Biology subjects

Wang, D.

Publications and source records attributed to Wang, D..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Transcription factors (TFs) regulate gene expression through specific interactions with genomic DNA. While TF binding motifs from public databases describe sequence preferences, quantifying genome-wide affinity (Kd) is highly desirable for a more accurate thermodynamic description. Here, we report ivtFOODIE (in vitro FOOtprinting with DeamInasE), an assay that leverages deaminase-mediated cytosine-to-uracil conversion to measure Kd values for a given TF across accessible genomic regions from human cells. By pre-training on TF binding sites from JASPAR and fine-tuning with our ivtFOODIE data from 46 TFs representing 13 different DNA-binding domains (DBDs), we developed Seq2Kd, a deep learning model capable of predicting a TFs absolute binding affinity on DNA sequences. Seq2Kd enables de novo motif discovery of [~]500 previously uncharacterized human TFs and reveals the effects of genetic variation both in TF-coding regions and DNA-binding sites on gene expression and disease susceptibility. By correlating predicted affinity changes with the sign and magnitude of expression quantitative trait locus (eQTL) effects, we stratified TFs into activator-like and repressor-like groups. Compared to clinically benign variants, pathogenic single-nucleotide variants (SNVs) within regulatory and protein-coding regions show significantly larger predicted shifts in Kd. We provide an interactive web portal, the ENcyclopedia of Transcription-factor Interactions with Regulatory Elements (ENTIRE), which integrates the Seq2Kd model with the ivtFOODIE dataset. This resource offers thermodynamic prediction for TF-DNA interactions for functional genomics and human disease.

biophysics↗

bioRxiv2026

Robust lesion network mapping reveals genuine symptom-specific networks

Lesion network mapping (LNM) and its derivatives successfully integrate anatomically distributed brain loci into common symptom-associated functional networks, however, their statistical validity and specificity have recently become topics of debate. Here, we introduce a null model-based robust LNM (rLNM) framework to perform sensitivity and symptom-specificity testing. Across multiple lesion-based (10 conditions, 333 lesions) and task-based (4 conditions, 706 experiments) datasets, rLNM reveals biologically meaningful and symptom-specific networks while effectively controlling for false positives.

neuroscience↗

bioRxiv2026

Developing a multi-modal neuroimaging-based BrainAge model across childhood

BrainAge models hold promise as a clinical biomarker for developmental brain health, especially in childhood when there is the potential for early intervention. To distinguish between normative developmental variance and pathological divergence, BrainAge models should reflect the dynamic and diverse neurodevelopmental processes that occur in distinct developmental windows across childhood. We utilized multi-modal neuroimaging data from three pediatric cohorts covering ages 4 to 13 years (n = 1005, 2126 scans), split into Train and Test datasets. Twelve sex-stratified BrainAge models were built stratified by type and different combinations of neuroimaging features. Model types were "Full-Span" models covering the full age range, and "Phase-Specific" models split into early- and late-childhood. We first compared BrainAge estimates in the Test dataset amongst our candidate models, then benchmarked the best-performing model against published pre-trained models and DNA-based biological age measures. Our findings show that a BrainAge model that was phase-specific and consisted of both structural and functional features (cortical thickness, subcortical volumes, and functional network integration measures) showed good prediction of age and best distinguished between healthy and symptomatic subgroups. We present a proof-of-concept for developmental models supporting building BrainAge models of higher temporal resolution that align to different childhood developmental phases.

