Biology subjects

Jin, P.

Publications and source records attributed to Jin, P..

At least 19 recordsLinked to original sources

bioRxiv2026

RegDesign-Bench: Benchmarking computational methods for cell-type-specific regulatory DNA design

The de novo design of regulatory DNA sequences that drive cell-type-specific gene expression could enable precise gene control in synthetic biology, cell engineering and gene therapy. Various computational methods have been developed for this task, yet systematic comparisons that guide method selection for different design objectives remain limited. Here, we introduce RegDesign-Bench, a standardized framework for evaluating cell-type-specific cis-regulatory element design through a comprehensive set of computational analyses. Among the methods tested, strong predicted activity was not consistently accompanied by cell-type-selective accessibility or prominent cell-type-associated motif signals. Similarity to reference sequence distributions also did not ensure high predicted target-cell activity or specificity. These findings demonstrate that favourable performance on individual measurements does not necessarily extend across regulatory properties and library characteristics, revealing application-dependent strengths and limitations of current design methods. Overall, RegDesign-Bench offers a general-purpose computational framework for comparing and selecting methods to design regulatory DNA that drives cell-type-specific gene expression.

genomics↗

bioRxiv2026

Structural basis for catalytic and inhibitory divergence between archaeal and bacterial ammonia monooxygenases

Ammonia oxidation initiates nitrification and is closely linked to microbial N2O production. Ammonia monooxygenase (AMO) catalyzes the first and rate-limiting step of nitrification and is widespread across evolutionarily distinct ammonia-oxidizing archaea (AOA) and bacteria (AOB). The ocean is the largest biome for AOA and AOB, which have distinct ecological niches and markedly different sensitivities to nitrification inhibitors. However, the lack of archaeal AMO structures and inhibitor-bound AMO complexes has hindered mechanistic understanding of the architectural, catalytic, and inhibitory divergence between these two enzyme systems. Here, we report high-resolution cryo-electron microscopy (cryo-EM) structures of marine archaeal AMO captured in active and inactivated states within its native membrane environment, together with inhibitor-bound structures of estuarine bacterial AMO. Archaeal AMO forms an unexpected cup-shaped homotrimer composed of eight subunits per protomer and exhibits substantial architectural divergence from bacterial AMO. Integrated structural, biochemical, kinetic, and computational analyses reveal distinct periplasmic architectures, copper-center organization, and hydrophobic channels between archaeal and bacterial AMOs for ammonium acquisition, catalysis and inhibitor response. These findings provide a structural and mechanistic framework for understanding how archaeal and bacterial AMOs have diverged to distinct ammonia-oxidizing strategies and inhibitor susceptibilities across environmentally important ammonia oxidizers.

molecular biology↗

bioRxiv2026

Aberrant expression of stress-related Hsrω-n lncRNA contributes to CGG repeat-mediated toxicity in a Drosophila model of FXTAS

