Biology subjects

Cheng, Q.

Publications and source records attributed to Cheng, Q..

51 records · Page 3Linked to original sources

bioRxiv2021

Redox regulation of PTPN22 affects the severity of T cell dependent autoimmune inflammation

Chronic autoimmune diseases are associated with mutations in PTPN22, a modifier of T cell receptor signaling. As with all protein tyrosine phosphatases the activity of PTPN22 is redox regulated, but if or how such regulation can modulate inflammatory pathways in vivo is not known. To determine this, we created a mouse with a cysteine-to-serine mutation at position 129 in PTPN22 (C129S), a residue proposed to alter the redox regulatory properties of PTPN22 by forming a disulfide with the catalytic C227 residue. The C129S mutant mouse showed a stronger T cell-dependent inflammatory response and development of T cell dependent autoimmune arthritis due to enhanced TCR signaling and activation of T cells, an effect neutralized by a mutation in Ncf1, a component of the NOX2 complex. Activity assays with purified proteins suggest that the functional results can be explained by an increased sensitivity to oxidation of the C129S mutated PTPN22 protein. We also observed that the disulfide of native PTPN22 can be directly reduced by the thioredoxin system, while the C129S mutant lacking this disulfide was less amenable to reductive reactivation. In conclusion, we show that PTPN22 functionally interacts with Ncf1 and is regulated by oxidation via the non-catalytic C129 residue and oxidation-prone PTPN22 leads to increased severity in the development of T cell-dependent autoimmunity. Significance statementA hitherto unstudied aspect of PTPN22 biology is its regulation by cell redox states. Here we created a mouse model where PTPN22 was mutated to respond differentially to redox levels in vivo and found that PTPN22 function is regulated by reactive oxygen species and that redox regulation of PTPN22 impacts T-cell-dependent autoimmune inflammation.

immunology↗

bioRxiv2021

Microscopic Quantification of Oxygen Consumption across Cortical Layers

The cerebral cortex is organized in cortical layers that differ in their cellular density, composition, and wiring. Cortical laminar architecture is also readily revealed by staining for cytochrome oxidase - the last enzyme in the respiratory electron transport chain located in the inner mitochondrial membrane. It has been hypothesized that a high-density band of cytochrome oxidase in cortical layer IV reflects higher oxygen consumption under baseline (unstimulated) conditions. Here, we tested the above hypothesis using direct measurements of the partial pressure of O2 (pO2) in cortical tissue by means of 2-photon phosphorescence lifetime microscopy (2PLM). We revisited our previously developed method for extraction of the cerebral metabolic rate of O2 (CMRO2) based on 2-photon pO2 measurements around diving arterioles and applied this method to estimate baseline CMRO2 in awake mice across cortical layers. To our surprise, our results revealed a decrease in baseline CMRO2 from layer I to layer IV. This decrease of CMRO2 with cortical depth was paralleled by an increase in tissue oxygenation. Higher baseline oxygenation and cytochrome density in layer IV may serve as an O2 reserve during surges of neuronal activity or certain metabolically active brain states rather than baseline energy needs. Our study provides the first quantification of microscopically resolved CMRO2 across cortical layers as a step towards informed interpretation and modeling of cortical-layer-specific Blood Oxygen Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) signals.

neuroscience↗

bioRxiv2020

A novel high-accuracy genome assembly method utilizing a high-throughput workflow

Across domains of biological research using genome sequence data, high-quality reference genome sequences are essential for characterizing genetic variation and understanding the genetic basis of phenotypes. However, the construction of genome assemblies for various species is often hampered by complexities of genome organization, especially repetitive and complex sequences, leading to mis-assembly and missing regions. Here, we describe a high-throughput gold standard genome assembly workflow using a large-scale bacterial artificial chromosome (BAC) library with a refined two-step pooling strategy and the Lamp assembler algorithm. This strategy minimizes the laborious processes of physical map construction and clone-by-clone sequencing, enabling inexpensive sequencing of several thousand BAC clones. By applying this strategy with a minimum tiling path BAC clone library for the short arm of chromosome 2D (2DS) of bread wheat, 98% of BAC sequences, covering 92.7% of the 2DS chromosome, were assembled correctly for this species with a highly complex and repetitive genome. We also identified 48 large mis-assemblies in the reference wheat genome assembly (IWGSC RefSeq v1.0) and corrected these large mis-assemblies in addition to filling 92.2% of the gaps in RefSeq v1.0. Our 2DS assembly represents a new benchmark for the assembly of complex genomes with both high accuracy and efficiency.

