Biology subjects

Li, R.

Publications and source records attributed to Li, R..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

p53 preserves genome stability in dividing cells under hypo-osmotic stress

Cells respond to frequent osmotic stress to maintain structural integrity and function. Whereas acute volume regulation is well-known, the cellular response to prolonged osmotic stress and its long-term consequences remain poorly understood. In this study, we investigate how lasting hypo-osmotic stress influences cell growth and division and the associated cellular response. We observe that cells undergoing their first division under hypo-osmotic stress experience delayed mitotic entry, lengthened mitotic duration, elevated mitotic errors and aneuploidy formation. However, continued cell division is halted through a p53/p21-dependent G1 arrest. p53 activation occurs post-mitotically and is associated with hyper-condensed chromatin and impaired nuclear expansion in daughter cells after mitotic exit. We show that the altered nuclear size dynamics lead to nuclear accumulation of p53 and activation of downstream gene expression. These findings uncover a mechanosensitive, p53-mediated mechanism that safeguards genome integrity by suppressing erroneous mitosis under hypo-osmotic stress.

cell biology↗

bioRxiv2025

Sequence and structural determinants of efficacious de novo chimeric antigen receptors

Advances in generative protein design using artificial intelligence (AI) have enabled the rapid development of binders against heterogeneous targets, including tumor-associated antigens. Despite extensive biochemical characterization, these novel protein binders have had limited evaluation as agents in candidate therapeutics, including chimeric antigen receptor (CAR) T cells. Here, we synthesize generative protein design workflows to screen 1,589 novel protein binders targeting BCMA, CD19, and CD22 for efficacy in scalable protein-binding and T cell assays. We identify three main challenges that hinder the utility of de novo protein binders as CARs, including tonic signaling, occluded epitope engagement, and off-target activity. We develop computational and experimental heuristics to overcome these limitations, including screens of sequence variants for individual parental structures, that restore on-target CAR activation while mitigating liabilities. Together, our framework accelerates the development of AI-designed proteins for future preclinical therapeutic screening, helping enable a new generation of cellular therapies.

bioengineering↗

bioRxiv2025

gFET-based aptasensors technology allows sensitive and specific quantification of the ESKAPE pathogens

The rapid rise of antimicrobial resistance among the ESKAPE pathogens, Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp., requires diagnostic technologies capable of fast, simplified and sensitive microbial detection. Conventional culture-based diagnostics remain too time-consuming to guide early therapeutic interventions. Here, we present a graphene field-effect transistor (gFET) aptasensor platform enabling rapid, label-free and highly sensitive quantification of all six ESKAPE pathogens. Each gFET device was functionalized with a specific DNA aptamer selected from literature sources and immobilized via a mixed pyrene-based linker strategy on reduced graphene oxide (rGO). Exposure of the functionalized sensors to logarithmically diluted bacterial suspensions (10-105 CFU mL-) produced characteristic and concentration-dependent shifts in source-drain current ({Delta}IDS). For all pathogen-specific aptamers, {Delta}IDS correlated linearly with bacterial load (R2 = 0.90-1.00), while non-target bacteria generated only low-level, unspecific fluctuations. Limits of detection ranged from 10 to 1000 bacterial cells depending on the aptamer. Together, these results demonstrate that aptamer-functionalized rGO-FETs provide a robust, scalable and highly specific electronic biosensing architecture capable of distinguishing clinically relevant multidrug-resistant pathogens with excellent analytical performance. Author summaryAntimicrobial-resistant bacteria pose a growing threat to global health, especially the so-called ESKAPE pathogens, which frequently cause hospital-acquired infections and are increasingly difficult to treat. Current diagnostic methods can take several days, delaying the start of effective therapy. In our work, we developed a fast and highly sensitive biosensor that uses electrically conductive GO and short DNA molecules, called aptamers, to recognize specific bacteria. When a pathogen binds to its matching aptamer on the sensor surface, the electrical signal of the graphene changes in a measurable way. We tested ESKAPE species and showed that all of the investigated aptamers detect only their intended bacterial targets, even at very low concentrations. Importantly, the sensors respond within minutes and do not require any labelling or complex sample preparation. Our technology demonstrates how graphene-based aptasensors can support rapid and accurate detection of dangerous bacterial pathogens and could ultimately help clinicians make faster decisions in treating infections.