Fragile X-associated tremor/ataxia syndrome (FXTAS) is an adult-onset neurodegenerative disease associated with carriers of premutation (PM) alleles of the fragile X messenger ribonucleoprotein 1 (FMR1) gene. Expanded CGG repeats in the PM alleles are sufficient to cause cellular stress and toxicity in animal and cellular models of FXTAS. Here, we show that in a Drosophila transgenic model of FXTAS, expanded CGG repeats result in the overexpression of heat shock RNA omega-nuclear (Hsr{omega}-n), a stress-related long non-coding RNA (lncRNA) that dominantly enhances expanded CGG-induced neurotoxicity, whereas its reduction suppresses CGG-induced neurotoxicity. Furthermore, overexpression of Hsr{omega}-n lncRNA concomitantly resulted in a significant enhancement in its association with Hrb87F, the fly ortholog of one of the previously identified CGG repeat RNA-binding proteins, hnRNP A2/B1, and with the imitation switch (ISWI) protein, a chromatin remodeling factor. We extended the findings of Hsr{omega}-n to show that the mammalian stress-related lncRNAs SatIII and Neat1 are elevated in cells expressing expanded CGG repeats. Together, these findings highlight the critical roles of stress-related lncRNAs in CGG repeat-mediated toxicity and support a model in which the total steady-state levels of stress-related lncRNAs increase in the presence of expanded CGG repeats as part of the normal regulatory response to combat stress. However, the effects of chronic expanded CGG repeat expression in post-mitotic neurons may result in altered distribution and/or sequestration of specific RNA-binding proteins or chromatin remodeling factors associated with stress-related lncRNAs. This could contribute to the altered cellular homeostasis and genomic instability associated with FXTAS. SignificanceIndividuals carrying the FMR1 allele with expanded CGG repeats (55-200) in the 5' untranslated region are predisposed to fragile X-associated tremor/ataxia syndrome (FXTAS), a late-onset neurodegenerative disorder. FXTAS demonstrates incomplete penetrance, suggesting the possible involvement of genetic modifiers in its pathogenesis. We used a transgenic Drosophila FXTAS model to conduct genetic, molecular, and biochemical analyses. Our findings revealed that the expression of expanded CGG repeats aberrantly activates stress-related Hsr{omega}-n lncRNA in Drosophila melanogaster. Overexpression of this lncRNA exacerbates CGG-mediated neurodegeneration, whereas its repression alleviates this effect. Extending these findings to mammalian systems, we observed the upregulation of SATIII and Neat I, functional homologs of Hsr{omega}-n lncRNA, in cells expressing expanded CGG repeats. Collectively, these findings identify stress-related lncRNAs as key mediators of CGG-mediated toxicity and underscore their potential as therapeutic targets for FXTAS.

genetics↗

bioRxiv2026

Reconstructing sequence-grammar trajectories enables interpretable and tunable cis- regulatory element design

Designing synthetic cis-regulatory elements (CREs) with cell-type-specific activity remains challenging, and optimization is usually treated as a black box, obscuring how regulatory grammar emerges and why design trajectories fail. Here, we present GO-CRE (Guided Optimization of Cis-Regulatory Elements), which combines efficient sequence generation, predictor-guided reinforcement learning, and trajectory-level interpretation. GO-CRE reconstructs iterative sequence changes in a shared sequence-grammar landscape and identifies coordinated update programs corresponding to search, commitment, and optimization. Productive trajectories in HepG2 and K562 progressively acquired cell-type-associated grammar, whereas SK-N-SH trajectories remained confined to local basins. In HepG2, trajectory analysis revealed a low-complexity polyG trap; introducing a polyG penalty redirected optimization toward HNF/FOXA-associated features. Final designs retained sequence diversity while converging on cell-type-associated motif patterns. Lentiviral MPRA validated cell-type-specific activity in K562 and HepG2 and showed higher average activity of HepG2 designs than endogenous CREs. Together, these findings establish sequence-grammar trajectory reconstruction as a basis for interpretable and tunable synthetic CRE design.

genomics↗

bioRxiv2026

HoloCell: A Generative Foundation Model for Holistic Cellular Modeling

Single-cell multi-omics technologies have recently advanced to enable the profiling of epigenomic, transcriptomic, and proteomic layers within individual cells, offering new opportunities to characterize cellular states as integrated biological systems. However, developing a unified framework that can seamlessly integrate diverse omics modalities and remain robust to heterogeneous modality missingness remains challenging. Existing methods are often designed for specific modalities or modality pairs, relying on dataset-specific training or paired measurements. Here we present HoloCell, to our knowledge the first generative foundation model for joint representation learning and generative modeling across all three major single-cell omics modalities, i.e., epigenomics, transcriptomics, and proteomics. HoloCell contains over 860 million parameters and is pretrained on the Human-Multi-Omics-Corpus, which comprises approximately 468 million single-cell profiles across these three omics layers, corresponding to over 425 billion tokens. HoloCell introduces a a simple yet biologically motivated hierarchical tokenization strategy that encodes cis-regulatory elements, genes, and proteins as structured tokens within a shared modeling framework. We evaluated HoloCell across single-omics representation learning, paired multi-omics integration, unpaired multi-omics alignment, and cross-modal generation via iterative diffusion and remasking, demonstrating its superior performance and flexibility across diverse omics tasks. From a representation perspective, HoloCell provides a unified digital mapping of cellular states across multiple omics layers, capturing cell heterogeneity as an integrated system. From a generation perspective, its iterative diffusion and remasking frame-work permits flexible generation orders beyond fixed left-to-right causality, enabling in silico simulation of multi-omics information flow. Together, these capabilities position HoloCell as a versatile foundation model toward the emerging concept of a virtual cell, offering both systematic characterization and generative simulation of cellular systems within a unified framework.

