Biology subjects

Ji, J.

Publications and source records attributed to Ji, J..

88 records · Page 5Linked to original sources

bioRxiv2020

High-resolution architecture of human epiphysis formation

Human limb skeletal system consists of both bone and cartilage which originated from fetal cartilage. However, the roadmap of chondrocyte divergent differentiation to bone and articular cartilage has yet to be established. Epiphysis possesses articular cartilage, growth plate and the secondary ossification center (SOC), making it an ideal model to uncover the trajectory of chondrocyte divergent differentiation. Here, we mapped differentiation trajectory of human chondrocyte during postnatal finger epiphysis development by using single-cell RNA sequencing. Our results uncovered that chondroprogenitors have two differentiation pathways to hypertrophic chondrocytes during ossification, and one pathway to articular chondrocytes for formation of cartilages. Interestingly, we found that, as an addition to the known typical endochondral ossification path from resting, proliferative to hypertrophic chondrocytes, there was a bypass by which chondroprogenitors differentiate into hypertrophic chondrocytes without proliferative stage. Furthermore, our results revealed two new chondrocyte subpopulations (bypass chondrocytes as it appeared in the ossification bypass, and ID1+ chondroblasts in articular chondrocyte path) during postnatal epiphysis development in addition to six well-known subpopulations. Overall, our study provides a comprehensive roadmap of chondrocyte differentiation in human epiphysis thereby expanding the knowledge of bone and articular cartilage, which could be utilized to design biotherapeutics for bone and articular cartilage regeneration.

developmental biology↗

bioRxiv2020

Vulnerability of ARID1A deficient cancer cells to pyrimidine synthesis blockade

Here we report the discovery and preclinical validation of a novel precision medicine strategy for ARID1A-mutated cancer. Unbiased proteomics reveals for the first time that ARID1A protein (BAF250a) binds aspartate transcarbamoylase (ATCase), a key regulatory enzyme of the de novo pyrimidine synthesis pathway. Using isogenic paired ARID1A proficient/deficient cancer cell lines, we show that ARID1A protein deficiency (as occurs in ARID1A mutant cancers) leads to metabolic reprogramming and pyrimidine synthesis dependency. Pyrimidine synthesis blockade using the FDA-approved drug teriflunomide (a DHODH inhibitor) suppresses tumor growth and selectively induces DNA damage in ARID1A-deficient tumor models. Combining pyrimidine synthesis inhibition with DNA damage repair blockade, using teriflunomide and AZD6738 (an ATR inhibitor), achieves potent synergy and induces sustained tumor regression in ARID1A-mutant ovarian cancer patient-derived xenografts (PDX). These compelling preclinical data support the evaluation of this novel combination treatment in patients with ARID1A-mutated cancers. SIGNIFICANCEWe identified that ARID1A-deficient cells are selectively vulnerable to pyrimidine synthesis blockade. Preclinical studies demonstrate the in vivo efficacy of a synergistic drug combination that concurrently inhibits the de novo pyrimidine synthesis pathway and DNA damage repair to induce regression in patient-derived xenograft models of ARID1A-mutated cancer.

cancer biology↗

bioRxiv2020

Hexosamine biosynthetic pathway integrates circadian and metabolic signals to regulate daily rhythms in protein O-linked N-acetylglucosaminylation

The integration of circadian and metabolic signals is essential for maintaining robust circadian rhythms and ensuring efficient metabolism and energy use. Using Drosophila as an animal model, we showed that cellular protein O-linked N-acetylglucosaminylation (O-GlcNAcylation) exhibits robust 24-hour rhythm and is a key post-translational mechanism that regulates circadian physiology. We observed strong correlation between protein O-GlcNAcylation rhythms and clock-controlled feeding-fasting cycles, suggesting that O-GlcNAcylation rhythms are primarily driven by nutrient input. Interestingly, daily O-GlcNAcylation rhythms were severely dampened when we subjected flies to time-restricted feeding (TRF) at unnatural feeding time. This suggests the presence of a clock-regulated buffering mechanism that prevents excessive O-GlcNAcylation at non-optimal times of the day-night cycle. We found that this buffering mechanism is mediated by glutamine-fructose-6-phosphate amidotransferase (GFAT) activity, which is regulated through integration of circadian and metabolic signals. Finally, we generated a mathematical model to describe the key factors that regulate daily O-GlcNAcylation rhythm.