microbiology↗

bioRxiv2025

Structure-Function Coupling Aligns with a Unimodal-to-Transmodal Gradient in the Mouse Cortex

Understanding how anatomical connectivity shapes brain activity is essential for clarifying brain function and disorders. In the human brain, the regional heterogeneity in structure-function (S-F) coupling is well characterized by measuring correlations between structural and functional connectivity. However, it remains unclear whether the same principles apply to the mouse cortex, where biological mechanisms can be studied directly. Here, we mapped S-F coupling across the mouse cortex by combining high-resolution structural connectivity derived from axonal tracing with resting-state functional connectivity measured by wide-field calcium imaging. Our findings revealed that structural connectivity imposes a robust yet regionally variable constraint on functional connectivity. As in humans, the S-F coupling was strong in primary sensorimotor areas and weaker in association areas, demonstrating a gradient from unimodal-to-transmodal cortical organization. This spatial variation covaried with intrinsic cellular properties, including myelination, excitation-inhibition balance, synaptic density, and gene expression profiles, but did not align with the anatomically defined cortical hierarchy. Our findings highlight graded S-F coupling as a common organizational principle in both the mouse and human cortex, providing a framework for future mechanistic studies using mouse models.

neuroscience↗

bioRxiv2025

Investigation of TRMT61B methyltransferase activity on mRNA and its effects on translation

Despite recent advances in technology to map RNA chemical modifications transcriptome-wide, the distribution of N{superscript 1}-methyladenosine (m{superscript 1}A) in mRNA remains contested, hindering a clear understanding of its function. Additionally, the enzyme(s) that installs the majority of reported mRNA m1A sites has yet to be identified. In this study, we characterized TRMT61B, an m1A methyltransferase known to methylate mitochondrial RNAs, but whose sequence preferences have been underexplored. By integrating cellular overexpression of TRMT61B and in vitro methylation of a synthetic pool of diverse human RNA sequences, we identified a preference for a YMRA consensus motif in single stranded RNA regions. In these experiments, TRMT61B methylated thousands of novel human mRNA sites, revealing activity on cytosolic mRNAs. We used these novel m1A-modifiable sequences to test the effects of m1A on translation of luciferase reporters and on ribosome recruitment to modified transcripts in the pool. We found that m1A addition can significantly affect translation and ribosome recruitment, but that these effects are vary by transcript. Taken together, our results can inform future studies of TRMT61B and mRNA, and emphasize that studies of m1A regulation of mRNA must be carried out and interpreted in a highly context-aware manner.

biochemistry↗

bioRxiv2025

Multiscale Spatial Transcriptomic Atlas of Human Basal Ganglia Cell-Type and Cellular Community Organization

We generated a multi-region, subcellular-resolution spatial transcriptomic atlas of the human basal ganglia by integrating MERFISH+ and Stereo-seq across four neurotypical donors. These datasets profiled [~]7 million cells spanning the caudate, putamen, nucleus accumbens, and globus pallidus, resolving 60 transcriptionally distinct cell types. We show region-selective, molecular and spatial diversification of medium-spiny-neuron cell types and multiple non-neuronal populations with distinct molecular identities and spatial localizations. Subcellular RNA localization captures somatic size and projection-inferred signatures that reflect direct and indirect pathway topology. Cellular community analyses reveal the enrichment of sub-clusters of astrocytes and oligodendrocytes at striosome-matrix borders, while primate-expanded interneurons are confined to matrix territories. Cross-species mapping uncovers orthologous striosome-matrix organization and conserved dorsolateral-ventromedial gene expression gradients. This atlas provides a foundational molecular and spatial framework for studying human basal ganglia architecture, offering a multi-centimeter scale resource that links cell types, spatial architecture, and subcellular transcript topography across multiple nuclei. HighlightsO_LIOur multi-centimeter scale spatial taxonomy identifies the precise locations of 60 neuronal and glial cell types of human basal ganglia. C_LIO_LIMERFISH+ and Stereo-seq platforms map consistent spatial modules that align with classical neuroanatomical nuclei. C_LIO_LID1D2 hybrid MSNs and primate-expanded interneurons show regional and domain specific organization C_LIO_LISubcellular RNA localization reports soma morphology and projection-inferred signatures. C_LI

neuroscience↗

bioRxiv2025

SuSiE 2.0: improved methods and implementations for genetic fine-mapping and phenotype prediction