genomics↗

bioRxiv2020

Structural Basis of Complex Formation Between Mitochondrial Anion Channel VDAC1 and Hexokinase-II

Complex formation between hexokinase-II (HKII) and the mitochondrial channel VDAC1 plays a crucial role in regulating cell growth and survival; however, structural details of this complex remain elusive. We hypothesize that a conserved, hydrophobic helix (H-anchor) of HKII first inserts into the outer membrane of mitochondria (OMM) and then interacts with VDAC1 on the cytosolic leaflet of OMM to form a binary complex. To systematically investigate this process, we adopted a hybrid approach: 1) the membrane binding of HKII was first described with molecular dynamics (MD) simulations employing a membrane mimetic model with enhanced lipid diffusion, then 2) the resulting membrane-bound HKII was used to form complex with VDAC1 in millisecond-scale Brownian dynamics (BD) simulations. We show that H-anchor inserts its first 10 residues into the membrane, substantiating previous experimental findings. The insertion depth of the H-anchor was used to derive positional restraints in subsequent BD simulations to preserve the membrane-bound pose of HKII during the formation of the HKII/VDAC1 binary complex. Multiple BD-derived structural models were further refined with MD simulations, resulting in one stable complex. A major feature in the complex is the partial (not complete) blockade of VDAC1s permeation pathway by HKII, a result supported by our comparative electrophysiological measurements of the channel in the presence and absence of HKII. Additionally, we showed how VDAC1 phosphorylation disrupts HKII binding, a feature that is verified by our electrophysiology recordings and have implications in mitochondria-mediated cell death.

biophysics↗

bioRxiv2020

MicroRNA variants and HLA-miRNA interactions are novel rheumatoid arthritis susceptibility factors

ObjectiveAlthogh Genome-wide association studies have identified >100 variants for rheumatoid arthritis (RA),the reported genetic variants only explain <40% of RA heritability. We conducted a systemic association study between common East-Asian miRNA SNPs with RA in a large Han Chinese cohort to explain missing heritability and identify miRNA epistatic interactions. Methods4 HLA SNPs (HLA-DRB1, HLA-DRB9, HLA-DQB1 and TNFAIP3) and 225 common SNPs located in miRNA which might influence the miRNA target binding or pre-miRNA stability were genotyped in 1,607 rheumatoid arthritis and 1,580 matched normal individuals. A meta-analysis with previous GWAS studies (4,873 RA cases and 17,642 controls) was performed to discovery another novel miRNA RA-associated SNPs. Results2 novel SNPs including rs1414273 (miR-548ac, OR=0.84, P=8.26x10-4) and rs2620381 (miR-627, OR=0.77, P=2.55x10-3) conferred significant association with RA. Individuals carried 8 risk alleles showed 15.38 (95%CI: 4.69-50.49, P<1.0x10-6) times more risk to be affected by RA. In addition, rs5997893 (miR-3928) showed significant epistasis effect with rs4947332 (HLA-DRB1, OR=4.23, P=0.04) and rs2967897 (miR-5695) with rs7752903 (TNFAIP3, OR=4.43, P=0.03). Finally, we demonstrated targets of the significant miRNAs showed enrichment in immune related genes (P=2.0x10-5) and FDA approved drug target genes (P=0.014). Conclusions6 novel miRNA SNPs including rs1414273 (miR-548ac, P=8.26x10-4), rs2620381 (miR-627, P=2.55x10-3), rs4285314 (miR-3135b, P=1.10x10-13), rs28477407 (miR-4308, P=3.44x10-5), rs5997893 (miR-3928, P=5.9x10-3) and rs45596840 (miR-4482, P=6.6x10-3) were confirmed to be significantly associated with RA in a Chinese population. Our study suggests that miRNAs might be interesting targets to accelerate the understanding of the pathogenesis and drug development for rheumatoid arthritis.

genetics↗

bioRxiv2020

Dexmedetomidine mediates microRNA-185 to suppress ovarian cancer growth via inhibiting the SOX9/Wnt/β-catenin signaling pathway

