Biology subjects

Zeng, J.

Publications and source records attributed to Zeng, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

Identifying SARS-CoV-2 Antiviral Compounds by Screening for Small Molecule Inhibitors of Nsp15 Endoribonuclease

SARS-CoV-2 is responsible for COVID-19, a human disease that has caused over 2 million deaths, stretched health systems to near-breaking point and endangered the economies of countries and families around the world. Antiviral treatments to combat COVID-19 are currently lacking. Remdesivir, the only antiviral drug approved for the treatment of COVID-19, can affect disease severity, but better treatments are needed. SARS-CoV-2 encodes 16 non-structural proteins (nsp) that possess different enzymatic activities with important roles in viral genome replication, transcription and host immune evasion. One key aspect of host immune evasion is performed by the uridine-directed endoribonuclease activity of nsp15. Here we describe the expression and purification of nsp15 recombinant protein. We have developed biochemical assays to follow its activity, and we have found evidence for allosteric behaviour. We screened a custom chemical library of over 5000 compounds to identify nsp15 endoribonuclease inhibitors, and we identified and validated NSC95397 as an inhibitor of nsp15 endoribonuclease in vitro. Although NSC95397 did not inhibit SARS-CoV-2 growth in VERO E6 cells, further studies will be required to determine the effect of nsp15 inhibition on host immune evasion.

biochemistry↗

bioRxiv2021

Identifying SARS-CoV-2 Antiviral Compounds by Screening for Small Molecule Inhibitors of Nsp14/nsp10 Exoribonuclease

SARS-CoV-2 is a coronavirus that emerged in 2019 and rapidly spread across the world causing a deadly pandemic with tremendous social and economic costs. Healthcare systems worldwide are under great pressure, and there is urgent need for effective antiviral treatments. The only currently approved antiviral treatment for COVID-19 is remdesivir, an inhibitor of viral genome replication. SARS-CoV-2 proliferation relies on the enzymatic activities of the non-structural proteins (nsp), which makes them interesting targets for the development of new antiviral treatments. With the aim to identify novel SARS-CoV-2 antivirals, we have purified the exoribonuclease/methyltransferase (nsp14) and its cofactor (nsp10) and developed biochemical assays compatible with high-throughput approaches to screen for exoribonuclease inhibitors. We have screened a library of over 5000 commercial compounds and identified patulin and aurintricarboxylic acid (ATA) as inhibitors of nsp14 exoribonuclease in vitro. We found that patulin and ATA inhibit replication of SARS-CoV-2 in a VERO E6 cell-culture model. These two new antiviral compounds will be valuable tools for further coronavirus research as well as potentially contributing to new therapeutic opportunities for COVID-19.

biochemistry↗

bioRxiv2021

Small but complete: liquid-liquid phase separation in minimalistic peptides

Liquid-liquid phase separation (LLPS) of proteins mediates the assembly of biomolecular condensates involved in physiological and pathological processes. Identifying the minimalistic building blocks and the sequence determinant of protein phase separation is of urgent importance but remains challenging due to the enormous sequence space and difficulties of existing methodologies in characterizing the phase behavior of ultrashort peptides. Here we demonstrate computational tools to efficiently quantify the microscopic fluidity and density of liquid-condensates/solid-aggregates and the temperature-dependent phase diagram of peptides. Utilizing our approaches, we comprehensively predict the LLPS abilities of all 400 dipeptide combinations of coded amino acids based on 492 micro-second molecular dynamics simulations, and observe the occurrences of spontaneous LLPS. We identify 54 dipeptides that form solid-like aggregates and three categories of dipeptides with high LLPS propensity. Our predictions are validated by turbidity assays and differential interference contrast (DIC) microscopy on four representative dipeptides (WW, QW, GF, and VI). Phase coexistence diagrams are constructed to explore the temperature dependence of LLPS. Our results reveal that aromatic moieties are crucial for a dipeptide to undergo LLPS, and hydrophobic and polar components are indispensable. We demonstrate for the first time that dipeptides, minimal but complete, possess multivalent interactions sufficient for LLPS, suggesting that LLPS is a general property of peptides/proteins, independent of their sequence length. This study provides a computational and experimental approach to the prediction and characterization of the phase behavior of minimalistic peptides, and will be helpful for understanding the sequence-dependence and molecular mechanism of protein phase separation. SignificanceProtein liquid-liquid phase separation (LLPS) is associated with human health and diseases. Identifying the minimalistic building blocks and sequence determinants of LLPS is of urgent importance but remains computationally challenging partially due to the lack of methodologies characterizing the liquid condensates. Herein we provide approaches to evaluate LLPS ability of dipeptides, and screen all 400 dipeptides by MD simulations combined with multi-bead-per-residue models which capture key interactions driving LLPS that are missing in one-bead-per-residue models. Three categories of LLPS dipeptides are identified and the experimentally-verified QW dipeptide is by far the smallest LLPS system. Our results suggest that dipeptides, minimal but complete, possess multivalent interactions sufficient for LLPS, and LLPS is a general property of peptides/proteins, independent of their length.

