Biology subjects

Xie, L.

Publications and source records attributed to Xie, L..

At least 181 records · Page 10Linked to original sources

bioRxiv2020

EcDNA hubs drive cooperative intermolecular oncogene expression

Extrachromosomal DNAs (ecDNAs) are prevalent in human cancers and mediate high oncogene expression through elevated copy number and altered gene regulation1. Gene expression typically involves distal enhancer DNA elements that contact and activate genes on the same chromosome2,3. Here we show that ecDNA hubs, comprised of ~10-100 ecDNAs clustered in the nucleus of interphase cells, drive intermolecular enhancer input for amplified oncogene expression. Single-molecule sequencing, single-cell multiome, and 3D enhancer connectome reveal subspecies of MYC-PVT1 ecDNAs lacking enhancers that access intermolecular and ectopic enhancer-promoter interactions in ecDNA hubs. ecDNA hubs persist without transcription and are tethered by BET protein BRD4. BET inhibitor JQ1 disperses ecDNA hubs, preferentially inhibits ecDNA oncogene transcription, and kills ecDNA+ cancer cells. Two amplified oncogenes MYC and FGFR2 intermix in ecDNA hubs, engage in intermolecular enhancer-promoter interactions, and transcription is uniformly sensitive to JQ1. Thus, ecDNA hubs are nuclear bodies of many ecDNAs tethered by proteins and platforms for cooperative transcription, leveraging the power of oncogene diversification and combinatorial DNA interactions. We suggest ecDNA hubs, rather than individual ecDNAs, as units of oncogene function, cooperative evolution, and new targets for cancer therapy.

cancer biology↗

bioRxiv2020

Genome-wide screen of genomic imprinting in endosperm and population-level analysis reveal allelic variation for imprinting in flax

Genomic imprinting is an epigenetic phenomenon caused by the biased expression of maternally and paternally inherited alleles. In flowering plants, genomic imprinting predominantly occurs in triploid endosperm and plays a vital role in seed development. In this study, we identified 241 candidate imprinted genes including 143 maternally expressed imprinted genes (MEGs) and 98 paternally expressed imprinted genes (PEGs) in flax (Linum usitatissimum L.) endosperm using deep RNA sequencing. The conservation of imprinting in plants is very limited and imprinting clustering is not a general feature. MEGs tends to be endosperm expression specific, while PEGs are non-tissue specific. Imprinted SNPs differentiated 200 flax cultivars into oil flax, oil-fiber dual purpose flax (OF) and fiber flax subgroups, suggesting that genomic imprinting contributes to intraspecific variation in flax. The nucleotide diversity ({pi}) of imprinted genes in oil flax subgroup is significantly higher than that in fiber flax subgroup, indicating that some imprinted genes undergo positive selection during flax domestication from oil flax to fiber flax. Imprinted genes undergo positive selection is related to the functions. Eleven imprinted genes related to seed size and weight were identified using the candidate gene-based association study. Our study provides information for further exploring the function and genomic variation of imprinted genes in flax population.

plant biology↗

bioRxiv2020

Novel gene-specific translation mechanism of dysregulated, chronic inflammation reveals promising, multifaceted COVID-19 therapeutics

Hyperinflammation and lymphopenia provoked by SARS-CoV-2-activated macrophages contribute to the high mortality of Coronavirus Disease 2019 (COVID-19) patients. Thus, defining host pathways aberrantly activated in patient macrophages is critical for developing effective therapeutics. We discovered that G9a, a histone methyltransferase that is overexpressed in COVID-19 patients with high viral load, activates translation of specific genes that induce hyperinflammation and impairment of T cell function or lymphopenia. This noncanonical, pro-translation activity of G9a contrasts with its canonical epigenetic function. In endotoxin-tolerant (ET) macrophages that mimic conditions which render patients with pre-existing chronic inflammatory diseases vulnerable to severe symptoms, our chemoproteomic approach with a biotinylated inhibitor of G9a identified multiple G9a-associated translation regulatory pathways that were upregulated by SARS-CoV-2 infection. Further, quantitative translatome analysis of ET macrophages treated progressively with the G9a inhibitor profiled G9a-translated proteins that unite the networks associated with viral replication and the SARS-CoV-2-induced host response in severe patients. Accordingly, inhibition of G9a-associated pathways produced multifaceted, systematic effects, namely, restoration of T cell function, mitigation of hyperinflammation, and suppression of viral replication. Importantly, as a host-directed mechanism, this G9a-targeted, combined therapeutics is refractory to emerging antiviral-resistant mutants of SARS-CoV-2, or any virus, that hijacks host responses.

