Biology subjects

Jiang, T.

Publications and source records attributed to Jiang, T..

At least 145 records · Page 8Linked to original sources

bioRxiv2020

Proteasome activator PA200 maintains stability of histone marks during transcription and aging

The epigenetic inheritance relies on stability of histone marks, but various diseases, including aging-related diseases, are usually associated with alterations of histone marks. How the stability of histone marks is maintained still remains unclear. The core histones can be degraded by the atypical proteasome, which contains the proteasome activator PA200, in an acetylation-dependent manner during somatic DNA damage response and spermiogenesis. Here we show that PA200 promotes the transcription-coupled degradation of the core histones, and plays an important role in maintaining the stability of histone marks. Degradation of the histone variant H3.3, which is incorporated into chromatin during transcription, was much faster than that of its canonical form H3.1, which is incorporated during DNA replication. This degradation of the core histones could be suppressed by the transcription inhibitor, the proteasome inhibitor or deletion of PA200. The histone deacetylase inhibitor accelerated the degradation rates of H3 in general, especially its variant H3.3, while the mutations of the putative acetyl-lysine-binding region of PA200 abolished histone degradation in the G1-arrested cells, supporting that acetylation is involved in the degradation of the core histones. Deletion of PA200 dramatically altered deposition of the active transcriptional hallmarks (H3K4me3 and H3K56ac) and transcription, especially during cellular aging. Furthermore, deletion of PA200 or its yeast ortholog Blm10 accelerated cellular aging. Notably, the PA200-deficient mice displayed a range of aging-related deteriorations, including immune malfunction, anxiety-like behaviors and shorter lifespan. Thus, the proteasome activator PA200 is critical to the maintenance of the stability of histone marks during transcription and aging.

cell biology↗

bioRxiv2020

Tracking crystallophore nucleating properties: setting-up a database for statistical analysis.

In this article, the principle of a database aimed at facilitating the understanding of the unique protein nucleating properties of the Crystallophore is presented. A first analysis allows us to compare the efficiency of Tb-Xo4 with the new Lu-Xo4 variant, featuring improved phasing properties. Then, the concept of subset-of-interest is introduced to reveal potential antagonistic/synergistic effects between Tb-Xo4 and physico-chemical parameters of the crystallisation kits such as pH. The overall approach may be of interest for any studies working on solutions dedicated to improve the nucleating step in protein crystallization. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/057596v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@351291org.highwire.dtl.DTLVardef@5620fcorg.highwire.dtl.DTLVardef@8fadb2org.highwire.dtl.DTLVardef@d4c208_HPS_FORMAT_FIGEXP M_FIG C_FIG SynopsisA database and associated representation tools are highlighted to understand nucleating properties of the crystallophore.

biochemistry↗

bioRxiv2020

CM93, a novel covalent small molecule inhibitor targeting lung cancer with mutant EGFR

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have provided successful targeted therapies for patients with EGFR-mutant non-small-cell lung cancer (NSCLC). Osimertinib (AZD9291) is a third-generation irreversible EGFR TKI that has received regulatory approval for overcoming resistance mediated by the EGFR T790M mutation as well as a first-line treatment targeting EGFR activating mutations. However, a significant fraction of patients cannot tolerate the adverse effect associated with AZD9291. In addition, brain metastases are common in patients with NSCLN and remain a major clinical challenge. Here, we report the development of a novel third-generation EGFR TKI, CM93. Compared to AZD9291, CM93 exhibits improved lung cancer targeting and brain penetration and has demonstrated promising antitumor efficacy in mouse models of both EGFR-mutant NSCLC orthotopic and brain metastases. In addition, we find that CM93 confers superior safety benefits in mice. Our results demonstrate that further evaluations of CM93 in clinical studies for patients with EGFR-mutant NSCLC and brain metastases are warranted.

