Biology subjects

Chen, T.

Publications and source records attributed to Chen, T..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Acquired resistance to the PRMT5 inhibitor confers collateral sensitivity to MEK inhibition in MTAP-null non-small cell lung cancer

Protein arginine methyltransferase 5 (PRMT5) is a synthetic lethal target in methylthioadenosine phosphorylase-deleted (MTAP-null) cancers. Second-generation MTA-cooperative PRMT5 inhibitors preferentially target MTAP-null cells while largely sparing MTAP-wildtype (MTAP-WT) cells, thereby improving tumor selectivity over first-generation PRMT5 inhibitors. Despite encouraging efficacy and safety signals in early clinical studies, the modest objective response rates (ORRs) observed with these inhibitors suggest that intrinsic or acquired resistance may limit their clinical benefit. Here, we investigated mechanisms of acquired resistance to the MTA-cooperative PRMT5 inhibitor BMS-986504/MRTX1719 in MTAP-null non-small cell lung cancer (NSCLC) cells and sought to identify therapeutic vulnerabilities that emerge upon resistance. Using multiple in vitro-derived resistant models, we found that acquired resistance was not fully explained by alterations in PRMT5 activity or reduced MTA levels. Instead, resistance was associated with collateral sensitivity to MEK inhibition and enrichment of MAPK-related transcriptional programs. Together, these findings identify MEK inhibition as an actionable collateral vulnerability in MTAP-null NSCLC cells that acquire resistance to PRMT5 inhibition.

cancer biology↗

bioRxiv2026

Virtual multiplex staining of the pancreatic islets across type 1 diabetes progression using a Schroedinger bridge

Classical hematoxylin and eosin (H&E) staining enables review of tissue morphology but lacks information regarding the molecular state of cells. Immunohistochemical (IHC) techniques label specific proteins in tissue, allowing differentiation of relevant structures that may go undetectable in H&E. However, the IHC process is complex, expensive, and time-consuming, especially for multiplex IHC (mIHC) limiting its use in large cohorts. Stain conversion of H&E to IHC using generative artificial intelligence models such as generative adversarial networks (GANs) represent one solution to this problem. However, GANs are unstable during out of distribution sampling and are prone to hallucinations or mode collapse, limiting their accuracy in challenging image conversion tasks. To address this, the field has recently turned to diffusion models. Here, we introduce Schrodinger-bridge for Multiplex ImmunoLabel Estimation (SMILE). Unlike conventional diffusion models that map from source to target through an intermediate Gaussian noise, Schrodinger-bridge diffusion models skip this step and have been shown to better preserve structures during image translation. To test the performance of SMILE, we generated a large cohort of high-fidelity H&E-mIHC image pairs from pancreatic organ donors, targeting insulin, glucagon, and CD3. Our dataset well-sampled across type-1 diabetes status, pancreas anatomical location, age, and sex. Using this cohort, we demonstrate the superiority of SMILE compared to GANs via a comprehensive evaluation framework incorporating texture, distribution, and antibody-specific metrics, as well as blinded pathologist reviews. We further confirmed the ability of SMILE to generate accurate mIHC images from H&Es generated at an external site, to perform whole slide image conversion, and to generate realistic three-dimensional maps of the pancreatic islets in non-diabetic, auto-antibody positive, and type-1 diabetic donor tissue. Finally, we performed stain conversion of paired H&E to HER2 and Ki67 images in breast cancer, confirming the superiority of SMILE in diverse stain conversion applications. Collectively, this framework provides a scalable pipeline for high-throughput proteomic inference from archival H&Es, providing transformative potential for pancreatic research and digital pathology.

bioinformatics↗

bioRxiv2026

Heterogeneous transmission estimation and strategy optimization for Chikungunya: a vector-borne modeling study differentiating age and sex

