Biology subjects

Ji, Z.

Publications and source records attributed to Ji, Z..

At least 55 records · Page 3Linked to original sources

bioRxiv2024

A systems model of alternating theta sweeps via firing rate adaptation

Place and grid cells provide a neural system for self-location and tend to fire in sequences within each cycle of the hippocampal theta rhythm when rodents run on a linear track. These sequences correspond to the decoded location of the animal sweeping forward from its current location ("theta sweeps"). However recent findings in open-field environments show alternating left-right theta sweeps, and propose a circuit for their generation. Here, we present a computational model of this circuit, comprising head direction cells, conjunctive grid x direction cells, and pure grid cells, based on continuous attractor dynamics, firing rate adaptation, and modulated by the medial-septal theta rhythm. Due to firing rate adaptation, the head-direction ring attractor exhibits left-right sweeps coding for internal direction, providing an input to the grid cell attractor network shifted along the internal direction, via an intermediate layer of conjunctive grid x direction cells, producing left-right sweeps of position by grid cells. Our model explains the empirical findings, including the alignment of internal position and direction sweeps and the dependence of sweep length on grid spacing. It makes predictions for thetamodulated head-direction cells, including specific relationships between theta phase precession during turning, theta skipping, anticipatory firing and directional tuning width. These predictions are verified in experimental data from anteroventral thalamus. The model also makes several predictions for the relationships between position and direction sweeps, running speed and dorsal-ventral location within the entorhinal cortex. Overall, a simple intrinsic mechanism explains the complex theta dynamics of the spatial circuit, with testable predictions.

neuroscience↗

bioRxiv2024

Dynamical Modulation of Hippocampal Replay Sequences through Firing Rate Adaptation

During periods of immobility and sleep, the hippocampus generates diverse self-sustaining sequences of "replay" activity, exhibiting stationary, diffusive, and super-diffusive dynamical patterns. However, the neural mechanisms underlying this diversity in hippocampal sequential dynamics remain largely unknown. Here, we propose such a mechanism demonstrating that modulation of firing rate adaptation in a continuous attractor model of place cells causes the emergence of different types of replay. Our model makes several key predictions. First, more diffusive replay sequences positively correlate with longer theta sequences across animals (both reflecting stronger adaptation). Second, replay diffusivity varies within an animal across behavioural states that affect adaptation (such as wake and sleep). Third, increases in neural excitability, incorporated with firing rate adaptation, reduce the step size of decoded movements within individual replay sequences. We provide new experimental evidence for all three predictions. These insights suggested that the diverse replay dynamics observed in the hippocampus can be reconciled through a simple yet effective neural mechanism, shedding light on its role in hippocampal-dependent cognitive functions and its relationship to other aspects of hippocampal electrophysiology.

neuroscience↗

bioRxiv2024

Vcam1+ Fibro-adipogenic Progenitors Mark Fatty Infiltration in Chronic Limb Threatening Ischemia

Skeletal muscle health and function is a critical determinant of clinical outcomes in patients with peripheral arterial disease (PAD). Herein, we identify fatty infiltration, the ectopic deposition of adipocytes in skeletal muscle, as a histological hallmark of end-stage PAD, also known as chronic limb threatening ischemia (CLTI). Leveraging single cell transcriptome mapping in mouse models of PAD, we identify a pro-adipogenic mesenchymal stromal cell population marked by expression of Vcam1 (termed Vcam1+ FAPs) that expands in the ischemic limb. Mechanistically, we identify Sfrp1 and Nr3c1 as regulators of Vcam1+ FAP adipogenic differentiation. Loss of Sfrp1 and Nr3c1 impair Vcam1+ FAP differentiation into adipocytes in vitro. Finally, we show that Vcam1+ FAPs are enriched in human CLTI patients. Collectively, our results identify a pro-adipogenic FAP subpopulation in CLTI patients and provide a potential therapeutic target for muscle regeneration in PAD.

genomics↗

bioRxiv2024

TurboID-mediated surface protein biotinylation to inhibit the growth of Staphylococcus aureus