systems biology↗

bioRxiv2020

Aberrant peripheral immune responses in acute Kawasaki disease with single-cell sequencing

Kawasaki disease (KD) is the most common cause of acquired heart disease in children in developed countries. Although diverse immune aberrance was reported, a global understanding of immune responses underlying acute KD was lacking. Based on single-cell sequencing, we profiled peripheral blood mononuclear cells from patients with acute KD before and after intravenous immunoglobulin therapy and from healthy controls. Most differentially expressed genes were derived from monocytes, with upregulation of immunoglobulin receptors, complement and receptors and downregulation of MHC class II receptors before therapy. The percentage of B cells was significantly increased before therapy and rapidly returned to normal after therapy. There was also an increased abundance of B-cell receptors with IGHA and IGHG after therapy, accompanied by massive oligoclonal expansion. The percentage of CD8 T cells was remarkably decreased during acute KD, especially the subset of effector memory CD8 T cells. All lymphocyte compartments were characterized by underexpressed interferon response pathways before therapy. The identification of unique innate and adaptive immune responses suggests potential mechanisms underlying pathogenesis and progression of KD.

immunology↗

bioRxiv2020

A deep learning framework for elucidating whole-genome chemical interaction space

Molecular interaction is the foundation of biological process. Elucidation of genome-wide binding partners of a biomolecule will address many questions in biomedicine. However, ligands of a vast number of proteins remain elusive. Existing methods mostly fail when the protein of interest is dissimilar from those with known functions or structures. We develop a new deep learning framework DISAE that incorporates biological knowledge into self-supervised learning techniques for predicting ligands of novel unannotated proteins on a genome-scale. In the rigorous benchmark studies, DISAE outperforms state-of-the-art methods by a significant margin. The interpretability analysis of DISAE suggests that it learns biologically meaningful information. We further use DISAE to assign ligands to human orphan G-Protein Coupled Receptors (GPCRs) and to cluster the human GPCRome by integrating their phylogenetic and ligand relationships. The promising results of DISAE open an avenue for exploring the chemical landscape of entire sequenced genomes.

bioinformatics↗

bioRxiv2020

Transferrin receptor (Tfr1) ablation in satellite cells impacts skeletal muscle regeneration through the activation of ferroptosis

Satellite cells (SCs) are critical to the postnatal development and skeletal muscle regeneration. Inactivation of SCs is linked with the skeletal muscle loss. Leveraging on the RNAseq screening, transferrin receptor (Tfr1) is identified to be associated with muscle/SC ageing and the declined regeneration potential. Muscle-specific deletion of Tfr1 results in the growth retardation, metabolic disorder and lethality, shedding light on the importance of Tfr1 in skeletal muscle physiology. Here, our investigation reported that conditional SC-ablation of Tfr1 leads to the SCs inactivation and skeletal muscle regeneration defects, followed by the labile iron accumulation, de novo lipogenesis via fibroadipogenic progenitors (FAPs) and Gpx4/Nrf2-mediated ROS-scavenger defects. These abnormal phenomena, such as Hmox1-mediated myoglobin degradation, Tfr1-Slc39a14 functional switch and the activation of unsaturated fatty acid biosynthesis pathway are orchestrated with the occurrence of ferroptosis in skeletal muscle. Ferroptosis may further prevent SC proliferation and skeletal muscle regeneration. Ferrostatin-1, a ferroptosis inhibitor could not rescue Tfr1-ablation induced ferroptosis. However, intramuscular administration of lentivirus expressing Tfr1 could partially reduce labile iron accumulation, decrease de novo lipogenesis and promote skeletal muscle regeneration. Most importantly, Tfr1/Slc39a14 functional switch, labile iron accumulation and fatty acid biosynthesis are recapitulated in aged skeletal muscle of rodents, indicating that ferroptosis occurs in the skeletal muscles of aged rodents. The present study also bridges the gap between pathogenesis of iron and functional defects in the skeletal muscle, providing mechanistic information to develop anti-aging strategies. One Sentence SummaryConditional ablation of Tfr1 in satellite cells (SCs) results in the SC inactivation, skeletal muscle regeneration defects, labile iron accumulation, and unsaturated fatty acid biosynthesis, leading to the activation of ferroptosis, which is recapitulated in skeletal muscles of aged rodents to be a new cell death form identified in skeletal muscle and sheds light on the development of novel anti-ageing strategies.

