Biology subjects

Min, Y.

Publications and source records attributed to Min, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

ASOFormer: a Transformer-based model for predicting antisense oligonucleotide efficacy to support therapeutic candidate prioritization

Antisense oligonucleotides (ASOs) represent a promising therapeutic modality for RNA-based disease mechanisms, but identifying high-efficacy candidates from large sequence spaces remains a major bottleneck in drug development. Here, we present ASOFormer, a novel Transformer-based neural network that predicts the inhibition efficiency of RNase H-mediated ASOs from sequence, predicted secondary structure, and chemical modification features. ASOFormer was trained on a knockdown efficacy dataset spanning over 170,000 ASO-target pairs compiled largely from published patents. Ablation analysis confirmed that the addition of chemical modification features to the primary sequence drove the largest performance gains, with predicted secondary structure providing complementary benefit when combined with modification information. Further analysis highlighted the importance of the wings of gapmer ASOs and other features. As independent validation, ASOFormer was applied to ASOs targeting seven genes that were not in the training dataset gene list. For four out of the seven genes, the ground truth ASO inhibition efficiency was obtained from a public dataset, whereas the others were measured in-house via qPCR. Compared to two published state-of-the-art methods, ASOFormer achieved the best overall accuracy and prioritized the strongest inhibitors. Crucially, only ASOFormer consistently exceeded random expectation for top-candidate recovery across all seven test genes.

bioinformatics↗

bioRxiv2026

OmniSyn unifies target-aware molecular generation and optimization within a synthesis-native LLM framework across the human proteome

Designing target-specific bioactive molecules with actionable synthesis routes for the human proteome holds enormous potential for expanding therapeutic discovery, but remains a challenge. Existing target-aware generative models often depend on protein structures and generate molecules before assessing synthetic feasibility. Here we present OmniSyn, a protein-sequence-conditioned Mixture-of-Experts (MoE) language model that couples a task-conditioned interaction module with a synthesis-action decoder to generate molecules with explicit synthesis traces, thereby unifying de novo ligand generation, synthesizability projection and hit-to-lead (H2L) optimization within synthesis-traceable chemical space. OmniSyn is pre-trained with self-distillation and post-trained with task-specific reinforcement learning (RL) to adapt expert routing and optimize molecular properties across design modes. On unseen protein targets from MolGenBench, a real-world drug-discovery benchmark, OmniSyn achieves state-of-the-art performance across de novo design and H2L optimization, including target-awareness and hit-rediscovery metrics, despite relying only on protein sequences rather than three-dimensional (3D) pocket structures. By embedding synthesis planning into the design process, OmniSyn shifts molecular generation from a generate-then-filter paradigm toward design-with-synthesis paradigm, transforming virtual predictions into experimentally actionable candidates. Independent AiZynthFinder evaluation yielded retrosynthetic success rates of 68.47% for de novo generation and 71.92% for H2L optimization, improving over the strongest baselines by 61.3% and 184.2%, respectively, and supporting the synthetic feasibility of OmniSyn-generated molecules. In synthesizability projection, OmniSyn further converts outputs from external generative models into close analogues with improved retrosynthetic feasibility while preserving molecular similarity. Having established strong benchmark performance and external retrosynthetic feasibility, we next applied OmniSyn at human-proteome scale, spanning more than 21,000 targets. Rapid sequence-conditioned sampling enabled the construction of, to our knowledge, the largest human-proteome-scale generative virtual library, comprising 2.7 billion target-specific molecules, each accompanied by model-derived synthesis traces and target-specific prioritization scores. By enabling scalable target-specific molecular design with synthesis-aware generation across the human proteome, OmniSyn opens new opportunities for exploring previously inaccessible therapeutic targets, including those lacking experimentally resolved structures.

