Biology subjects

Yu, R.

Publications and source records attributed to Yu, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

SWING domains prime chromatin for nuclear body mediated gene regulation

Nuclear speckles have long been recognized as RNA-rich nuclear bodies, yet their role in genome organization and gene regulation remains incompletely understood. Using a rapid dTAG-mediated degradation system to simultaneously deplete SON and SRRM2--the core structural components of nuclear speckles--we identify a novel class of genomic regions, which we term SWING regions. Upon speckle disruption, SWING regions relocate to the nuclear periphery and acquire repressive histone marks such as H3K9me3, accompanied by gene downregulation, particularly of genes involved in developmental pathways. Consistent with this, depletion of Lamin A reduces lamina association of SWING regions and enhances their association with nuclear speckles, supporting a bidirectional balance between these two nuclear compartments. Notably, human mutations in SON and SRRM2 are associated with neurodevelopmental disorders characterized by intellectual disability and global developmental delay. Patient-derived cells bearing such mutations similarly exhibit SWING-region relocalization and gene repression, underlining a role for speckles in developmental gene regulation. We also report that drug-induced speckle rejuvenation partially rescues aberrant SWING-region localization to the nuclear lamina in patient-derived cells and iPSCs with acute depletion of SON. These findings identify SWING regions as an intermediate chromatin state positioned between nuclear speckles and the lamina, maintained by opposing functions from both structures. Our work reveals a mechanism underlying the contribution of nuclear bodies to 3D genome organization, highlights the importance of nuclear speckles and SWING regions in developmental regulation, and provides a potential therapeutic intervention in speckle dysfunction. Impact StatementIdentification of nuclear speckles as determinants of specific 3D genome organization. Demonstration of functional interactions between opposing nuclear structures (speckle versus lamina) through SWING regions. Establishment of developmental and human disease relevance of speckle-mediated genome organization. Providing potential avenues for therapeutic intervention in speckleopathies.

genetics↗

bioRxiv2025

NQO1 phase condensation promotes stress granule assembly to facilitate pancreatic carcinogenesis

G3BP1 promotes pancreatic ductal adenocarcinoma (PDAC) tumorigenesis driven by oncogenic KRAS mutations through liquid-liquid phase separation (LLPS)-mediated assembly of stress granules (SGs). However, the regulatory mechanisms remain elusive. We identify the antioxidant enzyme NAD(P)H quinone dehydrogenase 1 (NQO1) as a novel SG regulator that enhances SG assembly in pancreatic cancer cells independently of reactive oxygen species (ROS). Mechanistic studies reveal that NQO1 does not regulate the expression of G3BP1 or other SG-associated genes. Instead, NQO1 undergoes LLPS dependent on its RNA-binding K homology (KH) like domain. Further analysis demonstrates that residues 121-131 of NQO1 directly interact with G3BP1s RNA-binding domain (RBD), enhancing the multivalency of G3BP1 complexes to potentiate LLPS-driven SG assembly. This interaction accelerates cell proliferation, KRAS mutation-induced acinar-to-ductal metaplasia (ADM), and pancreatic carcinogenesis. Notably, the interaction between NQO1 residues 121-131 and G3BP1s RBD is essential for NQO1 phase condensation under stress condition. Integrative analysis of human PDAC transcriptomic datasets reveals a weak association between NQO1 and G3BP1 levels. Importantly, both NQO1 and G3BP1 formed biological condensates and co-localized in the lesions of human PDAC tissue sections. Our study uncovers a novel KRAS mutation-driven mechanism of pancreatic carcinogenesis through the lens of phase separation, transcending conventional gene expression regulation and offering new insights into non-canonical KRAS-NQO1-G3BP1-SG regulatory networks in PDAC initiation.