neuroscience↗

bioRxiv2026

Piezo3 is a novel mechanosensitive Piezo ion channel in vertebrates

Mechanosensing and mechanotransduction are essential for all living cells. In mammals, Piezo1 and Piezo2 are two mechanically activated cation channels that serve as mechanosensors for a variety of physiological and pathological processes, ranging from touch sensing to sickle cell disease. These two channels are well evolutionarily conserved, and orthologous genes can be traced back to the origin of vertebrates, which underwent whole-genome duplications (WGDs). The number of paralogous genes originating from the vertebrate WGD varies across gene families. Thus, whether there are more PIEZO paralogous genes in vertebrates remains understudied. Here, we identified piezo3, a new paralog of the piezo gene family, and analyzed its evolutionary history using phylogenetic and synteny analyses. The piezo3 gene is present in most vertebrate lineages but absent in birds and most mammals, likely due to nonfunctionalization after WGDs. In addition, we demonstrated that this channel could mediate calcium flux in response to mechanical stimuli in HEK293T cells, suggesting that Piezo3 exhibits PIEZO1/2-like activation and conduction channel functions. Our CRISPR mutation analysis revealed that the zebrafish piezo3 gene is not developmentally essential, possibly because its expression overlaps with other PIEZO channels. Mutant zebrafish showed elevated sensitivity to mechanical force and increased locomotor activity under (photopic) light illumination. Our results suggest that this new mechanical-sensing Piezo channel is widespread in vertebrates and may be critical for vertebrate adaptation by modulating mechanical sensing and light responses during evolution. SIGNIFICANCEAll living cells must sense mechanical forces, whether endogenous or exogenous, and respond to them by transforming these forces into biological signals, which is essential to a wide range of cellular processes, including cell division, growth, and differentiation. PIEZO channels are well-characterized, critical, versatile mechanotransducers for touch and pain physiology and for human diseases. Currently, PIEZO1 and PIEZO2 are the only two known PIEZO channels in most vertebrates. In zebrafish, there are two Piezo2 channels (Piezo2a and Piezo2b) due to extra genome duplication in the ray-finned fishes. Here, we report Piezo3 channel, a long-missing paralog of Piezo1 and Piezo2, in most vertebrates. This channel is present in the majority of vertebrate lineages, except for most birds and mammals. The zebrafish piezo3 gene is expressed during early embryogenesis, and mutation of this gene leads to zebrafish larvae responding to tapping mechanical force and light with active movement. The widespread distribution of this Piezo3 channel across most vertebrate species, but its absence in birds and most mammals, suggests it may play important roles in vertebrate physiology and evolution.

evolutionary biology↗

bioRxiv2026

BAT protects against hepatic oxidative stress by remodeling the circulating metabolome

Brown adipose tissue (BAT) regulates systemic metabolism beyond thermogenesis, yet the circulating mediators through which BAT communicates with other organs remain less explored. Here, we performed comprehensive serum metabolomics and lipidomics in BAT-ablated mice and human cohorts with varying BAT activity to delineate how BAT activity shapes the circulating metabolome. By integrating datasets across serum, tissues, extracellular fluids, and conditioned media, we assembled BAT-linked circulating molecular signatures. The analyses support a critical role for BAT in the clearance of circulating branched-chain amino acids and triglycerides. We also identified a cold-inducible metabolite, 3-hydroxystearic acid (3-OHSA), produced primarily by BAT and released into circulation. 3-OHSA serves as a circulating readout of cold-activated BAT and acts on the liver to reduce mitochondrial membrane potential and reactive oxygen species production, thereby limiting oxidative stress. This work provides a framework for identifying BAT-derived mediators and uncovers a BAT-liver axis that coordinates adaptation to metabolic stress.

physiology↗

bioRxiv2026

CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data

Copy number variations (CNVs) are major structural genomic variants that contribute to a wide range of human diseases. Accurate detection of CNVs from whole-exome sequencing (WES) data has been a long-sought goal for clinical and population genetic studies. Despite recent progress, existing WES-based CNV callers still suffer from high false-positive rates and reduced recall for short-length variants, and current deep learning methods have not fully used complementary information in region-level genomic features. Here we present CN-RNN, a deep learning-based CNV caller for WES data. The model combines a bidirectional long short-term memory (BiLSTM) branch that captures local depth changes and contextual dependencies across neighboring exons with a parallel multi-layer perceptron (MLP) branch that encodes region-level metadata such as GC content, mappability, and exon length. CN-RNN was trained on the Autism Sequencing Consortium (ASC) parent-child trio cohort using the Mendelian rule of inheritance to ensure high-quality training sets. It was evaluated across three independent datasets, in which we showed that CN-RNN outperformed existing WES-based CNV callers and deep learning methods. CN-RNN offers a scalable, accurate tool for CNV profiling in WES-based studies and supports broader application of CNV analysis in population and clinical research. CN-RNN is available at https://github.com/FeifeiXiao-lab/CN-RNN.

