Biology subjects

Long, W.

Publications and source records attributed to Long, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Validating a high-throughput in vitro model for culturing antibiotic-altered microbiome communities

The gut microbiome plays a central role in host health, and its disruption is associated with metabolic, infectious, and immune conditions. Stool sampling is frequently used to characterize snapshots of the gut microbiota. To screen microbiota communities in vitro, multi-stage bioreactors have been developed that model distinct physiological environments along the gastrointestinal tract. However, such bioreactors are often operationally complex and provide limited throughput. Stool-derived in vitro communities offer several advantages to study microbiota due to their ease of use, high-throughput nature, and have previously been shown to remain stable following repeat passaging. Yet, prior characterization of stool-derived cultures has relied on 16S rRNA sequencing of unaltered gut microbiota, leaving several key questions unanswered: 1) how closely does in vitro culture recapitulate the functional potential and resistome composition of its inoculum and 2) can these cultures model antibiotic disrupted microbial communities? In this work, we generated stool-derived in vitro cultures from male and female mice with both unaltered and antibiotic altered gut microbiomes and applied shotgun metagenomic sequencing to characterize taxonomic composition, functional pathway capacity, and antimicrobial resistance gene (ARG) content. Antibiotic-altered microbiota communities, dominated by Enterobacteriaceae, were faithfully recapitulated in culture with preservation of both taxonomy and ARGs. By contrast, unaltered communities underwent substantial restructuring in culture, with Bifidobacterium and Enterococcus blooming and driving sex-divergent shifts in functional capacity and a substantial amplification of the resistome. Together, these findings validate a high-throughput stool-derived in vitro culture as a tractable proxy for an antibiotic altered gut microbiome to screen interventions.

microbiology↗

bioRxiv2026

Exploratory multi-omics analysis reveals sex-specific differences in microbial response to antibiotic exposure

Antibiotic exposure is a major driver of microbiome disruption and antimicrobial resistance gene (ARG) expansion. Yet, the role of biological sex in shaping these responses remains poorly understood. Most studies do not stratify antibiotic-induced microbiome changes by sex or integrate multi-omics datasets, limiting our understanding of how microbial, metabolic, and immune responses interact. Therefore, there remains a critical need for an integrative systems-level approach to determine how sex-specific disruptions under antibiotic pressure are paralleled across microbial, metabolic, and host immune layers. The objective of this work was to perform an exploratory study investigating how continuous antibiotic exposure reshaped the gut microbiome across sexual maturation and how these perturbations influenced downstream host responses in a sex-specific manner using an integrative multi-omics framework. Male and female mice that were exposed to continuous antibiotics were profiled over sexual maturation using shotgun metagenomics, untargeted metabolomics, and bulk RNA sequencing of the spleen to assess microbial composition, ARG dynamics, metabolic profiles, and immune responses. Overall, our results demonstrated sex-specific correlations at a systems-level that help provide valuable context to the differences observed in males and females upon antibiotic exposure.

microbiology↗

bioRxiv2026

Learning Perturbation Effects Through Contrastive Alignment of Multimodal Biological Embeddings

Multimodal single-cell perturbation screens offer a scalable approach for characterizing the effects of genetic and chemical interventions on cellular state. However, most existing representation-learning methods are tailored to a single perturbation modality and fail to explicitly incorporate external semantic knowledge, which limits their ability to generalize across datasets and perturbation types. Here, we introduce PertOmni, a CLIP-style multimodal representation-learning framework that aligns transcriptomic perturbation signatures with text-derived embeddings of curated genes and compound descriptions, as well as image-derived embeddings from cell paintings. PertOmni jointly trains a shared transcriptomic encoder and dataset-specific text encoders using a masked contrastive objective that emphasizes within-cell-type discrimination while mitigating confounding effects arising from cell-type heterogeneity. We evaluate the produced joint embedding space on bi-directional retrieval, drug-gene interaction inference, and perturbation prediction across both small-molecule and CRISPRi perturbation datasets, and demonstrate consistent improvements over strong baseline methods.

bioinformatics↗

bioRxiv2026

ContextTAD: Context-aware boundary learning for TAD calling from Hi-C contact maps