Against the backdrop of global climate change and accelerating population mobility in 2025, chikungunya fever (CHIKF) exhibited a trend of worldwide spread, significantly increasing the difficulty of controlling tropical mosquito-borne diseases. To enhance the precision of intervention strategies, this study developed an age- and sex-structured human-mosquito interaction dynamic model based on data from the largest CHIKF outbreak ever recorded in China, and conducted a targeted analysis of prevention and control strategies. By decomposing the basic reproduction number and examining population heterogeneity, asymptomatic males aged 15-59 years were identified as the core transmission group. Optimal control analysis revealed that the synergistic implementation of three measures-- reducing the effective human-to-mosquito transmission rate, reducing the effective mosquito-to-human transmission rate, and suppressing mosquito population density--could reduce the overall infection rate by 95.7586%. Among these, mosquito population suppression should be prioritized as a universal core strategy; however, its protective effect on females aged 60 years and above was relatively weak, warranting particular attention. The study further demonstrated that asymmetric intensity combinations targeting these three intervention pathways--such as intensity profiles of "10%, 90%, 90%" or "60%, 80%, 90%"--could achieve effective outbreak control. This research elucidates population-specific transmission patterns and key pathways for intervention intensity, providing a theoretical and strategic foundation for the precise control of mosquito-borne diseases. It also provides actionable operational insights to support rapid response and strategy optimization for future emerging outbreaks. Author summaryCHIKF is a mosquito-borne viral disease that is gradually spreading from tropical regions to other areas. To achieve more precise control of this disease, we developed an age- and sex-structured analytical model based on the largest CHIKF outbreak in China, aiming to provide a scientific basis for responding to potential future outbreaks with inherent uncertainties. The study found that asymptomatic males aged 15-59 years were the primary drivers of transmission and should be prioritized as a key population for reducing viral spread in prevention efforts. When evaluating the effectiveness of different intervention strategies, females aged 60 years and above were the least affected by the implemented measures, indicating that this group should strengthen personal protection to lower their infection risk. Among all control measures, mosquito suppression was the most effective, suggesting that vector control strategies should be prioritized in future outbreak responses.

pathology↗

bioRxiv2026

A correlational study of ABCA3 and SCN4B as exercise-related biomarkers of patients with Stanford type A aortic dissection

BackgroundAccumulating evidence indicates that moderate exercise may reduce the incidence of Stanford type A aortic dissection (TAAD), but the specific mechanisms remain unclear. This study aims to identify exercise-related biomarkers in TAAD patients and to investigate their underlying mechanisms. MethodsTranscriptome data related to TAAD and exercise-related genes were obtained from publicly available databases. Candidate biomarkers for TAAD were identified through an integrative approach incorporating differential expression analysis, machine learning, and expression level assessment, leading to the construction of a diagnostic model. Subsequently, functional enrichment, immune infiltration, regulatory network analysis, and computational drug prediction were conducted to systematically investigate the pathological mechanisms and translational potential of the indentified biomarkers. ResultsABCA3 and SCN4B were identified as exercise-related biomarkers in TAAD progression. A nomogram incorporating these two biomarkers exhibited strong diagnostic performance for identifying the disease. Functional enrichment analysis revealed potential involvement of these biomarkers in disease progression through pathways including circadian rhythm regulation and ribosome biosynthesis. Additionally, immune cells like M1 macrophages and naive B cells, as well as regulatory factors including hsa-miR-1343-3p and XIST, were found to be involved in this process. Finally, zonisamide and MRS1097 were identified through computation prediction as potential therapeutic drugs. ConclusionABCA3 and SCN4B were identified as exercise-related biomarkers associatied with TAAD and represent potential valuable targets for both diagnosis and treatment strategies.

bioinformatics↗

bioRxiv2026

BRD4 recruitment desilences transcription without erasure or depletion of repressive chromatin

