictly depend on poly(A) tail length, enabling selective decay of short-tailed viral transcripts while sparing host mRNAs. Importantly, inflammatory signaling enhances the expression of PABPC1 and EXD2, suggesting its role as an inducible host defense pathway activated during viral infection. Capitalizing on this discovery, we engineered a synthetic fusion protein mimicking the PABPC1-EXD2 complex and achieved efficient delivery using lipid nanoparticles (LNPs). This rationally designed therapeutic exhibits robust suppression of coronavirus replication in both cellular and murine models. Collectively, our findings uncover a novel host antiviral strategy that targets a conserved viral RNA structural element and provide a conceptual framework for developing host-derived and broad-spectrum anti-coronavirus therapeutics.

microbiology↗

bioRxiv2026

Abalone genomics reveals an ancient asymmetry axis and the multifunctionality of the mantle

Gastropod diversification represents one of the most spectacular evolutionary radiations, underpinned by a fundamentally asymmetric body plan and diverse pigmentation patterns. To understand the genetic underpinnings of these cardinal traits, we constructed a chromosome-level genome assembly for the rainbow abalone, Haliotis iris. Integrating systematic genomics, histoembryology, and molecular assays, we uncover an ancient regulatory axis, conserved for over 544 million years, in which the long non-coding RNA lncRNA1 functions as a competing endogenous RNA to shield pitx transcripts from miRNA-mediated degradation, thereby driving the establishment of asymmetric body patterning, including the mantle. Furthermore, we identify a wnt-mitf-tyr cascade as the driver of melanogenesis and demonstrate that the mantle is the primary site of melanin synthesis. Concurrently, we reveal the mantle as a multifunctional organ possessing mechanosensory capabilities. These findings elucidate the ancient genetic toolkit governing trait evolution in gastropods. TeaserA deeply conserved RNA switch directs gastropod body plans and reveals the mantles hidden talents.

genomics↗

bioRxiv2026

Genome-wide investigation of WRKY gene family in Lagenaria siceraria

BackgroundA significant family of transcription factors known as WRKY genes include many physiological functions and environmental adaptations. However, insufficient information was previously available about the WRKY genes in Lagenaria siceraria, a crucial crop with substantial economic significance. The recent publication of the whole-genome sequence of L. siceraria has allowed us to perform a genome-wide investigation of the organization of the WRKY genes in L. siceraria. ResultsIn the present study, 57 L. siceraria WRKY (LsiWRKY) genes were identified and given new names based on their relative chromosomal distribution. The 57 LsiWRKYs were further divided into three major groups and several subgroups based on their structural and phylogenetic properties. Segmentation duplication events have played a major role in the expansion of the WRKY gene family in L. siceraria. Phylogenetic comparisons of the Group III WRKY genes provide valuable insights into the evolutionary characteristics of WRKY genes in L. siceraria. Additionally, RNA-seq analysis revealed distinct expression pattern of WRKY genes across different tissues. ConclusionsThis study presents a preliminary analysis of the WRKY gene family in L. siceraria, including their structural characteristics, evolutionary traits, and tissue-specific expression patterns. The systematic insights provided here serve as a foundation for further functional studies aimed at enhancing L. siceraria crops. This knowledge holds promise for improving the cultivation and yield of L. siceraria, thereby contributing to agricultural advancements.

genomics↗