pharmacology and toxicology↗

bioRxiv2020

MGMT genomic rearrangements contribute to chemotherapy resistance in gliomas

Temozolomide (TMZ) is an oral alkylating agent used for the treatment of glioblastoma and is now becoming a chemotherapeutic option in patients diagnosed with high-risk low-grade gliomas. The O-6-methylguanine-DNA methyltransferase (MGMT) is responsible for the direct repair of the main TMZ-induced toxic DNA adduct, the O6-Methylguanine lesion. MGMT promoter hypermethylation is currently the only known biomarker for TMZ response in glioblastoma patients. Here we show that a subset of recurrent gliomas carries MGMT genomic rearrangements that lead to MGMT overexpression, independently from changes in its promoter methylation. By leveraging the CRISPR/Cas9 technology we generated some of these MGMT rearrangements in glioma cells and demonstrated that the MGMT genomic rearrangements contribute to TMZ resistance both in vitro and in vivo. Lastly, we showed that such fusions can be detected in tumor-derived exosomes and could potentially represent an early detection marker of tumor recurrence in a subset of patients treated with TMZ.

cancer biology↗

bioRxiv2020

Mutations, Recombination and Insertion in the Evolution of 2019-nCoV

BackgroundThe 2019 novel coronavirus (2019-nCoV or SARS-CoV-2) has spread more rapidly than any other betacoronavirus including SARS-CoV and MERS-CoV. However, the mechanisms responsible for infection and molecular evolution of this virus remained unclear. MethodsWe collected and analyzed 120 genomic sequences of 2019-nCoV including 11 novel genomes from patients in China. Through comprehensive analysis of the available genome sequences of 2019-nCoV strains, we have tracked multiple inheritable SNPs and determined the evolution of 2019-nCoV relative to other coronaviruses. ResultsSystematic analysis of 120 genomic sequences of 2019-nCoV revealed co-circulation of two genetic subgroups with distinct SNPs markers, which can be used to trace the 2019-nCoV spreading pathways to different regions and countries. Although 2019-nCoV, human and bat SARS-CoV share high homologous in overall genome structures, they evolved into two distinct groups with different receptor entry specificities through potential recombination in the receptor binding regions. In addition, 2019-nCoV has a unique four amino acid insertion between S1 and S2 domains of the spike protein, which created a potential furin or TMPRSS2 cleavage site. ConclusionsOur studies provided comprehensive insights into the evolution and spread of the 2019-nCoV. Our results provided evidence suggesting that 2019-nCoV may increase its infectivity through the receptor binding domain recombination and a cleavage site insertion. One Sentence SummaryNovel 2019-nCoV sequences revealed the evolution and specificity of betacoronavirus with possible mechanisms of enhanced infectivity.

microbiology↗

bioRxiv2020

Prediction of receptorome for human-infecting virome

The virus receptors are key for the viral infection of host cells. Identification of the virus receptors is still challenging at present. Our previous study has shown that human virus receptor proteins have some unique features including high N-glycosylation level, high number of interaction partners and high expression level. Here, a random-forest model was built to identify human virus receptorome from human cell membrane proteins with an accepted accuracy based on the combination of the unique features of human virus receptors and protein sequences. A total of 1380 human cell membrane proteins were predicted to constitute the receptorome of the human-infecting virome. In addition, the combination of the random-forest model with protein-protein interactions between human and viruses predicted in previous studies enabled further prediction of the receptors for 693 human-infecting viruses, such as the Enterovirus, Norovirus and West Nile virus. As far as we know, this study is the first attempt to predict the receptorome for the human-infecting virome and would greatly facilitate the identification of the receptors for viruses.