genetics↗

bioRxiv2026

A suture-specific oocyst wall protein COWP4 is essential for excystation and infectivity, while COWP6 links wall architecture to host interaction in Cryptosporidium parvum

The oocyst of Cryptosporidium is a highly resilient transmission stage that protects sporozoites in the environment and mediates infection through a specialized opening known as the suture. While recent studies have identified Cryptosporidium oocyst wall proteins (COWPs) as structural components of the oocyst wall, the functional roles of individual COWPs and the molecular basis of suture biology remain poorly understood. Here, we performed a detailed characterization of two COWP family members, COWP4 and COWP6, combining immunolocalization, ultrastructural analysis, protein interaction assays, and genetic manipulation. We show that COWP4 is strictly localized to the oocyst suture, whereas COWP6 is distributed throughout the oocyst wall inner layer and enriched at the suture. COWP6 is additionally present in sporozoites and is secreted during parasite motility and host cell invasion, where it exhibits high-affinity binding to host cells. Structural analyses indicate that both proteins are cysteine-rich and likely form disulfide-stabilized architectures consistent with roles in wall assembly. Functional analyses reveal that COWP4 is essential for proper suture formation, excystation, and parasite infectivity, establishing the suture as a genetically defined gatekeeper for parasite transmission. In contrast, COWP6 functions as a multifunctional protein linking oocyst wall architecture to host interaction. We further demonstrate that COWP4 and COWP6 interact, suggesting coordinated assembly within the oocyst wall. Together, these findings provide a functional dissection of the Cryptosporidium oocyst suture and reveal distinct, non-redundant roles for individual COWPs. This work advances our understanding of oocyst wall biology and identifies COWP4 as an essential determinant of parasite transmission that may be targeted to disrupt the infectious cycle. Author summaryCryptosporidium is a major cause of diarrheal disease worldwide and is transmitted through environmentally resistant oocysts that protect infectious stages of the parasite. A defining feature of the oocyst is a specialized seam-like structure, called the suture, through which parasites exit to initiate infection. Although oocyst wall proteins (COWPs) have been identified, how individual proteins contribute to the structure and function of the oocyst wall, particularly the suture, has remained unclear. In this study, we investigated two oocyst wall proteins, COWP4 and COWP6, and uncovered distinct roles for each. We found that COWP4 is specifically localized to the suture and is essential for its proper formation, enabling parasites to exit the oocyst and establish infection. In contrast, COWP6 is distributed more broadly in the oocyst wall and also functions beyond it, being secreted during parasite movement and interacting with host cells. These findings reveal that the oocyst wall is composed of specialized proteins with non-redundant functions and identify the suture as a critical control point for parasite transmission. Understanding these mechanisms may help guide the development of new strategies to block infection by disrupting oocyst integrity or parasite release. HighlightsO_LICOWP4 is a suture-specific protein essential for oocyst excystation and infectivity C_LIO_LIGenetic disruption of COWP4 produces non-infectious oocysts with impaired viability C_LIO_LIThe oocyst suture is a genetically defined gatekeeper for parasite transmission C_LIO_LICOWP6 localizes to the wall inner layer and is enriched at the suture C_LIO_LICOWP6 is secreted by sporozoites and binds host cells with nanomolar affinity C_LIO_LI{middle dot} COWP4 and COWP6 directly interact, linking wall architecture to host interaction C_LI

microbiology↗

bioRxiv2026

DPLM: Dynamics-aware Protein Language Model via contrastive learning between sequence and molecular dynamics simulation trajectory

Protein dynamics play a critical role in protein function, yet such important information is missing in many protein language models (PLM). We introduce DPLM, a dynamics-aware protein language model that aligns sequence embeddings with molecular dynamics (MD) trajectory embeddings via contrastive learning. Using MD features encoded by a pretrained video model, DPLM learns sequence representations that correlate with residue-level flexibility and improve protein-level functional clustering compared to static sequence- and structure-based PLMs. Without task-specific training, DPLM outperforms ESM-based representations in zero-shot mutation-effect prediction on multiple deep mutational scanning datasets. When adapted with lightweight task-specific heads, DPLM further achieves top-tier performance on protein stability prediction and intrinsic disorder region identification, demon-strating that contrastive alignment with MD trajectories enables PLMs to capture biologically meaningful dynamic properties.