bioinformatics↗

bioRxiv2026

AFP-R: An Open Resource Dedicated to Antifreeze Proteins

Antifreeze proteins (AFPs), lower the freezing point via thermal hysteresis activity and/or ice recrystallization inhibition, playing a crucial role in protecting organisms from freezing damage under sub-zero milieu. This property endows them with promising applications in biomedicine and agriculture, ranging from tissue-organ cryopreservation to the development of frost-resistant crops. However, the lack of comprehensive resources dedicated for AFPs hinders further progress in elucidating their functional mechanisms and advancing their applications. Here, we report AFP-R, an online resource comprising AFP-DB and AFP-Predictor. AFP-DB is a comprehensive database with manually curated proteins bearing experimentally validated antifreeze activity derived from published literature, whereas AFP-Predictor is a sequence-based machine-learning model to identify AFPs. AFP-DB stores diverse AFP-related information, including sequences, structures, post-translational modifications, taxonomy and annotations of antifreeze-activity experimental assays. It now holds 186 entries, 607 sub-entries, and 1444 experimental records. AFP-Predictor, an AFP-identification algorithm built on protein language model ESM2 (Evolutionary Scale Modeling2), is trained on data in AFP-DB and outperforms several existing models. This work offers a valuable resource for systematically dissecting the mechanisms underlying AFP antifreeze activity and will facilitate their broader applications.

bioinformatics↗

bioRxiv2026

ProMeta: A meta-learning framework for robust disease diagnosis and prediction from plasma proteomics

The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of [~] 0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts.

bioinformatics↗

bioRxiv2025

PRSNet-2: End-to-end genotype-to-phenotype prediction via hierarchical graph neural networks

The emergence of large-scale biobanks has opened unprecedented opportunities for the development of data-driven approaches, especially deep learning-based methods, for genotype-to-phenotype (G2P) prediction. However, designing an end-to-end framework capable of directly leveraging extremely high-dimensional genotypic data while simultaneously mitigating overfitting and ensuring robust prediction remains a significant challenge. In this study, we introduce PRSNet-2, an interpretable end-to-end deep learning framework designed to predict complex phenotypes directly from large-scale genotypic data. PRSNet-2 proposes a novel hierarchical graph neural network (GNN) architecture, which first employs a multi-kernel aggregator to map high-dimensional genotypic features to gene-level representations. Next, it models gene-gene interactions through message-passing operations and uses an attention-based readout module to generate interpretable phenotypic predictions. We further introduce significance-guided regularization strategies to boost models generalizability based on prior genetic associations. Extensive empirical evaluations across multiple complex traits and diseases demonstrate that PRSNet-2 consistently outperforms a variety of baseline methods and exhibits superior capabilities in overcoming overfitting, even when modeling around half a million single nucleotide polymorphisms (SNPs). Moreover, PRSNet-2 is shown to be easily extendable to integrate multiple genome-wide association study (GWAS) datasets into a single model, thereby enhancing predictive performance for both single-phenotype and multi-phenotype prediction tasks. The inherent interpretability of PRSNet-2 further facilitates the identification of disease-relevant genes, functional gene modules, and potential therapeutic targets. In summary, PRSNet-2 offers a powerful, versatile tool for both genetic risk stratification and the discovery of biological insights.

genetics↗

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

Illuminating the Virosphere's Dark Matter using Hierarchical Deep Learning

Systematic discovery of novel viruses is essential for pandemic preparedness, understanding tumor-associated viruses, developing viral delivery systems, and advancing biomedical applications. Yet, the majority of sequences in metagenomic datasets lack close relatives in existing references, representing a vast viral "dark matter" whose biology and evolution remain largely unknown. The central task is threefold: 1) to determine whether a genome is viral or non-viral, 2) to correctly assign viral genomes to known lineages when possible, and, critically, 3) to recognize when no existing lineage applies and thereby identify candidates for entirely novel viral groups. Existing approaches, which depend on sequence homology or narrow markers, struggle to capture this uncharted viral space. Here we present DeepVirus, a hierarchical transformer-based framework that models viral genomes as structured sequences of protein-coding genes. By combining protein-level embeddings from a foundation model with genome-aware representations, DeepVirus not only achieves accurate classification across deep taxonomic hierarchies, but also extends beyond conventional classification to detect and organize candidate novel viral lineages through open-set recognition. Applied to large-scale metagenomic resources, DeepVirus uncovered extensive viral diversity, including previously uncharacterized RNA-dependent RNA polymerases (RdRps), thereby expanding the known evolutionary space of RNA viruses. DeepVirus integrates deep learning with genome-aware open-set discovery to illuminate viral dark matter, providing a foundation for systematic viral taxonomy and advancing exploration of the global virosphere, with broad implications for safeguarding human health.

bioinformatics↗

bioRxiv2025

Evolutionary innovation within conserved gene regulatory networks underlying biomineralized skeletons in Bilateria

