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Zheng, J.

Publications and source records attributed to Zheng, J..

At least 181 records · Page 10Linked to original sources

TRIM72 restricts lyssavirus infection by inducing K48-linked ubiquitination and proteasome degradation of the matrix protein

The tripartite motif (TRIM) protein family is the largest subfamily of E3 ubiquitin ligases, playing a crucial role in the antiviral process. In this study, we found that TRIM72, a member of the TRIM protein family, was increased in neuronal cells and mouse brains following rabies lyssavirus (RABV) infection. Over-expression of TRIM72 significantly reduced the viral titer of RABV in neuronal cells and mitigated the pathogenicity of RABV in mice. Furthermore, we found that TRIM72 over-expression effectively prevents the release of RABV. In terms of the mechanism, TRIM72 promotes the K48-linked ubiquitination of RABV Matrix protein (M), leading to the degradation of M through the proteasome pathway. TRIM72 directly interacts with M and the interaction sites were identified and confirmed through TRIM72-M interaction model construction and mutation analysis. Further investigation revealed that the degradation of M induced by TRIM72 was attributed to TRIM72s promotion of ubiquitination at site K195 in M. Importantly, the K195 site was found to be partially conserved among lyssaviruss M proteins, and TRIM72 over-expression induced the degradation of these lyssavirus M proteins. In summary, our study has uncovered a TRIM family protein, TRIM72, that can restrict lyssavirus replication by degrading M, and we have identified a novel ubiquitination site (K195) in lyssavirus M. Author SummaryRabies, caused by lyssaviruses, most often by rabies virus, the most lethal zoonotic disease, is responsible for approximately 59000 human deaths globally each year. Lyssavirus M protein displays an essential role in lyssavirus assembly and budding. Here, we found that TRIM family protein TRIM72 directly interacts with lyssavirus M thereby promoting the K48-linked ubiquitination of M, leading to the degradation of M through the proteasome pathway, restricting lyssavirus release. Moreover, we identified a novel important ubiquitination site (K195) in lyssavirus M. Prior to our study, there had been no reports of a direct interaction between TRIM72 and viral proteins, an antiviral function of TRIM72 was proved in our study. It is possible that TRIM72 interacts and ubiquitinates other viral proteins from various viruses, which warrants further investigation. This research contributes to our understanding of how TRIM72 and other TRIM proteins play a role in defending against viral invasions and may inspire further research in this field.

microbiology↗

Maturation and detoxification of synphilin-1 inclusion bodies regulated by sphingolipids

Due to proteostasis stress induced by aging or disease, misfolded proteins can form toxic intermediate species of aggregates and eventually mature into less toxic inclusion bodies (IBs). Here, using a yeast imaging-based screen, we identified 84 potential synphilin-1 (SY1) IB regulators and isolated the conserved sphingolipid metabolic components in the most enriched groups. Furthermore, we show that, in both yeast cells and mammalian cells, SY1 IBs are associated with mitochondria. Pharmacological inhibition of the sphingolipid metabolism pathway or knockout of its key genes results in a delayed IB maturation and increased SY1 cytotoxicity. We postulate that SY1 IB matures by association with the mitochondrion membrane, and that sphingolipids stimulate the maturation via their membrane-modulating function and thereby protecting cells from SY1 cytotoxicity. Our findings identify a conserved cellular component essential for IB maturation and suggest a mechanism by which cells may detoxify the pathogenic protein aggregates through forming mitochondrion-associated IBs.

cell biology↗

Activation of a cortical neurogenesis transcriptional program during NEUROD1-induced astrocyte-to-neuron conversion

