Biology subjects

Gong, W.

Publications and source records attributed to Gong, W..

At least 73 records · Page 4Linked to original sources

bioRxiv2020

Integrating multimeric threading with high-throughput experiments for structural interactome of Escherichia coli

Genome-wide protein-protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline to address both problems, which first recognizes PPIs by combining multi-chain threading alignments with HTE results using naive Bayesian classifiers, where the quaternary complex structures are then constructed by mapping the monomer models with the dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks revealed a scale-free cluster size distribution, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6,771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5 and 93 showed a close consistency with the later released experimental structures with an average TM-score=0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.

bioinformatics↗

bioRxiv2020

YY1 cistrome analysis uncovers an essential requirement of the YY1:BRD4-PFKP regulatory axis for promoting tumorigenesis of castration-resistant prostate cancer

Castration-resistant prostate cancer (CRPC) is a terminal disease, demanding a better understanding of its pathogenesis. Targeted therapy needs to be developed for CRPC due to its heterogeneity and resistance to current treatments. Here, through cistrome study of YY1, a transcription factor significantly overexpressed during prostate cancer progression, we identify a YY1-PFKP axis to be essential for CRPC tumorigenesis. Depletion of YY1 in independent CRPC models dramatically reduced tumor cell growth in vitro and delayed oncogenic progression in vivo. Importantly, YY1 functions as a master regulator of prostate tumor metabolism including the Warburg effect and mitochondria respiration. Loss-of-function and rescue studies further reveals a mechanistic underpinning in which YY1 directly binds and trans-activates PFKP, a gene encoding the rate-limiting enzyme for glycolysis, significantly contributing to the YY1-enforced oncogenic phenotypes such as enhanced tumor cell glycolysis and malignant growth. Additionally, a vast majority of gene-regulatory element in advanced prostate cancer cells are bound by YY1, lending a support for its role as a master regulator of prostate cancer progression. YY1 interactome studies point to bromodomain-containing coactivators in prostate cancer, which act as functional partners of YY1 to potentiate YY1-related target gene activation. Altogether, this study unveils an unexplored YY1:BRD4-PFKP oncogenic axis operating in advanced prostate cancer with implications for therapy.

cancer biology↗

bioRxiv2020

Assessing Study Reproducibility through MMRI: A Novel Approach for Large-scale High-throughput Association Studies

Magnetic Resonance Imaging (MRI) technology has been increasingly used in large-scale association studies. Reproducibility of statistically significant findings generated by MRI-based association studies, especially structural MRI (sMRI) and functional MRI (fMRI), has been recently heavily debated. However, there is still a lack of overall reproducibility assessment for MRI-based association studies. It is also crucial to elucidate the relationship between overall reproducibility and sample size in an experimental design. In this study, we proposed an overall reproducibility index for large-scale high-throughput MRI-based association studies. We performed the overall reproducibility assessments for several recent large sMRI/fMRI databases and observed satisfactory overall reproducibility. Furthermore, we performed the sample size evaluation for the purpose of achieving a desirable overall reproducibility. Additionally, we evaluated the overall reproducibility of GMV changes for UKB vs. PPMI and UKB vs. HCP. We demonstrated that both sample size and some experimental factors play important roles in the overall reproducibility for different experiments. In summary, a systematic assessment of overall reproducibility is fundamental and crucial in the current large-scale high-throughput MRI-based research.

neuroscience↗

bioRxiv2020

Requirement of the antimicrobial peptide CRAMP for macrophages to eliminate phagocytosed E. coli through an autophagy pathway

Host-derived antimicrobial peptides play an important role in the defense against extracellular bacterial infections. However, the capacity of antimicrobial peptides derived from macrophages as potential antibacterial effectors against intracellular pathogens remains unknown. In this study, we report that normal (wild type, WT) mouse macrophages increased their expression of the cathelicidin-related antimicrobial peptide (CRAMP) after infection by viable E. coli or stimulation with inactivated E. coli and its product LPS, a process involving activation of NF-{kappa}B followed by protease-dependent conversion of CRAMP from an inactive precursor to an active form. The active CRAMP was required by WT macrophages to eliminate phagocytosed E. coli, with participation of autophagy-related proteins ATG5, LC3-II, and LAMP-1 as well as conjugation of the bacteria with p62. The autophagy-mediated elimination of E. coli was impaired in CRAMP-/- macrophages resulting in retention of intracellular bacteria and fragmentation of macrophages. These results indicate CRAMP as a critical component in autophagy-mediated clearance of intracellular E. coli by macrophages.

immunology↗

bioRxiv2020

Astrocytes express DMT1 and transferrin receptors, which transport iron thus activating Ca2+ signalling: possible role in neuroprotection against iron overload?