bioinformatics↗

bioRxiv2026

Structural and biochemical insights reveal substrate-modulated nuclease activity of ComEC during DNA processing

Natural transformation enables bacteria to internalise extracellular DNA, driving adaptation and the spread of antibiotic resistance. The membrane protein ComEC mediates translocation of single-stranded DNA across the cytoplasmic membrane while degrading the complementary strand, yet the structural basis of its activity remains incompletely defined. Here, we report a cryo-electron microscopy structure of full-length ComEC from Neomoorella carbonis in a pre-translocation state, revealing a three-domain architecture and a conserved transmembrane channel captured in a closed conformation. Structural analysis indicates that conformational rearrangements of channel-lining helices are required to accommodate single-stranded DNA. Biochemical assays show that, relative to the isolated {beta}-lactamase-like domain, full-length ComEC degrades DNA more efficiently. Importantly, coating of the DNA by the periplasmic DNA receptor ComEA suppresses endonucleolytic cleavage, thereby modulating nuclease activity. Together, these findings provide the first characterisation of the nuclease activity of full-length ComEC and show how ComEA-mediated protection of the substrate directs ComECs nuclease activity to ensure high fidelity during the natural transformation process.

biochemistry↗

bioRxiv2026

Spatial-neighbour encoding enables fast RNA 3D structure search

Experimental and predicted RNA three-dimensional structures are expanding rapidly, but RNA structure search still lacks a compact residue-level representation that supports database-scale comparison. Using family-held-out ablations across the currently available experimental RNA structure collection, we found that spatial-neighbour features are markedly more informative for family-level discrimination than conventional backbone and base descriptors. Building on this result, we developed RiboSeek, a search framework based on a 20-letter geometric alphabet (RS-20), an 80-letter structure-and-base composite alphabet (RS-80). Across family-level classification and retrieval benchmarks, RS-80 delivered the strongest overall performance, whereas RS-20 most closely tracked US-align TM-score, indicating better preservation of geometric similarity. RiboSeek searches the full experimental RNA structure database in 204 ms per query and can be applied to predicted RNA structure libraries to prioritize candidate structural relationships for downstream analysis.

bioinformatics↗

bioRxiv2026

Prpf8N1531S homozygous mutant mouse embryos have multiple defects in cardiac development and show aberrant splicing of the cardiac transcription factor Tead1

Mutations in the spliceosomal gene PRPF8 are associated with a range of human diseases. Studies in mouse and zebrafish suggest that Prpf8 also has a developmental function. Here, using a Prpf8 mutant mouse line isolated from a chemical induced mutagenesis screen, we uncover a previously unrecognised and essential role for Prpf8 in heart development, consistent with the embryonic lethality observed in Prpf8N1531S homozygous mutants. Prpf8N1531S mutant embryos display severe defects in ventricular trabeculation and compact zone formation, accompanied by increased cardiomyocyte proliferation specifically in the compact zone. Mutant embryonic hearts also exhibit disrupted cellular organisation, altered cytoskeletal architecture and changes in extracellular matrix protein expression. Notably, these cardiac abnormalities were exacerbated in embryos exhibiting cardiac looping defects. Transcriptomic analysis identified multiple aberrantly spliced transcripts in Prpf8N1531S mutant embryos, among which the cardiac transcription factor Tead1 was selected as a key functional candidate due to it known role in cardiac ventricle wall developemnt. Tead1 mis-splicing generated an in-frame, lower molecular weight protein isoform, associated with reduced overall TEAD1 expression. The Tead1 mis-spliced isoform showed altered nuclear localisation and dysregulation of TEAD1-dependent gene network important for heart development, including known cardiac sarcomeric genes. In addition, we observed reduced levels of the intracellular domain of the NOTCH1 receptor (NICD1), indicating impaired Notch signalling.. These findings suggest that impaired TEAD1-dependent transcription and Notch signalling contribute to abnormal cardiac trabeculation and compact zone development, highlighting a critical role for Prpf8 in maintaining proper heart development through the regulation of cardiac transcription factor expression and associated signalling networks. This study offers new mechanistic insights into congenital heart diseases linked to spliceosomal gene mutations.