biophysics↗

bioRxiv2021

Impact of blood storage conditions on the transcript profile of plasma cell-free RNA

BACKGROUNDPlasma cell-free RNA (cfRNA) are potential biomarkers for disease prediction and diagnosis. However, pre-analysis factors, such as the delay in blood processing and storage may lead to unreliable results, though no study has systematically evaluated the effect of blood storage conditions on the whole transcriptome of plasma cfRNA yet. METHODSWe collected peripheral blood samples from four healthy subjects and allowed them to stand at room temperature or 4{square} for different time periods (0h, 2h, 6h and 24h) prior to plasma separation. Then, plasma cfRNA stability was evaluated by measuring expression changes of cell-free mRNA, lncRNA and miRNA using high throughput sequencing-based profiling. Finally, their paired leukocyte RNA data were integrated to depict the effect of leukocytes on plasma cfRNA during storage. RESULTSPlasma mRNA and lncRNA presented high correlations (Pearson R2 [≥] 0.8) and fewer variations when blood was stored at 4{square} for 6 hours or stored at RT for 2 hours. miRNA was more stable, with minimal R2 of 0.86 at 4{square} for at least 24 hours or at RT for 6 hours. Correlations of plasma RNA and leukocyte RNA increased with the incubation time, and the relative proportion of neutrophils in plasma grown from 14.3% to 61.2% at RT (P = 0.004), indicating leukocyte RNA contamination. Besides, the tissue enriched genes in plasma were down-regulated with the extension of storage time. CONCLUSIONSOur results characterized the effects of short-term storage of blood samples on plasma cfRNA, which will facilitate further researches or clinical applications to avoid bias resulting from sample processing.

molecular biology↗

bioRxiv2021

Methylation of KRAS at lysine 182 and 184 by SETD7 promotes KRAS degradation

Oncogenic KRAS mutations are considered to be a key driver for initiation and progression in non-small-cell lung cancer (NSCLC). However, how post-translational modifications (PTMs) of KRAS, especially methylation, modify KRAS activity and downstream signals remain largely unclear. Here, we showed that SET domain containing histone lysine methyltransferase 7 (SETD7) interacts with KRAS and methylates KRAS at lysine 182 and 184. SETD7-mediated methylation of KRAS led to degradation of KRAS and attenuation of the RAS/MEK/ERK signaling cascade, endowing SETD7 with a potent tumor-suppressive role in NSCLC, both in vitro and in vivo. Mechanistically, RABGEF1, a ubiquitin E3 ligase of KRAS, was recruited and promoted KRAS degradation in a K182/K184 methylation-dependent manner. Notably, low SETD7 or RABGEF1 expression was associated with poor prognosis in lung adenocarcinoma patients. Taken together, our results establish a novel connection between lysine methylation and KRAS protein stability, in addition to elucidating a tumor-suppressive function of SETD7 that operates via modulation of oncogenic RAS signaling.

cancer biology↗

bioRxiv2021

ARGONAUTE10 is required for cell fate specification and the control of formative cell divisions in the Arabidopsis root meristem