Biomineralized skeletons have evolved convergently across animals, yet exhibit remarkable diversity in structure and development. However, the evolutionary origins of gene regulatory networks underlying biomineralized skeletons remains elusive. Here, we report comprehensive developmental profiling of transcriptomic and chromatin dynamics in a bivalve mollusc, Crassostrea nippona. We provide evidence for a biphasic regulatory program orchestrating larval and adult shell formation, involving the coordinated activity of ancient transcription factors and dynamic chromatin remodeling. Comparative analyses suggest a conserved developmental toolkit was co-opted for larval exoskeleton formation in the common lophotrochozoan ancestor. In contrast, limited regulatory conservation was observed between lophotrochozoans and echinoderms with regards to the formation of biomineralized skeletons, despite both relying on a heterochronic activation of ancestral regulators. Together, our findings support a hierarchical model where dynamic chromatin decouples rapidly evolving effectors from deeply conserved regulators, allowing modular innovations within conserved gene regulatory networks. This study highlights how epigenetic dynamics bridge evolutionary conservation and novelty, offering a framework for understanding the independent evolution of biomineralization across Bilateria through combinatorial regulatory evolution.

evolutionary biology↗

bioRxiv2025

STARNet enables spatially resolved inference of gene regulatory networks from spatial multi-omics data

Biological tissues are composed of distinct microenvironments that spatially orchestrate gene expression and cell identity. However, the regulatory principles governing domain-specific cellular functions remain poorly understood due to the lack of effective methods for mapping gene regulatory networks (GRNs) in situ. To address this gap, we introduce STARNet, a representation learning approach that leverages heterogeneous hypergraph modeling of spatial transcriptomic and epigenomic data to resolve tissue-domain-specific regulatory interactions. By integrating graph neural networks with contrastive learning in a self-supervised framework, STARNet learns unified embeddings that preserve both multi-modal molecular features and anatomical spatial context, enabling accurate and domain-resolved GRN reconstruction within complex tissues. Benchmarking on both simulated and real datasets demonstrates that STARNet achieves state-of-the-art performance. We further demonstrate its broad applicability across diverse biological contexts, including neural development, genetic disease risk, and drug-induced developmental toxicity. In the mouse brain, it delineates region-specific regulatory networks and reconstructs spatiotemporal programs underlying neural stem cell differentiation. In human genetics, it provides a mechanistic link between genotypes and phenotypes by showing how genome-wide association study (GWAS) variants for complex diseases perturb hippocampus-specific GRNs. In developmental toxicology, STARNet reveals that drug-induced disruptions of GRNs in defined embryonic regions underlie tissue-specific vulnerability. Collectively, STARNet offers a powerful and versatile framework for resolving the spatial regulatory logic of complex tissues, providing multi-angle insights into tissue patterning, development, and disease mechanisms.

bioinformatics↗

bioRxiv2025

The Moment Symmetry Breaks: Spatiotemporal Dynamics of CYCLOIDEA Expression During Early Floral Development

The establishment of bilateral symmetry in flowers depends on the precise regulation of CYCLOIDEA (CYC) gene expression along the dorsal-ventral axis. Although auxin and BLADE-ON-PETIOLE (BOP) have been implicated as regulators of CYC, when, where, and how they affect CYC expression remains unclear. Here, we combined transgenic manipulation and fluorescent confocal imaging to capture the spatiotemporal dynamics of the Mimulus parishii CYC genes (MpCYC2a and MpCYC2b) in relation to FM growth and auxin activity maxima in both wild type (WT) and bop mutants (mpbop). Strikingly, MpCYCs have already gained dorsal expression in the FM prior to any detectable auxin maxima, and no difference in MpCYC expression was observed between the wild type and mpbop during this initiation phase. We observed highly dynamic auxin maxima during FM expansion, when MpCYC expressions remained dorsally restricted in WT but expanded broadly in mpbop FMs. These findings suggest that early symmetry breaking in the FM is guided by positional cues independent of auxin or BOP, which are instead essential for refining and maintaining dorsal-specific CYC expression during later FM enlargement. Our work illustrated how combining advanced imaging with emerging model systems can yield fresh insights into long-standing questions in development and evolution, and laid the groundwork for further elucidating the mechanisms underlying the repeated symmetry breaking in FMs.

developmental biology↗

bioRxiv2025

Tau-seed interactome analysis reveals distinct functional signatures in Alzheimer's disease across model systems.