Sum of Single Effects regression (SuSiE) has become widely adopted for genetic fine-mapping, yet its original implementation faces architectural limitations that hinder extensibility and performance. We present SuSiE 2.0, featuring a modular redesign for extensibility, up to 5x speed improvements for summary statistics applications, and several useful extensions including SuSiE-ash, a new method that improves calibration when strong signals coexist with moderate effects. Simulations and real data benchmarks demonstrate performance across diverse genetic architectures, highlighting improved calibration of SuSiE-ash for fine-mapping under complex polygenic backgrounds with 1.5-3x FDR reduction while maintaining power, and revealing SuSiE-based methods as effective yet underappreciated tools for TWAS prediction.

bioinformatics↗

bioRxiv2025

DiCoLo: Integration-free and cluster-free detection of localized differential gene co-expression in single-cell data

Detecting changes in gene coordination patterns between biological conditions and identifying the cell populations in which these changes occur are key challenges in single-cell analysis. Existing approaches often compare gene co-expression between predefined cell clusters or rely on aligning cells across conditions. These strategies can be suboptimal when changes occur within small subpopulations or when batch effects obscure the underlying biological signal. To address these challenges, we introduce DiCoLo, a framework that identifies genes exhibiting differential co-localization, defined as changes in coordinated expression within localized cell neighborhoods-subsets of highly similar cells in the transcriptomic space. Importantly, DiCoLo does not rely on cell clustering or cross-condition alignment. For each condition, DiCoLo constructs a gene graph using Optimal Transport distances that reflect gene co-localization patterns across the cell manifold. Then, it identifies differential gene programs by detecting changes in connectivity patterns between the gene graphs. We show that DiCoLo robustly identifies differential gene co-localization even under weak signals or complex batch effects, outperforming existing methods across multiple benchmark datasets. When applied to mouse hair follicle development data, DiCoLo reveals coordinated gene programs and emerging cell populations driven by perturbations in morphogen signaling that underlie dermal condensate differentiation. Overall, these results establish DiCoLo as a powerful framework for uncovering localized differential transcriptional coordinated patterns in single-cell data.

bioinformatics↗

bioRxiv2025

Repurposed endogenous virus-like vesicles mediate dendritic cell long-range antigen presentation and T cell activation for enhanced cancer vaccination

Dendritic cells (DCs) release extracellular vesicles (DEVs) that amplify antigen presentation while incorporating patient-derived, rapidly evolving antigens. By enriching peptide-MHC and co-stimulatory ligands orders-of-magnitude above donor-cell levels, DEVs emerge as potent vesicle-vaccines, although efficacy remains limited by unclear mechanisms. We show that DCs repurpose viral components to generate endogenous virus-like vesicles (VLVs) that preferentially carry peptide-MHC and high-density co-stimulatory ligands, intensifying and extending antigen presentation. Upon antigen exposure, Arc partners with endogenous envelope proteins to assemble VLVs that directly engage T cells and trigger intrinsic adjuvanticity via viral mimicry. Arc-/- DEVs failed to prime antigen-specific T cell responses, whereas Arc overexpression with its 5'-UTR stem-loop shifted DEVs toward VLVs that trafficked to lymphoid organs, drove rapid CD4-assisted priming and durable CD8-biased memory, suppressed melanoma, and prolonged survival. These reveal a viral-mimicry mechanism enabling long-range immune activation and support Arc+ VLVs as an antigen-agnostic vaccine for cancer immunotherapy. HighlightsO_LIArc+ VLVs intensify and extend DC antigen presentation in vivo C_LIO_LIArc+ VLVs directly engage T cells and trigger viral-mimic adjuvanticity C_LIO_LIArc-/- DEVs fail to prime antigen-specific T cell responses C_LIO_LIEngineered Arc+ VLVs drive durable CD8-biased memory and tumor control C_LIO_LIArc+ VLV vaccination provides long-range and cross-tumor protection in vivo C_LI

bioengineering↗

bioRxiv2025

Learning dynamically regulates stimulus discrimination of ventral striatal D1 receptor expressing neurons

