Biology subjects

Luo, R.

Publications and source records attributed to Luo, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

PIEZO channels link mechanical forces to uterine contractions in parturition

AbstractMechanical forces are extensively involved in pregnancy and parturition, but their precise roles and mechanisms remain poorly understood. Here, we identify mechanically activated ion channels PIEZO1 and PIEZO2 as key mechanotransducers required for labor progression. Genetic deletion of Piezo1 and Piezo2 in mice resulted in weakened uterine contractions and severe parturition defects. Tissue-specific knockouts revealed that deletion in either the uterus or sensory neurons alone caused modest defects, whereas combined loss significantly impaired labor, demonstrating additive effects. Single-nuclei sequencing showed that loss of PIEZO reduced expression of connexin43 (Gja1), a gap junction protein in uterine smooth muscle cells, suggesting a mechanistic link to impaired contraction. These findings highlight the critical role of PIEZO channels in mechanotransduction during parturition and suggest therapeutic targets for labor dysfunction.

physiology↗

bioRxiv2025

Guess till correct: Gungnir codec enabling high error-tolerance and low-redundancy DNA storage through substantial computing power

DNA has emerged as a compelling archival storage medium, offering unprecedented information density and millennia-scale durability. Despite its promise, DNA-based data storage faces critical challenges due to error-prone processes during DNA synthesis, storage, and sequencing. In this study, we introduce Gungnir, a codec system using the proof-of-work idea to address substitution, insertion, and deletion errors in a sequence. With a hash signature for each data fragment, Gungnir corrects the errors by testing the educated guesses until the hash signature is matched. For practicality, especially when sequenced with nanopore long-read, Gungnir also considers biochemical constraints including GC-content, homopolymers, and error-prone motifs during encoding. In silico benchmarking demonstrates its outperforming error resilience capacity against the state-of-art methods and achieving complete binary data recovery from a single sequence copy containing 20% erroneous bases. Gungnir requires neither keeping many redundant sequence copies to address storage degradation, nor high-coverage sequencing to address sequencing error, reducing the overall cost of using DNA for storage.

bioinformatics↗

bioRxiv2025

Perturb-seq reveals distinct responses to pluripotency regulator dosages underlying the control of self-renewal and differentiation

Precise regulation of transcription factor (TF) expression is critical for maintaining cell identity, but studies on how graded expression levels affect cellular phenotypes are limited. To address this gap, we employed human embryonic stem cells (hESCs) as a dynamic model to study gene dosage effects and systematically titrated key TFs NANOG and OCT4 expression using CRISPR interference (CRISPRi). We then profiled transcriptomic changes in hESCs under self-renewal and differentiation conditions using single-cell RNA-seq (scRNA-seq). Quantitative modeling of these Perturb-seq datasets uncovers distinct response patterns for different types of genes, including a striking non-monotonic response of lineage-specific genes during differentiation, indicating that mild perturbations of hESC TFs promote differentiation while strong perturbations compromise it. These discoveries suggest that fine-tuning the dosage of stem cell TFs can enhance differentiation efficiency and underscore the importance of characterizing TF function across a gradient of expression levels.

developmental biology↗

bioRxiv2025

OmicsNavigator: an LLM-driven multi-agent system for autonomous zero-shot biological analysis in spatial omics

Translating high-dimensional, spatially resolved molecular datasets into testable biological findings remains a major research bottleneck. Here, we present Omic-sNavigator, an autonomous large language model-powered system for end-to-end data exploration and hypothesis validation on spatial omics data. OmicsNaviga-tor reasons directly over the multi-modal inputs of spatial omics data, including visual and molecular signatures, to perform knowledge-guided annotation of spatial structures. We show that by transforming high-dimensional data into textual interpretations, OmicsNavigator enables zero-shot semantic retrieval of tissue biomarkers and the reconstruction of patient-level disease profiles from raw omics observations. Furthermore, OmicsNavigator features an objective hypothesis validation engine governed by pre-registered, human-audited blueprints. By validating the system across datasets spanning diverse pathological conditions including diabetic kidney disease, kidney transplant rejection, and COVID-19 pulmonary pathology, we demonstrate that OmicsNavigator generates evidence-based, human-readable insights from spatial omics data, with potential to accelerate spatial biology discoveries.