bioinformatics↗

bioRxiv2020

Nucleosomal embedding reshapes the dynamics of abasic sites

Apurinic/apyrimidinic (AP) sites are the most common DNA lesions, which benefit from a most efficient repair by the base excision pathway. The impact of losing a nucleobase on the conformation and dynamics of B-DNA is well characterized. Yet AP sites seem to present an entirely different chemistry in nucleosomal DNA, with lifetimes reduced up to 100-fold, and the much increased formation of covalent DNA-protein cross-links, refractory to repair. We report microsecond range, all-atom molecular dynamics simulations that capture the conformational dynamics of AP sites and their tetrahydrofuran analogs at two symmetrical positions within a nucleosome core particle, starting from a recent crystal structure. Different behaviours between the deoxyribo-based and tetrahydrofuran-type abasic sites are evidenced. The two solvent-exposed lesion sites present contrasted extrahelicities, revealing the crucial role of the position of a defect around the histone core. Our all-atom simulations also identify and quantify the occurrence of several spontaneous, non-covalent interactions between AP and positively-charged residues from the histones H2A and H2B tails that prefigure DNA-protein cross-links. This study paves the way towards an in silico mapping of DNA-protein cross-links.

biochemistry↗

bioRxiv2020

Learning polygenic scores for human blood cell traits

Polygenic scores (PGSs) for blood cell traits can be constructed using summary statistics from genome-wide association studies. As the selection of variants and the modelling of their interactions in PGSs may be limited by univariate analysis, therefore, such a conventional method may yield sub-optional performance. This study evaluated the relative effectiveness of four machine learning and deep learning methods, as well as a univariate method, in the construction of PGSs for 26 blood cell traits, using data from UK Biobank (n=~400,000) and INTERVAL (n=~40,000). Our results showed that learning methods can improve PGSs construction for nearly every blood cell trait considered, with this superiority explained by the ability of machine learning methods to capture interactions among variants. This study also demonstrated that populations can be well stratified by the PGSs of these blood cell traits, even for traits that exhibit large differences between ages and sexes, suggesting potential for disease prevention. As our study found genetic correlations between the PGSs for blood cell traits and PGSs for several common human diseases (recapitulating well-known associations between the blood cell traits themselves and certain diseases), it suggests that blood cell traits may be indicators or/and mediators for a variety of common disorders via shared genetic variants and functional pathways.

genetics↗

bioRxiv2020

Genetic Analyses of Blood Cell Structure for Biological and Pharmacological Inference

Thousands of genetic associations with phenotypes of blood cells are known, but few are with phenotypes relevant to cell function. We performed GWAS of 63 flow-cytometry phenotypes, including measures of cell granularity, nucleic acid content, and reactivity, in 39,656 participants in the INTERVAL study, identifying 2,172 variant-trait associations. These include associations mediated by functional cellular structures such as secretory granules, implicated in vascular, thrombotic, inflammatory and neoplastic diseases. By integrating our results with epigenetic data and with signals from molecular abundance/disease GWAS, we infer the hematopoietic origins of population phenotypic variation and identify the transcription factor FOG2 as a regulator of platelet -granularity. We show how flow cytometry genetics can suggest cell types mediating complex disease risk and suggest efficacious drug targets, presenting Daclizumab/Vedolizumab in autoimmune disease as positive controls. Finally, we add to existing evidence supporting IL7/IL7-R as drug targets for multiple sclerosis.

genetics↗

bioRxiv2020

Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data for Chinese Glioma Patients

Gliomas are the most common and malignant intracranial tumours in adults. Recent studies have shown that functional genomics greatly aids in the understanding of the pathophysiology and therapy of glioma. However, comprehensive genomic data and analysis platforms are relatively limited. In this study, we developed the Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn), a user-friendly data portal for storage and interactive exploration of multi-dimensional functional genomic data that includes nearly 2,000 primary and recurrent glioma samples from Chinese cohorts. CGGA currently provides access to whole-exome sequencing (286 samples), messenger RNA sequencing (1,018 samples) and microarray (301 samples), DNA methylation microarray (159 samples), and microRNA microarray (198 samples) data, as well as detailed clinical data (e.g., WHO grade, histological type, critical molecular genetic information, age, sex, chemoradiotherapy status and survival data). In addition, we developed an analysis tool to allow users to browse mutational, mRNA/microRNA expression, and DNA methylation profiles and perform survival and correlation analyses of specific glioma subtypes. CGGA greatly reduces the barriers between complex functional genomic data and glioma researchers who seek rapid, intuitive, and high-quality access to data resources and enables researchers to use these immeasurable data sources for biological research and clinical application. Importantly, the free provision of data will allow researchers to quickly generate and provide data to the research community.