Animals encounter a barrage of sensory stimuli, but only a subset of these are associated with appetitive outcomes, highlighting the importance of neural mechanisms for learning to distinguish reward-paired from unpaired cues. The ventral striatum plays a critical role in both reinforcement learning and stimulus discrimination, but the effect of learning on the selectivity of different cell types remains unclear. Here we examined ventral striatal D1 and D2 medium spiny neuron (MSN) firing properties as mice learned to distinguish between reward-paired and unpaired cues. As learning progressed within a single session, D1 MSN selectivity increased linearly with behavioral selectivity, while D2 MSNs exhibited only modest, behaviorally uncorrelated changes in activity. Altered D1 MSN selectivity was primarily attributed to attenuated excitatory responses to the unrewarded cue, and increasing D1 MSN activity during the unrewarded cue impaired behavioral selectivity. Together, these findings reveal significantly more dynamic contributions of D1 MSNs to stimulus discrimination learning.

neuroscience↗

bioRxiv2025

Deconvolution of Sparse-count RNA Sequencing Data for Tumor Cells Using Embedded Negative Binomial Distributions

Estimating tumor-specific transcript proportions from mixed bulk samples has potential to inform novel biology. However, estimation accuracy using existing methods in sparse-count data such as microRNA-seq and spatial transcriptomics has yet to be established. We generated a mixed small RNA benchmark dataset to demonstrate analytical challenges. To resolve them, we developed DeMixNB, a semi-reference-based deconvolution model assuming a sum of negative binomial distributions. Applications to miRNA-seq from 856 patients with breast cancer and 3,755 spatial spots from lung cancer generated either clinical or mechanistic insights into tumor cell plasticity. This supports the important utility of DeMixNB to investigate cancer RNomes.

bioinformatics↗

bioRxiv2025

Toward Accurate RNA Non-Canonical Structure Prediction: The NC-Bench Benchmark and the NCfold Framework

AO_SCPLOWBSTRACTC_SCPLOWAccurate prediction of non-canonical (NC) base pairs is a pivotal step toward un-raveling the full functional landscape of RNA. This goal requires both a standardized dataset for evaluation and powerful models that overcome data limitations. Here, we make a dual contribution to this endeavor: the NC-Bench benchmark and the NCfold framework. NC-Bench offers a trusted ground for comparison with 925 sequences and 6,708 annotated NC pairs, defining tasks for edge and orientation prediction. NCfold provides a powerful analytical engine, a closed-loop dual-branch network that synergizes sequence features with structure-aware embeddings from RNA foundation models via our proposed REFweighted self-attention. This iterative design enables robust learning from scarce NC data. On the NC-Bench benchmark, NCfold establishes new state-of-the-art results, significantly outperforming existing methods. Comprehensive analyses validate its design and affirm the critical role of NC-Bench in driving future progress. NC-Bench and NCfold together form a cornerstone for the next generation of RNA structure prediction. The datasets and codes are publicly available at https://github.com/heqin-zhu/NCBench

bioinformatics↗

bioRxiv2025

mIDEA: An Interpretable Structure-Sequence Model for Methylation-Dependent Protein-DNA Binding Sensitivity

