Biology subjects

Yu, Z.

Publications and source records attributed to Yu, Z..

At least 145 records · Page 8Linked to original sources

bioRxiv2024

EPOP Restricts PRC2.1 Targeting to Chromatin by Directly Modulating Enzyme Complex Dimerization

Polycomb repressive complex 2 (PRC2) mediates developmental gene repression as two classes of holocomplexes, PRC2.1 and PRC2.2. EPOP is an accessory subunit specific to PRC2.1, which also contains PCL proteins. Unlike other accessory subunits that collectively facilitate PRC2 targeting, EPOP was implicated in an enigmatic inhibitory role, together with its interactor Elongin BC. We report an unusual molecular mechanism whereby EPOP regulates PRC2.1 by directly modulating its oligomerization state. EPOP disrupts the PRC2.1 dimer and weakens its chromatin association, likely by disabling the avidity effect conferred by the dimeric complex. Congruently, an EPOP mutant specifically defective in PRC2 binding enhances genome-wide enrichments of MTF2 and H3K27me3 in mouse epiblast-like cells. Elongin BC is largely dispensable for the EPOP-mediated inhibition of PRC2.1. EPOP defines a distinct subclass of PRC2.1, which uniquely maintains an epigenetic program by preventing the over-repression of key gene regulators along the continuum of early differentiation.

molecular biology↗

bioRxiv2024

Data Release: High-Resolution Imaging and Segmentation of P7 Mouse Tissue Microarchitecture Using FIB-SEM and Machine Learning

This report presents a comprehensive data release exploring the tissue microarchitecture of P7 aged mice using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) combined with machine learning-based segmentations of nuclei. The study includes high-resolution 3D volumes and nucleus segmentations for seven vital tissues--pancreas, liver, kidney, heart, thymus, hippocampus, and skin--from a single mouse. The detailed datasets are openly accessible onOpenOrganelle.org, providing a valuable resource for the scientific community to support further research and collaboration.

cell biology↗

bioRxiv2024

S-RNase Evolution in Self-Incompatibility: Phylogenomic Insights into Synteny with S-Like Genes

S-RNases are essential in the gametophytic self-incompatibility (GSI) system of many flowering plants, where they act as stylar-S determinants. Despite their significance, the syntenic genomic origin and evolutionary trajectory of S-RNase genes in eudicots have remained largely unclear. Here, we performed large-scale phylogenetic and microsynteny network analyses of RNase T2 genes across 130 angiosperm genomes, encompassing 35 orders and 56 families. Based on the connections observed in the synteny network, particularly the persisting synteny block in none-SI Cucurbitaceae species, we infer that well-characterized S-RNase genes (Class III-A RNase genes) were duplicated and evolved from Class I S-like RNase genes, possibly as a result of the gamma triplication event shared by core eudicots. Additionally, we identified frequent lineage-specific gene transpositions of S-RNase genes across diverse angiosperm lineages, including Rosaceae, Solanaceae, and Rutaceae families, accompanied by a significant increase in transposable element (TE) activity near these genes. Our findings delineate the precise genomic origin and evolutionary path of eudicot S-RNase genes, enhancing our understanding of the evolution of the S-RNase-based GSI system.

evolutionary biology↗

bioRxiv2024

RGAST: Relational Graph Attention Network for Spatial Transcriptome Analysis

Cell-cell communication (CCC) plays a fundamental role in tissue organization and function. Recent advances in spatial transcriptomics (ST) technologies have enabled high-resolution mapping of CCC at single-cell level. However, existing computational approaches for CCC inference face several limitations, including reliance on predefined ligand-receptor databases, loss of single-cell resolution, and inability to model long range communications. To address these challenges, we developed RGAST, a deep learning framework that integrates both spatial proximity and transcriptional profiles to reconstruct multi-scale CCC networks de novo. In our analysis, RGAST revealed directional communication from peripheral to central nuclei in mouse hypothalamus and tumor invasion signaling axis in breast cancer. Leveraging a relational graph attention network, RGAST effectively captures both local and global communication patterns while learning low-dimensional representations of ST data, which are versatile in multiple downstream tasks. Our results demonstrate that RGAST enhances spatial domain identification accuracy by approximately 10% compared to the second method in 10X Visium DLPFC dataset. Furthermore, RGAST facilitates the discovery of spatially variable genes, enables more precise cell trajectory inference and reveals intricate 3D spatial patterns across multiple sections of ST data.