How genes are desilenced within mesoscale repressive chromatin is a crucial yet poorly understood phenomenon. Prevailing models posit that methylation of lysine-9 of histone H3 (H3K9me3) engages heterochromatin protein 1 (HP1) to drive chromatin compaction and transcriptional silencing. The erasure of this repressive mark and its replacement with acetyl/acyl groups recruits positive factors such as BRD4/BET to elicit gene transcription. We report that in Friedreichs ataxia, a synthetic gene regulator (SynGR1) drives transcription across repressive chromatin without removal or replacement of H3K9me3 or HP1. By selectively recruiting BRD4/BET into repressive GAA-repeats in frataxin (FXN), SynGR1 creates a paradoxical state where gene transcription and repressive chromatin co-exist. Contrary to convention, we find that BRD4 readily partitions into phase separated HP1 condensates in vitro and into HP1 puncta in patient-derived cells, thus presenting a mechanistic explanation for desilencing transcription without the dispersal of mesoscale repressive chromatin. Epigenetic drugs that gate sequential steps in transcription, synergistically stimulate FXN expression while concomitantly increasing, rather than eliminating, repressive H3K9me3 and HP1 levels. More broadly, this study highlights the dynamic nature of repressive chromatin and the context-dependence of epigenetic marks in regulating gene expression.

cell biology↗

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

Wnt/β-catenin signaling restrains developmental beiging and imprints long-term energy expenditure

Beige adipocytes that emerge during the peri-weaning period support sympathetic nervous system (SNS)-independent thermogenesis, yet the mechanisms governing this spontaneous beiging remain unclear. Here, by integrating transcriptomic profiling with adipocyte-targeted Ctnnb1 deletion in mice, we identify the canonical Wnt signaling as an endogenous brake on developmental beige thermogenesis. Peri-weaning inguinal fat from adipocyte Ctnnb1 knockout mice exhibits enhanced beige adipocyte biogenesis, with increased thermogenesis-related gene expression and mitochondrial oxidative capacity, which programs durable activation of adaptive thermogenesis and augmented whole-body energy expenditure into adulthood. Mechanistically, suppression of Wnt/{beta}-catenin signaling induces a non-canonical Wnt5a-Ca2-AMPK axis that promotes triglyceride lipolysis and subsequent PPAR-driven fatty acid oxidation, thereby fueling mitochondrial respiration. Genetic or pharmacological disruption of this axis blunts thermogenic responses induced by {beta}-catenin inhibition in both murine and human subcutaneous adipocytes, indicating that Wnt5a-Ca2-AMPK axis is required for the cell-autonomous activation of beige fat. These results reveal Wnt/{beta}-catenin signaling as a developmental constraint on beige adipocyte formation and suggest an SNS-independent route to sustainably raise energy expenditure and improve metabolic health.

physiology↗

bioRxiv2026

A Deep Learning Framework for Predicting Gut Microbe-Host Receptor Interactions

MotivationGut microbiota regulates host health through complex protein-protein interactions. However, deciphering this specific interactions between microbiota and human receptors remains a significant challenge due to the lack of specialized computational tools. ResultsLeveraging the hypothesis of cell communication and relevant data, HMI-Pred initially builds an ensemble classifier to screen for potential ligand sequences within microbial genomes. It then jointly evaluates sequence semantics and molecular docking to predict potential microbe-host receptor interactions.HMI-Pred achieved robust performance with F1-scores of 0.901 for microbial ligand identification and 0.883 for interaction prediction. Application to 332,381 microbial proteins revealed distinct interaction patterns: histone deacetylases (HDACs) served as broad-spectrum targets (mean score > 0.80), while G protein-coupled receptors (GPCRs) exhibited high specificity (scores 0.42-0.61). Furthermore, literature mining validated over 47% of the functional predictions, and specific immunomodulatory interactions were confirmed in Akkermansia muciniphila.HMI-Pred provides a valuable computational tool for decoding host-microbe signaling networks and facilitating the discovery of microbiome-based therapeutic targets. AvailabilityThe source code and documentation are available at https://github.com/YangLab-BUPT/HMI-Pred. Contactlihm@bupt.edu.cn

microbiology↗

bioRxiv2026

Dual-Logistic Analysis of Time- and Concentration-Dependent Phenotypic Efficacy Evaluation Integrating Drug Targets Information