ABSTRACTIron is the fundamental element for numerous physiological functions. Reduced ferrous (Fe2+) and oxidized ferric (Fe3+) are the two ionized iron states in the living organisms. In the cell membrane, divalent metal ion transporter 1 (DMT1) is responsible for cellular uptake of Fe2+, whereas transferrin receptors (TFR) carry transferrin (TF)-bound Fe3+. In this study we performed, for the first time, detailed analysis of the action of Fe ions on cytoplasmic free calcium ion concentration ([Ca2+]i) in astrocytes. Using qPCR and immunocytochemistry we identified DMT1 and TFR in astrocytes in primary cultures, in acutely isolated astrocytes and in brain tissue preparations; in situ both DMT1 and TFR are concentrated in astroglial perivascular endfeet. Administration of Fe2+ or Fe3+ in low μM concentrations evoked Ca2+ signals in astrocytes in vitro and in vivo. Iron ions triggered increase in [Ca2+]i by acting through two distinct molecular cascades. Uptake of Fe2+ by DMT1 inhibited astroglial Na+-K+-ATPase (NKA), which led to an elevation in cytoplasmic Na+ concentration (as measured by SBFI probe), thus reversing Na+/Ca2+ exchanger (NCX) thereby generating Ca2+ influx. Uptake of Fe3+ by TF-TFR stimulated phospholipase C to produce inositol 1,4,5-trisphosphate (InsP3), thus trigering InsP3 receptor-mediated Ca2+ release from the endoplasmic reticulum. Iron-induced Ca2+ signals promote astroglial release of arachidonic acid and prostaglandin E2 cytokines by activating cytosolic phospholipase A2 (cPLA2) and NF-κB signalling cascade. In summary, these findings reveal new mechanisms of iron-induced astrocytic signalling operational in conditions of iron overload, in response to which astrocytes actively accumulate excessive iron and activate neuroprotective pathways.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗

bioRxiv2020

CRTC1-MAML2 Establishes a PGC1α-IGF1 Circuit that Confers Vulnerability to PPARγ Inhibition

Mucoepidermoid carcinoma (MEC) is a life-threatening salivary gland cancer that is driven primarily by the transcriptional co-activator fusion CRTC1-MAML2. The mechanisms by which the chimeric CRTC1-MAML2 oncoprotein rewires gene expression programs that promote tumorigenesis remain poorly understood. Here, we show that CRTC1-MAML2 induces transcriptional activation of the non-canonical PGC-1 splice variant PGC-14, which regulates PPAR{gamma}-dependent IGF-1 expression. This mitogenic transcriptional circuitry is consistent across cell lines and primary tumors. CRTC1-MAML2 positive tumors are dominated by IGF-1 pathway activation and small molecule drug screens reveal that tumor cells harboring the fusion gene are selectively sensitive to IGF-1R inhibition. Furthermore, this dependence on autocrine regulation of IGF-1 transcription renders MEC cells susceptible to PPAR{gamma}inhibition with inverse agonists. These results yield insights into the aberrant co-regulatory functions of CRTC1-MAML2 and identify a specific vulnerability that can be exploited for precision therapy.

cancer biology↗

bioRxiv2020

Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Existing labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescence indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended times. We report on a novel dual-mode high-density microelectrode array, which can simultaneously record in i) full-frame mode with 19,584 recording sites and ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we developed reliable analysis tools with drastically increased throughput for extracting axonal morphology and conduction parameters.