NEUROD1-induced astrocyte-to-neuron (AtN) conversion has garnered significant attention as a potential therapeutic intervention to neurological disorders. To gain insight into the molecular regulations underlying this neuronal reprogramming process, we applied single-cell multiomics analyses on in vitro ND1-induced AtN conversion to systematically investigate how ND1 changed the fate of astrocytes at transcriptomic and epigenetic levels. Our findings reveal that the initial immature astrocytes go through an intermediate state where both astrocytic and neuronal genes are activated at early stage of AtN conversion. ND1 directly reshapes the chromatin accessibility landscape of astrocytes to that of neurons, promoting expression of endogenous Neurod1 and other neurogenic genes such as Hes6, Insm1 etc. Interestingly, cell proliferation status is highly correlated with conversion rate, and inhibition of cell division significantly reduces the conversion ratio. Moreover, in comparison with another AtN reprogramming transcription factor, ASCL1, external ND1 can activate endogenous Neurod1 and directly promote neuronal gene transcription; whereas external ASCL1 hardly activates endogenous Ascl1, leading to slower and inefficient conversion. Together, our studies demonstrate that in vitro AtN conversion mimics neurogenic transcriptional program in embryonic neurogenesis.

neuroscience↗

Structural Basis of Streptomyces Antibiotic Regulatory Proteins Activating Transcription

Streptomycetes are renowned antibiotic producers, with Streptomyces antibiotic regulatory proteins (SARPs) acting as activators for antibiotic biosynthesis. However, the precise mechanism underlying SARPs transcriptional activation remains elusive. Here, we used cryo-electron microscopy (cryo-EM) to unravel the interplay between SARP, DNA, and RNA polymerase (RNAP) during transcriptional activation. The SARP domain of Streptomyces coelicolor AfsR (SAS) forms a side-by-side dimer contacting the afs box centered at -29.5 relative to the transcription start site. The upstream protomer binds to the direct repeat encompassing the -35 element while the {sigma}HrdB region 4 (R4) is positioned on top of both protomers, causing the removal of R4 from the major groove of the -35 element. Both SAS protomers establish interactions with C-terminal domain of one RNAP subunits, while specific regions of the RNAP {beta} flap tip helix and {beta} zinc-binding domain also engage with SAS. Key interfacial residues accounting for transcriptional activation were confirmed by mutational studies and in vitro transcriptional assays. Overall, our results present a detailed molecular view of how SARPs serve to activate transcription.

molecular biology↗

Enhancement of Protein Stability by Quenching Millisecond Conformational Dynamics

Protein folding may involve folding intermediates. Ubiquitin (Ub) is a 76-residue small protein essential in post-translational modification and cell signaling. Ub is also a model system of protein folding. Previous studies have indicated the involvement of a folding intermediate, as Ub C-terminal residues, including strand {beta}5, only dock correctly at a later stage. The natively folded Ub undergoes conformational dynamics over a vast range of timescales. At the millisecond timescale, Ub transiently digresses to a C-terminally retracted state, which is extremely rare and has only been recently identified at an elevated temperature. Herein through a conjoint use of NMR, MS, and MD simulations, we have established a link between Ub millisecond dynamics and protein stability. Among the alanine mutations that have been systematically introduced to the hydrophobic residues in {beta}5, L67A and L69A elevate the population of the retracted state and enhance conformational interconversion, which facilitates the undocking of {beta}5 and the exposure of protein hydrophobic core. Conversely, L71A and L73A mutations decrease the population of the retracted state and quench millisecond dynamics, which causes a significant enhancement of protein stability. As such, the transition state of Ub millisecond dynamics is the much sought-after folding intermediate, whereas C-terminal mutations alleviate the dependence on this intermediate and reduce the unfolding to an all-or-none process. Though having a negative impact on protein stability, Ub millisecond dynamics likely facilitate proper protein turnover and allow the fulfillment of its biological function.

biophysics↗

WGCNA and Machine Learning for Screening Potential Biomarkers in traumatic brain injury