genomics↗

bioRxiv2020

Glutamate dehydrogenase (Gdh2)-dependent alkalization is dispensable for escape from macrophages and virulence of Candida albicans

Candida albicans cells depend on the energy derived from amino acid catabolism to induce and sustain hyphal growth inside phagosomes of engulfing macrophages. The concomitant deamination of amino acids is thought to neutralize the acidic microenvironment of phagosomes, a presumed requisite for survival and initiation of hyphal growth. Here, in contrast to an existing model, we show that mitochondrial-localized NAD+-dependent glutamate dehydrogenase (GDH2) catalyzing the deamination of glutamate to -ketoglutarate, and not the cytosolic urea amidolyase (DUR1,2), accounts for the observed alkalization of media when amino acids are the sole sources of carbon and nitrogen. C. albicans strains lacking GDH2 (gdh2-/-) are viable and do not extrude ammonia on amino acid-based media. Environmental alkalization does not occur under conditions of high glucose (2%), a finding attributable to glucose-repression of GDH2 expression and mitochondrial function. Consistently, inhibition of oxidative phosphorylation or mitochondrial translation by antimycin A or chloramphenicol, respectively, prevents alkalization. GDH2 expression and mitochondrial function are derepressed as glucose levels are lowered from 2% ([~]110 mM) to 0.2% ([~]11 mM), or when glycerol is used as carbon source. Using time-lapse microscopy, we document that gdh2-/- cells survive, filament and escape from primary murine macrophages at rates indistinguishable from wildtype. Consistently, gdh2-/- strains are as virulent as wildtype in fly and murine models of systemic candidiasis. Thus, although Gdh2 has a critical role in central nitrogen metabolism, Gdh2-catalyzed deamination of glutamate is surprisingly dispensable for escape from macrophages and virulence, demonstrating that amino acid-dependent alkalization is not essential for hyphal growth, survival in macrophages and hosts. An accurate description of the microenvironment within the phagosomal compartment of macrophages and the metabolic events underlying the survival of phagocytosed C. albicans cells and their escape are critical to understanding the host-pathogen interactions that ultimately determine the pathogenic outcome. Author SummaryCandida albicans is a commensal component of the human microflora and the most common fungal pathogen. The incidence of candidiasis is low in healthy populations. Consequently, environmental factors, such as interactions with innate immune cells, play critical roles. Macrophages provide the first line of defense and rapidly internalize C. albicans cells within specialized intracellular compartments called phagosomes. The microenvironment within phagosomes is dynamic and ill defined, but has a low pH, and contains potent hydrolytic enzymes and oxidative stressors. Despite the inhospitable conditions, phagocytized C. albicans cells catabolize amino acids to obtain energy to survive and grow. Here, we have critically examined amino acid catabolism and ammonia extrusion in C. albicans, the latter is thought to neutralize the phagosomal pH and induce the switch of morphologies from round "yeast-like" to elongated hyphal cells that can pierce the phagosomal membrane leading to escape from macrophages. We report that Gdh2, which catalyzes the deamination of glutamate to -ketoglutarate, is responsible for the observed environmental alkalization when C. albicans catabolize amino acids. Strikingly, Gdh2 is dispensable for escape from macrophages and virulent growth. These results provide new insights into host-pathogen interactions that determine the pathogenic outcome of C. albicans infections.

microbiology↗

bioRxiv2020

Surveying Brain Tumor Heterogeneity by Single-Cell RNA Sequencing of Multi-sector Biopsies