ObjectiveCervical cancer (CC) is a serious health problem, causing heavy burden each year worldwide. Long non-coding RNA (lncRNA) is implicated in CC progression. This study aims to clarify the underlying mechanism of lncRNA HAND2-AS1 in CC cellular process. MethodLncRNA expression in CC tissues and adjacent tissues was analyzed via Gene Expression Omnibus microarray. The levels of HAND2-AS1, microRNA (miR)-21 and ALEX1 in 54 cases of CC tissues, adjacent tissues, CC cell lines and normal breast cells were detected. The malignant biological behaviors of CC cells were detected after overexpressing HAND2-AS1 or inhibiting miR-21 expression. Bioinformatics analysis, luciferase reporter assay and RNA-pull down assay were performed to verify the relationship among HAND2-AS1, miR-21 and ALEX1. The effects of HAND2-AS1 on CC cell growth in vivo were verified by nude mice transplantation experiment. ResultsHAND2-AS1 was lowly expressed in CC tissues and cells, and HAND2-AS1 functioned as a competitive endogenous RNA of miR-21 and enhanced ALEX1 expression and downregulated JAK1/STAT3 pathway to inhibit malignant biological behavior of CC cells. Overexpression of HAND2-AS1 or silencing of miR-21 can inhibit the malignant biological behavior of CC cells. Moreover, overexpression of HAND2-AS1 can inhibit the growth of CC cells in vivo. ConclusionOur data supported that lncRNA HAND2-AS1 promoted ALEX1 expression through competitively binding to miR-21, thereby inhibiting the proliferation and epithelial-mesenchymal transformation and promoting apoptosis of CC cells.

cell biology↗

bioRxiv2019

Oncogenic Gata1 causes stage-specific megakaryocyte differentiation delay

The megakaryocyte/erythroid Transient Myeloproliferative Disorder (TMD) in newborns with Down Syndrome (DS) occurs when N-terminal truncating mutations of the hemopoietic transcription factor GATA1, that produce GATA1short protein (GATA1s), are acquired early in development. Prior work has shown that murine GATA1s, by itself, causes a transient yolk sac myeloproliferative disorder. However, it is unclear where in the hemopoietic cellular hierarchy GATA1s exerts its effects to produce this myeloproliferative state. Here, through a detailed examination of hemopoiesis from murine GATA1s ES cells and GATA1s embryos we define defects in erythroid and megakaryocytic differentiation that occur relatively in hemopoiesis. GATA1s causes an arrest late in erythroid differentiation in vivo, and even more profoundly in ES-cell derived cultures, with a marked reduction of Ter-119 cells and reduced erythroid gene expression. In megakaryopoiesis, GATA1s causes a differentiation delay at a specific stage, with accumulation of immature, kit-expressing CD41hi megakaryocytic cells. In this specific megakaryocytic compartment, there are increased numbers of GATA1s cells in S-phase of cell cycle and reduced number of apoptotic cells compared to GATA1 cells in the same cell compartment. There is also a delay in maturation of these immature GATA1s megakaryocytic lineage cells compared to GATA1 cells at the same stage of differentiation. Finally, even when GATA1s megakaryocytic cells mature, they mature aberrantly with altered megakaryocyte-specific gene expression and activity of the mature megakaryocyte enzyme, acetylcholinesterase. These studies pinpoint the hemopoietic compartment where GATA1s megakaryocyte myeloproliferation occurs, defining where molecular studies should now be focussed to understand the oncogenic action of GATA1s.\n\nScientific CategoryHaematopoiesis and Stem Cells\n\nKey PointsGATA1s-induced stage-specific differentiation delay increases immature megakaryocytes in vivo and in vitro, during development.\n\nDifferentiation delay is associated with increased numbers of cells in S-phase and reduced apoptosis.

cell biology↗

bioRxiv2019

A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWAS in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. The major limitation of these methods is that they cannot be used to elucidate the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWAS, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make use of widely available GWAS summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and false positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWAS data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues.

bioinformatics↗

bioRxiv2019

MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting linkage disequilibrium and horizontal pleiotropy