MotivationReliable topologically associating domain (TAD) calling from Hi-C contact maps remains difficult at high resolution and realistic sequencing depth. A central reason is that many callers learn boundary evidence largely from local signals, while domain compatibility is handled mainly during downstream decoding, so the learned boundary scores are not explicitly optimized for the TAD assembly step that ultimately determines the final calls. ResultsWe present ContextTAD, a deep-learning TAD caller that learns boundary evidence from broader local Hi-C windows that capture TAD-scale structural context. Instead of treating boundary prediction as an isolated per-bin classification problem, ContextTAD uses a context-aware representation to produce left- and right-boundary tracks that are explicitly optimized for downstream TAD assembly. Concretely, the model combines multiscale feature extraction from 2D Hi-C windows with a pair objective that rewards compatible boundary combinations and a count objective that regularizes window-level boundary evidence. Due to the limited availability of high-quality TAD annotations, supervised deep-learning methods for TAD calling remain rare. To address this bottleneck, we construct improved training annotations by integrating high-coverage Hi-C structure with complementary boundary-associated genomic signals, thereby providing more reliable supervision for model training. We benchmarked ContextTAD against a broad panel of alternative TAD callers across standard comparative evaluation, sequencing-depth robustness analysis, and cross-cell-type transfer settings, and found that it performed strongly against alternative tools across this wide range of settings, with the best overall recovery of biologically supported TADs. Availabilityhttps://github.com/ai4nucleome/ContextTAD Contactyanlinzhang@hkust-gz.edu.cn

genomics↗

bioRxiv2026

PopGenAgent: Tool-Aware, Reproducible, Report-Oriented Workflows for Population Genomics

Population genetics analyses typically require orchestrating multiple tools across heterogeneous tasks, from data curation to inference and visualization. In practice, a central bottleneck lies not only in reproducibility, but in maintaining consistency between intermediate diagnostics, analytical decisions, and downstream results as analyses evolve. Here we present PopGenAgent, an agentic workflow that formalizes population-genetic analyses as explicit, multi-step plans with declared inputs, outputs, and dependencies. Each step is instantiated from curated tool and visualization templates, while all intermediate artefacts are preserved within a unified, inspectable context. An interactive agent interface supports iterative plan construction and revision, and leverages accumulated outputs to assist interpretation and report drafting. In a case study of 26 populations from the 1000 Genomes Project, starting from a filtered PLINK dataset, PopGenAgent coordinated analyses including ROH, LD decay, PCA, ADMIXTURE, TreeMix, and f3. By structuring analyses as explicit, revisable plans with preserved intermediate states, PopGenAgent provides a transparent and extensible environment for conducting population-genetic analyses.

bioinformatics↗

bioRxiv2025

Mut-BPE: A Modified BPE Strategy Improves Variant Effect Prediction

Byte Pair Encoding (BPE) is widely used in genome foundation models for its ability to compress long DNA sequences into fewer tokens. However, its variable-length tokens often span multiple nucleotides, limiting the models sensitivity to single-nucleotide variations--an essential requirement for accurate Variant Effect Prediction (VEP). We introduce Mut-BPE, a training-free, plug-and-play tokenization strategy that augments BPE with explicit single-nucleotide resolution at variant sites. Mut-BPE preserves the efficiency of BPE while enhancing its ability to represent subtle genomic alterations. To evaluate its effectiveness, we applied Mut-BPE to DNABERT-2 and conducted extensive experiments across six diverse datasets spanning gene expression, pathogenicity, and trait-associated variants, under zero-shot and fine-tuning settings. Mut-BPE consistently outperformed conventional BPE tokenization, yielding significant improvements in both AUROC and AUPRC, particularly in imbalanced datasets. These results highlight Mut-BPE as a practical enhancement for genomic foundation models in VEP tasks. Code availability: https://anonymous.4open.science/r/Mut-BPE-1182

bioinformatics↗

bioRxiv2025

The anti-retroviral therapy emtricitabine affects skeletal muscle DNA methylation and transcriptome patterns in a male HIV mouse model