bioinformatics↗

bioRxiv2025

Assessing large-scale genomic language models in predicting personal gene expression: promises and limitations

Large-scale genomic language models (gLMs) hold promise for modeling gene regulation, yet their ability to predict personal gene expression remains largely unexplored. We developed a framework, gLM2X-Tower, to benchmark gLMs and sequence-to-function (S2F) models on this task with paired personal genome-transcriptome data. With individual-level training, we found that similar to S2F models (e.g., AlphaGenome), gLMs (e.g., Evo2) remain incapable of predicting the inter-person variability on held-out genes. However, such training improves prediction for seen genes in new individuals, particularly by gLMs, highlighting the potential applications in few-shot settings like for rare variants

bioinformatics↗

bioRxiv2025

Pentose Sugars Encode Sequence-Dependent DNA-RNA Segregation for Biomimetic Multiphase Condensates

The nucleus segregates DNA and RNA with high precision to safeguard genome stability and gene regulation despite their homologous structures, yet the underlying molecular principles have long evaded both understanding and synthetic translation. Here we show that a single atomic difference between the pentose sugars, a 2-OH in RNA versus a 2-H in DNA, suffices to drive their phase segregation with cationic peptides. This subtle chemical distinction makes RNA bind peptides more strongly than sequence-identical DNA, generating an asymmetry that, when modulated by sequence-encoded homotypic interactions, drives the formation of core-shell multiphase condensates. Using a supervised machine-learning approach, we distill these molecular principles into a predictive design rule and harness it to program a library of oligonucleotide pairs, termed SEGREGamers, that self-assemble into droplets with coexisting DNA- and RNA-rich domains. These synthetic condensates recapitulate nuclear compartment functions, including selective molecular partitioning and enhanced RNA catalysis. Our results establish a chemically encoded platform for engineering synthetic nuclear mimics and programmable biomolecular condensates, and suggest that sugar identity may have served as an ancient physical mechanism for organizing nucleic acids.

biophysics↗

bioRxiv2025

Intravenous BCG immunization drives naive CD4+ T cell to Th1 differentiation via CD4+ T cell epigenetic reprogramming

Intravenous (IV) administration of live Bacille Calmette-Guerin (BCG) boosts potent immunity through coordinated interactions of innate immune response and adaptive immune response for BCG antiviral activity. The underlying mechanisms by which BCG initiates CD4+ T response remain incompletely understood. Here, we show that IV administration of BCG drives naive CD4+ T cell differentiation to Th1 cell through epigenetic reprogramming. Single-cell transcriptomic and epigenomic analyses revealed that IV BCG immunization induced metabolic reprogramming in monocytes and increased the proportion of T cells. The immunization induced chromatin openness for transcription factors regulating naive CD4+ T cells differentiation into Th1 cells. Importantly, we observed increases in Stat1, Irf1 and interferon-stimulated genes such as Igtp in Th1 cells, leading to upregulation of interferon related gene expression for antiviral response by BCG immunization. Activated transcription factors like Irf1 in CD4+ T cells promotes Il12rb1 expression to facilitate IL-12 signaling and drive naive CD4+ T cells differentiation into Th1 cells. We showed that BCG immunization reduced respiratory syncytial virus (RSV) infection. Thus, transcription regulation through epigenetic reprogramming plays a critical role in BCG-induced CD4+ T cell differentiation and to the broad antiviral activity. SignificanceBacille Calmette-Guerin (BCG) vaccination confers nonspecific protection against heterologous pathogens. Recent work demonstrated that coordinated interactions of innate immune and persistent CD4+ T cell response plays a critical role in BCG induced antiviral activity. In our study, we investigated BCG immunization on epigenetic reprogramming of CD4+ T cells and found that BCG immunization elicited antiviral activity by provoking T cell proliferation. We observed chromatin openness favoring naive T cell differentiation to Th1 response. Importantly, we observed STAT1 and interferon regulatory factor-1 (Irf1) activation. These findings provide mechanistic insights into BCG-induced Th1 response through epigenetic reprogramming and highlight the potential of BCG in offering a long-term protection against emerging and reemerging pathogens. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/655674v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@1c3cb94org.highwire.dtl.DTLVardef@16a8e8forg.highwire.dtl.DTLVardef@b58b88org.highwire.dtl.DTLVardef@dbc994_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIntravenous BCG immunization drives naive CD4+ T cell differentiation to Th1 response; C_LIO_LIBCG alters chromatin accessibility for transcriptional factor expression favoring Th1 response through a Irf1-Il12rb1-IL12 feedback loop; C_LIO_LIThe immunization elicits antiviral activity against RSV infection; C_LI