developmental biology↗

bioRxiv2026

A sequence-based proactive intelligence for influenza antigenic profiling improves vaccine strain selection

Seasonal influenza viruses accumulate antigenic changes, eroding population immunity and necessitating recurrent vaccine updates. Hemagglutination inhibition (HI) assays are the standard for measuring antigenic relationships between circulating and vaccine strains; however, their limited throughput constrains the scale and timeliness of surveillance. Here, we present fluProfiler, a foundation-model-based framework that learns a stable mapping from viral sequences to antigenic space and uses this representation to support influenza antigenic prediction, vaccine strain evaluation, and diversity-driven sampling. fluAgPredictor aligns hemagglutinin (HA) and neuraminidase (NA) sequence representations with HI-derived antigenicity, enabling accurate and consistent inference of pairwise antigenic distances across surveillance-aligned evaluation settings. Without prior annotation of antigenic sites, it identifies key residues in immunodominant epitopes and reveals the cooperative contributions of HA and NA to antigenic variation. Building on this antigenic-space representation, fluVacSelector provides antigenic coverage scores that are concordant with World Health Organization (WHO) vaccine recommendations while also flagging potential candidates that may offer broader coverage ahead of formal consultations. fluAgEnhancer further leverages the same representation to prioritize antigenically informative and diverse strains for experimental characterization, achieving comparable predictive accuracy with approximately 25% fewer HI measurements than random sampling. Together, these modules provide a high-throughput and interpretable complement to HI testing, converting routine genomic surveillance into a more proactive, data-driven support system for antigenic monitoring and vaccine strain selection.

bioinformatics↗

bioRxiv2026

Generation and long-term expansion of human pancreatic islet organoids in vitro

The scarcity of expandable, functional human islet cells remains a major barrier to diabetes therapy. Here, we identify PROCR+ cells within adult human islets and establish a defined culture system to generate pancreatic islet organoids. These organoids self-organize into -, {beta}-, {delta}-, and PP cells at near-native ratios, exhibit regulated insulin and glucagon secretion, and support exponential in vitro expansion. Single-cell transcriptomics reveals a unique progenitor-like cell population that is transcriptionally primed for endocrine differentiation but shares molecular features with fetal trunk cells and endocrine progenitors. When transplanted, the organoids rapidly ameliorate hyperglycemia in diabetic mice. Importantly, in a non-human primate model, intraportal transplantation of these organoids reduced exogenous insulin requirements, restored glucose-stimulated C-peptide secretion, and achieved sustained glycemic control-representing a critical step toward clinical translation. This study provides a strategy for expanding human islet organoids, offering a scalable platform for diabetes treatment, disease modeling, and regenerative medicine.

cell biology↗

bioRxiv2026

Combination of engineered cell type-specific promoters and a high-efficiency AAV capsid restores hearing in adult DFNB1 mice model with demonstrated safety in nonhuman primate

A major challenge in gene therapy for GJB2-related hearing loss (DFNB1)--the most common form of hereditary deafness--is achieving efficient and precise connexin 26 delivery. Herein, we engineered two cell type-specific promoters (GJB2-1 and WFS1-2274) and developed an AAV capsid, AAV-MAS012, with enhanced transduction efficiency in mature cochlear cells. Our AAV-mediated gene therapy systems restored hearing of low-to-mid-frequencies in newborn Gjb2 cKO mice to wild-type levels and maintained for 45 weeks. Additionally, our therapeutic systems restored low-to-mid-frequencies hearing function to wild-type levels in adult Gjb2 cKO mice. A humanized version of the therapy, AAV-MAS012-WFS1-2274-hGJB2, rescued hearing function in two distinct Gjb2-deficient mouse models, and demonstrated a favorable safety profile in nonhuman primates. This study represents the first successful hearing restoration in adult Gjb2-deficient mice. The significant therapeutic efficacy of the humanized gene therapy system shows great potential for clinical translation in DFNB1 patients.