bioinformatics↗

bioRxiv2026

A computational model for quantifying instability of tandem repeats across the genome

Tandem repeats (TRs) exhibit high levels of somatic mosaicism, which is increasingly recognized as an important modifier of repeat expansion disorders. Long-read sequencing can capture full-length repeat alleles, yet robust frameworks for quantifying instability across TRs genome-wide are still needed. Here, we introduce a general-purpose model for quantifying TR instability in a given long-read sequencing dataset, without explicitly distinguishing biological mosaicism from technical noise, and which is broadly applicable to both simple and structurally complex loci. This model accurately characterizes allelic instability at each TR locus by representing the distribution of read-to-consensus deviations for each allele. Using HiFi sequencing data from 256 HPRC cell line samples, we fitted models for 617,007 TR loci, including known pathogenic repeats. We observe that instability levels are generally low, but vary substantially across individual TRs, and are driven more strongly by repeat composition than overall repeat length. Furthermore, we applied our method to targeted PureTarget long-read data from samples with known repeat expansions and identified significant mosaicism in the majority of expanded alleles. Our model offers a practical way to quantify instability of tandem repeats across the genome and to detect unusually unstable repeat alleles.

bioinformatics↗

bioRxiv2026

Phenotype-Guided In Silico Molecular Generation Using Large Language Models

Complex diseases often emerge from coordinated, system-level cellular state changes that are difficult to address with target centric drug discovery. Phenotypic drug discovery offers a principled alternative but remains constrained by the cost and scalability of pathologically relevant assays. Here we present GEMGen, a large language model-based framework that performs in silico phenotypic drug discovery by generating small molecules directly from transcriptomic representations of cellular states. GEMGen encodes desired phenotypic transitions as text-based representations of up- and down-regulated gene sets, enabling transferable modeling across experimental platforms and data modalities. Trained on large scale chemical perturbation data, GEMGen robustly identifies phenotype-oriented compounds and mechanistically related but structurally distinct candidates across multiple benchmarks. Applied to signatures induced by genetic perturbations, GEMGen produces small molecules that phenocopy gene knockdown effects and identifies chemically novel inhibitors, including previously unreported KEAP1 inhibitors that activate NRF2 signaling. Extending this approach to a disease relevant model of fibrosis, GEMGen generates compounds that reverse profibrotic transcriptional programs and cellular phenotypes. These results establish a scalable framework for translating transcriptomic phenotypes into candidate therapeutic molecules, enabling systems-level exploration of vast chemical space and offering a complementary in silico counterpart to physical phenotypic drug screens.

cell biology↗

bioRxiv2025

S1PR3 mediates glial stimulated tumor invasion in response to interstitial fluid flow