BackgroundTraumatic brain injury (TBI) is more common than ever and is becoming a global public health issue. However, there are no sensitive diagnostic or prognostic biomarkers to identify TBI, which leads to long-term consequences. In this study, we aim to identify genes that contribute to brain injury and to identify potential mechanisms for its progression in the early stages. MethodFrom the Gene Expression Omnibus (GEO) database, we downloaded GSE2871s gene expression profiles. Weighted gene coexpression network analyses (WGCNA) were conducted on differentially expressed genes (DEGs), and the DEGs were analyzed by Gene Set Enrichment Analysis(GSEA). An enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed for understanding the biological functions of genes. The potential biomarkers were identified using 3 kinds of machine learning algorithms. Nomogram was constructed using the "rms" package. And the receiver operating characteristic curve (ROC) was plotted to detect and validate our prediction model sensitivity and specifificity. ResultsBetween samples with and without brain injury, 107 DEGs were identified, including 47 upregulated genes and 60 downregulated genes. On the basis of WGCNA and DEGs, 97 target genes were identified. In addition, biological function analysis indicated that target genes were primarily involved in the interaction of neuroactive ligands with receptors, taste transduction, cortisol synthesis and secretion, potassium ion transport. Based on machine learning algorithms, LOC103691092, Npw could be potentially useful biomarkers for TBI and showed good diagnostic values. Finally, a nomogram was constructed of the expression levels of these seven target genes to predict level of TBI, and the ROC showed that these genes can be used as hub genes after TBI. ConclusionLOC103691092, NPW, STK39, KCND3, APOC3, FOXE3, and CHRNB1 were identified as hub genes of TBI. These findings can provide a new direction for the diagnosis and treatment of TBI.

neuroscience↗

Disease-associated gut microbiome and metabolome changes in chronic low back pain patients with bone marrow lesions

Chronic low back pain (LBP) is the leading cause of global disability. Vertebral bone marrow lesions (BMLs), one etiological factor for chronic LBP, are MRI signal changes in the vertebral bone marrow that extend from the disc endplate. The adipogenesis of bone marrow mesenchymal stem cells (BM-MSCs) could explain fatty replacement (FR) in normal bone marrow. FR is the most common type of BMLs. Here we show how the gut microbiome and serum metabolome change and how they interact in LBP patients with or without FR. The serum metabolome of chronic LBP patients with FR is characterized by decreased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has important capability to regulate BCAA degradation pathway. Ruminococcus gnavus, Roseburia hominis and Lachnospiraceae bacterium 8 1 57FAA are identified as the main species driving the association between biosynthesis of BCAAs and BM-MSCs metabolism in LBP with FR individuals. In vitro work demonstrates that BCAAs can induce the adipogenesis of BM-MSCs by activating the SIRT4 pathway. Our findings provide a deep insight into understanding the role of the disturbed gut ecosystem in FR and LBP.

cell biology↗

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural needs significantly limit its clinical application, highlighting the need for more accessible alternatives. To bridge this gap, we introduce the Integrated Graph and Image Deep Learning (IGI-DL) model. This innovation, a fusion of Convolutional Neural Networks and Graph Neural Networks, is designed to predict gene spatial expression using HE images. The IGI-DL model outperforms its predecessors in analyzing colorectal cancer (CRC), breast cancer, and cutaneous squamous cell carcinoma (cSCC) by leveraging both pixel intensity and structural features in images. Significantly, across all cancer types, the IGI-DL model enhances the mean correlation of the top five genes by an average of 0.125 in internal and external test sets, rising from 0.306 to 0.431, surpassing existing state-of-the-art (SOTA) models. We further present a novel risk score derived from a super-patch graph, where gene expression predicted by IGI-DL serves as node features. Demonstrating superior prognostic accuracy, this risk score, with a C-index of 0.713 and 0.741 for CRC and breast cancer, supersedes traditional HE-based risk scores. In summary, the approach augments our understanding of the TME from the aspect of histological images, portending a transformation in cancer prognostics and treatment planning and ushering in a new era of personalized and precision oncology.

bioinformatics↗

Fully automated and integrated proteomics sample preparation platform for high-throughput drug target identification