bioinformatics↗

bioRxiv2024

Unveiling Fine-scale Spatial Structures and Amplifying Gene Expression Signals in Ultra-Large ST slices with HERGAST

We propose HERGAST, a system for spatial structure identification and signal amplification in ultra-large-scale and ultra-high-resolution spatial transcriptomics data. To handle ultra-large ST data, we consider the divide and conquer strategy and devise a Divide-Iterate-Conque framework specially for spatial transcriptomics data analysis, which can also be adopted by other computational methods for extending to ultra-large-scale ST data analysis. To tackle the potential oversmoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. In simulation, HERGAST consistently outperformed other methods across all settings with more than 10% average gaining. In real-world data, HERGASTs high-precision spatial clustering enabled finding SPP1+ macrophages intermingled in tumors in colorectal cancer, while the enhanced gene expression signal enabled discovering unique spatial expression pattern of key genes in breast cancer.

bioinformatics↗

bioRxiv2024

Synapse weakening-induced caspase-3 activity confers specificity to microglia-mediated synapse elimination

During brain development, synapses are initially formed in excess and are later eliminated in an activity-dependent manner. Weak synapses are preferentially removed, but the mechanism linking neuronal activity to synapse removal is unclear. Here we show that, in the developing mouse visual pathway, inhibiting synaptic transmission induces postsynaptic activation of caspase-3. Caspase-3 deficiency results in defects in synapse elimination driven by both spontaneous and experience-dependent neural activity. Notably, caspase-3 deficiency blocks activity-dependent synapse elimination, as evidenced by reduced engulfment of inactive synapses by microglia. Furthermore, in a mouse model of Alzheimers disease, caspase-3 deficiency protects against synapse loss induced by amyloid-{beta} deposition. Our results reveal caspase-3 activation as a key step in activity-dependent synapse elimination during development and synapse loss in neurodegeneration.

neuroscience↗

bioRxiv2024

Gene expression changes in long-term memory unlikely to replicate in the long term

Identifying the cellular effects of memory-forming experiences on neurons which enable subsequent memory recall is a fundamental aim of neuroscience. The search for the engram could benefit from single cell RNA sequencing, which can estimate the mRNA expression of all genes in large samples of individual brain cells from animals exposed to specific experiences. A recent study used spatial transcriptomics and single cell RNA-sequencing to identify "transcriptional signatures in subpopulations of neurons and astrocytes that were memory-specific and persisted for weeks"1. However, because the authors did not account for multiple statistical comparisons2 and instead used an "unadjusted" threshold for statistical significance, the reported findings are likely dominated by false positives3. Moreover, the statistical analysis treated individual cells as independent samples without accounting for correlations across cells derived from the same biological tissue sample. Reanalysis of the studys data using appropriate, widely accepted statistical procedures, identifies no significant differentially expressed genes. This suggests the data do not support the authors claim to have identified cell type-specific transcriptional signatures of memory in the mouse basolateral amygdala.

neuroscience↗

bioRxiv2024

Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases

Metagenomics, particularly genome-resolved metagenomics, has significantly deepened our understanding of microbes, illuminating their taxonomic and functional diversity and roles in ecology, physiology, and evolutions. However, eukaryotic populations within various microbiomes, including those in the mammalian gastrointestinal (GI) tract, remain relatively underexplored in metagenomic studies due to the lack of comprehensive reference genome databases and robust bioinformatics tools. The GI tract of ruminants, particularly the rumen, contains a high eukaryotic biomass although a relatively low diversity of ciliates and fungi, which significantly impact feed digestion, methane emissions, and rumen microbial ecology. In the present study, we developed GutEuk, a bioinformatics tool that improves upon the currently available Tiara and EukRep in accurately identifying metagenome eukaryotic sequences. GutEuk is optimized for high precision across different sequence lengths. It can also distinguish fungal and protozoal sequences, facilitating further elucidation of their unique ecological and physiological impacts. GutEuk was shown to facilitate a comprehensive analysis of protozoa and fungi within more than one thousand rumen metagenomes, revealing a greater genomic diversity among protozoa than previously documented. We further curated several ruminant eukaryotic protein databases, significantly enhancing our ability to distinguish the functional roles of ruminant fungi and protozoa from those of prokaryotes. Overall, the newly developed package GutEuk and its associated databases create new opportunities for in-depth study of GI tract eukaryotes.