immunology↗

bioRxiv2025

PET-derived amyloid patterns in gray and white matter across Alzheimer's disease: A high-model-order ICA

INTRODUCTIONAlzheimers Disease (AD) is a neurodegenerative disorder marked by gray matter (GM) changes driven by amyloid-beta (A{beta}) plaques and neurofibrillary tangles. While GM alterations are well documented, spatially distinct patterns of homogeneous A{beta} uptake and white matter (WM) involvement remain underexplored. METHODSWe applied high-order independent component analysis (ICA) to 716 [18F]Florbetapir PET scans, identifying 80 GM and 13 WM networks. Diagnostic and cognitive associations were evaluated via statistical modeling. RESULTSIdentified networks delineated a progression trajectory, with mild cognitive impairment (MCI) profiles in temporoparietal and frontal subdomains more closely aligned with AD than cognitively normal (CN) profiles. GM networks, including the hippocampal-entorhinal complex and precuneus, and WM networks, including the retrolenticular internal capsule, demonstrated robust associations with cognitive performance. DISCUSSIONOur findings highlight the utility of high-order ICA in identifying reproducible A{beta} networks and the contribution of WM networks, such as the posterior corpus callosum, in the early pathological landscape of AD.

neuroscience↗

bioRxiv2025

ClairS-TO: A deep-learning method for long-read tumor-only somatic small variant calling

Accurate identification of somatic variants in tumor is crucial but challenging, and typically requires a matched normal sample for reliable detection, which is often unavailable in real-world research and clinical scenarios, necessitating proficient algorithms to tell real somatic variants from germline variants and background noises. However, existing tumor-only somatic variant callers that were designed for short-read data dont work well with long-read. To fill the gap, we present ClairS-TO, a deep-learning-based method for long-read tumor-only somatic variant calling. ClairS-TO uses an ensemble of two disparate neural networks that were trained from the same samples but for opposite tasks - how likely/not likely a candidate is a somatic variant. ClairS-TO also applies multiple post-calling filters, including 1) nine hard-filters, 2) four public plus any number of user-supplied PoNs, and 3) a module that statistically separates somatic and germline variants using tumor purity and copy number profile. Benchmarks using COLO829 and HCC1395 show that ClairS-TO outperforms DeepSomatic in long-read. ClairS-TO is also applicable to short-read and outperforms Mutect2, Octopus, Pisces, and DeepSomatic. Extensive experiments across various sequencing coverages, VAF ranges, and tumor purities support that ClairS-TO has a broad coverage of usage scenarios. ClairS-TO is open-source, available at https://github.com/HKU-BAL/ClairS-TO.

bioinformatics↗

bioRxiv2024

Discovery of NANOG enhancers and their essential roles in self-renewal and differentiation in human embryonic stem cells