cancer biology↗

bioRxiv2025

Impact Induces Phagocytic Defect in Reactive Microglia

We have developed traumatic brain injury (TBI)-on-a-chip in vitro models using primary microglia and neuronal networks and recorded the molecular and cellular changes following impact to represent impact injury. Using a pH-responsive amyloid {beta} (A{beta}pH), we showed that microglial phagocytosis was reduced at 7 days post-impact on the chip. Simultaneously, neurons increased their uptake of A{beta}, and decreased neuronal firing frequency at 7 days post-impact based on electrophysiological recordings. Given the importance of lipid metabolism in brain trauma and neurodegeneration, the lipidome secreted by impacted cells was analyzed to understand changes in cellular processes. Interestingly, many lipid species from the sphingomyelin, glycerophospholipid, and phosphatidylserine classes were significantly affected by impact, which are known to play important roles in the resolution of neuroinflammation and the pathogenesis of neurodegeneration.

neuroscience↗

bioRxiv2025

Evidence for plastome loss in the holoparasitic Mystropetalaceae

Plastome loss is an extremely unusual phenomenon in land plants, even for those that lose their photosynthetic ability. To date, evidence for plastome loss has only been presented for two holoparasitic angiosperm lineages: two genera (Rafflesia and Sapria) of Rafflesiaceae and one section (Subulatae) of Cuscuta (Convolvulaceae). Here we propose plastome loss in holoparasitic Mystropetalaceae (Santalales), which consists of three monotypic genera: Dactylanthus, Hachettea, and Mystropetalon. Using Illumina DNA sequencing data, we successfully assembled the complete mitochondrial genomes of all three genera; however, we failed to recover any plastome sequences from them using reference-based and de novo assembly approaches. Illumina RNA sequencing data for Hachettea revealed the absence of plastid gene transcripts and losses of many nuclear-encoded genes with plastid function, including genes involved in DNA transcription, translation and protein degradation. Convergent loss of these key genes has also been documented in other plastome-less non-photosynthetic plant lineages. Taken together, we provided compelling evidence for plastome loss in the family Mystropetalaceae.

plant biology↗

bioRxiv2025

Circle-Mediated HGT shapes the multichromosomal mitochondrial genome of the endoparasite Mitrastemon yamamotoi

Horizontal gene transfer (HGT), a well-established driver of genome evolution in prokaryotes, was historically considered rare in plants. However, accumulating genomic evidence supports its occurrence in angiosperms, impacting both nuclear and mitochondrial genomes, particularly in parasitic species that establish vascular connections with their hosts. Despite the increasing recognition of HGT in a few clades of parasitic plants (e.g., Balanophoraceae, Rafflesiaceae, and Cynomoriaceae), the underlying mechanisms and evolutionary consequences of these transfers are still not fully understood. Mitrastemon yamamotoi, a holoparasitic endoparasite in the order Ericales, invades the roots of host trees in the Fagaceae family, creating favorable conditions for HGT. In this study, we assembled for the first time the mtDNA of Mitrastemon, revealing a multipartite structure consisting of 51 circular-mapping chromosomes. Phylogenetic and comparative genomic analyses uncovered extensive HGT from Fagaceae hosts, affecting both coding and non-coding regions. Notably, more than 60% of the Mitrastemon mtDNA is of foreign origin, and seven chromosomes are entirely foreign, with structural signatures in the donor mtDNA consistent with the recently proposed circle-mediated HGT model. Additionally, we detected six protein-coding genes of foreign origin and one chimeric gene. Remarkably, a foreign atp1 gene has replaced the missing native copy and represents a rare event of functional HGT in plant mitochondria. These results position Mitrastemon as a valuable model for studying mtDNA evolution and deepening our understanding of the HGT process. Our findings expand the range of lineages in which circle-mediated HGT has been documented, suggesting it is a more widespread and fundamental mode of mitochondrial HGT in plants. Significant StatementParasitic plants form intimate connections with their hosts, but how these interactions influence genome evolution remains poorly understood. Our study shows that Mitrastemon yamamotoi has acquired over 60% of its mitochondrial DNA from its host through horizontal gene transfer, including entire foreign chromosomes. These findings provide strong support for a recently proposed mechanism, circle-mediated HGT, and suggest that this process may be a more widespread driver of mitochondrial genome evolution in flowering plants.

evolutionary biology↗

bioRxiv2025

Neural correlates of evidence accumulation in social-affective decision-making under perceptual ambiguity

