Biology subjects

Wang, G.-Z.

Publications and source records attributed to Wang, G.-Z..

14 recordsLinked to original sources

bioRxiv2024

Cell types associated with human brain functional connectomes and their implications in psychiatric diseases

Cell types are fundamental to the functional organization of the human brain, yet the specific cell clusters contributing to functional connectomes remain unclear. Using human whole-brain single-cell RNA sequencing data, we investigated the relationship between cortical cell cluster distribution and functional connectomes. Our analysis identified dozens of cell clusters significantly associated with resting-state network connectivity, with excitatory neurons predominantly driving positive correlations and inhibitory neurons driving negative correlations. Many of these cell clusters are also conserved in macaques. Notably, functional network connectivity is predicted by cellular communication among these clusters. We further identified cell clusters linked to various neuropsychiatric disorders, with several clusters implicated in multiple conditions. Comparative analysis of schizophrenia and autism spectrum disorder revealed distinct expression patterns, highlighting disease-specific cellular mechanisms. These findings underscore the critical role of specific cell clusters in shaping functional connectomes and their implications for neuropsychiatric diseases.

neuroscience↗

bioRxiv2024

snCED-seq: High-fidelity cryogenic enzymatic dissociation of nuclei for single-nucleus RNA-seq of FFPE tissues

Profiling cellular heterogeneity in formalin-fixed paraffin-embedded (FFPE) tissues is key to characterizing clinical specimens for biomarkers, therapeutic targets, and drug responses. Recent advancements in single-nucleus RNA sequencing (snRNA-seq) techniques tailored for FFPE tissues have demonstrated their feasibility. However, isolation of high-quality nuclei from FFPE tissue with current methods remains challenging due to RNA cross-linking. We, therefore, proposed a novel strategy for the preparation of high-fidelity nuclei from FFPE samples, cryogenic enzymatic dissociation (CED) method, and performed snRandom-seq (snCED-seq) for polyformaldehyde (PFA)-fixed and FFPE brains to verify its applicability. The method is compatible with both PFA-based and FFPE brains or other organs with less hands-on time and lower reagent costs, and produced 10 times more nuclei than the homogenate method, without secondary degradation of RNA, and maximized the retention of RNA molecules within nuclei. snCED-seq shows 1.5-2 times gene and UMI numbers per nucleus, higher gene detection sensitivity and RNA coverage, and a minor rate of mitochondrial and ribosomal genes, compared with the nuclei from traditional method. The correlation gene expression of nucleus from the post-fixed and the frozen sample can be up to 94 %, and the gene expression of our nuclei was more abundant. Moreover, we applied snCED-seq to cellular heterogeneity study of the specimen on Alzheimers Disease (AD) to demonstrate a pilot application. Scarce Cajal Retzius cells in older mice were robustly detected in our data, and we successfully identified two subpopulations of disease-associated in astrocytes, microglia and oligodendrocytes, respectively. Meanwhile, we found that most cell types are affected at the transcriptional level by AD pathology, and there is a disease susceptibility gene set that affects these cell types similarly. Our method provides powerful nuclei for snRNA-seq studies for FFPE specimens, and even helps to reveal multi-omics information of clinical samples.

bioengineering↗

bioRxiv2024

Organizational principles of the primate cerebral cortex at the single-cell level

The primate cerebral cortex, the major organ for cognition, consists of an immense number of neurons. However, the organizational principles governing these neurons remain unclear. By accessing the single-cell spatial transcriptome of over 25 million neuron cells across the entire macaque cortex, we discovered that the distribution of neurons within cortical layers is highly non-random. Strikingly, over three-quarters of these neurons are located in distinct neuronal clusters. Within these clusters, different cell types tend to collaborate rather than function independently. Typically, excitatory neuron clusters mainly consist of excitatory-excitatory combinations, while inhibitory clusters primarily contain excitatory-inhibitory combinations. Both cluster types have roughly equal numbers of neurons in each layer. Importantly, most excitatory and inhibitory neuron clusters form spatial partnerships, indicating a balanced local neuronal network and correlating with specific functional regions. These findings suggest that different brain regions of the primate cortex may exhibit similar mechanisms at the neuronal population level.

neuroscience↗

bioRxiv2023

BrainCellR: A Precise Cell Type Nomenclature R Package for Comparative Analysis Across Brain Single-Cell Datasets

Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained cell types and establish standardized annotations at the cluster level, hindering comprehensive understanding of the brains cellular composition. To facilitate data integration across multiple models and datasets, we designed BrainCellR. This package provides researchers with a powerful and user-friendly tool for efficient cell type classification and nomination from single-cell transcriptomic data. BrainCellR goes beyond conventional classification approaches by incorporating a standardized nomenclature system for cell types at the cluster level. This feature enables consistent and comparable annotations across different studies, promoting data integration and providing deeper insights into the complex cellular landscape of the brain. Contactsimone.marini@ufl.edu or guangzhong.wang@picb.ac.cn

bioinformatics↗

bioRxiv2023

Feeding disruptions lead to a significant increase in disease modules in adult mice

Feeding disruption is intimately associated with numerous diseases, yet its molecular mechanisms remain to be elucidated in circadian biology. We discovered that in the liver of adult mice, feeding disruption can lead to a substantial increase in disease modules in the transcriptome. This increase in disease modules occurs far earlier than the individuals aging and disease onset. In contrast, calorie restriction significantly reduces the formation of disease modules. This offers a critical missing link between feeding disruption and the molecular systems mechanisms of disease.

neuroscience↗

bioRxiv2023

Transcriptomic profiling of nuclei from PFA-fixed and FFPE brain tissues

Formalin-fixed and paraffin-embedded (FFPE) tissue archives are the largest repository of clinically annotated specimens, and FFPE-compatible single cell gene expression workflow had been developed and applied recently. However, for tissues where cells are hard to dissociate or brains with complex neuronal cells, nuclear transcriptomic profiling are desirable. Moreover, the effects of standard pathological practice on the transcriptome of samples obtained from such archived specimens was also largely anecdotal. Here, we performed RNA-seq of nuclei from hippocampal of mice that underwent freezing, paraformaldehyde (PFA) fixation, and paraffin embedding. Then, we comprehensively evaluated the parameters affecting mRNA quality, transcription patterns, functional level and cell states of nuclei, including PFA fixation time and storage time of FFPE tissues. The results showed that the transcriptome signatures of nuclei isolated from fresh PFA-fixed and fresh FFPE tissues were more similar to matched frozen samples. By contrast, the brain fixed for more than 3 days had prominent impacts on the sequencing data, such as the numbers and biotypes of gene, GC content and ratio of reads interval. Commensurately, prolonged fixation time will result in more differentially expressed genes, especially those enriched in spliceosome and synaptic related pathways, affecting the analysis of gene splicing and neuron cells. MuSiC deconvolution results revealed that PFA infiltrating brains for 3 days will destroy the real cell states, and the proportion of neuron, endothelial and oligodendrocytes diminished while that of microglia was reversed. Yet the effect of storage time on cell composition was more neglectable for FFPE samples. In addition, oligodendrocyte precursor cells were most affected in all fixed samples, and their destruction was independent of fixation time and preservation time. The comprehensive results highlighted that fixation time had much more influences on the nuclear transcriptomic profiles than FFPE retention time, and the cliff-like effects appeared to occur over a fixed period of 1-3 days, with no more differences from additional fixation durations.

molecular biology↗

bioRxiv2022

RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization

Different RNAs have distinct subcellular localizations. However, nucleotide features that determine these distinct distributions of lncRNAs and mRNAs have yet to be fully addressed. Here, we develop RNAlight, a machine learning model based on LightGBM, to identify nucleotide k-mers contributing to the subcellular localizations of mRNAs and lncRNAs. With the Tree SHAP algorithm, RNAlight extracts nucleotide features for cytoplasmic or nuclear localization of RNAs, indicating the sequence basis for distinct RNA subcellular localizations. By assembling k-mers to sequence features and subsequently mapping to known RBP-associated motifs, different types of sequence features and their associated RBPs were additionally uncovered for lncRNAs and mRNAs with distinct subcellular localizations. Finally, we extended RNAlight to precisely predict the subcellular localizations of other types of RNAs, including snRNAs, snoRNAs and different circular RNA transcripts, suggesting the generality of using RNAlight for RNA subcellular localization prediction. Key pointsO_LIA machine learning model, RNAlight, is developed to efficiently and sensitively predict subcellular localizations of mRNAs and lncRNAs. C_LIO_LIWith embedded Tree SHAP algorithm, RNAlight further reveals distinct key sequence features and their associated RBPs for subcellular localizations of mRNAs or lncRNAs. C_LIO_LIRNAlight is successfully extended for the subcellular localization prediction of additional types of noncoding RNAs that were not used for model development, such as circular RNAs, suggesting its generality in RNA subcellular localization prediction. C_LIO_LIRNAlight is freely available at https://github.com/YangLab/RNAlight. C_LI