Human embryonic stem cells (hESCs) are notable for their ability to self-renew and to differentiate into all tissue types in the body. NANOG is a core regulator of hESC identity, and dynamic control of its expression is crucial to maintain the balance between self-renewal and differentiation. Transcriptional regulation depends on enhancers, but NANOG enhancers in hESCs are not well characterized. Here we report two NANOG enhancers discovered from a CRISPR interference screen in hESCs. Deletion of a single copy of either enhancer significantly reduced NANOG expression, compromising self-renewal and increasing differentiation propensity. Interestingly, these two NANOG enhancers are involved in a tandem duplication event found in certain primates including humans but not in mice. However, the duplicated counterparts do not regulate NANOG expression. This work expands our knowledge of functional enhancers in hESCs, and highlights the sensitivity of the hESC state to the dosage of core regulators and their enhancers.

developmental biology↗

bioRxiv2024

Long-read genomics reveal extensive nuclear-specific evolution and allele-specific expression in a dikaryotic fungus

Phased telomere to telomere (T2T) genome assemblies are revolutionising our understanding of long hidden genome biology "dark matter" such as centromeres, rDNA repeats, inter-haplotype variation, and allele specific expression (ASE). Yet insights into dikaryotic fungi that separate their haploid genomes into distinct nuclei is limited. Here we explore the impact of dikaryotism on the genome biology of a long-term asexual clone of the wheat pathogenic fungus Puccinia striiformis f. sp. tritici. We use Oxford Nanopore (ONT) duplex sequencing combined with Hi-C to generate a T2T nuclear-phased assembly with >99.999% consensus accuracy. We show that this fungus has large regional centromeres enriched in LTR retrotransposons, with a single centromeric dip in methylation that suggests one kinetochore attachment site per chromosomes. The centromeres of chromosomes pairs are most often highly diverse in sequence and kinetochore attachment sites are not always positionally conserved. Each nucleus carries a unique array of rDNAs with >200 copies that harbour nucleus-specific sequence variations. The inter-haplotype diversity between the two nuclear genomes is caused by large-scale structural variations linked to transposable elements. Nanopore long-read cDNA analysis across distinct infection conditions revealed pervasive allele specific expression for nearly 20% of all heterozygous gene pairs. Genes involved in plant infection were significantly enriched in ASE genes which appears to be mediated by elevated CpG gene body methylation of the lower expressed pair. This suggests that epigenetically regulated ASE is likely a previously overlooked mechanism facilitating plant infection. Overall, our study reveals how dikaryotism uniquely shapes key eukaryotic genome features.

genomics↗

bioRxiv2024

Employing Microsoft Sway as a Repository for Data Generated in Pharmaceutics Experimental Courses: A Case study on Topical Formulation Characterization

Microsoft Sway is an easy-to-use online creative software developed by Microsoft. It has many advantages as follows: easy to operate and does not require long-term practice and learning; network links can be created and accessed by devices such as Personal Computer (PC) and mobile terminals; Educators and Learners are allowed to browse this repository at anytime to facilitate the application prospects of mining relevant data. Therefore, the author proposes that Microsoft Sway may be a very promising data repository for pharmaceutical experimental teaching courses, and is expected to be extended to undergraduate experimental teaching in other natural science fields.

scientific communication and education↗

bioRxiv2024

Clair3-RNA: A deep learning-based small variant caller for long-read RNA sequencing data

Variant calling using long-read RNA sequencing (lrRNA-seq) can be applied to diverse tasks, such as capturing full-length isoforms and gene expression profiling. It poses challenges, however, due to higher error rates than DNA data, the complexities of transcript diversity, RNA editing events, etc. In this paper, we propose Clair3-RNA, the first deep learning-based variant caller tailored for lrRNA-seq data. Clair3-RNA leverages the strengths of the Clair series pipelines and incorporates several techniques optimized for lrRNA-seq data, such as uneven coverage normalization, refinement of training materials, editing site discovery, and the incorporation of phasing haplotype to enhance variant-calling performance. Clair3-RNA is available for various platforms, including PacBio and ONT complementary DNA sequencing (cDNA), and ONT direct RNA sequencing (dRNA). Our results demonstrated that Clair3-RNA achieved a [~]91% SNP F1-score on the ONT platform using the latest ONT SQK-RNA004 kit (dRNA004) and a [~]92% SNP F1-score in PacBio Iso-Seq and MAS-Seq for variants supported by at least four reads. The performance reached a [~]95% and [~]96% F1-score for ONT and PacBio, respectively, with at least ten supporting reads and disregarding the zygosity. With read phased, the performance reached [~]97% for ONT and [~]98% for PacBio. Extensive evaluation of various GIAB samples demonstrated that Clair3-RNA consistently outperformed existing callers and is capable of distinguishing RNA high-quality editing sites from variants accurately. Clair3-RNA is open-source and available at (https://github.com/HKU-BAL/Clair3-RNA).