Evidence accumulation models have been widely used to explain decision-making in sensory and cognitive domains, but how this process explains social-affective decision-making under perceptual ambiguity remains unclear. Here, we integrate computational modeling with multimodal neuroscience to investigate how perceptual ambiguity in emotion judgment influences decision dynamics. Participants viewed ambiguous stimuli--morphed images blending emotional expressions--and made binary categorizations. Using drift diffusion modeling (DDM), we show that drift rate, a core index of evidence accumulation, decreases as ambiguity increases. Scalp EEG data reveal that the late positive potential (LPP) tracks drift rate in both emotional and non-emotional decisions, but only when perceptual ambiguity is relevant to the task. Likewise, single-unite recordings from the dorsomedial prefrontal cortex (dmPFC) and amygdala neurons show that trial-by-trial firing rates in both regions encode drift rate, with opposite encoding patterns. fMRI-based connectivity analyses further reveal that the strength of amygdala-dmPFC coupling correlates with individual differences in the modulator effect of perceptual ambiguity on drift rate. Together, these findings identify a corticolimbic network that dynamically modulates evidence accumulation during social-affective decision-making under perceptual ambiguity. By linking computational parameters to neural activity across multiple modalities, this work advances our understanding of how the brain resolves emotionally ambiguous perceptual information. It provides a translational framework for studying emotion recognition and decision-making impairments in clinical populations.

neuroscience↗

bioRxiv2025

Improved flux profiling in genome-scale modeling of human cell metabolism

Understanding human cell metabolism through genome-scale flux profiling is of interest to diverse research areas of human health and disease. Metabolic modeling using genome-scale metabolic models (GEMs) has the potential to achieve this, but has been limited by a lack of appropriate input data as model constraints. Here we show that GEM-based flux profiling simulations can be improved with an appropriate input data collection procedure and exo-metabolite exchange flux calculation method, called regression during exponential growth phase (REGP). Our results show that the GEM-simulated feasible flux space is constrained to a biologically meaningful region, allowing an exploration of the basic organizing principles of the feasible flux space. These improvements help to fulfil the promise of GEMs as a valuable tool in the study of human metabolism and future development of translational applications.

systems biology↗

bioRxiv2025

Sequencing-free whole genome spatial transcriptomics at molecular resolution in intact tissue

Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, compositions, interactions, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution. Leading spatial-capture or spatial-tagging transcriptomics techniques that rely on in-vitro sequencing offer whole-transcriptome coverage, in principle, but at the cost of lower spatial resolution compared to image-based techniques. In contrast, high-performance image-based spatial transcriptomics techniques, which rely on in situ hybridization or in situ sequencing, achieve single-molecule spatial resolution and retain sub-cellular morphologies, but are limited by probe libraries that target only a subset of the transcriptome, typically covering several hundred to a few thousand transcript species. Together, these limitations hinder unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop a new image-based spatial transcriptomics technology termed Reverse-padlock Amplicon Encoding FISH (RAEFISH) with whole-genome level coverage while retaining single-molecule spatial resolution in intact tissues. We demonstrate image-based spatial transcriptomics targeting 23,000 human transcript species or 22,000 mouse transcript species, including nearly the entire protein-coding transcriptome and several thousand long-noncoding RNAs, in single cells in cultures and in tissue sections. Our analyses reveal differential subcellular localizations of diverse transcripts, cell-type-specific and cell-type-invariant tissue zonation dependent transcriptome, and gene expression programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.

genetics↗

bioRxiv2025

Flavoaffinins, elusive cellulose-binding natural products from an anaerobic bacterium

Cellulose is the most abundant polymer on earth. Many anaerobic cellulose-degrading bacteria produce uncharacterized yellow-orange, cellulose-binding pigments known as yellow affinity substances (here referred to as flavoaffinins) that are associated with cellulose degradation. Here, we isolate and structurally characterize the flavoaffinins from Clostridium (Hungateiclostridium) thermocellum, a key workhorse for the industrial conversion of cellulosic feedstocks to ethanol. Flavoaffinins represent an unprecedented structural juxtaposition of an aryl polyene chain with a hydroxy-diene {gamma}-lactone. We also shed light on their biosynthesis using stable-isotope feeding experiments. This effort lays the groundwork for understanding the biological function(s) of the flavoaffinins and expands the limited number of natural products isolated from obligately anaerobic microbes.