Tumor heterogeneity highlights the necessity of precision cancer medicine, making the evaluation and screening of anticancer drugs a core challenge in cancer therapy. However, current cell-based efficacy assessment methods struggle to quantify the holistic impact of drugs on cellular behavior through specific target engagement. Here, we proposed a novel approach (DL-TCP-FRET) that integrates phenotypic and target-related evaluations: the logistic fitting analysis is performed on time- and concentration-dependent cellular phenotypic characteristics to construct a phenotypic score (P), while a target score (T) is established based on the FRET efficiency between target proteins. These two scores were then further combined to generate a unified drug efficacy score (PT). Validation in A549 cells demonstrated that our method can reliably distinguish EGFR-TKIs from non-targeted drugs. DL-TCP-FRET simplifies the experimental workflow of drug efficacy evaluation and improves the accuracy of targeted drug identification, providing a novel strategy for advancing precision cancer therapy.

cell biology↗

bioRxiv2026

PRMT5-Mediated Arginine Methylation of HSP90AA1 Drives Esophageal Squamous Cell Carcinoma Progression

Dysregulated HSP90AA1 chaperone activity is a hallmark of multiple human cancers, yet its post-translational modifications in esophageal squamous cell carcinoma (ESCC) are poorly defined. Here, we identify a PRMT5-driven modification of HSP90AA1, symmetric dimethylation at arginine 182 (R182), as a pivotal switch that fuels ESCC progression. HSP90AA1 physically associates with PRMT5, and genetic or pharmacologic PRMT5 blockade diminishes HSP90AA1-R182 methylation and downstream oncogenic signaling. Functionally, HSP90AA1 loss suppresses ESCC cell proliferation, migration, and invasion; enforces cell cycle arrest and reverses epithelial-mesenchymal transition (EMT). Re-expression of wild-type HSP90AA1 rescues cancer cell growth and EMT in vitro and in vivo, whereas the methylation-deficient R182A mutant does not. Furthermore, clinically oriented analysis shows HSP90AA1 overexpression in ESCC tissues and indicates moderate prognostic utility. Moreover, dual targeting of the PRMT5/HSP90AA1 axis is therapeutically attractive, and combined PRMT5/HSP90AA1 inhibition acts synergistically to exert anti-tumor activity, markedly reducing clonogenicity, invasion, and tumor burden in both cell-derived and patient-derived xenograft models. These findings demonstrate that PRMT5/HSP90AA1 serve as mechanistic drivers and actionable biomarkers in ESCC and provide a rationale for biomarker-guided co-inhibition of PRMT5/HSP90AA1 in translational studies.

cancer biology↗

bioRxiv2026

DeepSRFusion: a point cloud deep learning framework for super-resolution particle fusion

Deciphering the spatial organization of macromolecular complexes in their native context is central to structural biology. Particle fusion in single-molecule localization microscopy offers a unique capability for high-resolution structural reconstruction in situ. However, existing methods face significant challenges from large rotational perturbations and sparse labeling, resulting in compromised accuracy and substantial computational cost. We present DeepSRFusion, a self-supervised pretraining framework for three-dimensional super-resolution particle fusion. By representing single-molecule point clouds as Gaussian Mixture Models, DeepSRFusion integrates data-driven feature learning with physical imaging constraints. A two-stage optimization strategy with dynamic template updating enhances robustness, and a novel Clustering Error metric quantifies fusion quality. Nanometer-scale validation on both simulated and experimental datasets demonstrates high reconstruction fidelity and structural consistency with cryo-electron microscopy and AlphaFold3. DeepSRFusion remains effective under challenging imaging conditions, including large 3D rotations, sparse labeling, high localization uncertainty, and limited particle numbers, while achieving over 100-fold speedups compared to current methods. It resolves fine structural features with a measured spatial resolution of 1.60 {+/-} 0.10 nm, sufficient to distinguish ~10 nm spaced protein pairs and visualize tilted internal substructures within macromolecular assemblies. DeepSRFusion provides a powerful tool for high-precision structural analysis in native cellular environments.

biophysics↗

bioRxiv2026

Modulation of rob expression accelerates development of antibiotic resistance in Yersinia enterocolitica