bioinformatics↗

bioRxiv2024

AutoPM3: Enhancing Variant Interpretation via LLM-driven PM3 Evidence Extraction from Scientific Literature

Rare diseases, affecting 300 million people globally, often result from genetic variants. Wholegenome sequencing has made variant detection more cost-effective, but interpreting these variants remains challenging. Current clinical practice combines quantitative evidence and literature, which is complex and time-consuming. We introduce AutoPM3, a method for automating the extraction of ACMG/AMP PM3 evidence from scientific literature using open-source LLMs. It combines an optimized RAG system for text comprehension and a TableLLM equipped with Text2SQL for data extraction. We evaluated AutoPM3 using our collected PM3-Bench, a dataset from ClinGen with 1,027 variant-publication pairs. AutoPM3 significantly outperformed other methods in variant hit and in trans variant identification, thanks to the four key modules. Additionally, we wrapped AutoPM3 with a user-friendly interface to enhance its accessibility. This study presents a powerful tool to improve rare disease diagnosis workflows by facilitating PM3-relevant evidence extraction from scientific literature.

bioinformatics↗

bioRxiv2024

EPInformer: A Scalable Deep Learning Framework for Gene Expression Prediction by Integrating Promoter-enhancer Sequences with Multimodal Epigenomic Data

Transcriptional regulation, critical for cellular differentiation and adaptation to environmental changes, involves coordinated interactions among DNA sequences, regulatory proteins, and chromatin architecture. Despite extensive data from consortia like ENCODE, understanding the dynamics of cis-regulatory elements (CREs) in gene expression remains challenging. Deep learning is a powerful tool for learning gene expression and epigenomic signals from DNA sequences, exhibiting superior performance compared to conventional machine learning approaches. However, even the most advanced deep learning-based methods may fall short in capturing the regulatory effects of distal elements such as enhancers, limiting their predictive accuracy. In addition, these methods may require significant resources to train or to adapt to newly generated data. To address these challenges, we present EPInformer, a scalable deep-learning framework for predicting gene expression by integrating promoter-enhancer interactions with their sequences, epigenomic signals, and chromatin contacts. Our model outperforms existing gene expression prediction models in rigorous cross-chromosome validation, accurately recapitulates enhancer-gene interactions validated by CRISPR perturbation experiments, and identifies crucial transcription factor motifs within regulatory sequences. EPInformer is available as open-source software at https://github.com/pinellolab/EPInformer.

genomics↗

bioRxiv2024

Towards a new standard in genomic data privacy: a realization of owner-governance

With the rapid developments in sequencing technologies, individuals now have unprecedented access to their genomic data. However, existing data management systems or protocols are inadequate for protecting privacy, limiting individuals control over their genomic information, hindering data sharing, and posing a challenge for biomedical research. To fill the gap, an owner-governed system that fulfills owner authority, lifecycle data encryption, and verifiability at the same time is prompted. In this paper, we realized Governome, an owner-governed data management system designed to empower individuals with absolute control over their genomic data during data sharing. Governome uses a blockchain to manage all transactions and permissions, enabling data owners with dynamic permission management and to be fully informed about every data usage. It uses homomorphic encryption and zero-knowledge proofs to enable genomic data storage and computation in an encrypted and verifiable form for its whole lifecycle. Governome supports genomic analysis tasks, including individual variant query, cohort study, GWAS analysis, and forensics. Query of a variants genotype distribution among 2,504 1kGP individuals in Governome can be efficiently completed in under 18 hours on an ordinary server. Governome is an open-source project available at https://github.com/HKU-BAL/Governome.