molecular biology↗

bioRxiv2020

Population Bottlenecks and Intra-host Evolution during Human-to-Human Transmission of SARS-CoV-2Population Bottlenecks and Intra-host Evolution during Human-to-Human Transmission of SARS-CoV-2

The emergence of the novel human coronavirus, SARS-CoV-2, causes a global COVID-19 (coronavirus disease 2019) pandemic. Here, we have characterized and compared viral populations of SARS-CoV-2 among COVID-19 patients within and across households. Our work showed an active viral replication activity in the human respiratory tract and the co-existence of genetically distinct viruses within the same host. The inter-host comparison among viral populations further revealed a narrow transmission bottleneck between patients from the same households, suggesting a dominated role of stochastic dynamics in both inter-host and intra-host evolutions. Author summaryIn this study, we compared SARS-CoV-2 populations of 13 Chinese COVID-19 patients. Those viral populations contained a considerable proportion of viral sub-genomic messenger RNAs (sgmRNA), reflecting an active viral replication activity in the respiratory tract tissues. The comparison of 66 identified intra-host variants further showed a low viral genetic distance between intra-household patients and a narrow transmission bottleneck size. Despite the co-existence of genetically distinct viruses within the same host, most intra-host minor variants were not shared between transmission pairs, suggesting a dominated role of stochastic dynamics in both inter-host and intra-host evolutions. Furthermore, the narrow bottleneck and active viral activity in the respiratory tract show that the passage of a small number of virions can cause infection. Our data have therefore delivered a key genomic resource for the SARS-CoV-2 transmission research and enhanced our understanding of the evolutionary dynamics of SARS-CoV-2.

genetics↗

bioRxiv2020

One-Step Rapid Quantification of SARS-CoV-2 Virus Particles via Low-Cost Nanoplasmonic Sensors in Generic Microplate Reader and Point-of-Care Device

The spread of SARS-CoV-2 virus in the ongoing global pandemics has led to infections of millions of people and losses of many lives. The rapid, accurate and convenient SARS-CoV-2 virus detection is crucial for controlling and stopping the pandemics. Diagnosis of patients in the early stage infection are so far limited to viral nucleic acid or antigen detection in human nasopharyngeal swab or saliva samples. Here we developed a method for rapid and direct optical measurement of SARS-CoV-2 virus particles in one step nearly without any sample preparation using a spike protein specific nanoplasmonic resonance sensor. We demonstrate that we can detect as few as 30 virus particles in one step within 15 minutes and can quantify the virus concentration linearly in the range of 103 vp/ml to 106 vp/ml. Measurements shown on both generic microplate reader and a handheld smartphone connected device suggest that our low-cost and rapid detection method may be adopted quickly under both regular clinical environment and resource-limited settings.

bioengineering↗

bioRxiv2020

Intra-host Variation and Evolutionary Dynamics of SARS-CoV-2 Population in COVID-19 Patients

As of middle May 2020, the causative agent of COVID-19, SARS-CoV-2, has infected over 4 million people with more than 300 thousand death as official reports1,2. The key to understanding the biology and virus-host interactions of SARS-CoV-2 requires the knowledge of mutation and evolution of this virus at both inter- and intra-host levels. However, despite quite a few polymorphic sites identified among SARS-CoV-2 populations, intra-host variant spectra and their evolutionary dynamics remain mostly unknown. Here, using deep sequencing data, we achieved and characterized consensus genomes and intra-host genomic variants from 32 serial samples collected from eight patients with COVID-19. The 32 consensus genomes revealed the coexistence of different genotypes within the same patient. We further identified 40 intra-host single nucleotide variants (iSNVs). Most (30/40) iSNVs presented in single patient, while ten iSNVs were found in at least two patients or identical to consensus variants. Comparison of allele frequencies of the iSNVs revealed genetic divergence between intra-host populations of the respiratory tract (RT) and gastrointestinal tract (GIT), mostly driven by bottleneck events among intra-host transmissions. Nonetheless, we observed a maintained viral genetic diversity within GIT, showing an increased population with accumulated mutations developed in the tissue-specific environments. The iSNVs identified here not only show spatial divergence of intra-host viral populations, but also provide new insights into the complex virus-host interactions.

genomics↗

bioRxiv2020

Multiple approaches for massively parallel sequencing of HCoV-19 genomes directly from clinical samples

