Biology subjects

Yuan, Z.

Publications and source records attributed to Yuan, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Deciphering the Cell Specific Transcript Heterogeneity and Alternative Splicing during the Early Embryonic Development of Zebrafish

Cell fate determination during early embryonic development is a complex process modulated by gene expression. The intricate interplay of transcriptional and post-transcriptional regulation is integral to the developmental trajectory of embryogenesis, yet how RNA processing may contribute to early development programming is largely elusive. Leveraging recent technological advances in single-molecule nanopore sequencing, we developed a single-cell long-read transcriptome sequencing technology, allowing a clear view of transcript diversity during zebrafish embryogenesis during pre- and post-zygotic genome activation (ZGA). A closer examination of the dynamic transcript usage and potential alternative splicing revealed that abundant stage-specific transcripts with differential coding potentials are involved in distinct biological functions. Specifically, we identified two cell populations at the onset of ZGA based on isoform diversity instead of gene profiling, which followed divergent developmental trajectories toward the ectoderm and the presumptive ectoderm. These two populations of cells were characterized by divergent splicing regulations linked to differential RNA-binding proteins, including SNRPA and SFPQ. Altogether, using the single-cell long-read transcriptome sequencing strategy, we work has revealed the cell-specific transcriptome dynamics contributing to the cell fate determination during embryogenesis.

developmental biology↗

bioRxiv2024

Wall teichoic acids regulate peptidoglycan synthesis by paving cell wall microstructure

The cell wall is a polymeric exoskeleton that defines the size and shape of bacteria; it is composed of peptidoglycan and, in Gram-positive bacteria, wall teichoic acid polymers. Two systems synthesize peptidoglycan in rod-shaped bacteria, which are found pervasively across bacterial clades: the multi-protein Rod complexes synthesize anisotropic peptidoglycan, which is required for rod shape because it reinforces the cell wall along its circumference1-3, whereas the non-essential enzyme PBP1 synthesizes isotropic peptidoglycan1,4 (Fig. 1A). In Gram-positive bacteria, rod shape also requires wall teichoic acids5 for unknown reasons. Here, we show that wall teichoic acids promote rod shape by preventing the formation of nanoscopic pores in the Bacillus subtilis cell wall, which lead to amorphous growth by activating PBP1 and inhibiting Rod complexes. Depleting wall teichoic acids resulted in pores within minutes, coinciding with a rapid increase in PBP1-mediated synthesis. PBP1s ability to sustain teichoic acid-less growth depended on its intrinsically disordered domain. In contrast to previous steady-state measurements6,7, we found that wall teichoic acid depletion caused the transient arrest of Rod complexes prior to the onset of amorphous growth. Finally, one of the two synthetically lethal cell wall hydrolases in B. subtilis8, LytE, became essential during wall teichoic acid depletion, meaning that PBP1 and LytE execute a novel, amorphous mode of growth. Collectively, our results identify the cell wall, via its molecular-scale structure, as a non-canonical auto-regulator of its own synthesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/610702v3_fig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@154996forg.highwire.dtl.DTLVardef@12561c3org.highwire.dtl.DTLVardef@1356cacorg.highwire.dtl.DTLVardef@719d22_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. 1:C_FLOATNO Inhibiting wall teichoic acid synthesis decreases Rod complex activity prior to cell shape loss.(A) Schematic of Gram-positive cell wall synthesis. (B) Cell length growth rate [Figure 1] for wild-type cells during 0.5 g/mL tunicamycin treatment. Blue line shows smoothed population median, error bars show standard deviation. Data shown for 2,048 discrete cell tracks from 3 biological replicates. Inset: micrographs taken (i) before and (ii) after 45 min tunicamycin treatment. Scale bars 5 m. (C) Time course for the percentage of processive Mbl filaments during 0.5 g/mL tunicamycin treatment. Error bars show 95% confidence intervals across biological replicates (bootstrap analysis). Analysis performed on 62,901 discrete filament tracks from 4 biological replicates. Inset shows representative fluorescent kymographs of Mbl motion across the cell waist in (i) LB and (ii) after 30 min tunicamycin treatment. Red dotted line follows a processive Mbl filament. Scale bars 1 m. (D) Mean fluorescent puncta per unit cell length for wild-type, {Delta}ponA and ponATP-cells during tunicamycin treatment, labeled with fluorescent D-amino acids. Error bars show 95% confidence intervals. Analysis performed over 2,017 segmentations from 4 biological replicates (wild-type), 857 segmentations from 3 biological replicates ({Delta}ponA) and 257 cells from 2 biological replicates (ponATP-). All timepoints show statistical significance for difference in mean between cell types (Students T-test, P<0.01). Dots show individual replicates. Measurements at 50 min tunicamycin treatment used a lower exposure time so are plotted separately. Inset shows micrographs of (i,iii,v) wild-type and (ii,iv,vi) {Delta}ponA cells at (i,ii) 0 min, (iii,iv) 30 min, and (v,vi) 50 min of treatment. Micrographs are identically saturated across cell types. Scale bars 5 m. (E) Percentage of peptidoglycan subunits in monomers, measured by HPLC (Methods). Two biological replicates per strain. Mean values calculated across biological replicates. Error bars show 95% confidence intervals. Individual datapoints shown in red. Statistical significance calculated using Students T-test, P<0.05. C_FIG