DNA methylation dynamically reshapes protein-DNA interaction landscapes, yet the mechanistic principles governing methylation-dependent recognition remain incompletely understood. Currently, there is no predictive framework that integrates methylation-aware binding specificity with 3D structure-based residue interactions for quantitatively assessing the impact of DNA methylation on protein-DNA interactions. To bridge this gap, we introduce mIDEA (Methylation-informed Interpretable protein-DNA Energy Associative model), a structure-aware, residue-level biophysical framework that predicts and interprets protein-methylated-DNA interactions by optimizing an amino acid-nucleotide energy matrix explicitly accounting for the modulatory effect of 5-methylcytosine. We validate mIDEA across diverse human transcription factors with distinct CpG methylation preferences. Using only structural templates and prior biochemical knowledge, mIDEA accurately recapitulates both "methyl-plus" and "methyl-minus" effects of DNA methylation on protein-DNA binding. Incorporating quantitative methylation-sensitive binding data further enables mIDEA to capture position-specific methylation effects. When integrated with whole-genome methylation profiles, the model enhances in vivo binding-site prediction by reducing false positives while preserving sensitivity compared to models trained on unmethylated sequences. Overall, mIDEA provides a principled framework for elucidating the molecular mechanisms underlying methylation-modulated protein-DNA recognition. The mIDEA source code is available at: https://github.com/LinResearchGroup-NCSU/IDEA_DNA_Methylation.

bioinformatics↗

bioRxiv2025

Diffusion MRI Processing in the HEALthy Brain and Child Development Study: Innovations and Applications

The landmark ongoing HEALthy Brain and Cognitive Development (HBCD) study will longitudinally chart brain development in a large sample (projected n=7,200) of infants through age 10 years with multimodal neuroimaging that includes an advanced diffusion MRI (dMRI) acquisition. Here, we detail advances in dMRI image processing developed for HBCD, incorporated into the widely used QSIPrep pipeline. Major changes to preprocessing include improvements in infant brain extraction, distortion correction, and normalization to infant-specific templates. Additionally, we describe a new software package - QSIRecon - that yields rich derived data including diverse maps of tissue microstructure as well as person-specific white matter bundles. Using dMRI data from a subset of the HBCD 1.0 release where age information was available (n=529 sessions across two time points), we observe critical improvements in data quality with preprocessing and see expected developmental patterns. Moving forward, the publicly-available data from HBCD will rapidly grow to become the largest study of brain development in infancy and early childhood using dMRI. QSIPrep and QSIRecon are openly available and can be applied to other infant and pediatric dMRI datasets.

neuroscience↗

bioRxiv2025

MEDUSA: Maintaining Entire DNA Duplexes for Utmost Sequencing Accuracy

Highly accurate DNA sequencing is essential for many applications. Duplex sequencing-based methods achieve unparalleled accuracy by requiring reads from both strands of the original DNA duplex to match. Yet, methods to prepare dsDNA for sequencing may resynthesize portions of each DNA duplex and cause base damage errors on one strand to become indistinguishable from true mutations on both strands. Here, we report MEDUSA (Maintaining Entire DNA Duplexes for Utmost Sequencing Accuracy) which minimizes dsDNA resynthesis to maximize duplex sequencing accuracy and yield. MEDUSA carefully repairs and blunts fragmented dsDNA, then employs apyrase to digest residual dNTPs, followed by restricted dA-tailing to prevent resynthesis. MEDUSA affords full genome coverage in a simplified protocol that is broadly compatible with dsDNA fragmentation and library preparation kits. We benchmarked MEDUSA on sheared genomic DNA and cell-free DNA and found a residual SNV frequency within a median 1.23-fold (range 0.92-1.88; p < 0.001) of what was expected if resynthesis was mostly blocked, but with full genome coverage and duplex yields within a median 1.02-fold (range 0.33 - 1.46; p = 0.258) of traditional methods that do not limit resynthesis. In all, MEDUSA could enable high breadth or depth of duplex sequencing while limiting false discovery. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/687202v1_ufig1.gif" ALT="Figure 1"> View larger version (12K): org.highwire.dtl.DTLVardef@1ddbc77org.highwire.dtl.DTLVardef@802ca9org.highwire.dtl.DTLVardef@f443ecorg.highwire.dtl.DTLVardef@974bdb_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

The scaffold protein PRR14L links the PP2A-TACC3 axis to mitotic fidelity and sensitivity to MPS1 inhibition