Staphylococcus aureus (S. aureus) infection is major cause of nosocomial infections. Antibiotic treatment for S. aureus remains the primary solution for managing S. aureus infections, which, however, increases the risk of antibiotic resistance. To broaden the resolutions on S. aureus infection, here we report TurboID-mediated protein proximity technologies to inhibit the growth of S. aureus. To achieve this goal, we utilized synthetic biology techniques to create a fusion protein named N-AgrD-TurboID (Agr-ID). The N-AgrD domain includes auto-inducer peptide (AIP) which combined to the surface AgrC protein on S. aureus. As such, TurboID then catalyzed the production of biotinoyl-5-AMP anhydride, triggering the biotinylation of surface proteins on S. aureus 25923 which were visualized by using fluorescence microscopy after incubating with Alexa Fluor 647-conjugated streptavidin. The biotinylation of surface protein on S. aureus 25923, S. aureus 43300, and S. aureus 6538 (MRSA) also resulted in growth inhibition and impaired colonization. Moreover, the biotinylation on surface protein further inhibited virulence protein production in S. aureus 25923, as indicated by reduced apoptosis of HEK 293T cells after treatment with S. aureus 25923 lysates. Overall, our work reveals that the biotinylation of surface proteins can inhibit the growth and toxicity of S. aureus 25923, S. aureus 43300, and S. aureus 6538 (MRSA), indicating therapeutic potential in clinical treatment.

microbiology↗

bioRxiv2024

ER-GUARD: an evolutionarily conserved antioxidant defense system at ER membranes

Oxidative protein folding in the endoplasmic reticulum (ER) is essential for all eukaryotic cells yet generates hydrogen peroxide (H2O2), a reactive oxygen species (ROS). The ER-transmembrane protein that provides reducing equivalents to ER and guards the cytosol for antioxidant defense remains unidentified. Here we combine AlphaFold2-based and functional reporter screens in C. elegans to discover a previously uncharacterized and evolutionarily conserved protein ERGU-1 that fulfills these roles. Deleting C. elegans ERGU-1 causes excessive H2O2 and transcriptional gene up-regulation through SKN-1, homolog of mammalian antioxidant master regulator NRF2. ERGU-1 deficiency also impairs organismal reproduction and behavioral responses to H2O2. Both C. elegans and human ERGU-1 proteins localize to ER membranes and form network reticulum structures. Human and Drosophila homologs of ERGU-1 can rescue C. elegans mutant phenotypes, demonstrating evolutionarily ancient and conserved functions. In addition, purified ERGU-1 and human homolog TMEM161B exhibit redox-modulated oligomeric states. Together, our results reveal an ER-membrane-specific protein machinery for peroxide detoxification and suggest a previously unknown and conserved mechanisms for antioxidant defense in animal cells.

cell biology↗

bioRxiv2024

Single-cell transcriptomics reveals immunosuppressive microenvironment and highlights stumor-promoting macrophage cells in Glioblastoma

Glioblastoma (GBM) is the most prevalent and aggressive primary brain malignancy in adults. Nevertheless, the cellular heterogeneity and complexity within the GBM microenvironment (TME) are still not fully understood, posing a significant obstacle in the advancement of more efficient immunotherapies for GBM. In this study, we conducted an integrated analysis of 48 tumor fragments from 24 GBM patients at the single-cell level, uncovering substantial molecular diversity within immune infiltrates. We characterized molecular signatures for five distinct tumor-associated macrophage (TAM) subtypes. Notably, the TAM_MRC1 subtype displayed a pronounced M2 polarization signature. Additionally, we identified a subtype of natural killer (NK) cells, designated CD56dim_DNAJB1. This subtype is characterized by an exhausted phenotype, evidenced by an elevated stress signature and enrichment in the PD-L1/PD-1 checkpoint pathway. Our findings also highlight significant cell-cell interactions among malignant glioma cells, TAM, and NK cells within the TME. Overall, this research sheds light on the functional heterogeneity of glioma and immune cells in the TME, providing potential targets for therapeutic intervention in this immunologically cold cancer.

cancer biology↗

bioRxiv2024

PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns

Modeling temporal and spatial gene expression patterns in large-scale single-cell and spatial transcriptomics data is a computationally intensive task. We present PreTSA, a method that offers computational efficiency in modeling these patterns and is applicable to single-cell and spatial transcriptomics data comprising millions of cells. PreTSA consistently matches the results of state-of-the-art methods while significantly reducing computational time. PreTSA provides a unique solution for studying gene expression patterns in extremely large datasets.

bioinformatics↗

bioRxiv2024

A systematic evaluation of computation methods for cell segmentation

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.

