Biology subjects

Zheng, S.

Publications and source records attributed to Zheng, S..

At least 181 records · Page 10Linked to original sources

bioRxiv2021

The Cdc42 GTPase activating protein Rga6 promotes the cortical localization of Septin

Septins are a family of filament-forming GTP-binding proteins that regulate fundamental cellular activities such as cytokinesis, cell polarity, and membrane remodelling. In general, Septin filaments function as barriers and scaffolds on the cell cortex. However, little is known about the mechanism that governs the recruitment and localization of the Septin complex to the cell cortex. Here, we identified the Cdc42 GTPase activating protein Rga6 as a key protein involved in promoting the localization of the Septin complex to the cell cortex in the fission yeast Schizosaccharomyces pombe. Rga6 interacts with the Septin complex and colocalizes with the Septin complex on the cell cortex. Live-cell microscopic analysis further showed Septin enrichment at the cortical regions adjacent to the growing cell tip. The Septin enrichment likely plays a crucial role in confining active Cdc42 to the growing cell tip. Hence, our findings support a model that Rga6 regulates polarized cell growth partly through promoting targeted localization of the Septin complex on the cell cortex.

cell biology↗

bioRxiv2021

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; 2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; 3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample; and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.

pharmacology and toxicology↗

bioRxiv2021

Facilitating the design of combination therapy in cancer using multipartite network models: Emphasis on acute myeloid leukemia

From the drug discovery perspective, combination therapy is recommended in cancer due to efficiency and safety compared to the common cytotoxic and single-targeted monotherapies. However, identifying effective drug combinations is time- and cost-consuming. Here, we offer a novel strategy of predicting potential drug combinations and patient subclasses by constructing multipartite networks using drug response data on patient samples. In the present study, we used Beat AML and GDSC, two comprehensive datasets based on patient-derived and cell line-based samples, to show the potential of multipartite network modeling in cancer combinatorial therapy. We used the median values of cell viability to compare drug potency and reconstruct a weighted bipartite network, which models the interaction of drugs and biological samples. Then, clusters of network communities were identified in two projected networks based on the topological structure of networks. Chemical structures, drug-target networks, protein-protein interactions, and signaling networks were used to corroborate the intra-cluster homogeneity. We further leveraged the community structures within the drug-based multipartite networks to discover effective multi-targeted drug combinations, and the synergy levels which were supported with more evidence using the DrugComb and the ALMANAC databases. Furthermore, we confirmed the potency of selective combinations of drugs against monotherapy in vitro experiment using three acute myeloid leukemia (AML) cell lines. Taken together, this study presents an innovative data-driven strategy based on multipartite networks to suggest potential drug combinations to improve treatment of AML.

systems biology↗

bioRxiv2021

Calling differential DNA methylation at cell-type resolution: avoiding misconceptions and promoting best practices

The accurate detection of cell-type specific DNA methylation alterations in the context of general epigenome studies is an important task to improve our understanding of epigenomics in disease development. Although a number of statistical algorithms designed to address this problem have emerged, the task remains challenging. Here we show that a recent commentary by Rahmani et al, that aims to address misconceptions and best practices in the field, continues to suffer from critical misconceptions in how statistical algorithms should be compared and evaluated. In addition, we report contradictory results on real EWAS datasets.

bioinformatics↗

bioRxiv2021

In vivo proximity labeling identifies cardiomyocyte protein networks during zebrafish heart regeneration

Strategies have not been available until recently to uncover interacting protein networks specific to key cell types, their subcellular compartments, and their major regulators during complex in vivo events. Here we apply BioID2 proximity labeling to capture protein networks acting within cardiomyocytes during a key model of innate heart regeneration in zebrafish. Transgenic zebrafish expressing a promiscuous BirA2 localized to the entire myocardial cell or membrane compartment were generated, each identifying unique proteomes in adult cardiomyocytes that became altered during regeneration. BioID2 profiling for interactors with ErbB2, a co-receptor for the cardiomyocyte mitogen Nrg1, implicated Rho A as a target of ErbB2 signaling in cardiomyocytes. Blockade of Rho A during heart regeneration, or during cardiogenic stimulation by the mitogenic influences Nrg1, Vegfaa or Vitamin D, disrupted muscle creation. Our findings reveal proximity labeling as a useful resource to interrogate cell proteomes and signaling networks during tissue regeneration in zebrafish.