Multidrug-resistant bacteria pose a severe threat to global health. Mutations in transcriptional regulators accelerate the emergence of multidrug resistance and may have a crucial impact on pathogen evolvability under antibiotic exposure. Here, we investigate these dynamics in the bacterial pathogen Yersinia enterocolitica. In this organism, we identified a high-frequency de novo mutation in the promoter of an AraC/XylS-family transcriptional regulator, Rob. This mutation arose independently during resistance evolution against three of six antibiotic classes. Sequence and structure alignments indicate that Rob is a previously uncharacterized, lineage-specific regulator in Y. enterocolitica, featuring a conserved promoter architecture. This promoter mutation resulted in robust rob overexpression, leading to the activation of multiple downstream efflux- and membrane-associated pathways. This regulatory mutation emerges early and shapes the acquisition of tetracycline resistance, while also enhancing high-level enrofloxacin resistance in combination with canonical mutations in gyrA and parC. Despite being favored under antibiotic selection, rob overexpression is costly, resulting in counter-selection of the -57 G>A rob allele in the absence of antibiotics. Together, these findings identify Rob as a Yersinia-specific efflux regulator and demonstrate how regulatory mutations can transiently accelerate antibiotic resistance. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/707304v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@107aa88org.highwire.dtl.DTLVardef@4ccd66org.highwire.dtl.DTLVardef@411e2aorg.highwire.dtl.DTLVardef@1235fbc_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

Reprogramming CAR T-Cells with designed bioPROTACs

Gene editing has been used to enhance CAR T-cell function by disrupting negative regulators but has limitations. Here we show that de novo-designed generated targeted degraders (bioPROTACs) provide an alternative approach. Expression of bioPROTACs in CAR T-cells targeting DNMT3A, a key regulator of T-cell exhaustion, phenocopied gene knockout. Our reversible, non-gene editing approach provides a tunable strategy to reprogram T-cell fate which should be broadly applicable for next-generation cell therapies.

immunology↗

bioRxiv2026

Environmental DNA/RNA metabarcoding in estuaries of Sao Paulo, Brazil, reveals fish diversity and the presence of invasive species

Tropical estuaries within the Brazilian Atlantic Forest are biodiversity hotspots facing escalating anthropogenic pressures, yet their ichthyofaunal assemblages remain incompletely documented. We evaluated the combined use of environmental DNA (eDNA) and environmental RNA (eRNA) metabarcoding to characterize fish communities in two estuaries with contrasting levels of urbanization (the Juqueriquere and Escuro rivers) on the northern coast of Sao Paulo, Brazil. Targeting the mitochondrial 12S rRNA (MiFish) fragment, we detected a diverse vertebrate assemblage totaling 93 species. eDNA identified 32 fish species across both systems, while eRNA detected 22 species in the preserved estuary, providing robust signals of metabolically active assemblages. The less impacted estuary exhibited significantly higher diversity indices and a more heterogeneous taxonomic composition. In contrast, the urbanized system displayed clear molecular signatures of anthropogenic influence, including the presence of invasive species (Oreochromis niloticus, O. aureus, and Clarias gariepinus) and domestic animals. This study constitutes the first application of fish eRNA metabarcoding in Brazil and demonstrates that integrating eDNA and eRNA refines ecological interpretation by coupling biodiversity detection with improved inference about contemporary community composition. Our findings highlight the potential of multi-molecule metabarcoding for routine, non-invasive biodiversity assessment in megadiverse and conservation-priority coastal ecosystems.

ecology↗

bioRxiv2026

Molecular basis of enfumafungin inhibition and resistance in Candida glabrata β-1,3-Glucan synthase FKS1