microbiology↗

bioRxiv2024

Identifying Differential Spatial Expression Patterns across Different Slices, Conditions and Developmental Stages with Interpretable Deep Learning

Spatially resolved transcriptomics technologies enable the mapping of multiplexed gene expression profiles within tissue contexts. To explore the gene spatial patterns in complex tissues, computational methods have been developed to identify spatially variable genes within single tissue slices. However, there is a lack of methods designed to identify genes with differential spatial expression patterns (DSEPs) across multiple slices or conditions, which becomes increasingly common in complex experimental designs. The challenges include the complexity of cross-slice gene expression and spatial information modeling, scalability issues in constructing large-scale cell graphs, and mixed factors of inter-slice heterogeneity. We propose DSEP gene identification as a new task and develop River, an interpretable deep learning-based method, to solve this task. River comprises a two-branch prediction model architecture and a post-hoc attribution method to prioritize DSEP genes that explain condition differences. Rivers special design for modeling spatial-informed gene expression makes it scalable to large-scale spatial omics datasets. We proposed strategies to decouple the spatial and non-spatial components of Rivers outcomes. We validated Rivers performance using simulated datasets and applied it to identify DSEP genes/proteins in diverse biological contexts, including embryo development, diabetes-induced alterations in spermatogenesis, and lupus-induced splenic changes. In a human triple-negative breast cancer dataset, River identified generalizable survival-related DSEPs, validated across unseen patient groups. River does not rely on specific data distribution assumptions and is compatible with various spatial omics data types, making it a versatile method for analyzing complex tissue architectures across multiple biological conditions.

bioinformatics↗

bioRxiv2024

Differential regulation of BAX and BAK apoptotic activity revealed by a novel small molecule

Defective apoptosis mediated by BAK or BAX underlies various human pathologies including autoimmune and degenerative conditions. The mitochondrial channel protein VDAC2 interacts with BAK and BAX through a common interface to either inhibit BAK or to facilitate BAX apoptotic activity. Using a newly developed small molecule (WEHI-3773) that inhibits the interaction between VDAC2 and BAK or BAX, we reveal contrasting effects on BAX and BAK apoptotic activity. WEHI-3773 inhibits apoptosis mediated by BAX by blocking VDAC2-mediated BAX recruitment to mitochondria. Conversely, WEHI-3773 primes BAK for apoptosis by impairing its inhibitory sequestration by VDAC2 on the mitochondrial membrane. In cells expressing both BAX and BAK, repressing their association with VDAC2 promotes apoptosis, because once BAK is activated, it further activates BAX through a feed-forward mechanism. In some leukemias, mutation or loss of BAX is a key driver of resistance to the BH3-mimetic anti-cancer drug venetoclax. Strikingly, promoting BAK-mediated killing by small molecule dissociation of the VDAC2 interaction can overcome this resistance in different leukemia models. These data reveal a hitherto unappreciated level of coordination of BAX and BAK apoptotic activity through their interaction with VDAC2 that may be targeted therapeutically.