developmental biology↗

bioRxiv2021

MolRep: A Deep Representation Learning Library for Molecular Property Prediction

SummaryRecently, novel representation learning algorithms have shown potential for predicting molecular properties. However, unified frameworks have not yet emerged for fairly measuring algorithmic progress, and experimental procedures of different representation models often lack rigorousness and are hardly reproducible. Herein, we have developed MolRep by unifying 16 state-of-the-art models across 4 popular molecular representations for application and comparison. Furthermore, we ran more than 12.5 million experiments to optimize hyperparameters for each method on 12 common benchmark data sets. As a result, CMPNN achieves the best results ranked the 1st in 5 out of 12 tasks with an average rank of 1.75. Relatively, ECC has good performance in classification tasks and MAT good for regression (both ranked 1st for 3 tasks) with an average rank of 2.71 and 2.6, respectively. AvailabilityThe source code is available at: https://github.com/biomed-AI/MolRep Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

cancer biology↗

bioRxiv2020

A functional artificial neural network for noninvasive presurgical evaluation of glioblastoma multiforme prognosis and radiosensitivity profiling

Background and PurposeGenetic profiling for glioblastoma multiforme (GBM) patients with intracranial biopsy carries a significant risk of permanent morbidity. We previously demonstrated that the CUL2 gene, encoding the scaffold cullin2 protein in the cullin2-RING E3 ligase (CRL2), can predict GBM radiosensitivity and prognosis mainly due to the functional involvement of CRL2 in mediating hypoxia-inducible factor 1 (HIF-1) and epidermal growth factor receptor (EGFR) degradation. Because CUL2 expression levels are closely regulated with its copy number variations (CNVs), this study aims to develop an artificial neural network (ANN) that can predict GBM prognosis and help optimize personalized GBM treatment planning. Materials and MethodsDatasets including Ivy-GAP, The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM), the Chinese Glioma Genome Atlas (CGGA) were analyzed. T1 images from corresponding cases were studied using automated segmentation for features of heterogeneity and tumor edge contouring. ResultsWe developed a 4-layer neural network that can consistently predict GBM prognosis with 80-85% accuracy with 3 inputs including CUL2 copy number, patients age at GBM diagnosis, and surface vs. volume (SvV) ratio. ConclusionA functional 4-layer neural network was constructed that can predict GBM prognosis and potential radiosensitivity.

bioinformatics↗

bioRxiv2020

Topoisomerase 1 inhibition therapy protects against SARS-CoV-2-induced inflammation and death in animal models.

The ongoing pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is currently affecting millions of lives worldwide. Large retrospective studies indicate that an elevated level of inflammatory cytokines and pro-inflammatory factors are associated with both increased disease severity and mortality. Here, using multidimensional epigenetic, transcriptional, in vitro and in vivo analyses, we report that Topoisomerase 1 (Top1) inhibition suppresses lethal inflammation induced by SARS-CoV-2. Therapeutic treatment with two doses of Topotecan (TPT), a FDA-approved Top1 inhibitor, suppresses infection-induced inflammation in hamsters. TPT treatment as late as four days post-infection reduces morbidity and rescues mortality in a transgenic mouse model. These results support the potential of Top1 inhibition as an effective host-directed therapy against severe SARS-CoV-2 infection. TPT and its derivatives are inexpensive clinical-grade inhibitors available in most countries. Clinical trials are needed to evaluate the efficacy of repurposing Top1 inhibitors for COVID-19 in humans.

microbiology↗

bioRxiv2020

Global architecture of the nucleus in single cells by DNA seqFISH+ and multiplexed immunofluorescence

Identifying the relationships between chromosome structures, chromatin states, and gene expression is an overarching goal of nuclear organization studies. Because individual cells are highly variable at all three levels, it is essential to map all three modalities in the same single cell, a task that has been difficult to accomplish with existing tools. Here, we report the direct super-resolution imaging of over 3,660 chromosomal loci in single mouse embryonic stem cells (mESCs) by DNA seqFISH+, along with 17 chromatin marks by sequential immunofluorescence (IF) and the expression profile of 70 RNAs, in the same cells. We discovered that the nucleus is separated into zones defined by distinct combinatorial chromatin marks. DNA loci and nascent transcripts are enriched at the interfaces between specific nuclear zones, and the level of gene expression correlates with an association between active or nuclear speckle zones. Our analysis also uncovered several distinct mESCs subpopulations with characteristic combinatorial chromatin states that extend beyond known transcriptional states, suggesting that the metastable states of mESCs are more complex than previously appreciated. Using clonal analysis, we show that the global levels of some chromatin marks, such as H3K27me3 and macroH2A1 (mH2A1), are heritable over at least 3-4 generations, whereas other marks fluctuate on a faster time scale. The long-lived chromatin states may represent "hidden variables" that explain the observed functional heterogeneity in differentiation decisions in single mESCs. Our integrated spatial genomics approach can be used to further explore the existence and biological relevance of molecular heterogeneity within cell populations in diverse biological systems.