Tau aggregates propagate through the brain in a prion-like manner in Alzheimers disease (AD) and other tauopathies, but the molecular identity and functional partners of the seeding-competent Tau species remain poorly defined. Here, we present an unbiased proteomic profiling of a high-molecular-weight (HMW) Tau-seed isolated from AD patient brains. We contrast this interactome with that of a biochemically similar, seeding-incompetent HMW-Tau species from age-matched healthy controls. Despite comprising less than 5% of total Tau in the brain, Tau-seed associates with a distinct set of proteins enriched in synaptic, mitochondrial, and vesicle-trafficking functions. Cross-species functional screening in Drosophila and mouse models identifies interactors that modulate Tau toxicity and seeding. Spatially resolved analysis of postmortem AD brains reveals heterogenous co-deposition of these proteins with Tau aggregates, suggesting functionally distinct Tau-seed complexes. Together, this dataset provides a framework for understanding selective Tau-seed toxicity and identifies candidate regulators of Tau propagation with therapeutic potential.

neuroscience↗

bioRxiv2025

Integrative Epigenomic Landscape of Alzheimer's Disease Brains Reveals Oligodendrocyte Molecular Perturbations Associated with Tau

Alzheimers disease (AD) brains are characterized by neuropathologic and biochemical changes that are highly variable across individuals. Capturing epigenetic factors that associate with this variability can reveal novel biological insights into AD pathophysiology. We conducted an epigenome-wide association study of DNA methylation (DNAm) in 472 AD brains with neuropathologic measures (Braak stage, Thal phase, and cerebral amyloid angiopathy score) and brain biochemical levels of five proteins (APOE, amyloid-{beta} (A{beta})40, A{beta}42, tau, and p-tau) core to AD pathogenesis. Using a novel regional methylation (rCpGm) approach, we identified 5,478 significant associations, 99.7% of which were with brain tau biochemical measures. Of the tau-associated rCpGms, 93 had concordant associations in external datasets comprising 1,337 brain samples. Integrative transcriptome-methylome analyses uncovered 535 significant gene expression associations for these 93 rCpGms. Genes with concurrent transcriptome-methylome perturbations were enriched in oligodendrocyte marker genes, including known AD risk genes such as BIN1, myelination genes MYRF, MBP and MAG previously implicated in AD, as well as novel genes like LDB3. We further annotated the top oligodendrocyte genes in an additional 6 brain single cell and 2 bulk transcriptome datasets from AD and two other tauopathies, Picks disease and progressive supranuclear palsy (PSP). Our findings support consistent rCpGm and gene expression associations with these tauopathies and tau-related phenotypes in both bulk brain tissue and oligodendrocyte clusters. In summary, we uncover the integrative epigenomic landscape of AD and demonstrate tau-related oligodendrocyte gene perturbations as a common potential pathomechanism across different tauopathies.

genomics↗

bioRxiv2024

Directing cellular transitions on gene graph-enhanced cell state manifold

A select few genes act as pivotal drivers in the process of cell state transitions. However, finding key genes involved in different transitions is challenging. To address this problem, we present CellNavi, a deep learning-based framework designed to predict genes that drive cell state transitions. CellNavi builds a driver gene predictor upon a cell state manifold, which captures the intrinsic features of cells by learning from large-scale, high-dimensional transcriptomics data and integrating gene graphs with directional connections. Our analysis shows that CellNavi can accurately predict driver genes for transitions induced by genetic, chemical, and cytokine perturbations across diverse cell types, conditions, and studies. By leveraging a biologically meaningful cell state manifold, it is proficient in tasks involving critical transitions such as cellular differentiation, disease progression, and drug response. CellNavi represents a substantial advancement in driver gene prediction and cell state manipulation, opening new avenues in disease biology and therapeutic discovery.

cell biology↗

bioRxiv2024

Limitations of the radiotheranostic concept in neuroendocrine tumors due to lineage-dependent somatostatin receptor expression on hematopoietic stem and progenitor cells