cell biology↗

bioRxiv2024

Cnidaria XIAP activates caspase-mediated cell death

In vertebrate, X-linked inhibitor of apoptosis (XIAP) is a potent inhibitor of apoptosis. XIAP inhibits apoptosis by interacting with proapoptotic caspases via the baculovirus IAP repeat (BIR) domains and mediating caspase ubiquitination via the really interesting new gene (RING) domain and ubiquitin-associated (UBA) domain. In invertebrate, mostly arthropods, XIAP is also known as an apoptosis inhibitor. To date, no study on basal metazoan XIAP has been documented. In the present work, we examined the biological activity of XIAP from jellyfish Aurelia coerulea (AcXIAP) and other non-bilaterians. AcXIAP possesses three BIRs and one RING domain but lacks the UBA domain. AcXIAP augmented the apoptosis-inducing activity of all of the four A. coerulea caspases, of both the initiator and the effector clades, identified in this study. AcXIAP activated caspase via one of the BIRs, which bound and stabilized the caspase, and the RING domain, which mediated ubiquitination on the p20 subunit of the caspase in a lysine-independent manner. Similar caspase-activating properties were also observed in the XIAP of hydra, coral, and sponge. In hydra, XIAP knockdown markedly decreased cell death induced by an apoptosis inducer. Together these results revealed the unconventional function and working mechanism of XIAP in Cnidaria, and shed new light into the functional and structural evolution of XIAP.

cell biology↗

bioRxiv2024

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

Spatial omics technologies enable the precise detection of proteins and RNAs at high spatial resolution. Designing spatial omics experiments requires careful consideration of "what" targets to measure and "where" to position the field of views (FOVs). Current FOV sampling strategies often involve acquiring densely sampled FOVs and stitching them together, which is time-consuming, resource-intensive, and sometimes impossible. To optimize FOV sampling strategies, we developed SOFisher, a reinforcement learning-based framework that harnesses the knowledge gained from the sequence of previously sampled FOVs to guide the selection of the next FOV position, to improve the efficiency of capturing more regions of interest. We rigorously evaluated SOFishers performance using comprehensive simulations based on real spatial datasets, and our results clearly demonstrated that SOFisher consistently outperformed the conventional approach across various metrics. SOFishers robustness and generalizability were further validated through cross-domain generalization tests and its adaptability to varying FOV sizes. On a real Alzheimers Disease (AD) dataset, SOFisher successfully guided the selection of FOVs containing neurofibrillary tangles and amyloid-{beta} plaques in both single and dual target tissue landmark scenarios. Remarkably, SOFisher-guided experiment design of spatial single-omics on limited tissue areas yielded insights into AD-related cell states, subtypes, and gene programs previously obtained through extensive spatial multi-omics experiments. SOFisher has the potential to revolutionize the experiment design of spatial biology.

bioinformatics↗

bioRxiv2024

scCancerExplorer: a comprehensive database for interactively exploring single-cell multi-omics data of human pan-cancer

Genomic, epigenomic, and transcriptomic alterations are hallmarks of cancer cells, and are closely connected. Especially, epigenetic regulation plays a critical role in tumorigenesis and progression. The growing single-cell epigenome data in cancer research provide new opportunities for data mining from a more comprehensive perspective. However, there is still a lack of databases designed for interactively exploring the single-cell multi-omics data of human pan-cancer, especially for the single-cell epigenome data. To fill in the gap, we developed scCancerExplorer, a comprehensive and user-friendly database to facilitate the exploration of the single-cell genome, epigenome (chromatin accessibility and DNA methylation), and transcriptome data of 50 cancer types. Five major modules were provided to explore those data interactively, including "integrated multi-omics analysis", "single-cell transcriptome", "single-cell epigenome", "single-cell genome", and "TCGA analysis". By simple clicking, users can easily investigate gene expression features, chromatin accessibility patterns, transcription factor activities, DNA methylation states, copy number variations, and TCGA survival analysis results. Taken together, scCancerExplorer is distinguished from previous databases with rich and interactive functions for exploring the single-cell multi-omics data of human pan-cancer. It bridges the gap between single-cell multi-omics data and the end-users, and will facilitate progress in the field of cancer research. scCancerExplorer is freely accessible via https://bianlab.cn/scCancerExplorer.