microbiology↗

bioRxiv2024

Grape extract and resveratrol mitigate sleep fragmentation, Aβ accumulation, and abnormal neuronal excitability in a Drosophila model of Alzheimer's disease

Consumption of red wine and grape extracts may offer a range of health benefits, largely attributable to the grapes rich content of vitamins, fiber, and antioxidant compounds, such as polyphenols. To determine if resveratrol (RES) present in grape extracts is responsible for these benefits, we conducted a study on the effects of red grape skin extract (GSKE), seed extract (GSEE), and RES on sleep patterns, amyloid-beta (A{beta}) deposition, neuronal excitability, and lifespan in a Drosophila model expressing A{beta}42. A{beta}42 flies experienced significant sleep fragmentation at night, yet their overall sleep duration was unaffected. Dietary GSKE significantly enhanced sleep duration and mitigated sleep fragmentation in these flies, whereas GSEE only increased the duration of sleep bouts during the day. RES demonstrated a similar effect, albeit to a lesser extent compared to GSKE. All three dietary interventions led to a reduction in A{beta}42 levels and an extension of the lifespan in A{beta}42 flies, with GSEE showing the least pronounced effects. Furthermore, GSEE and RES were able to reverse the hyperexcitability of mushroom body neurons (MBNs) caused by A{beta}42 expression. These results suggest that GSKE and RES are potent promoters of sleep and have the potential to ameliorate sleep disturbances. Additionally, the study highlights that other bioactive component in GSKE, beyond RES, may contribute to its diverse pharmacological activities, which could differ from those of GSEE or RES alone. This underscores the multifaceted nature of grape extracts and their potential therapeutic applications in addressing sleep disorders and neurodegenerative conditions associated with A{beta} deposition.

neuroscience↗

bioRxiv2024

A new alignment-free method: Subsequence Correlation Coefficient Vector(SCCFV) for influenza A comparison using virus genomes

Withdrawal StatementThe authors have withdrawn their manuscript because I discovered some important missing data that is crucial for supporting my viewpoint and research findings. Although I tried to supplement this data through other means, I was unable to complete it before the deadline. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2024

Endogenous FGFs drive ERK-dependent cell fate patterning in 2D human gastruloids

The role of FGF is the least understood of the morphogens driving mammalian gastrulation. Here we investigated the function of FGF in a stem cell model for human gastrulation known as a 2D gastruloid. We found a ring of FGF-dependent ERK activity that closely follows the emergence of primitive streak (PS)-like cells but expands further inward. We showed that this ERK activity pattern is required for PS-like differentiation and that loss of PS-like cells upon FGF receptor inhibition can be rescued by directly activating ERK. We further demonstrated that the ERK-ring depends on localized activation of basolaterally positioned FGF receptors (FGFR) by endogenous FGF gradients. We confirmed and extended previous studies in analyzing expression of FGF pathway components, showing FGFR1 is the main receptor, FGF2 is highly expressed across several cell types, and FGF4/17 are the main FGF ligands expressed in the PS-like cells, similar to the human and monkey embryo but different from the mouse. We found that knockdown of FGF4 greatly reduced PS-like differentiation while FGF17 knockdown primarily affected subsequent mesoderm differentiation. FGF8 expression was spatially displaced from PS-markers and FGF4 expression and peaked earlier, while knockdown led to an expansion in PS-like cells, suggesting FGF8 may counteract FGF4 to limit PS-like differentiation. Thus, we have identified a previously unknown role for FGF-dependent ERK signaling in 2D gastruloids and possibly the human embryo, driven by a mechanism where FGF4 and FGF17 signal through basally localized FGFR1 to induce PS-like cells and their derivatives, potentially restricted by FGF8.

developmental biology↗

bioRxiv2024

Deterministic processes played more important roles than stochastic processes in driving community assembly of ectomycorrhizal fungal associated with Pinus tabuliformis - an endemic Pinaceae species in China