COVID-19 has caused a major epidemic worldwide, however, much is yet to be known about the epidemiology and evolution of the virus. One reason is that the challenges underneath sequencing HCoV-19 directly from clinical samples have not been completely tackled. Here we illustrate the application of amplicon and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of HCoV-19 from clinical samples covering a range of sample types and viral load. We also examine and compare the bias, sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. This is, to date, the first work systematically implements amplicon and capture approaches in sequencing HCoV-19, as well as the first comparative study across methods. Our work offers practical solutions for genome sequencing and analyses of HCoV-19 and other emerging viruses.

genomics↗

bioRxiv2020

ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data and analysis results from various CPTAC projects, we built a prototype user-friendly web application called "ProTrack" with the CPTAC clear cell renal cell carcinoma (ccRCC) data set (http://ccrcc.cptac-data-view.org). Here we describe the salient features of this application which provides a dynamic, comprehensive, and granular visualization of the rich proteogenomic data. Statement of SignificanceThe CPTAC initiative (https://proteomics.cancer.gov/) has generated multi-omics data for multiple cancer types to understand the proteogenomic aberrations of these malignancies. Collectively this effort has so far produced a large data resource for the research community, including high-throughput profiles for proteome, phosphoproteome, whole exome, whole genome, transcriptome, and DNA methylome. To make this valuable data-resource useful to the larger research community, there is a pressing need for development of user-friendly, readily accessible, and easily shared analytic and visualization tools for aligning multi-omics data and exploring alterations in key cancer genes, to drive and support new biological hypotheses. To bridge this gap, we have developed CPTAC ProTrack, an interactive web application which uses a multilayered, client-server architecture in order to deliver an interactive web experience to any user of a modern web-browser. This tool is intentionally designed accessible for researchers, biologists, and clinicians who are interested in multi-omic data without any need to code.

bioinformatics↗

bioRxiv2019

Emergence and Genetic Analysis of Avian Gyrovirus 2 Variants-Related Gyrovirus in Farmed King-ratsnakes (Elaphe carinata): the First Report in China

Avian gyrovirus 2 (AGV2), which is similar to chicken infectious anemia virus, is a new member of the Circovirus genus. AGV2 has been detected not only in chicken but also in human tissues and feces. In this study, a total of 91 samples (8 liver tissues and 83 faecal samples) collected from king-ratsnakes (Elaphe carinata) at 7 separate farms in Hubei and Henan, China, were analyzed to detect AGV2 DNA via specific PCR. The results indicated a low positive rate of AGV2 (6.59%, 6/91) in the studied animals, and all of the positive samples were from the same farm. The AGV2 strain HB2018S1 was sequenced, and the genome with a total length of 2376 nt contained three partially overlapping open reading frames: VP1, VP2, and VP3. Phylogenetic tree analysis revealed that the HB2018S1, NX1506-1 strain from chickens in China belong to the same clade, with nucleotide homology as high as 99.5%. In total, 10 amino acid mutation sites, including 44R/K, 74T/A, 256 C/R, 279L/Q, and 373V/A in AGV2 VP1; 60I/T, 125T/I, 213D/N, and 215L/S in AGV2 VP2; and 83H/Y in AGV2 VP3, were found in the genome of HB2018S1 that were different from those observed in most reference strains, suggesting that the differences are related to an transboundary movement among hosts which needs to be further elucidated.\n\nIMPORTANCERecently, AGV2 has been detected in live poultry markets and human blood in mainland China. Previous findings indicated future studies should investigate the large geographic distribution of AGV2 and monitor the variants, the host range, and the associated diseases. To the best of our knowledge, this study is the first report on AGV2 infected poikilotherm, suggested that cross-host transmission of viruses with circular single-stranded DNA genomes would be a public health concern.

epidemiology↗

bioRxiv2018

Insights into impact of DNA copy number alteration and methylation on the proteogenomic landscape of human ovarian cancer via a multi-omics integrative analysis