Radiopharmaceutical therapy (RPT) has become an effective treatment option for neuroendocrine tumors (NETs) and castration-resistant prostate cancer and is in clinical development for a growing number of indications. One of the major advantages of theranostic RPT is that the distribution of radiopharmaceuticals in the human body can be imaged, and radiation doses to the patients organs can be calculated. However, accurate dosimetry may be fundamentally limited by microscopic heterogeneity of radiopharmaceutical distribution. We developed fluorescent analogs of somatostatin-receptor-subtype 2 (SSTR2) targeting Lutetium-177 labelled radiopharmaceuticals that are clinically used in patients with neuroendocrine tumors (NETs) and studied their uptake by hematopoietic stem and progenitor cells (HSPC). Hematopoietic stem cells (HSCs) and multipotent progenitor cells (MPPs) showed high and specific SSTR2-ligand uptake, which was at similar levels as neuroendocrine tumor cells. Furthermore, they displayed a several-fold higher uptake of SSTR2-antagonists than of SSTR2-agonists. HSPC treatment with a 177Lu-labelled antagonist and agonist showed a stronger reduction of HSC proliferation by the antagonist. Due to the scarcity of HSCs and MPPs, their contribution to total bone marrow uptake of SSTR2-ligands is not detectable in imaging-based dosimetry studies. This likely explains why SSTR2-antagonists caused pancytopenia in clinical trials despite safe dosimetry estimates. In conclusion, target expression heterogeneity can lead to underestimation of radiopharmaceutical toxicity and should be considered when designing clinical trials for new radiopharmaceuticals. The implications of our findings go beyond somatostatin receptor-targeted radiopharmaceuticals and suggest more generally that first-in-human studies should not only be guided by radiation dosimetry from imaging studies but should also include careful escalation of the administered therapeutic activity. The MMC technology is modular and can be applied to other peptide or protein-based radiopharmaceuticals to study cellular distribution and potential bone marrow uptake prior to clinical testing.

cancer biology↗

bioRxiv2024

ABCA7-dependent Neuropeptide-Y signalling is a resilience mechanism required for synaptic integrity in Alzheimer's disease

Alzheimers disease (AD) remains a complex challenge characterized by cognitive decline and memory loss. Genetic variations have emerged as crucial players in the etiology of AD, enabling hope for a better understanding of the disease mechanisms; yet the specific mechanism of action for those genetic variants remain uncertain. Animal models with reminiscent disease pathology could uncover previously uncharacterized roles of these genes. Using CRISPR/Cas9 gene editing, we generated a knockout model for abca7, orthologous to human ABCA7 - an established AD-risk gene. The abca7+/- zebrafish showed reduced astroglial proliferation, synaptic density, and microglial abundance in response to amyloid beta 42 (A{beta}42). Single-cell transcriptomics revealed abca7-dependent neuronal and glial cellular crosstalk through neuropeptide Y (NPY) signaling. The abca7 knockout reduced the expression of npy, bdnf and ngfra, which are required for synaptic integrity and astroglial proliferation. With clinical data in humans, we showed reduced NPY in AD correlates with elevated Braak stage, predicted regulatory interaction between NPY and BDNF, identified genetic variants in NPY associated with AD, found segregation of variants in ABCA7, BDNF and NGFR in AD families, and discovered epigenetic changes in the promoter regions of NPY, NGFR and BDNF in humans with specific single nucleotide polymorphisms in ABCA7. These results suggest that ABCA7-dependent NPY signaling is required for synaptic integrity, the impairment of which generates a risk factor for AD through compromised brain resilience. O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/573893v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@eecf7forg.highwire.dtl.DTLVardef@78d8e0org.highwire.dtl.DTLVardef@1e65d71org.highwire.dtl.DTLVardef@100db01_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

neuroscience↗

bioRxiv2023

Platelet-specific TGFβ1 deficiency aggravates atherosclerosis, vascular inflammation, and hypercholesterolemia in mice

Atherosclerosis involves inflammatory and thrombotic mechanisms, to which both platelets and transforming growth factor {beta} (TGF{beta}) contribute. The effect of platelet-derived TGF{beta} on atherosclerosis is, however, unknown and therefore investigated. Murine platelet-selective TGF{beta}-deficiency (plt-TGF{beta}-/-) was created by a Pf4-Cre approach, and an atherosclerotic mouse model was established by functional abrogation of Ldlr and 10-15 weeks of a high-fat diet in plt-TGF{beta}-/- mice and their non-plt-TGF{beta}-/- littermates. En face Oil Red O staining of the aorta showed more atherosclerotic lesion formation in plt-TGF{beta}-/- mice, with significant increases in both lesion size and lesion coverage of the total aortic area. Cryosections of the aortic root confirmed the aggravation of atherogenesis. Platelet-derived TGF{beta} deficiency increased circulating platelets and plasma levels of total cholesterol, LDL-cholesterol, and triglycerides after a 10 or 15 week high-fat diet period. RNA sequencing and proteomic analyses of the aorta showed signs of CD4+ T effector cell and macrophage activation in plt-TGF{beta}-/- mice. In conclusion, platelet-specific TGF{beta} deficiency aggravates atherosclerosis, via increasing arterial inflammation and plasma levels of cholesterol. Our findings demonstrate that platelet-derived TGF{beta} is prominently athero-protective. Key pointsO_LIPlatelet-specific transforming growth factor {beta} (TGF{beta}) deficiency markedly enhances atherosclerosis in a high-fat diet-fed murine model. C_LIO_LIPlatelet TGF{beta} deficiency aggravates hyperlipidemia, with further elevations of total cholesterol, LDL-cholesterol, and triglycerides. C_LI