cell biology↗

bioRxiv2020

Postsynaptic potential energy as determinant of synaptic plasticity

Metabolic energy can be used as a unified principle to control neuronal activity. However, whether and how metabolic energy alone can determine the outcome of synaptic plasticity remains unclear. In this study, a computational model of synaptic plasticity that is completely determined by energy is proposed. A simple quantitative relationship between synaptic plasticity and postsynaptic potential energy is established. Synaptic weight is directly proportional to the difference between the baseline potential energy and the suprathreshold potential energy and is constrained by the maximum energy supply. Results show that the energy constraint improves the performance of synaptic plasticity and avoids setting the hard boundary of synaptic weights. With the same set of model parameters, our model can reproduce several classical experiments in homo and heterosynaptic plasticity. The proposed model can explain the interaction mechanism of Hebbian and homeostatic plasticity at the cellular level, thereby providing a new way to deeply understand the characteristics of learning and memory.

neuroscience↗

bioRxiv2020

Deuteration improves small-molecule fluorophores

Fluorescence microscopy relies on dyes that absorb short-wavelength photons and emit longer-wavelength light. In addition to this fluorescence process, dyes can undergo other photochemical reactions that result in spectral shifts and irreversible photobleaching. Increases in brightness, chromostability, and photostability of fluorescent dyes are therefore crucial for advancing the frontier of bioimaging. Here, we describe a general approach to improve small-molecule fluorophores using deuteration. Incorporating deuterium into the alkylamino substituents of rhodamines and other dyes improves fluorescence quantum yield, inhibits photochemically induced spectral shifts, and slows irreparable photobleaching. These compounds are easily synthesized and show improved performance in cellular imaging experiments.

cell biology↗

bioRxiv2020

Exploration of Chemical Space with Partial Labeled Noisy Student Self-Training for Improving Deep Learning: Application to Drug Metabolism

MotivationDrug discovery is time-consuming and costly. Machine learning, especially deep learning, shows a great potential in accelerating the drug discovery process and reducing its cost. A big challenge in developing robust and generalizable deep learning models for drug design is the lack of a large amount of data with high quality and balanced labels. To address this challenge, we developed a self-training method PLANS that exploits millions of unlabeled chemical compounds as well as partially labeled pharmacological data to improve the performance of neural network models. ResultWe evaluated the self-training with PLANS for Cytochrome P450 binding activity prediction task, and proved that our method could significantly improve the performance of the neural network model with a large margin. Compared with the baseline deep neural network model, the PLANS-trained neural network model improved accuracy, precision, recall, and F1 score by 13.4%, 12.5%, 8.3%, and 10.3%, respectively. The self-training with PLANS is model agnostic, and can be applied to any deep learning architectures. Thus, PLANS provides a general solution to utilize unlabeled and partially labeled data to improve the predictive modeling for drug discovery. AvailabilityThe code that implements PLANS is available at https://github.com/XieResearchGroup/PLANS

bioinformatics↗

bioRxiv2020

Genome-wide Prediction of Small Molecule Binding to Remote Orphan Proteins Using Distilled Sequence Alignment Embedding

Endogenous or surrogate ligands of a vast number of proteins remain unknown. Identification of small molecules that bind to these orphan proteins will not only shed new light into their biological functions but also provide new opportunities for drug discovery. Deep learning plays an increasing role in the prediction of chemical-protein interactions, but it faces several challenges in protein deorphanization. Bioassay data are highly biased to certain proteins, making it difficult to train a generalizable machine learning model for the proteins that are dissimilar from the ones in the training data set. Pre-training offers a general solution to improving the model generalization, but needs incorporation of domain knowledge and customization of task-specific supervised learning. To address these challenges, we develop a novel protein pre-training method, DIstilled Sequence Alignment Embedding (DISAE), and a module-based fine-tuning strategy for the protein deorphanization. In the benchmark studies, DISAE significantly improves the generalizability and outperforms the state-of-the-art methods with a large margin. The interpretability analysis of pre-trained model suggests that it learns biologically meaningful information. We further use DISAE to assign ligands to 649 human orphan G-Protein Coupled Receptors (GPCRs) and to cluster the human GPCRome by integrating their phylogenetic and ligand relationships. The promising results of DISAE open an avenue for exploring the chemical landscape of entire sequenced genomes.