cancer biology↗

bioRxiv2024

Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi

Tissues are composed of heterogeneous cell niches, which can be investigated using spatial omics technologies. Large consortia have accumulated vast amounts of spatially resolved data, which typically assign slice-level condition labels without considering intra-slice heterogeneity, particularly differential cell niches that respond to certain perturbations. Here, we present Taichi, an efficient and scalable method for condition-relevant cell niche analysis that does not rely on pre-defined discrete spatial clustering. Taichi utilizes a scalable spatial co-embedding approach that effectively accounts for batch effects, incorporating advanced label refinement and graph heat diffusion techniques to explore condition-relevant cell niches across extensive multi-slice and multi-condition spatial omics datasets. Comprehensive benchmarks demonstrate Taichis ability to precisely identify condition-relevant niches under various levels of perturbations. We showcase Taichis effectiveness in accurately delineating major shifts in cell niches in a mouse model of diabetic kidney disease compared to a normal group, revealing disease-specific cell-cell interactions and spatial gene expression patterns. Furthermore, Taichi can identify key subtype-relevant niches between colorectal cancer patient groups with significantly different survival outcomes. Moreover, we demonstrate that Taichi can help discover more fine-grained clinical properties within the originally coarse-defined patient groups in large-scale tumor spatial atlases, reflecting intra-group heterogeneity obscured previously. Additionally, we combine Taichi and tensor decomposition to discover higher-order biomarkers relevant to the immunotherapy response of triple-negative breast cancer. Finally, we highlight Taichis speed and scalability by confirming its unique applicability in large-scale scenarios containing up to 16 million cells in [~] 12 minutes. Taichi provides a powerful tool for mining disease-relevant spatially resolved insights in the era of big data in spatial biology.

bioinformatics↗

bioRxiv2024

Unveiling Tissue Structure and Tumor Microenvironment from Spatially Resolved Transcriptomics by Hypergraph Learning

Spatial omics technologies have revolutionized life sciences by enabling the simultaneous acquisition of biomolecular and spatial information. Identifying spatial patterns is crucial for understanding organ development and tumor microenvironments. However, the emergence of diverse spatial omics resolutions in these technologies has made it challenging to accurately characterize spatial domains at finer resolutions. To address this, we propose HyperSTAR, a hypergraph-based method designed to precisely identify spatial domains across varying resolutions by leveraging higher-order relationships among spatially adjacent tissue programs. Specifically, a gene expression-guided hyperedge decomposition module is introduced to refine the hypergraph structure to accurately delineate spatial domains boundaries. Additionally, a hypergraph attention convolutional neural network is designed to adaptively learn the importance of each hyperedge, enhancing the models ability to capture complex higher-order relationships within spatially neighboring multi-spots and/or single cells. HyperSTAR outperforms existing graph neural network models in tasks such as uncovering tissue substructures, inferring spatiotemporal patterns, and denoising spatially resolved gene expressions. It effectively handles diverse spatial omics data types and scales seamlessly to large datasets. The method successfully reveals spatial heterogeneity in breast cancer sections, with findings validated through functional and survival analyses of independent clinical data. HyperSTAR represents a significant advancement in spatial omics analysis, representing a robust tool for exploring complex spatial patterns across varying resolutions and data types. Its ability to capture intricate higher-order relationships among spatially neighboring spots/cells makes it an invaluable tool for advancing research in life sciences, particularly in cancer and developmental biology.

bioinformatics↗

bioRxiv2024

GABA and astrocytic cholesterol determine the lipid environment of GABAAR in cultured cortical neurons.