biochemistry↗

bioRxiv2024

Scoring Information Integration with Statistical Quality Control Enhanced Cross-Run Analysis of Data-Independent Acquisition Proteomics Data

The peptide-centric strategy is widely applied in data-independent acquisition (DIA) proteomics to analyze multiplexed MS2 spectra. However, current software tools often rely on single-run data for peptide peak identification, leading to inconsistent quantification across heterogeneous datasets. Match-between-runs (MBR) algorithms address this by aligning peaks or elution profiles across runs post-analysis but they are often ad-hoc and lack statistical frameworks for controlling peak quality, resulting in false positives and reduced quantitative reproducibility. Here we present DreamDIAlignR, a cross-run peptide-centric tool that integrates peptide elution behavior across runs with a deep learning peak identifier and signal alignment algorithm for consistent peak picking and FDR-controlled scoring. DreamDIAlignR outperformed state-of-the-art MBR methods, identifying up to 25.6% more quantitatively changing proteins on a benchmark dataset and 38.5% more on a cancer dataset. Additionally, DreamDIAlignR establishes an improved methodology for performing MBR compatible with existing DIA analysis tools, thereby enhancing the overall quality of DIA analysis.

bioinformatics↗

bioRxiv2024

Prediction of Cell States and Key Transcription Factors of the Human Cornea through Integrated Single-Cell Omics Analyses

The cornea, a transparent tissue composed of multiple layers, allows light to enter the eye. Several single-cell RNA-seq analyses have been performed to explore the cell states and to understand the cellular composition of the human cornea. However, the inconsistences in cell state annotations between these studies complicate the application of these findings in corneal studies. To address this, we integrated single-cell RNA-seq data from four published studies and created a human corneal cell state meta-atlas. This meta-atlas was subsequently evaluated in two applications. First, we developed a machine learning pipeline cPredictor, using the human corneal cell state meta-atlas as input, to annotate corneal cell states. We demonstrated the accuracy of cPredictor and its ability to identify novel marker genes and rare cell states in the human cornea. Furthermore, cPredictor revealed the differences of the cell states between pluripotent stem cell-derived corneal organoids and the human cornea. Second, we integrated the single-cell RNA-seq based cell state meta-atlas with chromatin accessibility data, conducting motif-focused and gene regulatory network analyses. These approaches identified distinct transcription factors driving cell states of the human cornea. The novel marker genes and transcription factors were validated by immunohistochemistry. Overall, this study offers a reliable and accessible reference for profiling corneal cell states, which facilitates future research in cornea development, disease and regeneration. Significance statementThis study creates a human corneal cell state meta-atlas that provides a common nomenclature of cells in the human cornea, through integrating multiple single-cell RNA-seq analyses. Using this meta-atlas, we developed a machine learning pipeline, cPredictor, to accurately annotate cell states in corneal studies using single-cell RNA-seq. Additionally, we identified distinct transcription factors driving cell states through integrating the atlas with chromatin accessibility data. This meta-atlas and the computational tool cPredictor enable future research in cornea development, disease, and regeneration.

cell biology↗

bioRxiv2024

Accurate Cell Abundance Quantification using Multi-positive and Unlabeled Self-learning

Quantifying the abundance of different cell types in pathological samples can help to uncover the correlations between cell composition and pathological conditions, offering deeper insights into the roles of different cell types in complex diseases. Conventional methods for cell abundance estimation often employ unsupervised clustering or supervised learning to identify cell types and estimate their proportions. However, these methods face challenges in accurately quantifying cell abundances, as clustering results could be unreliable and supervised methods may misclassify cell types not presented in the training data. We introduce CleverXMBD1 (denoted as Clever) for quantifying cell abundance from complex samples using a multi-class classifier trained with a confidence-based multi-positive and unlabeled loss function. Our evaluations show that Clever consistently and substantially outperforms existing methods in quantifying cell type abundance across multiple single cell datasets derived from different modalities, including CyTOF and image mass cytometry.