systems biology↗

bioRxiv2020

The SWELL1-LRRC8 complex regulates endothelial AKT-eNOS-mTOR signaling and vascular function

The endothelium responds to a multitude of chemical and mechanical factors in regulating vascular tone, angiogenesis, blood pressure and blood flow. The endothelial volume regulatory anion channel (VRAC) has been proposed to be mechano-sensitive, to activate in response to fluid flow/hydrostatic pressure and putatively regulate vascular reactivity and angiogenesis. Here, we show that the Leucine Rich Repeat Containing Protein 8a, LRRC8a (SWELL1) functionally encodes VRAC in human umbilical vein endothelial cells (HUVECs). Endothelial SWELL1 (SWELL1) expression positively regulates AKT-eNOS signaling while negatively regulating mTOR signaling, via a SWELL1-GRB2-Cav1-eNOS signaling complex. Endothelium-restricted SWELL1 KO (SWELL1 KO) mice exhibit enhanced tube formation from ex-vivo aortic ring explants in matrigel angiogenesis assays, develop hypertension in response to chronic angiotensin II infusion and have impaired retinal blood flow with both diffuse and focal blood vessel narrowing in the setting of Type 2 diabetes (T2D). These data demonstrate that SWELL1 antithetically regulates AKT-eNOS and mTOR signaling in endothelium and is required for maintaining vascular function, particularly in the setting of T2D.

cell biology↗

bioRxiv2020

AD-linked R47H-TREM2 mutation induces disease-enhancing proinflammatory microglial states in mice and humans

The hemizygous R47H variant of TREM2, a microglia-specific gene in the brain, increases risk for late-onset Alzheimers disease (AD). In this study, we identified a subpopulation of microglia with disease-enhancing proinflammatory signatures associated with the R47H mutation in human AD brains and tauopathy mouse brains. Using transcriptomic analysis at the single-nuclei level from AD patients with the R47H or the common variant (CV)-TREM2 with matched sex, pathology and APOE status, we found that the R47H mutation was associated with cell type- and sex-specific transcriptional changes in human AD brains, with microglia exhibiting the most robust alterations. Further characterization revealed that R47H-associated microglial subpopulations had enhanced inflammatory signatures including hyperactivation of Akt, one of the signaling pathways downstream of TREM2. In a newly-generated tauopathy knock-in mouse model expressing one allele of human TREM2 (hTREM2) with either the R47H mutation or CV, R47H induced and exacerbated tau-mediated spatial memory deficits in female mice. Single-cell transcriptomic analysis of microglia from these mice also revealed transcriptomic changes induced by R47H that had significant overlaps with R47H microglia in human AD brains, including robust increases in proinflammatory cytokines, activation of Syk-Akt-signaling, and elevation of a subset of disease-associated microglial signatures. Strikingly, pharmacological Akt inhibition largely reversed the enhanced inflammatory signatures induced by R47H in primary microglia treated with tau fibrils. By unraveling the disease-enhancing properties of the R47H mutation in mouse and human, our findings shed light on an immune-linked AD subtype and provide new directions for modulating microglial immune responses to treat AD.

neuroscience↗

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↗

bioRxiv2020

SWELL1-LRRC8 complex regulates skeletal muscle cell size, intracellular signalling, adiposity and glucose metabolism

Maintenance of skeletal muscle is beneficial in obesity and Type 2 diabetes. Mechanical stimulation can regulate skeletal muscle differentiation, growth and metabolism, however the molecular mechanosensor remains unknown. Here, we show that SWELL1 (LRRC8a) functionally encodes a swell-activated anion channel that regulates PI3K-AKT, ERK1/2, mTOR signaling, muscle differentiation, myoblast fusion, cellular oxygen consumption, and glycolysis in skeletal muscle cells. SWELL1 over-expression in SWELL1 KO myotubes boosts PI3K-AKT-mTOR signaling to supra-normal levels and fully rescues myotube formation. Skeletal muscle targeted SWELL1 KO mice have smaller myofibers, generate less force ex vivo, and exhibit reduced exercise endurance, associated with increased adiposity under basal conditions, and glucose intolerance and insulin resistance when raised on a high-fat diet, compared to WT mice. These results reveal that the SWELL1-LRRC8 complex regulates insulin-PI3K-AKT-mTOR signalling in skeletal muscle to influence skeletal muscle differentiation in vitro and skeletal myofiber size, muscle function, adiposity and systemic metabolism in vivo.