bioinformatics↗

bioRxiv2024

OsAAI1-OsMADS25 module orchestrates root morphogenesis by fine-tuning IAA in drought stressed rice

Indole-3-acetic acid (IAA) plays a critical role as a plant hormone in regulating the growth and development of the root system in plants, particularly in enhancing their ability to withstand abiotic stress. In this study, we found that overexpression of OsAAI1 promoted the growth of rice root system. The length of primary root, the number of lateral roots, the density of lateral roots, and the number of adventitious roots of overexpression of OsAAI1 (OE19) were significantly better than those of the wild type (ZH11) and the mutant line (osaai1), and the IAA content of OE19 was significantly higher than those of ZH11 and osaai1. We also found that exogenous application of IAA could compensate for the root growth defect caused by the osaai1 mutation. OE19 had the highest number and widest distribution of total roots under the water-cut drought treatment, and exogenous application of IAA attenuated the growth inhibitory effect of drought stress on osaai1. Our study also revealed that OsAAI1 interacts with the MADS-box family transcription factor OsMASD25. Additionally, the application of IAA helped alleviate the growth inhibitory effects of drought stress on osmads25.Importantly, OsMADS25 interaction with OsAAI1 was found to enhance the transcriptional expression of its downstream target genes LAX1 and OsBAG4, which are crucial genes in rices response to drought stress. These findings suggest that OsAAI1 and OsMADS25 are crucial in rices drought acclimation process by regulating downstream gene expression and influencing the IAA signaling pathway. Author summaryThe root system is a crucial organ for crop plants as it facilitates the absorption of water and nutrients, contributing to their drought resistance. Indole-3-acetic acid (IAA) plays a pivotal role in the growth of various types of roots in plants. Under drought stress conditions, changes in IAA levels and transport can impact the morphology of plant roots. This research illustrates that OsAAI1 positively influences rice root development and enhances the plants response to drought stress through the auxin signaling pathway. The study reveals a physical interaction between OsAAI1 and the transcription factor OsMADS25. This interaction boosts the expression of the auxin synthesis gene OsYUC4 and suppresses the auxin inhibitory factor OsIAA14, thereby promoting the auxin signaling pathway, stimulating rice root growth, and enhancing the plants ability to withstand drought. Furthermore, the interaction between OsAAI1 and OsMADS25 has been found to also positively affect the expression of the genes LAX1 and OsBAG4, which is associated with activated drought resistance in rice plants.

plant biology↗

bioRxiv2024

CRISPR Screening Uncovers a Long-Range Enhancer for ONECUT1 in Pancreatic Differentiation and Links a Diabetes Risk Variant

Functional enhancer annotation is a valuable first step for understanding tissue-specific transcriptional regulation and prioritizing disease-associated non-coding variants for investigation. However, unbiased enhancer discovery in physiologically relevant contexts remains a major challenge. To discover regulatory elements pertinent to diabetes, we conducted a CRISPR interference screen in the human pluripotent stem cell (hPSC) pancreatic differentiation system. Among the enhancers uncovered, we focused on a long-range enhancer [~]664 kb from the ONECUT1 promoter, since coding mutations in ONECUT1 cause pancreatic hypoplasia and neonatal diabetes. Homozygous enhancer deletion in hPSCs was associated with a near-complete loss of ONECUT1 gene expression and compromised pancreatic differentiation. This enhancer contains a confidently fine-mapped type 2 diabetes associated variant (rs528350911) which disrupts a GATA motif. Introduction of the risk variant into hPSCs revealed substantially reduced binding of key pancreatic transcription factors (GATA4, GATA6 and FOXA2) on the edited allele, accompanied by a slight reduction of ONECUT1 transcription, supporting a causal role for this risk variant in metabolic disease. This work expands our knowledge about transcriptional regulation in pancreatic development through the characterization of a long-range enhancer and highlights the utility of enhancer discovery in disease-relevant settings for understanding monogenic and complex disease.

developmental biology↗