The {gamma}-aminobutyric acid (GABA) type A receptor (GABAAR), a GABA activated pentameric chloride channel, mediates fast inhibitory neurotransmission in the brain. The lipid environment is critical for GABAAR function. How lipids regulate the channel in the cell membrane is not fully understood. Here we employed super resolution imaging of lipids to demonstrate that the agonist GABA induces a rapid and reversible membrane translocation of GABAAR to phosphatidylinositol 4,5-bisphosphate (PIP2) clusters in mouse primary cortical neurons. This translocation relies on nanoscopic separation of PIP2 clusters and lipid rafts (cholesterol-dependent ganglioside clusters). In a resting state, the GABAAR associates with lipid rafts and this colocalization is enhanced by uptake of astrocytic secretions. These astrocytic secretions enhance endocytosis and delay desensitization. Our findings suggest intercellular signaling from astrocytes regulates GABAAR location based on lipid uptake in neurons. The findings have implications for treating mood disorders associated with altered neural excitability.

neuroscience↗

bioRxiv2024

Scale-free dynamics of cerebrospinal fluid regions is associated with Alzheimer's disease-related pathology

Cerebrospinal Fluid (CSF) clearance is one of the central mechanisms for removal of amyloid-{beta} (A{beta}) and tau proteins from the central nervous system that are closely linked to Alzheimers Disease (AD) pathology. Conventional functional imaging studies in AD have primarily focused on activities in the brain while ignoring macroscopic cerebrospinal fluid (CSF) activities. In the current study, we utilized a public dataset from the Alzheimers Disease Neuroimaging Initiative. Our analysis spanned both brain and cerebrospinal fluid (CSF) areas. We compared the scale-free dynamics in fMRI signals across three groups: cognitively normal individuals (CN), people with Alzheimers Disease (AD), and those with mild cognitive impairments (MCI). Scale-free dynamics are patterns of brain activity that remain consistent across different time scales. Our comparison focused on the Hurst exponent (H), a measure derived from detrended fluctuation analysis (DFA), to characterize these dynamics. This comparison revealed clusters in the fourth ventricle and subarachnoid space (medioventral channel along the spinal axis). In these CSF-filled structures, H was significantly higher in AD group comparing to CN. Moreover, H in these two clusters correlated with AD pathological biomarkers including CSF A{beta} and tau in the AD group. These findings suggested scale-free properties of macroscopic CSF flow as a potential imaging biomarker for AD. This biomarker can be readily acquired from common resting-state fMRI scans and therefore may be valuable for AD diagnosis.

neuroscience↗

bioRxiv2024

Regional Glymphatic Dysfunction is linked to Spinocerebellar Ataxia Type 3 pathophysiology

Spinocerebellar ataxia type 3 (SCA3) involves neuroinflammation and imbalance between production and clearance of proteins which affects the glymphatic system, the lymphatic-like, fluid-transport system in the brain. However, it is unclear whether SCA3 is related to impairments in glymphatic function. Using multimodal imaging data, 34 SCA3 patients and 36 age-, sex- and educational matched healthy controls (HCs) were compared using multiple glymphatic measurements, including choroid plexus (CP) and cerebrospinal fluid (CSF) volume, diffusion tensor imaging along the perivascular (DTI-ALPS) index, and coupling relationship between blood-oxygen-level-dependent signals and CSF flow (BOLD-CSF coupling). Then, we evaluated regional glymphatic function by dividing DTI-ALPS and BOLD-CSF coupling into anterior, middle, posterior, and cerebellum regions, thereby identifying the spatial variation of glymphatic function in the two groups. We demonstrated that compared with HCs, larger CP and CSF volumes were found in SCA3 patients. More importantly, for DTI-ALPS index and BOLD-CSF coupling, these surrogate markers for glymphatic clearance were weaker in SCA3 patients. Furthermore, altered regional glymphatic functions were most prominent in midbrain, cerebellum and middle regions. Crucially, the altered midbrain, cerebellum, middle and global glymphatic functions were accompanied by the severity of ataxia and other SCA3 symptoms. Similar to other neurodegenerative disorders, the association between multiple glymphatic indexes and SCA3 symptoms suggested that waste clearance is disrupted in SCA3 patients, which shed light on the pathogenesis of this disease from a glymphatic lens. Our findings highlighted the dysregulated glymphatic function as a novel diagnostic marker for SCA3.

neuroscience↗

bioRxiv2024

Dysregulated Neurofluid Coupling as a New Noninvasive Biomarker for Primary Progressive Aphasia