Brain tumors are among the most challenging human tumors for which the mechanisms driving progression and heterogeneity remain poorly understood. We combined single-cell RNA-seq with multisector biopsies to sample and analyze single-cell expression profiles of gliomas from 13 Chinese patients. After classifying individual cells, we generated a spatial and temporal landscape of glioma that revealed the patterns of invasion between the different sub-regions of gliomas. We also used single-cell inferred CNVs and pseudotime trajectories to inform on the crucial branches that dominate tumor progression. The dynamic cell components of the multi-region biopsy analysis allowed us to spatially deconvolute with unprecedented accuracy the transcriptomic features of the core and those of the periphery of glioma at single cell level. Through this rich and geographically detailed dataset, we were also able to characterize and construct the chemokine and chemokine receptor interactions that exist among different tumor and non-tumor cells. This study provides the first spatial-level analysis of the cellular states that characterize human gliomas. It also presents an initial molecular map of the crosstalks between glioma cells and the surrounding microenvironment with single cell resolution.

genomics↗

bioRxiv2019

MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

Computational approaches for inferring the mechanisms of compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, although a number of deep learning based methods have been proposed to predict binding affinities and attempt to capture local interaction sites in compounds and proteins through neural attentions, they still lack a systematic evaluation on the interpretability of the identified local features. In addition, in these previous approaches, the exact matchings between interaction sites from compounds and proteins, which are generally important for understanding drug mechanisms of action, still remain unknown. Here, we compiled the first benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs, and used it to systematically evaluate the interpretability of neural attentions in existing prediction models. We developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinity for a given compound-protein pair. MONN uses convolution neural networks on molecular graphs of compounds and primary sequences of proteins to effectively capture the intrinsic features from both inputs, and also takes advantage of the predicted non-covalent interactions to further boost the accuracy of binding affinity prediction. Comprehensive evaluation demonstrated that while the previous neural attention based approaches fail to exhibit satisfactory interpretability results without extra supervision, MONN can successfully predict non-covalent interactions on our benchmark dataset as well as another independent dataset derived from the Protein Data Bank (PDB). Moreover, MONN can outperform other state-of-the-art methods in predicting compound-protein binding affinities. In addition, the pairwise interactions predicted by MONN displayed compatible and accordant patterns in chemical properties, which provided another evidence to support the strong predictive power of MONN. These results suggested that MONN can offer a powerful tool in predicting binding affinities of compound-protein pairs and also provide useful insights into understanding the molecular mechanisms of compound-protein interactions, which thus can greatly advance the drug discovery process. The source code of the MONN model and the dataset creation process can be downloaded from https://github.com/lishuya17/MONN.

bioinformatics↗

bioRxiv2019

Structural characterization of an intermediate reveals a unified mechanism for the CLC Cl-/H+ transport cycle

Among coupled exchangers, CLCs uniquely catalyze the exchange of oppositely charged ions (Cl- for H+). Transport-cycle models to describe and explain this unusual mechanism have been proposed based on known CLC structures. While the proposed models harmonize many experimental findings, there have remained gaps and inconsistencies in our understanding. One limitation has been that global conformational change - which occurs in all conventional transporter mechanisms - has not been observed in any high-resolution structure. Here, we describe the 2.6 [A] structure of a CLC mutant designed to mimic the fully H+-loaded transporter. This structure reveals a global conformational change to a state that has improved accessibility for the Cl- substrate from the extracellular side and new conformations for two key glutamate residues. Based on this new structure, together with DEER measurements, MD simulations, and functional studies, we propose a unified model of the CLC transport mechanism that reconciles existing data on all CLC-type proteins.

biophysics↗

bioRxiv2019

Diagnostic value of circulating tumor DNA as an effective biomarker in cervical cancer: a meta-analysis