bioinformatics↗

bioRxiv2024

A multi-step completion process model of cell plasticity

Plasticity is the potential for cells or cell populations to change their phenotypes and behaviours in response to internal or external cues. A common way to study a plasticity programme is to track the underlying molecular changes over time, by collecting omics time-series data. However, there remains a lack of mathematical models to elucidate and predict molecular behaviours in a plasticity programme using omics time-series data. Here we report a new mathematical framework that models cell plasticity as a multi-step completion process, where the system moves from the initial state along a path guided by multiple intermediate attractors until the final state (i.e. a new homeostasis) is reached. In benchmarking tests, we show that our developed Control Point (CP) model fits omics time-series data and identifies attractor states that are well-aligned with prior knowledge. Importantly, we show that the CP model can make non-trivial predictions such as the molecular outcomes of blocking a plasticity programme from reaching completion, in a quantitative and time-resolved manner. Significance statementCell plasticity is fundamental to a wide range of complex biological processes, for example in embryonic development, immunology, regenerative medicine, aging, cancer, and others. We developed a new mathematical model that leverages omics time-series data to understand and make accurate predictions about cell plasticity. This can lead to new discoveries and open up new avenues of research in medicine and bioengineering.

systems biology↗

bioRxiv2024

The Human T-cell Leukemia Virus capsid protein is a potential drug target

Human T-cell Leukemia Virus type 1 (HTLV-1) is an untreatable retrovirus that causes lethal malignancies and degenerative inflammatory conditions. Effective treatments have been delayed by substantial gaps in our knowledge of the fundamental virology, especially when compared to the closely related virus, HIV. A recently developed and highly effective anti-HIV strategy is to target the virus with drugs that interfere with capsid integrity and interactions with the host. Importantly, the first in class anti-capsid drug approved, lenacapavir, can provide long-acting pre-exposure prophylaxis. Such a property would provide a means to prevent the transmission of HTLV-1, but its capsid has not previously been considered as a drug target. Here we describe the first high-resolution crystal structures of the HTLV-1 capsid protein, define essential lattice interfaces, and identify a previously unknown ligand-binding pocket. We show that this pocket is essential for virus infectivity, providing a potential target for future anti-capsid drug development.

molecular biology↗

bioRxiv2024

Simplified and Rapid Workflow Enhances Throughput of De Novo Sequencing of COVID-19 Neutralizing Antibodies

During the current COVID-19 pandemic, precise antibody sequencing is crucial for the rapid development of broad-spectrum or pan-{beta}-coronavirus neutralizing antibodies (NAbs) to prevent new variants of the coronavirus and other highly pathogenic {beta}-coronaviruses. However, mass spectrometry-based de novo sequencing remains challenging due to its high cost, low throughput, and unexpected missing of certain ion information. We developed an innovative approach using solely bottom-up to provide a rapid, robust, and refined solution for the current de novo sequencing challenges. The methodology, referred to as SP-MEGD for Single-Pot and Multi-Enzymatic Gradient Digestion, capitalizes on a five-protease gradient digestion by sampling every two hours for a total of 6 hours in an integrative reactor. The SP-MEGD method could engender numerous missed cleavage events and produce various peptide products of diverse lengths with overlapping stretches of residues, enabling efficient database-free de novo sequencing. Antibody assembly for single or mixed COVID-19-NAbs with publicly available sequences and commercial antibodies with unknown sequences was efficiently deciphered using our previously proposed optimization method, Fusion assembler. Our innovative study represents the first successful simultaneous discrimination and achieving over 99% accurate sequence coverage of a mixture containing three humanized COVID-19-NAbs (S2P6LH, BD5514LH, and BD5840LH). Furthermore, we utilized SP-MEGD and Fusion to sequence commonly used anti-CD8 and anti-CD4, and successfully confirmed their impact on immune cell clearance through in vivo experiments. Overall, our developed workflow demonstrates promise in facilitating the discovery and development of vaccines and antibody therapeutics while providing deeper insights into monoclonal antibodies (mAbs).