ObjectiveAccumulation of pathological tau is one of the primary causes of Primary Progressive Aphasia (PPA). The glymphatic system is crucial for removing metabolic waste from the brain, whereas the underlying mechanism on the interplay between impairments in glymphatic clearance and PPA is poorly understood. Therefore, the aim of this study is to investigate the role that dysregulated macroscopic cerebrospinal fluid (CSF) movement plays in the pathology of PPA. Methods56 PPA patients and 94 healthy controls (HCs) participated this work. The coupling strength between blood-oxygen-level-dependent (BOLD) signals in the gray matter and CSF flow within the subarachnoid space and ventricular system was calculated by using Pearson correlation and made comparison between the two groups. Its associations with clinical characteristics including scores from Clinical Dementia Rating (CDR), Mini-Mental State Exam, Geriatric Depression Scale and with morphological measures in the hippocampus and entorhinal cortex were also quantified. ResultsThe PPA group exhibited decreased global BOLD and CSF coupling as compared to that of HCs, indicating impaired glymphatic functions of the patients (p = 0.006). More importantly, it was discovered that BOLD-CSF coupling of PPA group rather than that of the HCs demonstrated significant correlations with the CDR scores (p = 0.04), hippocampal volume (p = 0.005), and entorhinal cortex thickness (p = 0.04). InterpretationThe measured decoupling between global brain activation (hemodynamic response) and CSF flow and its association with symptomology and brain structural changes in PPA revealed the glymphatic dysregulation in PPA. Herein, this evidence supports the potential role of BOLD-CSF coupling as a noninvasive biomarker for the detection and prediction of PPA.

neuroscience↗

bioRxiv2024

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value for the characterizing and understanding of tissue architecture. However, the inherent heterogeneity and varying spatial resolutions present challenges in the joint analysis of multi-modal SRT data. We introduce a multi-modal geometric deep learning method, named stMMR, to effectively integrate gene expression, spatial location and histological information for accurate identifying spatial domains from SRT data. stMMR uses graph convolutional networks (GCN) and self-attention module for deep embedding of features within unimodal and incorporates similarity contrastive learning for integrating features across modalities. Comprehensive benchmark analysis on various types of spatial data shows superior performance of stMMR in multiple analyses, including spatial domain identification, pseudo-spatiotemporal analysis, and domain-specific gene discovery. In chicken heart development, stMMR reconstruct the spatiotemporal lineage structures indicating accurate developmental sequence. In breast cancer and lung cancer, stMMR clearly delineated the tumor microenvironment and identified marker genes associated with diagnosis and prognosis. Overall, stMMR is capable of effectively utilizing the multi-modal information of various SRT data to explore and characterize tissue architectures of homeostasis, development and tumor.

bioinformatics↗

bioRxiv2023

Molecular definition of the BAK:VDAC2 interaction as a target to manipulate apoptosis

BAK and BAX execute intrinsic apoptosis by permeabilising the mitochondrial outer membrane. Their activity is regulated through interactions with pro-survival BCL-2 family proteins and with non-BCL-2 proteins including the mitochondrial porin VDAC2. VDAC2 is important for bringing both BAK and BAX to mitochondria where they execute their apoptotic function. Despite this important function in apoptosis, whilst interactions with pro-survival family members are well characterised and have culminated in the development of drugs that target these interfaces to induce cancer cell apoptosis, the interaction between BAK and VDAC2 remains largely undefined. Deep scanning mutagenesis coupled with cysteine linkage identified key residues in the interaction between BAK and VDAC2. Obstructive labelling of specific residues in the BH3 domain or hydrophobic groove of BAK disrupted this interaction. Conversely, mutating specific residues in a cytosol-exposed region of VDAC2 stabilised the interaction with BAK, and inhibited BAK apoptotic activity. Thus, this VDAC2-BAK interaction site can potentially be targeted to either inhibit BAK-mediated apoptosis in scenarios where excessive apoptosis contributes to disease, or to promote BAK-mediated apoptosis for cancer therapy.

cell biology↗

bioRxiv2023

Regulation of blood pressure by METTL3 via RUNX1b-eNOS pathway in endothelial cells