Pinus tabuliformis represents a typical and essential woody ectomycorrhizae (EM) host plant growing in North China. EM fungi contribute to the host healthy and the stability of the forest ecosystem. However, EM fungal community associated with this tree is less documented. We examined EM fungal diversity and composition of P. tabuliformis from three sites in Inner Mongolia, China by using Illumina MiSeq sequencing on the rDNA ITS2 region. Our results showed that a total of 105 EM fungal operational taxonomic units (OTUs) were identified from 15 composite root samples, and the dominant lineages were /suillus-rhizopogon, /tomentella-thelephora, /tricholoma, /amphinema-tylospora, /wilcoxina, /inocybe and /Sebacina. A high proportion of unique EM fungal OTUs were detected and some abundant OTUs preferred to exist in specific sites. EM fungal communities were significantly different among the sites, with soil, climatic and spatial variables being responsible for the community variations. The EM fungal community assembly was mainly governed by deterministic processes. These findings suggest that this endemic Pinaceae species in China also harbored a rich and distinctive EM fungal community and deterministic processes played more important roles than stochastic in shaping the symbiotic fungal community. Our study improves our understanding of EM fungal diversity and community structure from the perspective of a single host plant that has not been investigated exclusively before.

ecology↗

bioRxiv2024

Decoding dynamic visual scenes across the brain hierarchy

Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a paramount investigation within the domain of neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding dataset and utilize the capabilities of deep learning neural network models to study the question of neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. We find that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within both the visual cortex and subcortical nuclei, in contrast to a relatively diminished encoding activity within hippocampal neurons. Strikingly, our results reveal a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings not only corroborate existing knowledge in visual coding using artificial visual stimuli but illuminate the functional role of these deeper brain regions using dynamic natural scenes. Consequently, our results proffer a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding of dynamic natural visual scenes, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.

neuroscience↗

bioRxiv2024

Structure of the E3 ligase CRL2-ZYG11B with substrates reveals the molecular basis for N-degron recognition and ubiquitination

ZYG11B is a substrate specificity factor for Cullin-RING ubiquitin ligase (CRL2) involved in many biological processes, including Gly/N-degron pathways. Yet how the binding of ZYG11B with CRL2 is coupled to substrate recognition and ubiquitination is unknown. We present the Cryo-EM structures of the CRL2-ZYG11B holoenzyme alone and in complex with a Gly/N-peptide from the inflammasome-forming pathogen sensor NLRP1. The structures indicate ZYG11B folds into a Leucine-Rich Repeat followed by two armadillo repeat domains that promote assembly with CRL2 and recognition of NLRP1 Gly/N-degron. ZYG11B promotes activation of the NLRP1 inflammasome through recognition and subsequent ubiquitination of the NLRP1 Gly/N-degron revealed by viral protease cleavage. Our structural and functional data indicate that blocking ZYG11B recognition of the NLRP1 Gly/N-degron inhibits NLRP1 inflammasome activation by a viral protease. Overall, we show how the CRL2-ZYG11B E3 ligase complex recognizes Gly/N-degron substrates, including those that are involved in viral protease-mediated activation of the NLRP1 inflammasome.

molecular biology↗

bioRxiv2024

The effects of 17α-estradiol treatment on endocrine system revealed by single-nucleus transcriptomic sequencing of hypothalamus

In this study, we investigated 17-estradiols role in lifespan extension from long-term administration. Pooled hypothalami from aged male Norway brown rats treated with 17-estradiol (O.T), aged male controls (O), and young male controls (Y) underwent single-nucleus transcriptomic sequencing (snRNA-seq). To evaluate the effects of 17-estradiol on aging neurons, supervised clustering of neurons based on neuropeptides and their receptors were used to evaluate the responses of each neuron subtype during aging and after 17-estradiol treatment. The elevated cellular metabolism, stress and decreased expression levels of pathways involved in synapse formation in neurons initiated by aging were significantly attenuated by 17-estradiol. Assessment of changes in neuron populations showed that neurons related to food intake, reproduction, blood pressure, stress response, and electrolyte balance were sensitive to 17-estradiol treatment. 17-estradiol treatment not only increased serum Oxytocin (Oxt), but also heightened the activity of hypothalamic-pituitary-gonadal (HPG) axis, as evidenced by significantly elevated levels of plasma Gnrh, total testosterone, and decreased estradiol. Elevated Gnrh1 was confirmed to be one of the causal effects mediating the role of 17-estradiol in energy homeostasis, neural synapse, and stress response. Notably, Crh neurons exhibited prominent stressed phenotype in O.T, distinct to appetite-stimulating neurons Agrp and Ghrl. Therefore, the HPG axis and energy metabolism may be key targets of 17-estradiol in male hypothalamus. Additionally, supervised clustering of neurons was shown to be a useful method for assessing treatment responses and cellular perturbation among different neuron subtypes in the hypothalamus.