With the increased demand of large-cohort proteomic analysis, fast and reproducible sample preparation has become the critical issue that needs to be solved. Herein, we developed a fully automated and integrated proteomics sample preparation workflow (autoSISPROT), enabling the simultaneous processing of 96 samples in less than 2.5 hours. Benefiting from its 96-channel all-in-tip operation, protein digestion, peptide desalting, and TMT labeling could be achieved in a fully automated manner. The autoSISPROT demonstrated good sample preparation performances, including >94% of digestion efficiency, nearly 100% of alkylation efficiency, >98% of TMT labeling efficiency, and >0.9 of intra- and inter-batch Pearson correlation coefficients. Furthermore, by combining with cellular thermal shift assay-coupled to mass spectrometry (CETSA-MS), the autoSISPROT was able to process and TMT-label 40 samples automatically and accurately identify the known target of methotrexate. Importantly, taking advantage of the data independent acquisition and isothermal CETSA-MS, the autoSISPROT was well applied for identifying known targets and potential off-targets of 20 kinase inhibitors by automatedly processing 87 samples, affording over a 10-fold improvement in throughput when compared to classical CETSA-MS. Collectively, we developed a fully automated and integrated workflow for high-throughput proteomics sample preparation and drug target identification.

systems biology↗

Learning Complete Protein Representation by Deep Coupling of Sequence and Structure

Learning effective representations is crucial for understanding proteins and their biological functions. Recent advancements in language models and graph neural networks have enabled protein models to leverage primary or tertiary structure information to learn representations. However, the lack of practical methods to deeply co-model the relationships between protein sequences and structures has led to suboptimal embeddings. In this work, we propose CoupleNet, a network that couples protein sequence and structure to obtain informative protein representations. CoupleNet incorporates multiple levels of features in proteins, including the residue identities and positions for sequences, as well as geometric representations for tertiary structures. We construct two types of graphs to model the extracted sequential features and structural geometries, achieving completeness on these graphs, respectively, and perform convolution on nodes and edges simultaneously to obtain superior embeddings. Experimental results on a range of tasks, such as protein fold classification and function prediction, demonstrate that our proposed model outperforms the state-of-the-art methods by large margins.

bioinformatics↗

Dendrimeric DNA Coordinate Barcoding Design for Spatial RNA Sequencing

Spatially resolved transcriptomic technologies show promise in revealing complex pathophysiological processes, but developing sensitive, high-resolution, and cost-effective methodology is challenging. Here, we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq). This technology combined dendrimeric nano-substrates with microfluidic coordinate barcoding to generate high-density spatial DNA arrays with deterministically combinatorial barcodes in a resolution-flexible and cost-effective manner ([~]$0.5/mm2). Decoder-seq achieved high RNA capture efficiency, [~]68.9% that of in situ sequencing, and enhanced the detection of lowly expressed genes by [~]five-fold compared to 10x Visium. Decoder-seq visualized a spatial single-cell atlas of mouse hippocampus at near-cellular resolution (15 m) and revealed dendrite-enriched mRNAs. Application to renal cancers dissected the heterogeneous tumor microenvironment of two subtypes, and identified spatial gradient expressed genes with the potential in predicting tumor prognosis and progression. Decoder-seq is compatible with sensitivity, resolution, and cost, making spatial transcriptomic analysis accessible to wider biomedical applications and researchers.

genomics↗

Metagenomic survey of antimicrobial resistance (AMR) in Maryland surface waters differentiated by high and low human impact

In alignment with the One Health paradigm, surface waters are being evaluated as a modality to better understand baseline antimicrobial resistance (AMR) across the environment to supplement existing AMR monitoring in pathogens associated with humans, foods, and animals. Here, we use metagenomic and quasimetagenomic sequence data to describe AMR in Maryland surface waters from developed (high human impact) and natural (low human impact) classifications by the National Land Cover Database (NLCD). Critically important {beta}-lactamase genes were observed in twice as many high human impact zones. All data are available under BioProject PRJNA79347. https://www.ncbi.nlm.nih.gov/bioproject/794347

microbiology↗

Microglial P2Y6 calcium signaling promotes phagocytosis and shapes neuroimmune responses in epileptogenesis