bioinformatics↗

bioRxiv2024

Plural molecular and cellular mechanisms of pore domain KCNQ2 encephalopathy

KCNQ2 variants in children with neurodevelopmental impairment are difficult to assess due to their heterogeneity and unclear pathogenic mechanisms. We describe a child with neonatal-onset epilepsy, developmental impairment of intermediate severity, and KCNQ2 G256W heterozygosity. Analyzing prior KCNQ2 channel cryoelectron microscopy models revealed G256 as a node of an arch-shaped non-covalent bond network linking S5, the pore turret, and the ion path. Co-expression with G256W dominantly suppressed conduction by wild-type subunits in heterologous cells. Ezogabine partly reversed this suppression. G256W/+ mice have epilepsy leading to premature deaths. Hippocampal CA1 pyramidal cells from G256W/+ brain slices showed hyperexcitability. G256W/+ pyramidal cell KCNQ2 and KCNQ3 immunolabeling was significantly shifted from axon initial segments to neuronal somata. Despite normal mRNA levels, G256W/+ mouse KCNQ2 protein levels were reduced by about 50%. Our findings indicate that G256W pathogenicity results from multiplicative effects, including reductions in intrinsic conduction, subcellular targeting, and protein stability. These studies provide evidence for an unexpected and novel role for the KCNQ2 pore turret and introduce a valid animal model of KCNQ2 encephalopathy. Our results, spanning structure to behavior, may be broadly applicable because the majority of KCNQ2 encephalopathy patients share variants near the selectivity filter.

neuroscience↗

bioRxiv2024

GPT-4V exhibits human-like performance in biomedical image classification

Image classification plays a pivotal role in analyzing biomedical images, serving as a cornerstone for both biological research and clinical diagnostics. We demonstrate that large multimodal models (LMMs), like GPT-4, excel in one-shot learning, generalization, interpretability, and text-driven image classification across diverse biomedical tasks. These tasks include the classification of tissues, cell types, cellular states, and disease status. LMMs stand out from traditional single-modal classification approaches, which often require large training datasets and offer limited interpretability.

bioinformatics↗

bioRxiv2023

Bidirectional substrate shuttling between the 26S proteasome and the Cdc48 ATPase promotes protein degradation

Most eukaryotic proteins are degraded by the 26S proteasome after modification with a polyubiquitin chain. Substrates lacking unstructured segments cannot be degraded directly and require prior unfolding by the Cdc48 ATPase (p97 or VCP in mammals) in complex with its ubiquitin-binding partner Ufd1-Npl4 (UN). Here, we use purified yeast components to reconstitute Cdc48-dependent degradation of well-folded model substrates by the proteasome. We show that a minimal system consists of the 26S proteasome, the Cdc48-UN ATPase complex, the proteasome cofactor Rad23, and the Cdc48 cofactors Ubx5 and Shp1. Rad23 and Ubx5 stimulate polyubiquitin binding to the 26S proteasome and the Cdc48-UN complex, respectively, allowing these machines to compete for substrates before and after their unfolding. Shp1 stimulates protein unfolding by the Cdc48-UN complex, rather than substrate recruitment. In vivo experiments confirm that many proteins undergo bidirectional substrate shuttling between the 26S proteasome and Cdc48 ATPase before being degraded.

biochemistry↗

bioRxiv2023

Automating literature screening and curation with applications to computational neuroscience

ObjectiveModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1800 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. We estimate we have captured only around one-third of NEURON models and lower fractions for other simulators. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (e.g. cell types, research topics). Materials and MethodsKnown computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2, GPT-3.5 and GPT-4 were used to identify likely computational neuroscience work and their relevant metadata. ResultsSPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. DiscussionDue to computational limitations, we only used each papers title and abstract, partially leading to false negatives. Further efforts should be devoted to including more training data and further improving current LLMs through fine-tuning approaches. ConclusionNLP and LLM techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.

neuroscience↗

bioRxiv2023

HGNNPIP: A Hybrid Graph Neural Network framework for Protein-protein Interaction Prediction

A deep understanding of Protein-protein interactions (PPIs) can provide comprehensive insights into many biological functions, thereby facilitating drug target identification and novel therapeutic design. Recent developments in artificial intelligence (AI)-driven computational methods have enabled the discovery of previously uncharacterized PPIs from large-scale interactome datasets. Almost all existing machine learning methods rely on Subcellular Localization (SL) to construct balanced datasets based on positive interactions to achieve predictions. Despite high fitting accuracy, the generalization ability of these models is questionable. To solve this problem, we analyzed existing methods and found that the high false positives in these methods are due to the bias in data distribution caused by SL. Therefore, we proposed a new strategy for negative instance sampling in PPI prediction and developed a Hybrid Graph Neural Network framework for Protein-protein Interaction Prediction (HGNNPIP). The experimental results showed that HGNNPIP works well on six benchmark datasets. Comparison analysis demonstrated that our model outperformed the other four existing methods. We also used HGNNPIP to explore the molecular contacts involved in the rice-pathogen interaction system. In vivo experiments confirmed multiple regulations related to disease resistance in rice. In summary, this study provides new insights into establishing a computational framework for PPI prediction with high reliability.