Cellular invasion is a primary challenge to complete resection and treatment of glioblastoma, the most aggressive and deadly primary brain tumor. The brain tumor microenvironment actively stimulates glioma invasion through a multitude of cellular, chemical, and biophysical cues. We and others have shown elevated interstitial fluid flow at the tumor border is one such biophysical cue that directly stimulates invasion through tumor-intrinsic signaling and, in other tumor types, priming of cancer-associated stromal cells. It is currently unclear if interstitial flow similarly primes neuroglial cells to promote glioma cell dissemination and can be targeted for therapeutic purposes. Here, we show elevated interstitial flow upregulates expression of sphingosine-1-phosphate receptor 3 (S1PR3) in glial astrocytes and microglia, which drives glioma cell invasion via chemotaxis. Flow-induced expression of glial S1PR3 is tumor-independent and displays a biphasic relationship to fluid shear stress magnitude in vitro and flow rate in vivo. Inhibition of glial S1PR3 in a tissue engineered culture model and orthotopic mouse model abrogates flow-stimulated invasion, demonstrating a tumor-extrinsic approach to limiting glioblastoma progression. Given prior evidence of a pro-inflammatory role for glial S1PR3, identification of S1PR3 as a disease-agnostic marker of flow-stimulated glia may also have therapeutic implications across myriad neuropathologies.

cancer biology↗

bioRxiv2025

FlexRibbon: Joint Sequence and Structure Pretraining for Protein Modeling

AO_SCPLOWBSTRACTC_SCPLOWProtein foundation models have advanced rapidly, with most approaches falling into two dominant paradigms. Sequence-based language models (e.g., ESM-2) capture sequence semantics at scale, and a number of recent works incorporate structural signals into sequence encoders. MSA-based predictors (e.g., AlphaFold 2/3) achieve accurate folding by exploiting evolutionary couplings, but their reliance on homologous sequences makes them less reliable in highly mutated or alignment-sparse regimes. We present FlexRibbon{ddagger}, a pretrained protein model that jointly learns from amino acid sequences and three-dimensional structures. Our pretraining strategy combines masked language modeling with diffusion-based denoising, enabling bidirectional sequence-structure learning without requiring MSAs. Trained on both experimentally resolved structures and AlphaFold 2 predictions, FlexRibbon captures global folds as well as flexible conformations critical for biological function. Evaluated across diverse tasks spanning interface design, intermolecular interaction prediction, and protein function prediction, FlexRibbon establishes new state-of-the-art performance on 12 different tasks, with particularly strong gains in mutation-rich settings where MSA-based methods often struggle.

molecular biology↗

bioRxiv2025

Single-Cell and Spatial Profiling of Tumor Microenvironment Heterogeneity in Human Osteosarcomas

Osteosarcoma, a highly heterogeneous malignant bone tumor, exhibits substantial interpatient and intratumoral heterogeneity. Utilizing integrated single-cell RNA sequencing and spatial transcriptomics, we uncovered distinct tumor microenvironment (TME) transcriptional profiles across patients, highlighting profound interpatient heterogeneity. Intratumorally, malignant cells primarily followed two differentiation trajectories, converging into osteoblastic and chondroblastic functional states, and hypoxia may be the influencer; notably, within individual patients, tumor cells exhibited greater transcriptional similarity driven by specific transcription factors, despite these divergent states. Spatial analysis revealed patient-specific TME cellular co-localization patterns, alongside conserved spatial relationships: vascular components (endothelial cells, pericytes) demonstrated strong co-localization, while immune cells (T cells, myeloid cells) clustered within shared regions. Crucially, these functional states occupied distinct microniches: osteoblastic regions were enriched with osteoclasts, vascular components, and immune cells, whereas chondroblastic regions displayed the opposite composition and were preferentially located in hypoxic, vascular-poor niches, exhibiting significant enrichment of hypoxia-related signaling pathways. Furthermore, our data suggest osteosarcoma cells may activate fibroblasts via the SPP1 signaling pathway, indicating fibroblasts act as key intermediaries in tumor-directed TME modulation. This study comprehensively delineates the intricate landscape of osteosarcoma heterogeneity, defining distinct functional cellular states, their spatially organized TME niches, and a potential SPP1-mediated tumor-fibroblast regulatory axis. This comprehensive analysis elucidates the intricate interpatient and intratumoral heterogeneity in osteosarcoma, revealing functional and spatial organization within the tumor and its microenvironment.