bioinformatics↗

bioRxiv2022

Dominant constraints on the evolution of rhythmic gene expression

Although the individual transcriptional regulators of the core circadian clock are distinct amongst different organisms, the autoregulatory feedback loops they form are conserved. This unified design principle explains how daily physiological activities oscillate across species. However, whether analogous design principles govern the gene expression output of circadian clocks is unknown. Herein, we performed a comparative analysis of rhythmic gene expression in eight diverse species and captured four common distribution patterns of cycling gene expression across these species. We hypothesized that maintenance of reduced energetic costs constrains the evolution of rhythmic gene expression. Our large-scale computational simulations support this hypothesis by showing that selection against high-energy expenditure completely regenerates all cycling gene patterns. Moreover, we find that the late- and early-phase peaks of rhythmic expression have been subjected to this type of selective pressure. Therefore, energetic costs have constricted the cycling transcriptome throughout evolutionary history.

cell biology↗

bioRxiv2022

MvPPT: a highly efficient and sensitive pathogenicity prediction tool for missense variants

Next generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant identification. In this study, we developed mvPPT (Pathogenicity Prediction Tool for missense variants), a highly sensitive and accurate missense variant classifier based on gradient boosting. MvPPT adopts high-confidence training sets with a wide spectrum of variant profiles, and extracts three categories of features, including scores from existing prediction tools, allele, amino acid and genotype frequencies, and genomic context. Compared with established predictors, mvPPT achieved superior performance in all test sets, regardless of data source. In addition, our study also provides guidance for training set and feature selection strategies, as well as reveals highly relevant features, which may further provide biological insights of variant pathogenicity.

bioinformatics↗

bioRxiv2021

Gene networks under circadian control exhibit diurnal organization in primate organs

Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and off-target genes across time. While interactions between these molecular clocks are likely necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8,120) of baboon genes are rhythmically expressed body-wide. In addition, >30% of gene-gene interactions exhibit periodic co-expression patterns, with core circadian genes more cyclically co-expressed than others. Moreover, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, we found that 216 of 313 genes encoding products that interact with SARS-CoV-2 are rhythmically expressed throughout the body. Importantly, more than 80% of SARS-CoV-2 related genes enriched modules are rhythmically expressed, and have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.

systems biology↗

bioRxiv2020

Degradation of SARS-CoV-2 receptor ACE2 by tobacco carcinogen-induced Skp2 in lung epithelial cells

An unexpected observation among the COVID-19 pandemic is that smokers constituted only 1.4-18.5% of hospitalized adults, calling for an urgent investigation to determine the role of smoking in SARS-CoV-2 infection. Here, we show that cigarette smoke extract (CSE) and carcinogen benzo(a)pyrene (BaP) increase ACE2 mRNA but trigger ACE2 protein catabolism. BaP induces an aryl hydrocarbon receptor (AhR)-dependent upregulation of the ubiquitin E3 ligase Skp2 for ACE2 ubiquitination. ACE2 in lung tissues of non-smokers is higher than in smokers, consistent with the findings that tobacco carcinogens downregulate ACE2 in mice. Tobacco carcinogens inhibit SARS-CoV-2 Spike protein pseudovirions infection of the cells. Given that tobacco smoke accounts for 8 million deaths including 2.1 million cancer deaths annually and Skp2 is an oncoprotein, tobacco use should not be recommended and cessation plan should be prepared for smokers in COVID-19 pandemic.

cell biology↗

bioRxiv2019

MECP2 duplication causes aberrant GABA pathways, circuits and behaviors in transgenic monkeys: neural mappings to patients with autism