bioinformatics↗

bioRxiv2023

T-cell receptor sequences correlate with and predict gene expression levels in T cells

T cells exhibit high heterogeneity in both their gene expression profiles and antigen specificities, but the precise relationship between these two features remains largely unknown. Utilizing a large-scale single-cell immune-profiling dataset that includes both T cell receptor (TCR) sequences and gene expression profiles of individual cells, we systematically investigated how the transcriptional profiles of T cells are linked to their TCR sequence similarity. We observed that T cells sharing the same clonotype often belong to the same cell type, and that gene expression similarity among T cells correlates with TCR sequence similarity. Furthermore, we identified features such as clonality, VJ gene usage, sequence motifs, and amino acid preferences that are distinctly associated with specific T cell subtypes. These findings suggest that T cell transcriptional landscapes are broadly shared and influenced by TCR sequence similarity.

bioinformatics↗

bioRxiv2023

A tissue ubiquitous gene set for cellular senescence

Accurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, substantially outperforming existing methods.

cell biology↗

bioRxiv2023

Delineating yeast cleavage and polyadenylation signals using deep learning

3-end cleavage and polyadenylation is an essential process for eukaryotic mRNA maturation. In yeast species, the polyadenylation signals that recruit the processing machinery are degenerate and remain poorly characterized compared to well-defined regulatory elements in mammals. Especially, recent deep sequencing experiments showed extensive cleavage heterogeneity for some mRNAs in Saccharomyces cerevisiae and uncovered the polyA motif differences between S. cerevisiae vs. Schizosaccharomyces pombe. The findings raised the fundamental question of how polyadenylation signals are formed in yeast. Here we addressed this question by developing deep learning models to deconvolute degenerate cis-regulatory elements and quantify their positional importance in mediating yeast polyA site formation, cleavage heterogeneity, and strength. In S. cerevisiae, cleavage heterogeneity is promoted by the depletion of U-rich elements around polyA sites as well as multiple occurrences of upstream UA-rich elements. Sites with high cleavage heterogeneity show overall lower strength. The site strength and tandem site distances modulate alternative polyadenylation (APA) under the diauxic stress. Finally, we developed a deep learning model to reveal the distinct motif configuration of S. pombe polyA sites which show more precise cleavage than S. cerevisiae. Altogether, our deep learning models provide unprecedented insights into polyA site formation across yeast species.

genomics↗

bioRxiv2023

The 14-3-3/spastin pathway coordinates neurite regeneration and recovery of spinal cord injury by targeting microtubule dynamics

Axon regeneration is abortive in the central nervous system following injury. Orchestrating microtubule dynamics has emerged as a promising approach to improve axonal regeneration. The microtubule severing enzyme spastin is essential for axonal development and regeneration through remodeling of microtubule arrangement. To date, however, little is known regarding the mechanisms underlying spastin action in neural regeneration after spinal cord injury. Here, we use glutathione transferase pulldown and immunoprecipitation assays to demonstrate that 14-3-3 interacts with spastin, both in vivo and in vitro, via spastin Ser233 phosphorylation. Moreover, we show that 14-3-3 protects spastin from degradation by inhibiting the ubiquitination pathway and upregulates the spastin-dependent severing ability. Furthermore, the 14-3-3 agonist Fusicoccin (FC-A) promotes neurite outgrowth and regeneration in vitro which needs spastin activation. Western blot and immunofluorescence results revealed that 14-3-3 protein is upregulated in the neuronal compartment after spinal cord injury in vivo. In addition, administration of FC-A not only promotes locomotor recovery, but also nerve regeneration following spinal cord injury in both contusion and lateral hemisection models; however, application of spastin inhibitor spastazoline successfully reverses these phenomena. Taken together, these results indicate that 14-3-3 is a molecular switch that regulates spastin protein levels, and the small molecule 14-3-3 agonist FC-A effectively mediates the recovery of spinal cord injury in mice which requires spastin participation. HighlightsO_LIThe formation of the 14-3-3 and spastin protein complex requires phosphorylation of spastin at the S233 site. C_LIO_LI14-3-3 is involved in the phosphorylation-ubiquitination crosstalk of spastin, thus impacting the protein stability. C_LIO_LI14-3-3 agonists Fusicoccin-A can facilitate the repair of spinal cord injury in mice. C_LIO_LIActivation of spastin function is a prerequisite for nerve regeneration and recovery of spinal cord injury in mice. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/550600v3_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@ebabb6org.highwire.dtl.DTLVardef@5651caorg.highwire.dtl.DTLVardef@113df6borg.highwire.dtl.DTLVardef@1b0ea5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.

bioinformatics↗