A key question in plant biology is how oriented cell divisions are integrated with patterning mechanisms to generate organs with adequate cell type allocation. In the root vasculature, a miRNA gradient controls the abundance of HD-ZIP III transcription factors, which in turn control cell fate and spatially restrict vascular cell proliferation to specific cells. Here, we show that a functional miRNA gradient requires an opposing gradient of ARGONAUTE10, which sequesters miRNAs to protect HD-ZIP III transcripts from degradation. In the absence of ARGONAUTE10, xylem precursor cells undergo periclinal divisions that lead to continuous strands of differentiated xylem elements at ectopic positions. Notably, periclinal daughter cells maintain xylem identity even when they are located outside of the xylem axis, resulting in disrupted tissue boundaries. We further demonstrate that ARGONAUTE10 and HD-ZIP IIIs buffer cytokinin signalling to control formative cell divisions, providing a framework for integration of phytohormone and miRNA-mediated patterning.

plant biology↗

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

Localization, proteomics and metabolite profiling reveal a putative vesicular transporter for UDP-glucose

Vesicular neurotransmitter transporters (VNTs) mediate the selective uptake and enrichment of small molecule neurotransmitters into synaptic vesicles (SVs) and are therefore a major determinant of the synaptic output of specific neurons. To identify novel VNTs expressed on SVs (thus identifying new neurotransmitters and/or neuromodulators), we conducted localization profiling of 361 solute carrier (SLC) transporters tagging with a fluorescent protein in neurons, which revealed 40 possible candidates through comparison with a known SV marker. We parallelly performed proteomics analysis of immunoisolated SVs and identified 7 transporters in overlap. Ultrastructural analysis confirmed one of the transporters, SLC35D3, localized to SVs. Finally, by combining metabolite profiling with a radiolabeled substrate transport assay, we identified UDP-glucose as the principal substrate for SLC35D3. These results provide new insights into the functional role of SLC transporters in neurotransmission and improve our understanding of the molecular diversity of chemical transmitters.

neuroscience↗

bioRxiv2020

CAMP: a Convolutional Attention-based Neural Network for Multifaceted Peptide-protein Interaction Prediction

Peptide-protein interactions (PepPIs) are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. To facilitate the peptide drug discovery process, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Although several deep-learning-based frameworks have been proposed to predict compound-protein interactions or protein-protein interactions, few of them are particularly designed to specifically predict peptide-protein interactions. In this paper, We present a sequence-based Convolutional Attention-based neural network for Multifaceted prediction of Peptide-protein interactions, called CAMP, including predicting binary peptide-protein interactions and corresponding binding residues in the peptides. We also construct a benchmark dataset containing high-quality peptide-protein interaction pairs with the corresponding peptide binding residues for model training and evaluation. CAMP incorporates convolution neural network architectures and attention mechanism to fully exploit informative sequence-based features, including secondary structures, physicochemical properties, intrinsic disorder features and position-specific scoring matrix of the protein. Systematical evaluation of our benchmark dataset demonstrates that CAMP outperforms the state-of-the-art baseline methods on binary peptide-protein interaction prediction. In addition, CAMP can successfully identify the binding residues involved non-covalent interactions for peptides. These results indicate that CAMP can serve as a useful tool in peptide-protein interaction prediction and peptide binding site identification, which can thus greatly facilitate the peptide drug discovery process. The source code of CAMP can be found in https://github.com/twopin/CAMP.

bioinformatics↗

bioRxiv2020

Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2

The ongoing coronavirus disease 2019 (COVID-19) pandemic has raised an urgent need to develop effective therapeutics against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As a potential antiviral drug target, the nucleocapsid (N) protein of SARS-CoV-2 functions as a viral RNA chaperone and plays vital and multifunctional roles during the life cycle of coronavirus1-3. In this study, we discovered that the N protein of SARS-CoV-2 undergoes liquid-liquid phase separation (LLPS) both in vitro and in vivo, which is further modulated by viral RNA. In addition, we found that, the core component of the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2, nsp12, preferentially partitions into the N protein condensates. Moreover, we revealed that, two small molecules, i.e., CVL218 and PJ34, can be used to intervene the N protein driven phase separation and loosen the compact structures of the condensates of the N-RNA-nsp12 complex of SARS-CoV-2. The discovery of the LLPS-mediated interplay between N protein and nsp12 and the corresponding modulating compounds illuminates a feasible way to improve the accessibility of antiviral drugs (e.g., remdesivir) to their targets (e.g., nsp12/RdRp), and thus may provide useful hints for further development of effective therapeutic strategies against SARS-CoV-2.