MECP2 gain- and loss-of-function in genetically-engineered monkeys demonstrably recapitulate typical phenotypes in patients, yet where MECP2 mutation affects the monkey brain and whether/how it relates to autism pathology remains unknown. Using expression profiles of 13,888 genes in 182 macaque neocortical samples, we first show that MECP2 coexpressed genes are enriched in GABA-related signaling pathways. We then perform analyses on multiple phenotypic levels including locomotive and cognitive behavior, resting-state electroencephalography and fMRI in MECP2 overexpressed and wild-type macaque monkeys. Behaviorally, transgenic monkeys exhibit hyperactive and repetitive locomotion, greater separation anxiety response, and less flexibility in rule switching. Moreover, decreased neural synchronization at beta frequency (12-30 Hz) is associated with greater locomotion after peer separation. Further analysis of fMRI-derived connectomics reveals widespread hyper- and hypo-connectivity, where hyper-connectivity prominently involving prefrontal and cingulate networks accounts for deficits in cognitive flexibility. To map MECP2-related aberrant circuits of monkeys to the pathological circuits of autistic patients, individuals in a large public neuroimaging database of autism were clustered using community detection on functional connectivity patterns. In a stratified cohort of 49 autisms and 72 controls, the dysfunctional connectivity profile particularly in prefrontal and temporal networks is highly correlated with that of transgenic monkeys, as is further responsible for the severity of social communicative deficits in patients. Through establishing a circuit-based construct link between transgenic animal models and stratified clinical patients, the present findings with explicable biological causes are potentially amenable to translation for accurate diagnosis and evaluation of future treatments in autism-related disorders.\n\nOne sentence summaryWe identify shared circuit-level abnormalities between MECP2 transgenic monkeys and a stratified subgroup of human autism, and demonstrate the translational need of a multimodal approach to capture multifaceted effects triggered by a single genetic event in a genetically-engineered primate model.

neuroscience↗

bioRxiv2018

Genome-wide identification of CDC34 that stabilizes EGFR and promotes lung carcinogenesis

To systematically identify ubiquitin pathway genes that are critical to lung carcinogenesis, we used a genome-wide silencing method in this study to knockdown 696 genes in non-small cell lung cancer (NSCLC) cells. We identified 31 candidates that were required for cell proliferation in two NSCLC lines, among which the E2 ubiquitin conjugase CDC34 represented the most significant one. CDC34 was elevated in tumor tissues in 67 of 102 (65.7%) NSCLCs, and smokers had higher CDC34 than nonsmokers. The expression of CDC34 was inversely associated with overall survival of the patients. Forced expression of CDC34 promoted, whereas knockdown of CDC34 inhibited lung cancer in vitro and in vivo. CDC34 bound EGFR and competed with E3 ligase c-Cbl to inhibit the polyubiquitination and subsequent degradation of EGFR. In EGFR-L858R and EGFR-T790M/Del(exon 19)-driven lung cancer in mice, knockdown of CDC34 by lentivirus mediated transfection of short hairpin RNA significantly inhibited tumor formation. These results demonstrate that an E2 enzyme is capable of competing with E3 ligase to inhibit ubiquitination and subsequent degradation of oncoprotein substrate, and CDC34 represents an attractive therapeutic target for NSCLCs with or without drug-resistant EGFR mutations.

cancer biology↗

bioRxiv2017

Genomic variations in paired normal controls for lung adenocarcinomas

Somatic genomic mutations in lung adenocarcinomas (LUADs) have been extensively dissected, but whether the counterpart normal lung tissues that are exposed to ambient air or tobacco smoke as the tumor tissues do, harbor genomic variations, remains unclear. Here, the genome of normal lung tissues and paired tumors of 11 patients with LUAD were sequenced, the genome sequences of counterpart normal controls (CNCs) and tumor tissues of 513 patients were downloaded from TCGA database and analyzed. In the initial screening, genomic alterations were identified in the \"normal\" lung tissues and verified by Sanger capillary sequencing. In CNCs of TCGA datasets, a mean of 0.2721 exonic variations/Mb and 5.2885 altered genes per sample were uncovered. The C:G[->]T:A transitions, a signature of tobacco carcinogen N-methyl-N-nitro-N-nitrosoguanidine, were the predominant nucleotide changes in CNCs. 16 genes had a variant rate of more than 2%, and CNC variations in MUC5B, ZXDB, PLIN4, CCDC144NL, CNTNAP3B, and CCDC180 were associated with poor prognosis whereas alterations in CHD3 and KRTAP5-5 were associated with favorable clinical outcome of the patients. This study identified the genomic alterations in CNC samples of LUADs, and further highlighted the DNA damage effect of tobacco on lung epithelial cells.

cancer biology↗