BackgroundAlthough antiretroviral therapies (ART) have substantially reduced HIV-associated mortality, the increased lifespan achieved by widespread ART deployment has revealed that HIV infection is linked to an unexplained earlier onset and increased incidence of aging-associated conditions like sarcopenia. Complex syndromes, like sarcopenia, often arise from a combination of genetic and environmental factors, so in this study, we investigated effects of short-term treatment with emtricitabine (2,3-dideoxy-5-fluoro-3-thiacytidine; FTC), an FDA approved ART, on skeletal muscle DNA methylation patterns and transcriptome-wide responses in a male murine model of HIV phenotypic biology (Tg26 mice). MethodsWe treated 6 month old male Tg26 (+/-) mice or wildtype (WT) littermates on a C57BL/6 genetic background with FTC in the drinking water for one month; control groups received drinking water vehicle alone (VEH). Muscle function and body composition were measured longitudinally. Skeletal muscle methylation patterns, transcriptional changes, and histological features were quantified at sacrifice. ResultsAlthough neither gross structural nor functional muscle deficits were observed in this short-term study with ART usage, relative decreases in muscle endurance measured by hang time over the study were 2-fold more severe in the Tg26 as compared to WT mice (p=0.0453), and markers of myogenic cell maturation (Myf5, Myf6) were disrupted in the Tg26 HIV model as compared to WT littermates in a manner exacerbated by FTC treatment. Fat mass, measured by DXA, also tended (p=0.085) to be uniquely increased by FTC treatment in the Tg26 mice over the study. Differential methylation patterns and pathway enrichment data suggested that the presence of an HIV phenotype and exposure to an ART regimen altered the methylation status in skeletal muscle genes such as Camk2B, Pcolce2 and Lima1 in a manner consistent with promoting eventual functional impairment in muscle. Additionally, RNAseq revealed differential gene expression profiles and key regulatory pathways including cellular differentiation, regulation of lipid metabolism, and neuroactive ligand-receptor interactions. Lipodystrophy-related genes including LEP, ADIPOQ and PPAR involved in fat distribution and metabolism along with skeletal genes related to regulation of muscle strength were affected by the presence of an HIV phenotype and ART treatment. ConclusionsThe current study provides insights into mechanisms by which a clinically relevant ART may influence DNA methylation and transcriptome changes in skeletal muscle in the context of HIV biology. The differentially regulated pathways suggest novel targets for understanding, and eventually abrogating, the harmful effects of long-term ART use in PLWH on skeletal muscle mass and function.

cell biology↗

bioRxiv2025

NyxBind: enhancing DNN representations via contrastive learning for TFBS prediction

While pretrained genomic language models effectively capture general DNA sequence patterns through masked language modeling, they often struggle to discriminate subtle yet biologically critical differences among transcription factor binding site (TFBS) motifs. Recent studies suggest that contrastive learning can enhance the discriminative power of embeddings by explicitly modeling inter-instance similarities and differences. Building on this insight, we introduce NyxBind, the first TFBS prediction model that applies contrastive learning across multiple TFBS types to enhance regulatory sequence representations. NyxBind better captures discriminative sequence features, enabling more accurate and biologically meaningful TFBS prediction. Extensive evaluations show that NyxBind consistently outperforms alternative models across multiple TFBS classification benchmarks, demonstrating strong robustness and generalizability. Moreover, NyxBind supports both full-parameter and parameter-efficient fine-tuning while maintaining high performance, and supports accurate motif visualization, aligning closely with experimentally validated transcription factor binding profiles. The code are available at https://github.com/ai4nucleome/NyxBind.

bioinformatics↗

bioRxiv2025

spVelo: RNA velocity inference for multi-batch spatial transcriptomics data

RNA velocity has emerged as a powerful tool to interpret transcriptional dynamics and infer trajectory from snapshot datasets. However, current methods fail to utilize the spatial information inherent in spatial transcriptomics and lack scalability in multi-batch datasets. Here, we introduce spVelo (spatial Velocity inference), a scalable framework for RNA velocity inference in multi-batch spatial transcriptomics data. Our model comparison studies show that spVelo compares favorably to existing methods regarding velocity consistency, transition accuracy, and direction correctness across expression levels, spatial graphs in each batch, and MNN graphs between batches. Furthermore, spVelo supports several downstream applications, including uncertainty quantification, complex trajectory pattern discovery, biologically significant state driver marker identification, gene regulatory network inference and temporal cell-cell communication inference. In conclusion, spVelo has the potential to provide deeper insights into complex tissue organization and underscore their biological mechanisms based on spatially-resolved patterns.

bioinformatics↗

bioRxiv2025

HGCPep: A Hypergraph-based Deep Learning Model for Enhancing Representation of Peptide Features in Cancer-associated ncPEPs Identification