neuroscience↗

bioRxiv2024

Muscone-specific olfactory protein reveals the putative scent-marking pheromone in the Sunda pangolin (Manis javanica)

The Sunda pangolin (Manis javanica) is a burrowing and nocturnal animal, and they have poor vision, thus, intraspecies communication relies on olfaction, such as mating, warning, and scent-marking. However, the intraspecies pheromone in pangolins remains unknown. In this study, all the odorant-binding proteins in Sunda pangolins were functionally expressed, and they were screened against a panel of 32 volatiles that were derived from the pangolins urine, feces, and anal gland secretions. Reverse chemical ecology identified that M. javanica odorant-binding protein 3 (MjavOBP3) possesses the highest binding affinity to muscone. A subsequent behavior-tracking assay showed that only males can sense muscone; thus, we hypothesize that muscone is a male-specific scent-marking pheromone. Meanwhile, the structural study showed that Tyr117 contributes the most to muscones binding, which was further validated by site-directed mutagenesis. The findings clarify the scent-marking mechanism in pangolins, and muscone could potentially be used to support the monitoring and conservation of this endangered animal. Author SummaryThe Sunda pangolin is an endangered mammal that is native to Southeast Asia and is threatened due to its economic value. They are cave-living and nocturnal, poor vision; thus, their intraspecies communication is highly reliant on olfaction. Although they are generally solitary, they have been observed to have some social aspects in the wild, such as breeding and territorial behaviors, which are mediated by scents. However, no previous study has investigated the type of pheromones and how they are detected. Using the reverse chemical ecology approach, MjavOBP3 was found to bind to muscone with high affinity, and behavior-tracking assay was performed under well-controlled artificial rearing conditions, which showed only male pangolins can recognize muscone, suggesting its potential male-specific pheromone role.

zoology↗

bioRxiv2024

Structural and functional analysis of the Nipah virus polymerase complex

Nipah virus (NiV) is a bat-borne, zoonotic RNA virus that is among the most pathogenic viruses known to humans. The NiV polymerase, which mediates viral genome replication and mRNA transcription, is a drug target. However, NiV polymerase structures were previously unavailable. We determined the cryo-EM structure of the NiV polymerase complex, comprising the large protein (L) and its associated co-factor (P), and performed structural, biophysical, and functional analyses of the NiV polymerase. The complex assembles with a long P tetrameric coiled-coil that is capped by a bundle of -helices that we show are likely dynamic in solution. Highly conserved zinc-binding modules in the capping domain and a large insert in the RdRp palm domain that is short or absent in most other non-segmented negative strand RNA viruses are critical for replication and transcription. Our findings have the potential to aid in the rational development of drugs to combat NiV infection.

microbiology↗

bioRxiv2024

A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images

Computational pathology, utilizing whole slide image (WSI) for pathological diagnosis, has advanced the development of intelligent healthcare. However, the scarcity of annotated data and histological differences hinder the general application of existing methods. Extensive histopathological data and the robustness of self-supervised models in small-scale data demonstrate promising prospects for developing foundation pathology models. Due to the need for deployment, lightweight foundation models also need to be developed. In this work, we propose the BEPH (BEiT-based model Pre-training on Histopathological images), a general lightweight foundation model that leverages self-supervised learning to learn meaningful representations from 11 million unlabeled histopathological images. These representations are then efficiently adapted to various tasks, including 2 cancer patch-level recognition tasks, 3 cancer WSI-level classification tasks, and 6 cancer subtypes survival prediction tasks. Experimental results demonstrate that our model consistently outperforms several comparative models with similar parameters, even with limited training data reduced to 50%. Especially when the downstream structure is the same, the model can improve ResNet and DINO by up to a maximum increase of 8.8% and 7.2% (WSI level classification), and 6.44% and 3.28% on average (survival prediction), respectively. Therefore, BEPH offers a universal solution to enhance model performance, reduce the burden of expert annotations, and enable widespread clinical applications of artificial intelligence. The code and models can be obtained at https://github.com/Zhcyoung/BEPH. And currently, online fine-tuning of WSI classification tasks is available for use on http://yulab-sjtu.natapp1.cc/BEPH.

pathology↗