Microglial calcium signaling is rare in a baseline state but shows strong engagement during early epilepsy development. The mechanism and purpose behind microglial calcium signaling is not known. By developing an in vivo UDP fluorescent sensor, GRABUDP1.0, we discovered that UDP release is a conserved response to seizures and excitotoxicity across brain regions. UDP signals to the microglial P2Y6 receptor for broad increases in calcium signaling during epileptogenesis. UDP-P2Y6 signaling is necessary for lysosome upregulation across limbic brain regions and enhances production of pro-inflammatory cytokines--TNF and IL-1{beta}. Failures in lysosome upregulation, observed in P2Y6 KO mice, can also be phenocopied by attenuating microglial calcium signaling in Calcium Extruder ("CalEx") mice. In the hippocampus, only microglia with P2Y6 expression can perform full neuronal engulfment, which substantially reduces CA3 neuron survival and impairs cognition. Our results demonstrate that calcium activity, driven by UDP-P2Y6 signaling, is a signature of phagocytic and pro-inflammatory function in microglia during epileptogenesis.

neuroscience↗

Hippocampal Theta Phase Precession Supports Memory Formation and Retrieval of Naturalistic Experience in Humans

Linking different experiences together is a key aspect of episodic memory. A potential neural mechanism for linking sequential events over time is phase precession, which causes neurons to fire progressively earlier in time relative to theta-frequency local field potential oscillations. However, no direct link between phase precession and behaviorally assessed memory encoding or retrieval success has been established. We recorded the activity of single neurons and local field potentials in the human medial temporal lobe (MTL) while participants encoded and retrieved memories of movie clips. Transient brief theta bouts and theta phase precession were observed following cognitive boundaries during movie watching as well as following stimulus onset during memory retrieval. Phase precession was dynamic, with different neurons exhibiting phase precession in different task periods. The strength of phase precession provided information about memory encoding and retrieval success that was not available in firing rates, thereby linking the temporal code established by phase precession to behaviorally assessed memory strength. These data reveal phase precession during non spatial memory in humans and provide direct neural evidence for a functional role of phase precession in episodic memory.

neuroscience↗

TMELand: An end-to-end pipeline for quantification and visualization of Waddington's epigenetic landscape based on gene regulatory network

Waddingtons epigenetic landscape is a framework depicting the processes of cell differentiation and reprogramming under the control of a gene regulatory network (GRN). Traditional model-driven methods for landscape quantification focus on the Boolean network or differential equation-based models of GRN, which need sophisticated prior knowledge and hence hamper their practical applications. To resolve this problem, we combine data-driven methods for inferring GRNs from gene expression data with model-driven approach to the landscape mapping. Specifically, we build an end-to-end pipeline to link data-driven and model-driven methods and develop a software tool named TMELand for GRN inference, visualizing Waddingtons epigenetic landscape, and calculating state transition paths between attractors to uncover the intrinsic mechanism of cellular transition dynamics. By integrating GRN inference from real transcriptomic data with landscape modeling, TMELand can facilitate studies of computational systems biology, such as predicting cellular states and visualizing the dynamical trends of cell fate determination and transition dynamics from single-cell transcriptomic data. The source code of TMELand, a user manual, and model files of case studies can be downloaded freely from https://github.com/JieZheng-ShanghaiTech/TMELand.

systems biology↗

Chemogenetic approaches reveal dual functions of microglia in epilepsy

Microglia are key players in maintaining brain homeostasis and exhibit phenotypic alterations in response to epileptic stimuli. However, it is still relatively unknown if these alterations are pro- or anti-epileptic. To unravel this dilemma, we employed chemogenetic manipulation of microglia via of the artificial Gi-Dreadd receptor within a kainic acid (KA) induced murine seizure model. Our results indicate that Gi-Dreadd activation can reduce seizure severity. Additionally, we observed increased interaction between microglia and neuronal soma, which correlated with reduced neuronal hyperactivity. Interestingly, prolonged activation of microglial Gi-Dreadds by repeated doses over 3 days, arrested microglia in a less active, homeostatic-like state, which associated with increased neuronal loss after KA induced seizures. RNAseq analysis revealed that prolonged activation of Gi-Dreadd interferes with interferon {beta} signaling and microglia proliferation. Thus, our findings highlight the importance of microglial activation not only during status epilepticus (SE) but also within later seizure induced pathology.