bioengineering↗

bioRxiv2024

Comprehensive assembly of monoclonal and mixed antibody sequences

The elucidation of antibody sequence information is crucial for understanding antigen binding and advancing therapeutic and research applications. However, complete de novo assembly of monoclonal antibody sequences remains challenging due to accuracy and robustness limitations. To address this issue, we introduce Fusion, an innovative de novo assembler that integrates overlapping peptides and template information into complete sequences using a beam search strategy. We demonstrate Fusions performance by reconstructing multiple human and murine antibodies with highest accuracy (100% and over 99%, respectively). Biological validation of the recombinantly expressed AFS98 antibody with unknown sequences further supports its effectiveness. Furthermore, current methods are applicable only to traditional monoclonal antibody sequencing assembly, presenting a significant bottleneck in achieving higher throughput. In contrast, Fusion can assemble peptide sequences from mixtures of two or three monoclonal antibodies into complete individual sequences with the same accuracy as traditional sequencing, significantly enhancing throughput. To our knowledge, this is the first study enabling high-throughput sequencing of multiple antibodies using only bottom-up mass spectrometry. The duration, expense, and reagent consumption of mass spectrometry detection are comparable to those required for sequencing a single monoclonal antibody. In summary, Fusions superior performance in handling the complex antibody sequencing represents a significant advancement in antibody research.

bioinformatics↗

bioRxiv2024

An aging-sensitive compensatory secretory phospholipase that confers neuroprotection and cognitive resilience

Breakdown of lipid homeostasis is thought to contribute to pathological aging, the largest risk factor for neurodegenerative disorders such as Alzheimers Disease (AD). Cognitive reserve theory posits a role for compensatory mechanisms in the aging brain in preserving neuronal circuit functions, staving off cognitive decline, and mitigating risk for AD. However, the identities of such mechanisms have remained elusive. A screen for hippocampal dentate granule cell (DGC) synapse loss-induced factors identified a secreted phospholipase, Pla2g2f, whose expression increases in DGCs during aging. Pla2g2f deletion in DGCs exacerbates aging-associated pathophysiological changes including synapse loss, inflammatory microglia, reactive astrogliosis, impaired neurogenesis, lipid dysregulation and hippocampal-dependent memory loss. Conversely, boosting Pla2g2f in DGCs during aging is sufficient to preserve synapses, reduce inflammatory microglia and reactive gliosis, prevent hippocampal-dependent memory impairment and modify trajectory of cognitive decline. Ex vivo, neuronal-PLA2G2F mediates intercellular signaling to decrease lipid droplet burden in microglia. Boosting Pla2g2f expression in DGCs of an aging-sensitive AD model reduces amyloid load and improves memory. Our findings implicate PLA2G2F as a compensatory neuroprotective factor that maintains lipid homeostasis to counteract aging-associated cognitive decline.

neuroscience↗

bioRxiv2024

Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits

Unraveling the regulatory mechanisms that govern complex traits is pivotal for advancing crop improvement. Here we present a comprehensive regulome atlas for rice (Oryza sativa), charting the chromatin accessibility across 23 distinct tissues from three representative varieties. Our study uncovers 117,176 unique open chromatin regions (OCRs), accounting for [~]15% of the rice genome, a notably higher proportion compared to previous reports in plants. Integrating RNA-seq data from matched tissues, we confidently predict 59,075 OCR-to-gene links, with enhancers constituting 69.54% of these associations, including many known enhancer-to-gene links. Leveraging this resource, we re-evaluate genome-wide association study results and discover a previously unknown function of OsbZIP06 in seed germination, which we subsequently confirm through experimental validation. We optimize deep learning models to decode regulatory grammar, achieving robust modeling of tissue-specific chromatin accessibility. This approach allows to predict cross-variety regulatory dynamics from genomic sequences, shedding light on the genetic underpinnings of cis-regulatory divergence and morphological disparities between varieties. Overall, our study establishes a foundational resource for rice functional genomics and precision molecular breeding, providing valuable insights into regulatory mechanisms governing complex traits.

genomics↗