Aneuploidy is a hallmark of cancer and is a potential vulnerability that can be selectively targeted. To systematically identify genes that affect the incidence and fitness of aneuploid cells, we conducted a genome-wide CRISPR/Cas9 screen using NMS-P715, an inhibitor of the spindle assembly checkpoint (SAC) kinase MPS1/TTK. In this study, we identified a number of genes known to regulate aneuploidy and mitosis, and subsequently focused on PRR14L, a ubiquitously expressed gene previously implicated in chronic myelomonocytic leukemia (CMML). Proximity labeling of PRR14L using TurboID revealed several cell division proteins, including the PP2A-B56 phosphatase complex and the spindle assembly factor TACC3, as PRR14L-interacting proteins. Loss of PRR14L prolongs SAC-dependent mitotic arrest in response to microtubule depolymerization but, paradoxically, leads to catastrophic mitotic errors upon SAC abrogation by MPS1 inhibitors. A model derived from our findings provides a rationale for exploiting MPS1 inhibition as a potential vulnerability in cancers containing either PRR14L loss of function mutations or FGFR-TACC3 fusions. Significance StatementO_LIAneuploidy is a hallmark of cancer. Whether aneuploidy can be selectively targeted is not known. C_LIO_LIUtilizing a CRISPR/Cas9 screen, the authors found that loss of the gene PRR14L sensitizes cells to aneuploidy induction. Taking advantage of live cell imaging and proximity labeling, they linked PRR14L to TACC3 and mitosis. C_LIO_LIThese findings suggest that spindle checkpoint inhibitors may have therapeutic potential in cancers with either loss-of-function PRR14L and/or gain of function TACC3 mutations. C_LI

cell biology↗

bioRxiv2025

A multi-tissue atlas of genetic regulatory effects in sheep

Characterizing the impact of genomic variants on genome function and ultimately complex traits in livestock is essential for the development of sustainable precision agriculture and comparative genomics. Here, as part of the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we present the pilot phase of the SheepGTEx resource through analyzing 6,761 RNA-sequencing samples of 51 primary tissues in a multi-breed population of sheep. We identify millions of regulatory variants associated with seven types of molecular phenotypes, and fine-map 322,468 primary and 114,017 non-primary effects, revealing a high degree of regulatory allelic heterogeneity. We systematically characterize the pleiotropic effects of these variants on molecular phenotypes, assess their context-specific regulatory patterns across tissues, breeds, sexes, and developmental stages, as well as explore their evolutionary constraints across mammals. Finally, we demonstrate the substantial potential of the SheepGTEx resource (https://sheepgtex.farmgtex.org), by providing examples of regulatory mechanisms underpinning 34 complex traits, population divergence between European and Asian breeds, and adaptive evolution in sheep over the past ten millennia.

genomics↗

bioRxiv2025

Zwitterionic hydrogel designs for conducting polymers enable bioelectronics with suppressed foreign body response

For long-term, continuous operation of implantable biosensors and electrophysiological devices, the foreign body response (FBR) is a major obstacle that needs to be overcome. As the FBR progresses, any implanted device will become damaged and isolated from its physiological environment, due to encapsulation by fibrotic tissue and inflammatory immune cells. To achieve more compatible and low-impedance biointerfaces, conducting polymers, such as PEDOT:PSS, have been extensively explored as ideal materials. However, FBR on such conducting polymers remains an unmet challenge. We report a zwitteronic-hydrogel-based double-network design for PEDOT:PSS that can significantly suppress the FBR by 64%, in addition to improving conductivity by more than one order of magnitude. Surprisingly, the FBR level of this design is even lower than that of the parent zwitteronic hydrogel by 53%. Our further immunological investigations at the histological, cellular, and transcriptomic levels give deeper insights into the unique effects that come from the chemical heterogeneity. Furthermore, chronic electrocardiographic recording in mice demonstrate the benefit of this material design to long-term, implanted electrophysiology, which provides indications for the future development of immunocompatible electronic polymers.

bioengineering↗