neuroscience↗

Development of Synthetic Modulator Enabling Long-Term Propagation and Neurogenesis of Human-Derived Neural Progenitor Cells

Neural progenitor cells (NPCs) are important cells for in vitro drug screening and the cell-based therapy for brain-related disorders, which requires well-defined and reproducible culture systems. Current strategy the use of protein growth factors presents challenges in terms of reproducibility and cost. In this study, we have developed a novel DNA-based modulator to regulate FGFR signaling of NPCs, enabling maintenance of the stemness over 50 passages and neurogenesis towards neurons. The DNA-based FGFR-agonist effectively promoted FGFR1 phosphorylation and activated the downstream ERK signaling pathway in FGFR1-positive cells. Using human embryonic stem cell lines, we differentiated them into NPCs and replaced basic fibroblast growth factor (bFGF) in the regulator culture medium with DNA-based FGFR-agonist for artificially elicited FGFR signaling. The results demonstrated that the FGFR-agonist could promote NPCs proliferation and neurosphere formation, recapitulating the function of bFGF. Notably, transcriptomic analysis revealed that FGFR-agonist could customize the stemness-associated transcription program, while decouples the neuronal differentiation program, highly resembling that the native ligand, bFGF. Moreover, our culture condition facilitated the successful propagation of NPCs for over 50 passages, while retaining their ability to efficiently differentiate into neurons. Overall, our approach provides a highly effective method for expanding NPCs, offering new opportunities for disease-in-dish research and drug screening for neural degeneration.

neuroscience↗

bettercallsal: better calling of Salmonella serotypes from enrichment cultures using shotgun metagenomic profiling and its application in an outbreak setting.

Precise and rapid identification of Salmonella serotypes from suspect food matrices is critical for successful source attribution of illness outbreaks (Scallan et al., 2011). Currently, close to 3% of U.S. foodborne Salmonella outbreaks have been attributed to multiple Salmonella serotypes (2.85%, 2000 - 2020) (CDC, 2022). Recent foodborne outbreaks that have been attributed to multiple Salmonella serotypes force us to question whether these are rare events or if previous methods simply did not have the throughput to provide an accurate picture of the complex ecology that is connected to outbreak etiologies. (Hassan et al., 2019; FDA, 2021; Whitney et al., 2021). An in-silico benchmark dataset, comprising 29 unique Salmonella, 46 non-Salmonella bacterial and 10 viral genomes, was generated with varying read depths. For outbreak samples, analysis was performed on previously sequenced pre-enrichments and selective enrichments of papayas and peaches (fruits and leaves) that led to the identification of multiple serovars. Data analyses was performed using a custom-built k-mer tool, SeqSero2, Kallisto and bettercallsal. The in-silico dataset analyzed with bettercallsal had accuracy, recall and specificity of 95%, 96% and 98 % respectively. In the papaya outbreak samples, bettercallsal identified multiple serovar presence in concordance with Bioplex assay results and the genome hits assigned to the samples are Salmonella isolates from the papaya outbreak as evident by NCBI SNP cluster information. In peach outbreak samples, bettercallsal identified both the serovars (Alachua and Gaminara) in concordance with k-mer analysis and the Luminex xMap assay. bettercallsal outperformed k-mer, Kallisto and Seqsero2 in identifying multiple serovars from enrichment cultures using shotgun metagenomics sequencing. Most Salmonella subtyping work has relied upon WGS methods which focuses on the high-resolution analysis of single genomes, or multiple single genomes picked from colonies on agar. Here we introduce laboratory and bioinformatics innovations for a metagenomic outbreak response workflow that accurately identifies multiple Salmonella serovars at the same time in a much higher throughput approach. bettercallsal is one of the first analysis tools that can potentially identify multiple Salmonella spp. serotypes from a metagenomic or quasi-metagenomic datasets with accuracy and can provide early insights into the etiology of the sample.

genomics↗