The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IV) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we propose a probabilistic model for MR analysis to identify the casual effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP). MR-LDP utilizes a computationally efficient parameter-expanded variational Bayes expectation-maximization (PX-VBEM) algorithm to estimate the parameter of interest and further calibrates the evidence lower bound (ELBO) for a likelihood ratio test. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods in terms of both type-I error control and point estimates. Moreover, we used two real exposure-outcome pairs (CAD-CAD and Height-Height; CAD for coronary artery disease) to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol (HDL-C) on peripheral vascular disease (PVD), and a positive causal effect of body mass index (BMI) on hemorrhoids.

genetics↗

bioRxiv2018

Dynamic alterations of retinal EphA5 expression in retinocollicular map plasticity

The topographically ordered retinocollicular projection is an excellent system for studying the mechanism of axon guidance. Gradients of EphA receptors in the retina and ephrin-As in the superior colliculus (SC) pattern the anteroposterior axis of the retinocollicular map, but whether they are involved in map plasticity after injury is unknown. Partial damage to the caudal SC at birth creates a compressed, complete retinotopic map in the remaining SC without affecting visual response properties. Previously, we found that the gradient of ephrinA expression in compressed maps is steeper than normal, suggesting an instructive role in compression (Tadesse et al., 2013). Here we measured EphA5 mRNA and protein levels after caudal SC damage in order to test the hypothesis that changes in retinal EphA5 expression occur that are complementary to the changes in collicular ephrin-A expression. We find that the nasotemporal gradient of EphA5 receptor expression steepens in the retina and overall expression levels change dynamically, especially in temporal retina, supporting the hypothesis. This change in receptor expression occurs after the change in ephrin-A ligand expression. We propose that changes in the retinal EphA5 gradient guide recovery of the retinocollicular projection from early injury. This could occur directly through the change in EphA5 expression instructing retino-SC map compression, or through ephrinA ligand signaling instructing a change in EphA5 receptor expression that in turn signals the retinocollicular map to compress. Understanding what molecular signals direct compensation for injury is essential to developing rehabilitative strategies and maximizing the potential for recovery.

neuroscience↗

bioRxiv2018

System-wide Identification of Enzyme Substrates by Thermal Analysis (SIESTA)

Despite the immense importance of enzyme-substrate reactions, there is a lack of generic and unbiased tools for identifying and prioritizing substrate proteins which are modulated in the structural and functional levels through modification. Here we describe a high-throughput unbiased proteomic method called System-wide Identification and prioritization of Enzyme Substrates by Thermal Analysis (SIESTA). The approach assumes that enzymatic post-translational modification of substrate proteins might change their thermal stability. SIESTA successfully identifies several known and novel substrate candidates for selenoprotein thioredoxin reductase 1, protein kinase B (AKT1) and poly-(ADP-ribose) polymerase-10 systems in up to a depth of 7179 proteins. Wider application of SIESTA can enhance our understanding of the role of enzymes in homeostasis and disease, open new opportunities in investigating the effect of PTMs on signal transduction, and facilitate drug discovery.

biochemistry↗

bioRxiv2018

Integrative data analysis predicts YY1 as a Cis-regulator in the 3D Cell Culture Models of MCF10A at the Stiffness Level of High Mammographic Density

Previous studies have shown that in 3D cell culture models of human mammary cells (HMEC) (i) colony organizations are heterogeneous, and (ii) ERBB2 is overexpressed in MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). The goal of the current study is to identify transcription factors that regulate processes associated with the increased stiffness of the microenvironment. Two HMEC premalignant lines of MCF7 and 184A1 are cultured in 3D, colonies are imaged using confocal microscopy, and colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment. In parallel and surrogate assays, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices, from 3D culture, and a subset of transcriptome data is selected. This subset is then processed with Model-based Analysis of Regulation of Gene Expression (MARGE) and publicly available ChIP-seq data to predict regulatory transcription factors. The integrative analysis indicated that YY1 regulates ERBB2 in the 3D cell culture of MCF10A when the stiffness of the microenvironment is increased to that of high MD. Subsequent experimental validation confirmed that YY1 is only expressed at the high stiffness value of the microenvironment concomitant with the overexpression of ERBB2 in MCF10A. Furthermore, using ERBB2 positive SKBR3 cell line, co-expression of YY1 and ERBB2 is absent, which indicates that YY1 regulates tumorigenicity through multiple pathways.\n\nAuthors summaryMCF10A is a premalignant immortalized human mammary cell that has been isolated from a patient with fibrocystic and lost several barriers toward transformation. In an earlier study, we showed that ERBB2 is upregulated in 3D cultures of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density. Here, we leverage publicly available ChIP-seq data to predict and validate the cis-regulator of ERBB2. Our integrated experimental and computation protocol provides a pathway for elucidating regulators that can potentially be targeted for intervention.