In this work, we propose iProFun, an integrative analysis tool to screen for Proteogenomic Functional traits perturbed by DNA copy number alterations (CNA) and DNA methylations. The goal is to characterize functional consequences of DNA copy number and methylation alterations in tumors and to facilitate screening for cancer drivers contributing to tumor initiation and progression. Specifically, we consider three functional molecular quantitative traits: mRNA expression levels, global protein abundances, and phosphoprotein abundances. We aim to identify those genes whose CNAs and/or DNA methylations have cis-associations with either some or all three types of molecular traits. In comparison with analyzing each molecular trait separately, the joint modeling of multi-omics data enjoys several benefits: iProFun experienced enhanced power for detecting significant cis-associations shared across different omics data types; and it also achieved better accuracy in inferring cis-associations unique to certain type(s) of molecular trait(s). For example, unique associations of CNA/methylations to global/phospho protein abundances may imply post-translational regulations.\n\nWe applied iProFun to ovarian high-grade serous carcinoma tumor data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium, and identified CNAs and methylations of 500 and 122 genes, respectively, affecting the cis-functional molecular quantitative traits of the corresponding genes. We observed substantial power gain via the joint analysis of iProFun. For example, iProFun identified 130 genes whose CNAs were associated with phosphoprotein abundances by leveraging mRNA expression levels and global protein abundances. By comparison, analyses based on phosphoprotein data alone identified none. A group of these 130 genes clustered in a small region on Chromosome 14q, harboring the known oncogene, AKT1. In addition, iProFun identified one gene, CANX, whose DNA methylation has a cis-association with its global protein abundances but not its mRNA expression levels These and other genes identified by iProFun could serve as potential drug targets for ovarian cancer.

bioinformatics↗

bioRxiv2018

Dynamic Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model

MotivationNowadays brain connectivity analysis has attracted tremendous attention and has been at the foreground of neuroscience research. Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activity. Growing evidence now suggests that the brain connectivity network experiences alternations with the presence of numerous neurological disorders, thus differential brain network analysis may provides new insights into disease pathologies. For the matrix-valued data in brain connectivity analysis, existing graphical model estimation methods assume a vector normal distribution that in essence requires the columns of the matrix data to be independent. It is obviously not true, they have limited applications. Among the few solutions on graphical model estimation under a matrix normal distribution, none of them tackle the estimation of differential graphs across different populations. This motivates us to consider the differential network for matrix-variate data to detect the brain connectivity alternation.\n\nResultsThe primary interest is to detect spatial locations where the connectivity, in terms of the spatial partial correlation, differ across the two groups. To detect the brain connectivity alternation, we innovatively propose a Matrix-Variate Differential Network (MVDN) model. MVDN assumes that the matrix-variate data follows a matrix-normal distribution. We exploit the D-trace loss function and a Lasso-type penalty to directly estimate the spatial differential partial correlation matrix where the temporal information is fully excavated. We propose an ADMM algorithm for the Lasso penalized D-trace loss optimization problem. We investigate theoretical properties of the estimator. We show that under mild and regular conditions, the proposed method can identify all differential edges accurately with probability tending to 1 in high-dimensional setting where dimensions of matrix-valued data p, q and sample size n are all allowed to go to infinity. Simulation studies demonstrate that MVDN provides more accurate differential network estimation than that achieved by other state-of-the-art methods. We apply MVDN to Electroencephalography (EEG) dataset, which consists of 77 alcoholic individuals and 45 controls. The hub genes and differential interaction patterns identified are consistent with existing experimental studies.\n\nContactheyong@sdufe.edu.cn\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2018

Inhibitor of the spindle assembly checkpoint surpasses apoptosis sensitizer in synergy with taxanes

The antitumor effect of taxanes have been attributed to their ability to induce mitotic arrest through activation of the spindle assembly checkpoint. Cell death following prolonged mitotic arrest is mediated by the intrinsic apoptosis pathway. Thus, apoptosis sensitizers which inhibit antiapoptotic Bcl-2 family proteins has been shown to enhance taxanes-induced cell death. By contrast, spindle checkpoint disruption facilitates mitotic slippage and is thought to promote taxanes resistance. Notably, other modes of cell death also contribute to treatment outcomes. Here we show that inhibition of the spindle checkpoint suppresses taxanes induced apoptosis but increases terminal growth arrest of tumor cells with features of cellular senescence. By using clonogenic assay which measures the net result of multiple forms of cell death and is more reflective of therapeutic response, our finding suggests apoptosis is not a major determinant of antitumor efficacy of taxanes, whereas spindle checkpoint inhibitor displays a long-term advantage over apoptosis sensitizer in blocking colony outgrowth of tumor cells when combined with different microtubule toxins, therefore represents a superior therapeutic strategy.\n\nSIGNIFICANCEApoptosis has long been regarded as the primary mechanism of anti-cancer efficacy of taxanes, while the role of the spindle assembly checkpoint (SAC) in treatment response to taxanes has been controversial. Either apoptosis sensitizer or inhibitor of SAC has been reported to synergize with taxanes. While inhibitor of antiapoptotic proteins potentiates taxanes induced apoptosis, inhibitor of SAC suppresses apoptosis by facilitating mitotic slippage, that is why it is implicated in taxanes resistance. By demonstrating that apoptotic rates are not associate with long-term treatment response, not only do we find that inhibitor of SAC displays a long-term advantage over apoptosis sensitizer in combination with taxanes, but we also resolve the dispute around the role of SAC in cellular response to taxanes.