cancer biology↗

bioRxiv2025

mcDETECT: Decoding 3D Spatial Synaptic Transcriptomes with Subcellular-Resolution Spatial Transcriptomics

Spatial transcriptomics (ST) is a powerful tool for studying the molecular basis of brain diseases. However, most current analyses focus only on nuclear or somatic expression, overlooking distally localized mRNAs, known as the "dark transcriptome." These transcripts, which make up nearly 40% of the brain transcriptome, are packaged into RNA granules and transported to polarized neuronal structures critical for neuronal function. Here, we present mcDETECT, a machine learning framework that leverages in situ ST (iST) data to detect RNA granules and resolve their RNA composition. Applying mcDETECT to simulated and real datasets from multiple iST platforms, we demonstrate robust granule detection and molecular subtyping. Granule-informed downstream analysis further revealed functionally distinct neuronal substates not captured by somatic expression alone. In an Alzheimers disease (AD) mouse model, mcDETECT uncovered alterations in RNA granule distributions and associated neuronal substates prior to measurable neuronal loss, highlighting potential therapeutic targets for early AD pathology.

bioinformatics↗

bioRxiv2025

Stochastically Emergent Tumors offer in vivo whole genome interrogation of cancer evolution from non-malignant precursors

Interrogating the stochastic events underlying tumor evolution from non-malignant precursors is crucial for understanding therapy resistance. Current methods are complicated by chromosomal instability, obscuring driver identification and yielding non-representative genetics. Inspired by patient tumors that evolve without chromosomal instability, we developed Stochastically Emergent Tumors (SETs) by inducing mismatch repair deficiency in non-malignant precursors, then engrafting in mice. Barcoded SETs exhibited increased tumoral and drug target heterogeneity over current models. SETs delineated the stochastic contributions, mutational landscapes, and selective pressures distinguishing tumorigenesis from non-malignant precursor in vitro growth. SETs are an unlimited source for diverse Stochastically Emergent cell Lines (SELs), bolstering under-represented cancers. Since SETs composition dynamically reflects therapy exposure, they are a whole-genome platform for precision oncology. We identified three novel genetic drivers (ZFHX3, CIC, KMT2D) of differential prostate cancer therapy responses. These alterations are enriched in patients of African and Chinese ancestry and correlate with significant differences in survival.

cancer biology↗

bioRxiv2024

ANKRD5: a key component of the axoneme required for spermmotility and male fertility

Sperm motility is essential for male fertility and depends on the structural integrity of the sperm axoneme, which features a canonical "9+2" microtubule arrangement. This structure comprises nine outer doublet microtubules (DMTs) that are associated with various macromolecular complexes. Among them, the nexin-dynein regulatory complex (N-DRC) forms crossbridges between adjacent DMTs, contributing to their stabilization and enabling flagellar bending. In this study, we investigated Ankyrin repeat domain 5 (ANKRD5, also known as ANK5 or ANKEF1), a protein highly expressed in the sperm axoneme. We found that ANKRD5 interacts with DRC5/TCTE1 and DRC4/GAS8, two key components of the N-DRC, and these interactions occur independently of calcium regulation. Male Ankrd5-/- mice exhibited impaired sperm motility and infertility. Cryo-electron tomography revealed a typical "9+2" axoneme structure with intact DMTs in Ankrd5 null sperm; however, the DMTs showed pronounced morphological variability and increased structural heterogeneity. Notably, ANKRD5 deficiency did not alter ATP levels, reactive oxygen species (ROS) levels, or mitochondrial membrane potential. These findings suggest that ANKRD5 may attenuate the N-DRCs mechanical buffering-akin to a "car bumper"-between adjacent DMTs, thereby compromising axonemal stability under high mechanical stress during vigorous flagellar beating. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=184 HEIGHT=200 SRC="FIGDIR/small/626701v3_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1ca36eforg.highwire.dtl.DTLVardef@dcafe3org.highwire.dtl.DTLVardef@9f6086org.highwire.dtl.DTLVardef@17a0a0b_HPS_FORMAT_FIGEXP M_FIG C_FIG Significance StatementMale infertility affects approximately 8%-12% of men globally, with defects in sperm motility accounting for over 80% of these cases. The axoneme, which functions as the motor apparatus of the sperm, adopts a canonical "9+2" microtubule arrangement, where the nexin-dynein regulatory complex (N-DRC) plays a critical role in providing structural support between adjacent outer microtubule doublets. Elucidating the interplay between the structural organization and protein composition of the N-DRC is essential for advancing the understanding of male reproductive biology. In this study, we identify ANKRD5 as a new component of N-DRC that is essential for maintaining normal sperm motility. These findings contribute to the molecular understanding of sperm motility and highlight ANKRD5 as a potential target for the development of novel male contraceptives.