systems biology↗

bioRxiv2020

Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

Efficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

bioinformatics↗

bioRxiv2020

Graph theoretic analysis reveals intranasal oxytocininduced network changes over frontal regions

In this study, we aim to elucidate how intranasal oxytocin modulates brain network characteristics, especially over the frontal network. As an essential brain hub of social cognition and emotion regulation, we will also explore the association between graphic properties of the frontal network and individual personality traits under oxytocin (OT) administration. 59 male participants administered intranasal OT or placebo were followed by restingstate fMRI scanning. The Correlation-based network model was applied to study OT modulation effects. We performed community detection algorithms and conducted further network analyses, including clustering coefficient, average shortest path and eigenvector centrality. In addition, we conducted a correlation analysis between clustering coefficients and the self-assessed psychological scales. Modular organizations in the OT group reveal integrations of the frontoparietal network (FPN) and the default mode network (DMN) over frontal regions. Results show that frontal nodes within the FPN are characterized by lower clustering coefficients and higher average shortest path values compared to the placebo group. Notably, these modulation effects on frontal network property are associated with Interpersonal Reactivity Index (IRI) fantasy value. Our results suggest that OT elevates integrations between FPN, DMN and limbic system as well as reduces small-worldness within the FPN. Our results support graph theoretic analysis as a potential tool to assess OT induced effects on the information integration in the frontal network.

animal behavior and cognition↗

bioRxiv2020

BRAF controls the effects of metformin on neuroblast cell divisions in C. elegans

Metformin has demonstrated substantial potential for use in cancer treatments. LKB-AMPK and mTOR are reported to be the main targets of metformin in relation to its ability to prevent cancer cell proliferation. However, the role of metformin in the control of neoplastic cancer cell growth is possibly independent of LKB-AMPK and mTOR. Using C. elegans as a model, we found that the neuronal Q-cell divisions in L1-arrested worms were suppressed following metformin treatment in AMPK-deficient mutants, suggesting that the mechanism by which metformin suppresses these cell divisions is independent of AMPK. Our results showed that the mTOR pathway indeed played a role in controlling germ cell proliferation, but it was not involved in the neuronal Q-cell divisions occurring in L1-arrested worms. We found that the neuronal Q-cells divisions were hold at G1/S cell stage by metformin in vivo. Additionally, we demonstrated that metformin could reduce the phosphorylation activity of BRAF and block the BRAF-MAPK oncogenesis pathway to regulate neuronal Q-cell divisions during L1 arrest. This work discloses a new mechanism by which metformin treatment acts to promote neuronal cancer prevention, and these results will help promote the study of the anticancer mechanisms underlying metformin treatments.

developmental biology↗

bioRxiv2020

Evaluation of azithromycin or hydroxychloroquine plus azithromycin combination therapy on cardiac conduction and function in guinea pigs

BackgroundIn the early stages of the coronavirus disease pandemic, the anti-malarial drug hydroxychloroquine (HCQ) and the antibiotic drug azithromycin (AZM) were widely used as emerging treatments. However, controversial cardiac toxicity results obtained from clinical trials and epidemic studies suggest that the cardiotoxicity of these two drugs should be re-evaluated. In the present study, we aimed to assess the impact of a short course of AZM or HCQ + AZM combination treatment on ECG and cardiac function in healthy guinea pigs. MethodsThirty-two male guinea pigs were randomly divided into four groups: control; AZM; HCQ; and HCQ + AZM groups. At 3, 6, and 9 days after treatment, electrocardiograms (ECGs) and echocardiographic techniques were used to determine important ECG parameters and cardiac functional parameters of the left ventricle (including posterior wall thickness, end systolic/end diastolic volume, ejection fraction, and fractional shortening). ResultsAlthough AZM decreased the heart rates of guinea pigs on day 9 (under anesthetized conditions), HCQ + AZM decreased heart rates on days 3, 6, and 9. The corrected QT intervals of guinea pigs after AZM and HCQ + AZM treatments were significantly increased, compared with CON and HCQ treatment respectively, on days 3, 6, and 9. However, QRS complex durations were not significantly different between the groups. AZM significantly decreased left ventricular ejection fraction (LVEF) and left ventricular fraction shortening (LVFS) on days 3, 6, and 9, whereas HCQ + AZM only decreased LVEF and LVFS on day 9. Posterior wall thickness and of the left ventricle in the diastolic and systolic states were not significantly different between these groups. In addition, compared with CON, AZM and HCQ decreased the EDV. And, in comparison with HCQ treatment, HCQ + AZM treatment increased ESV on day 9. ConclusionsAccording to our study, AZM significantly prolongs the QT interval and damages cardiac function. Moreover, HCQ + AZM treatment increased the risk of cardiac dysfunction compared with HCQ treatment.