cancer biology↗

bioRxiv2018

A new insight into underlying disease mechanism through semi-parametric latent differential network model

BackgroundIn genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types.\n\nResultsWe propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive estimation approaches do not require precision matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Theoretical analysis shows that the proposed methods achieve the same parametric convergence rate for both the difference of the precision matrices estimation and differential structure recovery, which means that the extra modeling flexibility comes at almost no cost of statistical efficiency. Besides theoretical analysis, thorough numerical simulations are conducted to compare the empirical performance of the proposed methods with some other state-of-the-art methods. The result shows that the proposed methods work quite well for various data types. The proposed method is then applied on gene expression data associated with lung cancer to illustrate its empirical usefulness.\n\nConclusionsThe proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended.

bioinformatics↗

bioRxiv2017

The role of Cdx2 as a lineage specific transcriptional repressor for pluripotent network during trophectoderm and inner cell mass specification

The first cellular differentiation event in mouse development leads to the formation of the blastocyst consisting of the inner cell mass (ICM) and an outer functional epithelium called trophectoderm (TE). The lineage specific transcription factor CDX2 is required for proper TE specification, where it promotes expression of TE genes, and represses expression of Pou5f1 (OCT4) by inhibiting OCT4 from promoting its own expression. However its downstream network in the developing early embryo is not fully characterized. Here, we performed high-throughput single embryo qPCR analysis in Cdx2 null embryos to identify components of the CDX2-regulated network in vivo. To identify genes likely to be regulated by CDX2 directly, we performed CDX2 ChIP-Seq on trophoblast stem (TS) cells, derived from the TE. In addition, we examined the dynamics of gene expression changes using an inducible CDX2 embryonic stem (ES) cell system, so that we could predict which CDX2-bound genes are activated or repressed by CDX2 binding. By integrating these data with observations of chromatin modifications, we were able to identify novel regulatory elements that are likely to repress gene expression in a lineage-specific manner. Interestingly, we found CDX2 binding sites within regulatory elements of key pluripotent genes such as Pou5f1 and Nanog, pointing to the existence of a novel mechanism by which CDX2 maintains repression of OCT4 in trophoblast. Our study proposes a general mechanism in regulating lineage segregation during mammalian development.

cell biology↗

bioRxiv2017

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

MotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.\n\nResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.\n\nAvailabilityR scripts available at https://github.com/jijiadong/JDINAC\n\nContactlxie@iscb.org\n\nSupplementary informationSupplementary data are available at bioRxiv online.

systems biology↗

bioRxiv2016

DNA damage in 3D constricted migration or after lamin-A depletion in 2D: shared mechanisms of repair factor mis-localization under nuclear stress

Cells that migrate through small, rigid pores and that have normal levels of the nuclear structure protein lamin-A exhibit an increase in DNA damage, which is also observed with lamin-A depletion in diseases such as cancer and with many lamin-A mutations. Here we show nuclear envelope rupture is a shared feature that increases in standard culture after lamin-A knockdown, which causes nuclear loss of multiple DNA repair factors and increased DNA damage. Some repair factors are merely mis-localized to cytoplasm whereas others are partially depleted unless rescued by lamin-A expression. Compared to standard cultures on rigid glass coverslips, the growth of lamin-A low cells on soft matrix relaxes cytoskeletal stress on the nucleus, suppresses the mis-localization of DNA repair factors, and minimizes DNA damage nearly to wildtype levels. Conversely, constricted migration of the lamin-A low cells causes abnormally high levels of DNA damage, consistent with sustained loss of repair factors. The findings add insight into why monogenic progeroid syndromes that often associate with increased DNA damage and predominantly impact cells in stiff tissues result from mutations only in lamin-A or DNA repair factors.

cell biology↗