developmental biology↗

bioRxiv2024

Epigenetic remodeling and 3D chromatin reorganization governed by NKX2-1 drive neuroendocrine prostate cancer

A significant number of castration-resistant prostate cancer (CRPC) evolve into a neuroendocrine (NE) subtype termed NEPC, leading to resistance to androgen receptor (AR) pathway inhibitors and poor clinical outcomes. Through Hi-C analyses of a panel of patient-derived xenograft tumors, here we report drastically different 3D chromatin architectures between NEPC and CRPC samples. Such chromatin re-organization was faithfully recapitulated in vitro on isogenic cells undergoing NE transformation (NET). Mechanistically, neural transcription factor (TF) NKX2-1 is selectively and highly expressed in NEPC tumors and is indispensable for NET across various models. NKX2-1 preferentially binds to gene promoters, but it interacts with chromatin-pioneering factors such as FOXA2 at enhancer elements through chromatin looping, further strengthening FOXA2 binding at NE enhancers. Conversely, FOXA2 mediates regional DNA demethylation, attributing to NE enhancer priming and inducing NKX2-1 expression, forming a feed-forward loop. Single-cell multiome analyses of isogenic cells over time-course NET cells identify individual cells amid luminal-to-NE transformation, exhibiting intermediate epigenetic and transcriptome states. Lastly, NKX2-1/FOXA2 interacts with, and recruits CBP/p300 proteins to activate NE enhancers, and pharmacological inhibitors of CBP/p300 effectively blunted NE gene expression and abolished NEPC tumor growth. Thus, our study reports a hierarchical network of TFs governed by NKX2-1 in regulating the 2D and 3D chromatin re-organization during NET and uncovers a promising therapeutic approach to eradicate NEPC.

cancer biology↗

bioRxiv2024

Modeling Thoracic Aortic Dissection Using Patient-Specific iPSCs Reveals VSMC Dysfunction and Extracellular Matrix Dysregulation

Thoracic aortic dissection (TAD) is a life-threatening condition characterized by medial degeneration and vascular smooth muscle cell (VSMC) dysfunction, with no effective medical therapy currently available. The underlying pathological mechanisms of TAD remain incompletely understood. In this study, we used a non-integrated episomal vector-based reprogramming system to generate induced pluripotent stem cells (iPSCs) from TAD patients and healthy controls. Both TAD and normal iPSCs expressed key pluripotency markers and were capable of differentiating into the three germ layers in vitro. These iPSCs were differentiated into VSMCs through a mesodermal intermediate for disease modeling. VSMCs derived from both TAD and normal iPSCs expressed smooth muscle -actin (-SMA), calponin, and SM22. However, TAD-iPSC-derived VSMCs exhibited significantly reduced contraction in response to carbachol stimulation compared to their normal counterparts. Whole-exome sequencing identified a mutation in the COL4A2 gene (c.392G>T, p. R131M) in TAD-iPSCs. This mutation was associated with reduced collagen IV expression and increased expression of collagen I and III in TAD-VSMCs, both with and without TGF-{beta} stimulation. Furthermore, noncanonical TGF-{beta} signaling was hyperactivated in TAD-VSMCs, accompanied by elevated MMP9 expression. This patient-specific iPSC model reveals key dysfunctions in VSMC contractility, extracellular matrix protein expression, and dysregulated TGF-{beta} signaling, which may contribute to TAD pathogenesis. Our findings provide new insights into the molecular mechanisms driving TAD and offer a platform for future therapeutic development.