genetics↗

bioRxiv2026

The Common Fund Data Ecosystem (CFDE)

The NIH Common Fund Data Ecosystem (CFDE) integrates data resources from 18 NIH Common Fund programs for discovery and integrative analysis. These programs generate valuable but heterogeneous datasets that can be difficult to discover, access, and reuse. CFDE aims to provide a collaborative, community-built infrastructure that links and enriches Common Fund programs. We describe the evolution, structure, and core technologies of CFDE, including practical approaches that support submission, integration, visualization, and public release of multimodal data. Training programs and workforce initiatives lower barriers to adoption. CFDE has devised solutions to critical issues facing cross-program initiatives, including data scale and heterogeneity, dataset integration, and long-term sustainability. We demonstrate the utility of linking Common Fund resources through integrative tools and cross-dataset queries to yield insights that would otherwise be infeasible. Collectively, CFDE shows that a standards-driven, federated approach enhances and unifies cross-disciplinary resources, fostering collaboration and data-driven discovery.

scientific communication and education↗

bioRxiv2026

A unified spatial transcriptome profiling of ten mouse organs

Spatial transcriptomics has enabled numerous deep learning models in this area, and training them requires large amounts of high-quality data, especially expression matrices paired with histological images. Here, we present a unified spatial transcriptomic dataset generated using the Stereo-seq platform, covering 10 mouse organs--including brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterus--encompassing 23 tissue sections generated from 21 chips, each with matched ssDNA or H&E staining images. The dataset comprises single-cell-resolution (cell-bin) or square bin-50 (25 {micro}m x 25 {micro}m) expression matrices for each sample, accompanied by corresponding cell type annotations. Annotation robustness was further supported by concordance across different sections of the same tissue and corroboration with canonical marker gene expression patterns. Finally, we compared the characteristics of the cell-bin and bin-50 expression matrices and demonstrated the advantages of cell-bin resolution for cell annotation. This dataset provides a standardized resource for spatial transcriptomics method development, benchmarking, and multimodal analysis.

bioinformatics↗

bioRxiv2026

Topographic structure and function of locus coeruleus norepinephrine neurons

Norepinephrine (NE) is released throughout most of the central nervous system by neurons in the locus coeruleus (LC). We found a relationship between the morphologies, gene expression, and activity of LC-NE neurons in mice. Axonal projections of individual neurons were extensive but largely confined to subsets of brain regions. Axonal projections and graded gene expression correlated with locations of cell bodies in LC. In a behavioral task requiring ongoing learning from actions, neurons in dorsal LC projecting to the cerebral cortex were excited when mice made a different choice from the previous one and by reward prediction errors, a signal driving learning. Background activity of neurons in ventral LC was higher when mice ignored stimuli indicating potential reward availability. These observations reveal a topographically organized structure and function of a neurotransmitter system and show that it contains learning signals for flexible behavior.

neuroscience↗

bioRxiv2026

Nonlinear mixed-effect models and tailored parametrization schemes enables integration of single cell and bulk data

Experimental methods for characterizing single cells and cell populations have improved tremendously over the past decades. This progress has enabled the development of quantitative, mechanistic models for cellular processes based on either single cell or bulk data. However, coherent statistical frameworks for the model-based integration of different data types at the single-cell and population levels are still missing. In this work, we present a mathematical modeling approach for integrating single-cell time-lapse, single-cell snapshot, single-cell time-to-event and population-average data. Utilizing a formulation based on nonlinear mixed-effect modeling, we enable the description of multiple data types, with and without single-cell resolution, and we propose a tailored parameter estimation method. Furthermore, we propose a tailored parameter estimation scheme that facilitates the assessment of underlying process parameters. Our study demonstrates that the proposed approach can reliably integrate diverse data types, thereby improving parameter identifiability and prediction accuracy. Applying this framework of extrinsic apoptosis reveals that simultaneously considering multiple data types can be essential, particularly when experimental constraints limit data availability. The proposed approach is broadly applicable and may significantly advance our understanding of complex biological processes.

systems biology↗