neuroscience↗

bioRxiv2020

SARS-CoV-2 neutralizing serum antibodies in cats: a serological investigation

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, China, and rapidly spread worldwide. Previous studies suggested cat could be a potential susceptible animal of SARS-CoV-2. Here, we investigated the infection of SARS-CoV-2 in cats by detecting specific serum antibodies. A cohort of serum samples were collected from cats in Wuhan, including 102 sampled after COVID-19 outbreak, and 39 prior to the outbreak. 15 of 102 (14.7%) cat sera collected after the outbreak were positive for the receptor binding domain (RBD) of SARS-CoV-2 by indirect enzyme linked immunosorbent assay (ELISA). Among the positive samples, 11 had SARS-CoV-2 neutralizing antibodies with a titer ranging from 1/20 to 1/1080. No serological cross-reactivity was detected between the SARS-CoV-2 and type I or II feline infectious peritonitis virus (FIPV). Our data demonstrates that SARS-CoV-2 has infected cat population in Wuhan during the outbreak.

microbiology↗

bioRxiv2020

Iatrogenic Iron Promotes Neurodegeneration and Activates Self-protection of Neural Cells against Exogenous Iron Attacks

Metal implants are used worldwide, with millions of metal nails, plates and fixtures grafted during orthopaedic surgeries. Iron is the most common element of these metal implants. As time passes metal elements can be corroded and iron can be released from the implants in the form of ferric (Fe3+) or ferrous (Fe2+). These iron ions can permeate the surrounding tissues and enter circulation; importantly both Fe3+ and Fe2+ freely pass blood brain barrier (BBB). Can iron from implants represent a risk factor for neurological diseases? This remains an unanswered question. In this study, we discovered that the probability of metal implants delivered through orthopaedic surgeries was higher in patients of Parkinsons diseases (PD) or ischemic stroke than in healthy subjects. This finding instigated subsequent study of iron effects on neuronal cells. In experiments in vivo, we found that iron selectively decreased presence of divalent metal transporter 1 (DMT1) in neurones through increasing the expression of Ndfip1, which degrades DMT1 and rarely exists in glial cells. At the same time iron accumulation increased expression of DMT1 in astrocytes and microglial cells and triggered reactive astrogliosis and microglial activation. Facing the attack of excess iron, glial cells act as neuroprotectors to uptake more extracellular iron by up-regulating DMT1, whereas neurones limit iron uptake through decreasing DMT1 operation. Cerebral accumulation of iron was associated with impaired cognition, locomotion and mood. Excess iron thus affects neural cells and could increase the risk of neurodegeneration.

neuroscience↗

bioRxiv2020

Phenotype Discovery from Population Brain Imaging

Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.

neuroscience↗

bioRxiv2019

Accurate brain age prediction with lightweight deep neural networks

Deep learning has huge potential for accurate disease prediction with neuroimaging data, but the prediction performance is often limited by training-dataset size and computing memory requirements. To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data. Compared with other popular deep network architectures, SFCN has fewer parameters, so is more compatible with small dataset size and 3D volume data. The network architecture was combined with several techniques for boosting performance, including data augmentation, pre-training, model regularization, model ensemble and prediction bias correction. We compared our overall SFCN approach with several widely-used machine learning models. It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.5% in sex classification. SFCN also won (both parts of) the 2019 Predictive Analysis Challenge for brain age prediction, involving 79 competing teams (N = 2,638, MAE = 2.90y). We describe here the details of our approach, and its optimisation and validation. Our approach can easily be generalised to other tasks using different image modalities, and is released on GitHub. HighlightsO_LIA lightweight deep learning model, Simple Fully Convolutional Network (SFCN), is presented, achieving state-of-the-art brain age prediction and sex classification performance in UK Biobank MRI brain imaging data. C_LIO_LIEven with limited number of training subjects (e.g., 50), SFCN performs better than widely-used regression models. C_LIO_LIA semi-multimodal ensemble strategy is proposed and achieved first place in the PAC 2019 brain age prediction challenge. C_LIO_LILinear regression can remove brain age prediction bias (even on unlabelled data) while maintaining state-of-the-art performance. C_LI