BackgroundEndothelial cells regulate vascular tone to control the blood pressure (BP) by producing both relaxing and contracting factors. Previously, we identified methyltransferase-like 3 (METTL3), a primary N6-methyladenosine (m6A) methyltransferase, as a key player in alleviating endothelial atherogenic progression. However, its involvement in BP regulation remains unclear. MethodsTo evaluate the role of METTL3 in vivo, mice with EC specific METTL3 deficiency (EC-Mettl3KO) with or without Ang II infusion were used to create a hypertensive model. Functional and MeRIP sequencing analysis were performed to explore the mechanism of METTL3-mediated hypertension. ResultsWe observed a reduction in endothelial METTL3 activity by Ang II in vitro and in vivo. Endothelial METTL3-deficient mice exhibited higher BP than controls, both before and after Ang II infusion. Through m6A sequencing and functional analysis, we identified m6A modification of various RUNX1 monomers resulted in endothelial dysfunction. Mutations in the 3'UTR region of RUNX1b abolished its luciferase reporter activity, and enhanced eNOS promoter luciferase reporter activity with or without METTL3 overexpression. Overexpression of METTL3 by adeno-associated virus reduced Ang II-induced BP elevation. ConclusionThis study reveals that METTL3 alleviates hypertension through m6A-dependent stabilization of RUNX1b mRNA, leading to upregulation of eNOS, thus underscoring the pivotal role of RNA transcriptomics in the regulation of hypertension.

cell biology↗

bioRxiv2023

Cooperative Gsx2-DNA Binding Requires DNA Bending and a Novel Gsx2 Homeodomain Interface

The conserved Gsx homeodomain (HD) transcription factors specify neural cell fates in animals from flies to mammals. Like many HD proteins, Gsx factors bind A/T-rich DNA sequences prompting the question -- how do HD factors that bind similar DNA sequences in vitro regulate specific target genes in vivo? Prior studies revealed that Gsx factors bind DNA both as a monomer on individual A/T-rich sites and as a cooperative homodimer to two sites spaced precisely seven base pairs apart. However, the mechanistic basis for Gsx DNA binding and cooperativity are poorly understood. Here, we used biochemical, biophysical, structural, and modeling approaches to (1) show that Gsx factors are monomers in solution and require DNA for cooperative complex formation; (2) define the affinity and thermodynamic binding parameters of Gsx2/DNA interactions; (3) solve a high-resolution monomer/DNA structure that reveals Gsx2 induces a 20{degrees} bend in DNA; (4) identify a Gsx2 protein-protein interface required for cooperative DNA binding; and (5) determine that flexible spacer DNA sequences enhance Gsx2 cooperativity on dimer sites. Altogether, our results provide a mechanistic basis for understanding the protein and DNA structural determinants that underlie cooperative DNA binding by Gsx factors, thereby providing a deeper understanding of HD specificity.

biochemistry↗

bioRxiv2023

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Tracing cellular dynamic changes across conditions, time, and space is crucial for understanding the molecular mechanisms underlying complex biological systems. However, integrating multi-sample data in a unified and flexible way to explore cellular heterogeneity remains a major challenge. Here, we present Stereopy, a flexible and versatile framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this flexible framework, we have developed three key components: a multi-sample tailored data container, a scope controller, and an analysis transformer. Furthermore, Stereopy showcases three transformative applications supported by pivotal algorithms. Firstly, the multi-sample cell community detection (CCD) algorithm introduces an innovative capability to detect specific cell communities and identify genes responsible for pathological changes in comparable datasets. Secondly, the spatially resolved temporal gene pattern inference (TGPI) algorithm represents a notable advancement in detecting important spatiotemporal gene patterns while concurrently considering spatial and temporal features, which enhances the identification of important genes, domains and regulatory factors closely associated with temporal datasets. Finally, the 3D niche-based regulation inference tool, named NicheReg3D, reconstructs the 3D cell niches to enable the inference of cell-gene interaction network within the spatial texture, thus bridging intercellular communications and intracellular regulations to unravel the intricate regulatory mechanisms that govern cellular behavior. Overall, Stereopy serves as both a bioinformatics toolbox and an extensible framework that provides researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data.

bioinformatics↗