The emergence of non-coding RNA-encoded small peptides (ncPEPs) has sparked significant interest within the realm of cancer immunotherapy, owing to their potential as valuable therapeutic targets and biomarkers. The identification and characterization of cancer-associated ncPEPs assume a crucial role in propelling cancer research forward and augmenting our comprehension of immune-related processes. However, prevailing methods for cancer-associated ncPEPs identification primarily rely on sequence order, neglecting the latent relationships between peptides. As widely acknowledged in the central dogma, peptides are translated from RNA via a many-to-many mapping. In this study, we capitalize on the translation relationship and introduce a hypergraph-based model called HGCPep to enhance the representation of peptide features. Specifically, RNA is regarded as a hyperedge connecting the peptides that are translated from it. Our experimental results validate that the HGCPep approach, leveraging the strengths of hypergraph and convolutional neural networks, outperforms alternative methodologies in the recognition of ncPEPs. Additionally, employing a reduction tool, we visualize the results of multi-label classification within a 2D latent space, shedding light on how multi-label classification task influence the representation of peptides. The dataset and source code of our proposed method can be found via https://github.com/Longwt123/HGCPep_Github.

bioinformatics↗

bioRxiv2025

BioMaster: Multi-agent System for Automated Bioinformatics Analysis Workflow

MotivationThe rapid expansion of biological data has significantly increased the complexity of bioinformatics workflows, which often involve intricate, multi-step processes. These tasks demand considerable manual effort from bioinformaticians, creating inefficiencies and limiting scalability. Recent advancements in large language model (LLM)-powered agents offer promising solutions to streamline and automate these workflows. However, existing automated systems, while effective for short, well-defined tasks, often struggle with long, multi-step workflows due to challenges such as error propagation, limited adaptability to emerging tools, and the inability of LLMs to generalize to niche bioinformatics tasks. Achieving effective workflow automation requires robust task coordination, dynamic knowledge retrieval, and mechanisms to ensure errors are identified and resolved before they impact downstream processes. ResultsWe present BioMaster, a multi-agent framework designed to automate and streamline complex bioinformatics workflows. BioMaster incorporates specialized agents with role-based responsibilities, enabling precise task decomposition, execution, and validation. It leverages Retrieval-Augmented Generation (RAG) to dynamically retrieve domain-specific knowledge, improving adaptability to new tools and niche analyses. BioMaster also introduces enhanced control over input and output validation to ensure pipeline consistency and employs a memory management strategy optimized for handling long workflows. Experiments across diverse bioinformatics tasks, including RNA-seq, ChIP-seq, single-cell analysis, and Hi-C processing, demonstrate that BioMaster significantly outperforms existing methods in accuracy, efficiency, and scalability. By addressing key limitations in workflow automation, BioMaster offers a robust solution for modern bioinformatics challenges. Availabilityhttps://github.com/ai4nucleome/BioMaster Contactyanlinzhang@hkust-gz.edu.cn

bioinformatics↗

bioRxiv2025

MutBERT: Probabilistic Genome Representation Improves Genomics Foundation Models

MotivationUnderstanding the genomic foundation of human diversity and disease requires models that effectively capture sequence variation, such as single nucleotide polymorphisms (SNPs). While recent genomic foundation models have scaled to larger datasets and multi-species inputs, they often fail to account for the sparsity and redundancy inherent in human population data, such as those in the 1000 Genomes Project. SNPs are rare in humans, and current masked language models (MLMs) trained directly on whole-genome sequences may struggle to efficiently learn these variations. Additionally, training on the entire dataset without prioritizing regions of genetic variation results in inefficiencies and negligible gains in performance. ResultsWe present MutBERT, a probabilistic genome-based masked language model that efficiently utilizes SNP information from population-scale genomic data. By representing the entire genome as a probabilistic distribution over observed allele frequencies, MutBERT focuses on informative genomic variations while maintaining computational efficiency. We evaluated MutBERT against DNABERT-2, various versions of Nucleotide Transformer, and modified versions of MutBERT across multiple downstream prediction tasks. MutBERT consistently ranked as one of the top-performing models, demonstrating that this novel representation strategy enables better utilization of biobank-scale genomic data in building pretrained genomic foundation models. Availabilityhttps://github.com/ai4nucleome/mutBERT Contactyanlinzhang@hkust-gz.edu.cn

bioinformatics↗

bioRxiv2024

CosGeneGate Selects Multi-functional and Credible Biomarkers for Single-cell Analysis

Selecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and/or do not show cell-type-specific expression patterns to distinguish cell types. Here we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https://github.com/VivLon/CosGeneGate/blob/main/marker gene list.xlsx.

bioinformatics↗

bioRxiv2024

The Sodium/Glucose Cotransporter 2 Inhibitor Empagliflozin Inhibits Long QT 3 Late Sodium Currents in a Mutation Specific Manner

BackgroundSodium/glucose cotransporter 2 inhibitors (SGLT2is) such as empagliflozin have demonstrated substantial cardioprotective effects in patients with or without diabetes. The SGLT2is have been shown to selectively inhibit the late component of cardiac sodium current (late INa). Induction of late INa is also the primary mechanism involved in the pathophysiology of congenital long QT syndrome type 3 (LQT3) gain-of-function mutations in the SCN5A gene that encodes the major cardiac sodium channel isoform Nav1.5. Therefore, we investigated the effect of empagliflozin on late INa in thirteen known LQT3 mutations located in distinct regions of the channel structure. MethodsThe whole-cell patch-clamp technique was used to investigate the effect of empagliflozin (10 {micro}M) on late INa in recombinantly expressed Nav1.5 channels containing different LQT3 mutations. Molecular modeling of human Nav1.5 and simulations in a mathematical model of human ventricular myocytes were used to extrapolate our experimental results to excitation contraction coupling. ResultsEmpagliflozin selectively inhibited late INa in LQT3 mutations residing in the inactivation gate region of Nav1.5, with no effect on either peak current or channel kinetics. In contrast, empagliflozin caused inhibition of both peak and late INa in mutations in the S4 voltage-sensing regions as well as changes in activation and inactivation kinetics and a slowing of recovery from inactivation. Empagliflozin had no effect on late/peak INa or channel kinetics in channels containing LQT3 mutations located in the putative empagliflozin binding region. Simulation of our experimental findings in a mathematical model of human ventricular myocytes predicts that empagliflozin may have a desirable therapeutic effect in LQT3 mutations located in the inactivation gate region. ConclusionsOur results show that empagliflozin selectively inhibits late INa, without affecting gating kinetics, in LQT3 mutations residing in the inactivation gate region. Patients with mutations in voltage-sensing regions are less suitable candidates as empagliflozin may prevent action potential firing. The SGLT2is may therefore be a promising novel precision medicine approach for patients with certain LQT3 mutations.

pharmacology and toxicology↗

bioRxiv2024

The Origin and Evolution of Orphan Genes: A Case Study in Tea Plant Family

Orphan genes and transcription factor genes (TFs) are pervasive across genomes, play pivotal roles as regulators in a myriad of biological processes. Despite their ubiquity, the evolutionary trajectories and functional divergence of these genes remain largely unexplored. Theaceae family, encompassing the economically and culturally significant tea plant, presents a unique opportunity to study these dynamics. Here, we decoded a nearly complete, chromosome-scale reference genome of Stewartia gemmata spanning 2.95 Gb. This study is enhanced by integrating the genome of S. gemmata, an early-diverging species within Theaceae, crucial for phylogenomic analyses and understanding the functional dynamics of orphan genes in this family. Our analysis confirmed the absence of a recent specific whole-genome duplication (WGD) event, with tandem duplications emerging as the predominant mechanism for gene duplication at ancestral nodes within Theaceae. By conducting an extensive comparative genomics analysis across 13 Theaceae and comparing these with a wide array of eukaryotic and prokaryotic proteins, we identified 37,618 orphan genes and 25,884 TFs in Theaceae. Interestingly, some orphan genes appear to have ancient origins in tea plant ancestors, suggesting relatively early origins with frequent gains and losses, conversely, many others seem more specific and recent. Notably, the orphan genes are characterized by shorter lengths, fewer exons and functional domains than TFs, implying relatively simpler functional roles. These orphan genes demonstrate diverse cellular localization and functions as predicted by GO/KEGG analysis, and are implicated in environmental response and flavor formation in tea plants. This study not only sheds light on the distinct evolutionary histories and functional divergences between orphan genes and TFs in Theaceae, but also contributes to our understanding of the genetic complexity and adaptability of this economically and culturally valuable plant family. Short summary: The nearly complete genome of an early-diverging species Stewartia gemmata and phylogenomic studies provide insights into new gene evolution in Theaceae.