neuroscience↗

bioRxiv2019

Genome-wide quantitative trait loci mapping on Verticilliumw

Cotton Verticillium wilt (VW) is a devastating disease seriously affecting fiber yield and quality, and the most effective and economical prevention measure at present is selection and extension of Gossypium varieties harboring high resistant VW. However, multiple attempts to improve the VW resistance of the most widely cultivated Upland cotton have brought in little significant progress, and it seems necessary and urgent to develop Chromosome segment substitution lines (CSSLs) for merging the superior genes related with high yield and wide adaptation from G. hirsutum and VW resistance and excellent fiber quality from G. barbadense. In this study, 300 CSSLs were chosen from the developed BC5F3:5 CSSLs constructed by G. hirsutum CCRI36 and G. barbadense Hai1 to conduct quantitative trait locus (QTL) mapping on VW resistance, and a total of 53 QTLs relevant to VW disease index (DI) were identified together with the phenotypic data of 2 years investigations in two fields with two replications per year. All the QTLs were distributed on 20 chromosomes with phenotypic variation of 3.74-11.89%, of which 29 stable ones were consistent in at least two environments. Based on Meta-analysis on the 53 QTLs, 43 novel ones were identified, while 10 ones consistent to previously identified QTLs. Meanwhile, 32 QTL hotspot regions were detected, including 15 ones were novel. This study concentrates on QTL identification and screening hotspot region related with VW in the 300 CSSLs, which lay a solid platform not only for revealing the genetic and molecular mechanisms of VW resistance, but also for further fine mapping, gene cloning and molecular designing in breeding program for resistant cotton varieties.

plant biology↗

bioRxiv2019

A novel algorithm for the collective integration of single cell RNA-seq during embryogenesis

Single cell RNA-seq (scRNA-seq) over specified time periods has been widely used to dissect the cell populations during mammalian embryogenesis. Integrating such scRNA-seq data from different developmental stages and from different laboratories is critical to comprehensively define and understand the molecular dynamics and systematically reconstruct the lineage trajectories. Here, we describe a novel algorithm to integrate heterogenous temporal scRNA-seq datasets and to preserve the global developmental trajectories. We applied this algorithm and approach to integrate 3,387 single cells from seven heterogenous temporal scRNA-seq datasets, and reconstructed the cell atlas of early mouse cardiovascular development from E6.5 to E9.5. Using this integrated atlas, we identified an Etv2 downstream target, Ebf1, as an important transcription factor for mouse endothelial development.

bioinformatics↗

bioRxiv2018

High glucose-induced ubiquitylation of G6PD leads to the injury of podocyte

Oxidative stress contributes substantially to podocyte injury in diabetic kidney disease. The mechanism of hyperglycemia-induced oxidative stress in podocytes is not fully understood. Glucose-6-phosphate dehydrogenase is critical in maintaining NADPH, an important cofactor for antioxidant system. Here, we hypothesized that high glucose induces ubiquitylation and degradation of G6PD, which injures podocytes by reactive oxygen species (ROS) accumulation. We found that both G6PD protein expression and G6PD activity was decreased in kidneys of both diabetic patients and diabetic rodents. Overexpressing G6PD reversed redox imbalance and podocyte apoptosis induced by high glucose and palmitate. Inhibition of G6PD induced podocyte apoptosis. In G6PD deficient mice, podocyte apoptosis was also largely increased. High glucose had no effect on G6PD mRNA level but it caused decreased G6PD protein expression, which was mediated by the ubiquitin proteasome pathway. Furthermore, von Hippel-Lindau (VHL), an E3 ubiquitin ligase subunit, directly bound to G6PD and degraded G6PD through ubiquitylating G6PD on lysine residues 366/403. Our data suggest that high glucose induces ubiquitylation of G6PD by VHL, which leads to ROS accumulation and podocyte injury.