biophysics↗

bioRxiv2020

Prime-boost vaccination of mice and Rhesus macaques with two novel adenovirus vectored COVID-19 vaccine candidates

COVID-19 vaccines are being developed urgently worldwide, among which single-shot adenovirus vectored vaccines represent a major approach. Here, we constructed two novel adenovirus vectored COVID-19 vaccine candidates on simian adenovirus serotype 23 (Sad23L) and human adenovirus serotype 49 vectors (Ad49L) carrying the full-length gene of SARS-CoV-2 spike protein (S), designated Sad23L-nCoV-S and Ad49L-nCoV-S vaccines, respectively. The immunogenicity elicited by these two vaccine strains was individually evaluated in mice. Specific humoral and cellular immune responses were proportionally observed in a dose-dependent manner, and stronger response was obtained by boosting. Furthermore, five rhesus macaques were intramuscularly injected with a dose of 5x109 PFU Sad23L-nCoV-S vaccine for prime vaccination, followed by boosting with 5x109 PFU of Ad49L-nCoV-S vaccine at 4-week interval. Three macaques were injected with Sad23L-GFP and Ad49L-GFP vectorial viruses as negative controls. Both mice and macaques tolerated well the vaccine inoculations without detectable clinical or pathologic changes. In macaques, prime-boost vaccination regimen induced high titers of 103.16 S-binding antibody (S-BAb), 102.75 cell receptor binding domain (RBD)-BAb and 102.38 neutralizing antibody (NAb) to pseudovirus a week after boosting injection, followed by sustained high levels over 10 weeks of observation. Robust IFN-{gamma} secreting T-cell response (712.6 SFCs/106 cells), IL-2 secreting T-cell response (334 SFCs/106 cells) and intracellular IFN-{gamma} expressing CD4+/CD8+ T cell response (0.39%/0.55%) to S peptides were detected in the vaccinated macaques. It was concluded that prime-boost immunization with Sad23L-nCoV-S and Ad49L-nCoV-S vaccines can safely elicit strong immunity in animals in preparation of clinical phase 1/2 trials.

immunology↗

bioRxiv2020

An interbacterial DNA deaminase toxin directly mutagenizes surviving target populations

When bacterial cells come in contact, antagonism mediated by the delivery of toxins frequently ensues. The potential for such encounters to have long-term beneficial consequences in recipient cells has not been investigated. Here we examined the effects of intoxication by DddA, a cytosine deaminase delivered via the type VI secretion system (T6SS) of Burkholderia cenocepacia. Despite its killing potential, we observed that several bacterial species resist DddA and instead accumulate mutations installed by the toxin, indicating that even in the absence of killing, interbacterial toxins can have profound consequences on target populations. Investigation of additional toxins from the deaminase superfamily revealed that mutagenic activity is a common feature of these proteins, including a representative we show targets single-stranded DNA and displays a markedly divergent structure. Our findings suggest that a surprising consequence of antagonistic interactions between bacteria could be the promotion of adaptation via the action of directly mutagenic toxins.

microbiology↗

bioRxiv2020

Bat and pangolin coronavirus spike glycoprotein structures provide insights into SARS-CoV-2 evolution

In recognizing the host cellular receptor and mediating fusion of virus and cell membranes, the spike (S) glycoprotein of coronaviruses is the most critical viral protein for cross-species transmission and infection. Here we determined the cryo-EM structures of the spikes from bat (RaTG13) and pangolin (PCoV_GX) coronaviruses, which are closely related to SARS-CoV-2. All three receptor-binding domains (RBDs) of these two spike trimers are in the "down" conformation, indicating they are more prone to adopt this receptor-binding inactive state. However, we found that the PCoV_GX, but not the RaTG13, spike is comparable to the SARS-CoV-2 spike in binding the human ACE2 receptor and supporting pseudovirus cell entry. Through structure and sequence comparisons, we identified critical residues in the RBD that underlie the different activities of the RaTG13 and PCoV_GX/SARS-CoV-2 spikes and propose that N-linked glycans serve as conformational control elements of the RBD. These results collectively indicate that strong RBD-ACE2 binding and efficient RBD conformational sampling are required for the evolution of SARS-CoV-2 to gain highly efficient infection.