{beta}-1,3-Glucan synthase FKS1 is essential for fungal cell wall biogenesis and serves as a validated target of widely prescribed antifungal drugs. However, the molecular mechanism of pathogenic FKS1, its modes of inhibition, and the associated resistance mechanisms remain elusive, hindering the antifungal development. Here, we focus on Candida glabrata FKS1, a clinically relevant target frequently linked to antifungal resistance. We present cryo-EM structures of C. glabrata FKS1 in multiple detergent environments and in complex with the triterpenoid antifungal enfumafungin. Integrated functional studies revealed a key catalytic residue within the conserved ED motif and uncovered structural adaptations to different membrane-mimetic environments, rationalizing FKS1s sensitivity to its surrounding membrane context. Enfumafungin binds at the extracellular membrane leaflet of cgFKS1 transmembrane domain, engaging a convex surface separated from the cytosolic active site, consistent with its role as a non-competitive inhibitor. Further analyses identified key enfumafungin-binding residues from TM5-TM6 and elucidated the molecular basis of drug resistance in C. glabrata. Collectively, these findings establish a mechanistic framework for pathogenic FKS1 function and inhibition and provide a molecular basis for the rational design of next-generation antifungal therapeutics.

microbiology↗

bioRxiv2026

Chronic Unpredictable Stress Drives Non-pathological, Adaptive Metabolic Hormone Reprogramming with Reduced Insulin Resistance Linked to Depressive-like Behaviors

Major Depressive Disorder (MDD) is increasingly conceptualized as a neuroimmune-metabolic-oxidative stress (NIMETOX) condition, partly driven by environmental stressors. While new antidepressant strategies have emerged to target NIMETOX pathways, the mechanism by which chronic stress reshapes metabolic regulation-particularly in the absence of clinically overt metabolic disease-remain poorly understood. Using a 6-week chronic unpredictable mild stress (CUMS) paradigm in metabolically healthy male and female mice, we evaluated depressive-like behaviors alongside circulating glucose and metabolic hormones, including insulin, resistin, Glucose-dependent Insulinotropic Polypeptide (GIP), Glucagon-like Peptide-1 (GLP-1), glucagon, ghrelin, leptin, and Plasminogen Activator Inhibitor-1 (PAI-1). We further assessed whether simvastatin, curcumin, S-adenosyl methionine (SAMe), or pyrrolidine dithiocarbamate (PDTC), an NF-{kappa}B inhibitor, normalize CUMS-induced metabolic and behavioral alterations, using fluoxetine as a reference antidepressant. CUMS induced a hypoinsulinemic state accompanied by enhanced basal insulin sensitivity and reduced insulin resistance, while central appetite regulation and adipose inflammatory profiles remained preserved. Indices of lower insulin resistance were strongly associated with sucrose preference and immobility time. Glucagon and PAI-1 showed positive associations with novel object recognition performance, whereas GIP and leptin were inversely related to open-field activity. Glucose levels correlated positively with rearing behavior. Despite significant improvements in depressive-like behaviors, none of the pharmacological interventions normalized the stress-induced metabolic hormone profile. Marked sex differences were observed, with females displaying a catabolic, immune-ready phenotype and males showing a relative anabolic bias. These findings indicate that CUMS induces a non-pathological, adaptive metabolic hormone reprogramming rather than metabolic dysfunction, supporting the interpretation of stress-related metabolic changes as resilient adaptations within the NIMETOX framework.

neuroscience↗

bioRxiv2026

PeptiVerse: A Unified Platform for Therapeutic Peptide Property Prediction

Therapeutic peptides combine the advantages of small molecules and antibodies, offering target flexibility and low immunogenicity, yet their successful translation requires careful evaluation of multiple developability properties beyond binding alone. As chemically modified peptides become increasingly common in drug design, no unified platform currently supports systematic property assessment across both canonical sequences and SMILES-based representations. Leveraging the generalizability of large foundational models trained on protein and chemical data, we introduce PeptiVerse, a universal therapeutic peptide property prediction platform. PeptiVerse accepts either amino acid sequences or chemically modified peptide SMILES, delivers state-of-the-art performance across diverse property prediction tasks, and provides both a web interface and open-source implementation for rapid, accessible, and scalable pep-tide developability analysis. By unifying property prediction across representations, PeptiVerse directly supports early-stage peptide therapeutic development campaigns and property-aware generative design workflows.

bioinformatics↗

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↗