cell biology↗

bioRxiv2018

LADL: Light-activated dynamic looping for endogenous gene expression control

Mammalian genomes are folded into tens of thousands of long-range looping interactions1,2. The cause and effect relationship between looping and genome function is poorly understood, and the extent to which chromatin loops are dynamic on short time scales remains a fundamental unanswered question. Currently available strategies for loop engineering involve synthetic transcription factors tethered to dCas93,4 or zinc fingers5,6, which are constitutively expressed5,6 or induced on long time scales by the presence of a small molecule3. Here we report a new class of 3-D optoepigenetic tools for the directed rearrangement of 3-D chromatin looping on short time scales using blue light. We create synthetic architectural proteins by fusing the CIBN protein subunit from Arabidopsis thaliana with enzymatically dead Cas9 (dCas9). We target our light-activated dynamic looping system (LADL) to two genomic anchors with CRISPR guide RNAs and engineer their spatial co-localization via light-induced heterodimerization of the cryptochrome 2 (CRY2) protein with dCas9-CIBN. We apply LADL to redirect a stretch enhancer (SE) away from its endogenous Klf4 target gene and to the Zfp462 promoter. Looping changes occur as early as four hours after light induction. Using single molecule RNA FISH, we observe a LADL-induced increase in the total nascent Zfp462 transcripts and the number of Zfp462 alleles expressing simultaneously per cell. Moreover, LADL also increased synchronous Sox2 expression after reinforcement of a known Sox2-SE looping interaction. LADL facilitates loop synchronization across a large population of cells without exogenous chemical cofactors and can enable future efforts to engineer reversible and oscillatory looping on short time scales.

molecular biology↗

bioRxiv2018

5C-ID: Increased resolution Chromosome-Conformation-Capture-Carbon-Copy with in situ 3C and double alternating primer design

Mammalian genomes are folded in a hierarchy of compartments, topologically associating domains (TADs), subTADs and looping interactions. Currently, there is a great need to evaluate the link between chromatin topology and genome function across many biological conditions and genetic perturbations. Hi-C generates high quality, high resolution maps of looping interactions genome-wide, but is intractable for high-throughput screening of loops across conditions due to the requirement of an enormous number of reads (>6 Billion) per library. Here, we describe 5C-ID, an updated version of Chromosome-Conformation-Capture-Carbon-Copy (5C) with restriction digest and ligation performed in the nucleus (in situ Chromosome-Conformation-Capture (3C)) and ligation-mediated amplification performed with a new double alternating design. 5C-ID reduces spatial noise and enables higher resolution 3D genome folding maps than canonical 5C, allowing for a marked improvement in sensitivity and specificity of loop detection. 5C-ID enables the creation of high-resolution, high-coverage maps of chromatin loops in up to a 30 Megabase subset of the genome at a fraction of the cost of Hi-C.

genomics↗

bioRxiv2018

Identifying associations in dense connectomes using structured kernel principal component regression

A powerful and computationally efficient multivariate approach is proposed here, called structured kernel principal component regression (sKPCR), for the identification of associations in the voxel-level dense connectome. The method can identify voxel-phenotype associations based on the voxels whole-brain connectivity pattern, which is applicable to detect linear and non-linear signals for both volume-based and surface-based functional magnetic resonance imaging (fMRI) data. For each voxel, our approach first extracts signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations via a general linear model. The method derives its power by appropriately modelling the spatial structure of the data. Simulations based on dense connectome data have shown that our method can accurately control the false-positive rate, and it is more powerful than many state-of-the-art approaches, such as the connectivity-wise general linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). To demonstrate the utility of our approach in real data analysis, we apply these methods to identify voxel-wise difference between schizophrenic patients and healthy controls in two independent resting-state fMRI datasets. The findings of our approach have a better between-sites reproducibility, and a larger proportion of overlap with existing schizophrenia findings. Code for our approach can be downloaded from https://github.com/weikanggong/vBWAS.

neuroscience↗

bioRxiv2017

DrImpute: Imputing dropout events in single cell RNA sequencing data

The single cell RNA sequencing (scRNA-seq) technique began a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events. However, many statistical methods used for analyzing scRNA-seq data in cell type identification, visualization, and lineage reconstruction do not model for dropout events. We have developed DrImpute to impute dropout events, and it improves many of the statistical tools used for scRNA-seq analysis that do not account for dropout events. Our numerical studies with real data demonstrate the promising performance of the proposed method, which has been implemented in R.

bioinformatics↗