bioinformatics↗

bioRxiv2022

Facemap: a framework for modeling neural activity based on orofacial tracking

Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracking algorithm and a deep neural network encoder for predicting neural activity. We used the Facemap keypoints as input for the deep neural network to predict the activity of [~]50,000 simultaneously-recorded neurons and in visual cortex we doubled the amount of explained variance compared to previous methods. Our keypoint tracking algorithm was more accurate than existing pose estimation tools, while the inference speed was several times faster, making it a powerful tool for closed-loop behavioral experiments. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used Facemap to find that the neuronal activity clusters which were highly driven by behaviors were more spatially spread-out across cortex. We also found that the deep keypoint features inferred by the model had time-asymmetrical state dynamics that were not apparent in the raw keypoint data. In summary, Facemap provides a stepping stone towards understanding the function of the brainwide neural signals and their relation to behavior.

neuroscience↗

bioRxiv2022

Development and application of an uncapped mRNA platform

A novel uncapped mRNA platform was developed. Five lipid nanoparticle (LNP)-encapsulated mRNA constructs were made to evaluate several aspects of our platform, including transfection efficiency and durability in vitro and in vivo and the activation of humoral and cellular immunity in several animal models. The constructs were eGFP-mRNA-LNP (for enhanced green fluorescence mRNA), Fluc-mRNA-LNP (for firefly luciferase mRNA), S{delta}T-mRNA-LNP (for Delta strain SARS-CoV-2 spike protein trimer mRNA), gDED-mRNA-LNP (for truncated glycoprotein D mRNA coding ectodomain from herpes simplex virus type 2 (HSV2)) and gDFR-mRNA-LNP (for truncated HSV2 glycoprotein D mRNA coding amino acids 1[~]400). Quantifiable target protein expression was achieved in vitro and in vivo with eGFP-and Fluc-mRNA-LNP. S{delta}T-mRNA-LNP, gDED-mRNA-LNP and gDFR-mRNA-LNP induced both humoral and cellular immune responses comparable to those obtained by previously reported capped mRNA-LNP constructs. Notably, S{delta}T-mRNA-LNP elicited neutralizing antibodies in hamsters against the Omicron and Delta strains. Additionally, gDED-mRNA-LNP and gDFR-mRNA-LNP induced potent neutralizing antibodies in rabbits and mice. The mRNA constructs with uridine triphosphate (UTP) outperformed those with N1-methylpseudouridine triphosphate (N1m{psi}TP) in the induction of antibodies via S{delta}T-mRNA-LNP. Our uncapped, process-simplified, and economical mRNA platform may have broad utility in vaccines and protein replacement drugs.

bioengineering↗

bioRxiv2021

TACAN is a novel regulator of PKD2

Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in membrane receptor PKD1 or cation channel PKD2. TACAN (also named TMEM120A), recently reported as an ion channel in neuron cells for mechano and pain sensing, is also distributed in diverse non-neuronal tissues such as kidney, heart and intestine, suggesting its involvement in other functions. In this study, we found that TACAN is in complex with PKD2 in native renal cell lines. Using the two-electrode voltage clamp in Xenopus oocytes we found that TACAN inhibited the channel activity of PKD2 gain-of-function mutant F604P. The first and last transmembrane domains of TACAN were found to interact with the PKD2 C-and N-terminal portions, respectively. We showed that the TACAN N-terminus acted as a blocking peptide and that TACAN inhibits the PKD2 function through the PKD2/TACAN binding. By patch clamping in mammalian cells, we found that TACAN inhibits both the single channel conductance and open probability of PKD2 and mutant F604P. PKD2 co-expressed with TACAN, but not PKD2 alone, exhibited pressure sensitivity. Furthermore, we also found that TACAN aggravates PKD2-dependent tail curvature and pronephric cysts in larval zebrafish, in support of the in vitro inhibitory effects of TACAN. In summary, this study revealed that TACAN acts as a PKD2 inhibitor and mediates mechano sensitivity of the PKD2/TACAN channel complex.

physiology↗