cancer biology↗

bioRxiv2018

Circadian gene Clock regulates mitochondrial morphology and functions by posttranscriptional way

Many daily activities are under the control of circadian clock, including nutrition metabolism and energy generation. Mitochondria, as the core factories of oxidizing substrates and producing ATP, undergo changes in quantity and morphology to adapt to the demand for energy. It has been demonstrated that mitochondrial gene expression, dynamics and functions are all affected by circadian clock. Here, we demonstrated that circadian gene Clock affects the number, architecture and function of mitochondria via posttranscriptional regulation of Drp1. Clock{Delta}19 leads to fragmented mitochondria accompanied with the loss of membrane potential, excessive ROS accumulation and decreased mitochondrial respiration and ATP generation. Clock{Delta}19 mice exhibit disordered lipid metabolism and evident nonalcoholic fatty liver disease (NAFLD), which are rescued by treatment with the mitochondrial fission inhibitor Mdivi-1. These results suggest a strong relationship between Clock, mitochondrial dynamics and metabolic diseases and provide a new perspective on disordered circadian clock and related diseases.

cell biology↗

bioRxiv2017

DeepGS: Predicting phenotypes from genotypes using Deep Learning

MotivationGenomic selection (GS) is a new breeding strategy by which the phenotypes of quantitative traits are usually predicted based on genome-wide markers of genotypes using conventional statistical models. However, the GS prediction models typically make strong assumptions and perform linear regression analysis, limiting their accuracies since they do not capture the complex, non-linear relationships within genotypes, and between genotypes and phenotypes.\n\nResultsWe present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypic markers when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional marker data. We used a large GS dataset to train DeepGS and compare its performance with other methods. In terms of mean normalized discounted cumulative gain value, DeepGS achieves an increase of 27.70%~246.34% over a conventional neural network in selecting top-ranked 1% individuals with high phenotypic values for the eight tested traits. Additionally, compared with the widely used method RR-BLUP, DeepGS still yields a relative improvement ranging from 1.44% to 65.24%. Through extensive simulation experiments, we also demonstrated the effectiveness and robustness of DeepGS for the absent of outlier individuals and subsets of genotypic markers. Finally, we illustrated the complementarity of DeepGS and RR-BLUP with an ensemble learning approach for further improving prediction performance.\n\nAvailabilityDeepGS is provided as an open source R package available at https://github.com/cma2015/DeepGS.

bioinformatics↗

bioRxiv2017

PEA: an integrated R toolkit for plant epitranscriptome analysis

MotivationThe epitranscriptome, also known as chemical modifications of RNA (CMRs), is a newly discovered layer of gene regulation, the biological importance of which emerged through analysis of only a small fraction of CMRs detected by high-throughput sequencing technologies. Understanding of the epitranscriptome is hampered by the absence of computational tools for the systematic analysis of epitranscriptome sequencing data. In addition, no tools have yet been designed for accurate prediction of CMRs in plants, or to extend epitranscriptome analysis from a fraction of the transcriptome to its entirety.\n\nResultsHere, we introduce PEA, an integrated R toolkit to facilitate the analysis of plant epitranscriptome data. The PEA toolkit contains a comprehensive collection of functions required for read mapping, CMR calling, motif scanning and discovery, and gene functional enrichment analysis. PEA also takes advantage of machine learning technologies for transcriptome-scale CMR prediction, with high prediction accuracy, using the Positive Samples Only Learning algorithm, which addresses the two-class classification problem by using only positive samples (CMRs), in the absence of negative samples (non-CMRs). Hence PEA is a versatile epitranscriptome analysis pipeline covering CMR calling, prediction, and annotation, and we describe its application to predict N6-methyladenosine (m6A) modifications in Arabidopsis thaliana. Experimental results demonstrate that the toolkit achieved 71.6% sensitivity and 73.7% specificity, which is superior to existing m6A predictors. PEA is potentially broadly applicable to the in-depth study of epitranscriptomics.\n\nAvailabilityPEA is implemented using R and available at https://github.com/cma2015/PEA.

bioinformatics↗