biophysics↗

bioRxiv2020

ZNF410 represses fetal globin by devoted control of CHD4/NuRD

Major effectors of adult-stage fetal globin silencing include the transcription factors (TFs) BCL11A and ZBTB7A/LRF and the NuRD chromatin complex, although each has potential on-target liabilities for rational {beta}-hemoglobinopathy therapeutic inhibition. Here through CRISPR screening we discover ZNF410 to be a novel fetal hemoglobin (HbF) repressing TF. ZNF410 does not bind directly to the{gamma} -globin genes but rather its chromatin occupancy is solely concentrated at CHD4, encoding the NuRD nucleosome remodeler, itself required for HbF repression. CHD4 has two ZNF410-bound regulatory elements with 27 combined ZNF410 binding motifs constituting unparalleled genomic clusters. These elements completely account for ZNF410s effects on{gamma} -globin repression. Knockout of ZNF410 reduces CHD4 by 60%, enough to substantially de-repress HbF while avoiding the cellular toxicity of complete CHD4 loss. Mice with constitutive deficiency of the homolog Zfp410 are born at expected Mendelian ratios with unremarkable hematology. ZNF410 is dispensable for human hematopoietic engraftment potential and erythroid maturation unlike known HbF repressors. These studies identify a new rational target for HbF induction for the {beta}-hemoglobin disorders with a wide therapeutic index. More broadly, ZNF410 represents a special class of gene regulator, a conserved transcription factor with singular devotion to regulation of a chromatin subcomplex.

genetics↗

bioRxiv2020

The global landscape of SARS-CoV-2 genomes, variants, and haplotypes in 2019nCoVR

On 22 January 2020, the National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), created the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access SARS-CoV-2 information resource. 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by our in-house automated pipeline. Of particular note, 2019nCoVR performs systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. It also generates visualization of the spatiotemporal change for each variant and yields historical viral haplotype network maps for the course of the outbreak from all complete and high-quality genomes. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on COVID-19 (Coronavirus Disease 2019), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB-NGDC, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with National Center for Biotechnology Information. Collectively, all SARS-CoV-2 genome sequences, variants, haplotypes and literature are updated daily to provide timely information, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.

genomics↗

bioRxiv2020

SARS-CoV-2 Spike Protein Interacts with Multiple Innate Immune Receptors

The spike (S) glycoprotein in the envelope of SARS-CoV-2 is densely glycosylated but the functions of its glycosylation are unknown. Here we demonstrate that S is recognized in a glycan-dependent manner by multiple innate immune receptors including the mannose receptor MR/CD206, DC-SIGN/CD209, L-SIGN/CD209L, and MGL/CLEC10A/CD301. Single-cell RNA sequencing analyses indicate that such receptors are highly expressed in innate immune cells in tissues susceptible to SARS-CoV-2 infection. Binding of the above receptors to S is characterized by affinities in the picomolar range and consistent with S glycosylation analysis demonstrating a variety of N- and O-glycans as receptor ligands. These results indicate multiple routes for SARS-CoV-2 to interact with human cells and suggest alternative strategies for therapeutic intervention.

biochemistry↗

bioRxiv2020

Substrate-induced clustering activates Trim-Away of pathogens and proteins

Trim-Away is a powerful new technology that exploits off-the-shelf antibodies and the E3 RING ligase and cytosolic antibody receptor TRIM21 to carry out rapid protein depletion. How TRIM21 is catalytically-activated upon substrate engagement during either its normal immune function or when re-purposed for targeted protein degradation is unknown. Here we show that a mechanism of substrate-induced clustering triggers intermolecular dimerization of the RING domain to switch on the ubiquitination activity of TRIM21 and induce an antiviral response or drive Trim-Away. We harness this mechanism to expand the Trim-Away toolbox with highly-active TRIM21-nanobody chimeras that can also be controlled optogenetically. This work provides a mechanism for cellular activation of TRIM RING ligases and has important implications for targeted protein degradation technologies.

molecular biology↗

bioRxiv2020

A deep learning framework for high-throughput mechanism-driven phenotype compound screening

Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

bioinformatics↗