physiology↗

bioRxiv2020

Drug synergy of combinatory treatment with remdesivir and the repurposed drugs fluoxetine and itraconazole effectively impairs SARS-CoV-2 infection in vitro

The SARS-COV-2 pandemic and the global spread of coronavirus disease 2019 (COVID-19) urgently calls for efficient and safe antiviral treatment strategies. A straightforward approach to speed up drug development at lower costs is drug repurposing. Here we investigated the therapeutic potential of targeting the host- SARS-CoV-2 interface via repurposing of clinically licensed drugs and evaluated their use in combinatory treatments with virus- and host-directed drugs. We tested the antiviral potential of repurposing the antifungal itraconazole and the antidepressant fluoxetine on the production of infectious SARS-CoV-2 particles in the polarized Calu-3 cell culture model and evaluated the added benefit of a combinatory use of these host-directed drugs with remdesivir, an inhibitor of viral RNA polymerase. Drug treatments were well-tolerated and potent impaired viral replication was observed with all drug treatments. Importantly, both itraconazole-remdesivir and fluoxetine-remdesivir combinations inhibited the production of infectious SARS-CoV-2 particles > 90% and displayed synergistic effects in commonly used reference models for drug interaction. Itraconazole-Remdesivir and Fluoxetine-Remdesivir combinations are promising therapeutic options to control SARS-CoV-2 infection and severe progression of COVID-19.

pharmacology and toxicology↗

bioRxiv2020

Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce weighted-nearest neighbor analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. AvailabilityInstallation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

genomics↗

bioRxiv2020

Cryo-EM structure of S-Trimer, a subunit vaccine candidate for COVID-19

Less than a year after its emergence, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over 22 million people worldwide with a death toll approaching 1 million. Vaccination remains the best hope to ultimately put this pandemic to an end. Here, using Trimer-Tag technology, we produced both wild-type (WT) and furin site mutant (MT) S-Trimers for COVID-19 vaccine studies. Cryo-EM structures of the WT and MT S-Trimers, determined at 3.2 [A] and 2.6 [A] respectively, revealed that both antigens adopt a tightly closed conformation and their structures are essentially identical to that of the previously solved full-length WT S protein in detergent. These results validate Trimer-Tag as a platform technology in production of metastable WT S-Trimer as a candidate for COVID-19 subunit vaccine.

biophysics↗

bioRxiv2020

Proteasome Regulation by Reversible Tyrosine Phosphorylation at the Membrane

Reversible phosphorylation has emerged as an important mechanism for regulating 26S proteasome function in health and disease. Over 100 phospho-tyrosine (pTyr) sites of the human proteasome have been detected, and yet their function and regulation remain poorly understood. Here we show that the 19S subunit Rpt2 is phosphorylated at Tyr439, a strictly conserved residue within the C-terminal HbYX motif of Rpt2 that is essential for 26S proteasome assembly. Unexpectedly, we found that Y439 phosphorylation depends on Rpt2 membrane localization mediated by its N-myristoylation. Multiple receptor tyrosine kinases (RTKs) can trigger Rpt2-Y439 phosphorylation by activating Src, a N-myristoylated tyrosine kinase. Src directly phosphorylates Rpt2-Y439 in vitro and negatively regulates 26S proteasome integrity and activity at cellular membranes, which can be reversed by the membrane-associated isoform of protein tyrosine phosphatase non-receptor type 2 (PTPN2). In H1975 lung cancer cells with activated Src, blocking Rpt2-Y439 phosphorylation by the Y439F mutation conferred partial resistance to the Src inhibitor saracatinib both in vitro and in a mouse xenograft tumor model, and caused significant changes of cellular responses to saracatinib at the proteome level. Our study has defined a novel mechanism involved in the spatial regulation of proteasome function and provided new insights into tyrosine kinase inhibitor-based anti-cancer therapies.

biochemistry↗