The applications of liquid biopsy have attracted much attention in biomedical research in recent years. Circulating cell-free DNA (cfDNA) in the serum may serve as a unique tumor marker in various types of cancer. Circulating tumor DNA (ctDNA) is a type of serum cfDNA found in patients with cancer and contains abundant information regarding tumor characteristics, highlighting its potential diagnostic value in the clinical setting. However, the diagnostic value of cfDNA as a biomarker in cervical cancer remains unclear. Here, we performed a meta-analysis to evaluate the applications of ctDNA as a biomarker in cervical cancer. A systematic literature search was performed using PubMed, Embase, and WANFANG MED ONLINE databases up to March 18, 2019. All literature was analyzed using Meta Disc 1.4 and STATA 14.0 software. Diagnostic measures of accuracy of ctDNA in cervical cancer were pooled and investigated. Fifteen studies comprising 1109 patients with cervical cancer met our inclusion criteria and were subjected to analysis. The pooled sensitivity and specificity were 0.52 (95% confidence interval [CI], 0.33-0.71) and 0.97 (95% CI, 0.91-0.99), respectively. The pooled positive likelihood ratio and negative likelihood ratio were 16.0 (95% CI, 5.5-46.4) and 0.50 (95% CI, 0.33-0.75), respectively. The diagnostic odds ratio was 32 (95% CI, 10-108), and the area under the summary receiver operating characteristic curve was 0.92 (95% CI, 0.90- 0.94). There was no significant publication bias observed. In the included studies, ctDNA showed clear diagnostic value for diagnosing and monitoring cervical cancer. Our meta-analysis suggested that detection of human papilloma virus ctDNA in patients with cervical cancer could be used as a noninvasive early dynamic biomarker of tumors, with high specificity and moderate sensitivity. Further large-scale prospective studies are required to validate the factors that may influence the accuracy of cervical cancer diagnosis and monitoring.

bioinformatics↗

bioRxiv2019

Long Read based Human Genomic Structural Variation Detection with cuteSV

Long-read sequencing enables the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high sensitivity and performance simultaneously due to the complex SV characteristics implied by noisy long reads. Therefore, we propose cuteSV, a sensitive, fast and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to analyze the signatures to implement sensitive SV detection. Benchmarks on real PacBio and ONT datasets demonstrate that cuteSV has better yields and scalability than state-of-the-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV.

bioinformatics↗

bioRxiv2019

Direct comparison of clathrin-mediated endocytosis in budding and fission yeast reveals conserved and evolvable features

AbstractConserved proteins drive clathrin-mediated endocytosis (CME), which universally involves a burst of actin assembly. To gain fundamental mechanistic insights into this process, a side-by-side quantitative comparison of CME was performed on two distantly related yeast species. Though endocytic protein abundance in S. pombe and S. cerevisiae are more similar than previously thought, membrane invagination speed and depth are two-fold greater in fission yeast than in budding yeast. In both yeasts, Arp2/3 complex activation drives membrane invagination when triggered by the accumulation of [~]70 WASP molecules. In contrast to budding yeast, WASP-mediated actin nucleation activity plays an essential role in fission yeast endocytosis. Genetics and live-cell imaging revealed core CME spatiodynamic similarities between the two yeasts, though two-zone actin assembly is a fission yeast-specific mechanism, which is not essential for CME. These studies identified conserved CME mechanisms and species-specific adaptations and have broad implications that extend from yeast to humans.

cell biology↗

bioRxiv2019

From phages to mammalian viruses: viral receptors play a central role in protein-protein interaction network

MotivationReceptors on host cells play a critical role in viral infection. How phages select receptors is still unknown.\n\nResultsHere, we manually curated a high-quality database named phageReceptor, including 355 pairs of phage-host receptor interactions, 280 unique viral species or sub-species and 64 bacterial species. Sugars and proteins were most widely used by phages as receptors. The receptor usage of phages in Gram-positive bacteria was different from that in Gram-negative bacteria. Most protein receptors were located on the outer membrane. The protein receptors were highly diverse in their structures, and had little homology with mammalian virus receptors. Further functional characterization of phage protein receptors in Escherichia coli showed that they had larger node degrees and betweennesses in the protein-protein interaction (PPI) network, and higher expression levels, than other outer membrane proteins, plasma membrane proteins, or other intracellular proteins. These findings were consistent with what observed for mammalian virus receptors, suggesting that viral protein receptors play a central role in the hosts PPI network. The study deepens our understanding of virus-host interactions.\n\nAvailabilityThe database of phageReceptor is publicly accessible at http://www.computationalbiology.cn/viralRecepetor/index.html.

microbiology↗