pathology↗

bioRxiv2024

Cell-type-specific mapping of enhancers and target genes from single-cell multimodal data

Mapping enhancers and target genes in disease-related cell types has provided critical insights into the functional mechanisms of genetic variants identified by genomewide association studies (GWAS). However, most existing analyses rely on bulk data or cultured cell lines, which may fail to identify cell-type-specific enhancers and target genes. Recently, single-cell multimodal data measuring both gene expression and chromatin accessibility within the same cells have enabled the inference of enhancer-gene pairs in a cell-type-specific and context-specific manner. However, this task is challenged by the datas high sparsity, sequencing depth variation, and the computational burden of analyzing a large number of enhancer-gene pairs. To address these challenges, we propose scMultiMap, a statistical method that infers enhancer-gene association from sparse multimodal counts using a joint latent-variable model. It adjusts for technical confounding, permits fast moment-based estimation and provides analytically derived p-values. In systematic analyses of blood and brain data, scMultiMap shows appropriate type I error control, high statistical power with greater reproducibility across independent datasets and stronger consistency with orthogonal data modalities. Meanwhile, its computational cost is less than 1% of existing methods. When applied to single-cell multimodal data from postmortem brain samples from Alzheimers disease (AD) patients and controls, scMultiMap gave the highest heritability enrichment in microglia and revealed new insights into the regulatory mechanisms of AD GWAS variants in microglia.

bioinformatics↗

bioRxiv2024

Nonlinear compliance of NompC gating spring and its implication in mechanotransduction

Cytoskeleton-tethered mechanosensitive channels (MSCs) utilize compliant proteins or protein domains called gating springs to convert mechanical stimuli into electric signals, enabling sound and touch sensation and proprioception. The mechanical properties of these gating springs, however, remain elusive. Here, we explored the mechanical properties of the homotetrameric NompC complex containing long ankyrin-repeat domains (ARDs). We developed a toehold-mediated strand displacement approach to tether single membrane proteins, allowing us to exert force on them and precisely measure their absolute extension using optical tweezers. Our findings revealed that each ARD has a low stiffness of [~]0.7 pN/nm and begins to unfold stepwise at [~]7 pN, leading to nonlinear compliance. Our calculations indicate that this nonlinear compliance may help regulate NompCs sensitivity, dynamic range, and kinetics to detect mechanical stimuli. Overall, our research highlights the importance of a compliant and unfolding-refolding gating spring in facilitating a graded response of MSC ion transduction across a wide spectrum of mechanical stimuli.

biophysics↗

bioRxiv2024

Deep5hmC: Predicting genome-wide 5-Hydroxymethylcytosine landscape via a multimodal deep learning model

5-hydroxymethylcytosine (5hmC), a critical epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Using tissue-specific 5hmC sequencing data, we introduce Deep5hmC, a multimodal deep learning framework that integrates both the DNA sequence and the histone modification information to predict genome-wide 5hmC modification. The multimodal design of Deep5hmC demonstrates remarkable improvement in predicting both qualitative and quantitative 5hmC modification compared to unimodal versions of Deep5hmC and state-of-the-art machine learning methods. This improvement is demonstrated through benchmarking on a comprehensive set of 5hmC sequencing data collected at four time points during forebrain organoid development and across 17 human tissues. Notably, Deep5hmC showcases its practical utility by accurately predicting gene expression and identifying differentially hydroxymethylated regions in a case-control study of Alzheimers disease.

bioinformatics↗