cell biology↗

bioRxiv2023

H2A.X mutants exhibit enhanced DNA demethylation in Arabidopsis thaliana

H2A.X is an H2A variant histone in eukaryotes, unique for its ability to respond to DNA damage, initiating the DNA repair pathway. H2A.X replacement within the histone octamer is mediated by the FAcilitates Chromatin Transactions (FACT) complex, a key chromatin remodeler. FACT is required for DEMETER (DME)-mediated DNA demethylation at certain loci in Arabidopsis thaliana female gametophytes during reproduction, though it is not known how FACT targets DME sites. Here, we investigated whether H2AX is involved in DME- and FACT-mediated DNA demethylation during Arabidopsis reproduction. We show that h2a.x mutants are more sensitive to genotoxic stress, consistent with previous reports. H2A.X fused to the Green Fluorescent Protein (GFP) gene under the H2A.X promoter was highly expressed in newly developing Arabidopsis tissues, including in male and female gametophytes. We examined DNA methylation in h2a.x developing seeds using whole genome bisulfite sequencing, and found that CG DNA methylation in the developing endosperm, but not the embryo, is decreased genome-wide in h2a.x mutants, predominately in transposons and intergenic DNA. Hypomethylated sites overlapped 62 % with canonical DME loci. These data indicate that H2A.X is not required for DME function, but is important for DNA methylation homeostasis in the unique chromatin environment of Arabidopsis endosperm.

plant biology↗

bioRxiv2021

Single Nuclei Transcriptome Reveals Perturbed Brain Vascular Molecules in Alzheimer's Disease

Blood-brain barrier (BBB) dysfunction is well-known in Alzheimers disease (AD), but the precise molecular changes contributing to its pathophysiology are unclear. To understand the transcriptional changes in brain vascular cells, we performed single nucleus RNA sequencing (snRNAseq) of temporal cortex tissue in 24 AD and control brains resulting in 79,751 nuclei, 4,604 of which formed three distinct vascular clusters characterized as activated pericytes, endothelia and resting pericytes. We identified differentially expressed genes (DEGs) and their enriched pathways in these clusters and detected the most transcriptional changes within activated pericytes. Using our data and a knowledge-based predictive algorithm, we discovered and prioritized molecular interactions between vascular and astrocyte clusters, the main cell types of the gliovascular unit (GVU) of the BBB. Vascular targets predicted to interact with astrocytic ligands have biological functions in signalling, angiogenesis, amyloid {beta} metabolism and cytoskeletal structure. Top astrocytic and vascular interacting molecules include both novel and known AD risk genes such as APOE, APP and ECE1. Our findings provide information on transcriptional changes in predicted vascular-astrocytic partners at the GVU, bringing insights to the molecular mechanisms of BBB breakdown in AD. Graphical AbstractPericytes (yellow), endothelia (salmon) and astrocytes (purple) that form the gliovascular unit (GVU) at the blood brain barrier (BBB) were interrogated for their differentially expressed genes (DEG) and vascular cell (pericyte or endothelia) to astrocyte interactions using single nucleus RNA sequencing (RNAseq) transcriptome obtained from brains of Alzheimers disease (AD) patients and controls. We identified many upregulated (red) or downregulated (blue) DEGs in AD brains in these cell types. These genes have known biological functions in amyloid {beta} (A{beta}) clearance, immune modulation, astrogliosis and neuronal death. Novel predicted interactions were identified between vascular cells and astrocytic DEGs. Collectively, our findings highlight the vast transcriptome changes that occur at the GVU and provide mechanistic insights into BBB dysfunction in AD. This figure was created with Biorender.com. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/474255v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@f48748org.highwire.dtl.DTLVardef@1ddc3f4org.highwire.dtl.DTLVardef@6bfa5eorg.highwire.dtl.DTLVardef@1024506_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