cell biology↗

bioRxiv2020

Loss of Bone Morphogenetic Protein-binding Endothelial Regulator Causes Insulin Resistance

Accumulating evidence suggests chronic inflammation of metabolic tissues plays a causal role in obesity-induced insulin resistance. Yet, how specific endothelial factors exert impacts in metabolic tissues remains undefined. Bone morphogenetic protein (BMP)–binding endothelial regulator (BMPER) adapts endothelial cells to inflammatory stress in diverse organ microenvironments. Here we demonstrate BMPER is a driver of insulin sensitivity. Inducible knockout (iKO) of BMPER causes hyperinsulinemia, glucose intolerance and insulin resistance without increasing inflammation in metabolic tissues. Interestingly, BMPER can directly activate insulin signaling, which requires its internalization and interaction with Niemann-Pick C1 (NPC1), an integral membrane protein that transports intracellular cholesterol. These results suggest the endocrine function of the vascular endothelium maintains glucose homeostasis. Of potential clinical significance, the delivery of BMPER recombinant protein or its overexpression significantly alleviates insulin resistance and hyperglycemia in high-fat diet (HFD)-fed mice and Leprdb/db (db/db) diabetic mice. We conclude that BMPER exhibits therapeutic potential for the treatment of diabetes.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pathology↗

bioRxiv2020

Structural basis for neutralization of SARS-CoV-2 and SARS-CoV by a potent therapeutic antibody

The COVID-19 pandemic caused by the SARS-CoV-2 virus has resulted in an unprecedented public health crisis. There are no approved vaccines or therapeutics for treating COVID-19. Here we reported a humanized monoclonal antibody, H014, efficiently neutralizes SARS-CoV-2 and SARS-CoV pseudoviruses as well as authentic SARS-CoV-2 at nM level by engaging the S receptor binding domain (RBD). Importantly, H014 administration reduced SARS-CoV-2 titers in the infected lungs and prevented pulmonary pathology in hACE2 mouse model. Cryo-EM characterization of the SARS-CoV-2 S trimer in complex with the H014 Fab fragment unveiled a novel conformational epitope, which is only accessible when the RBD is in open conformation. Biochemical, cellular, virological and structural studies demonstrated that H014 prevents attachment of SARS-CoV-2 to its host cell receptors. Epitope analysis of available neutralizing antibodies against SARS-CoV and SARS-CoV-2 uncover broad cross-protective epitopes. Our results highlight a key role for antibody-based therapeutic interventions in the treatment of COVID-19. One sentence summaryA potent neutralizing antibody conferred protection against SARS-CoV-2 in an hACE2 humanized mouse model by sterically blocking the interaction of the virus with its receptor.

biophysics↗

bioRxiv2020

Small Molecule Modulation of Microbiome Ecosystem: A Systems Pharmacology Perspective

An increasing body of evidence suggests that microbes are not only strongly associated with many human diseases but also responsible for the efficacy, resistance, and toxicity of drugs. Small-molecule drugs which can precisely fine-tune the microbial ecosystem on the basis of individual patients may revolutionize biomedicine. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional "one-drug-one-target-one-disease" process. It is often insufficient and less successful in tackling complex systematic diseases. A systematic pharmacology approach that intervenes multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution. Advances in the Human Microbiome Project have provided numerous genomics data to study microbial interactions in the complex microbiome community. Integrating microbiome data with chemical genomics and other biological information enables us to delineate the landscape for the small molecule modulation of the human microbiome network. In this paper, we construct a disease-centric signed microbe-microbe interaction network using metabolite information of microbes and curated microbe effects on human health from published work. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of pathogenic and commensal species. With a survey on the druggable and evolutionary space of microbe proteins, we find that 8-10% of them can be targeted by existing drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We also demonstrate that drugs for diabetes are enriched in the potential inhibitors that target pathogenic microbe without affecting the commensal microbe, thus can be repurposed to modulate the microbiome ecosystem. We further show that periplasmic and cellular outer membrane proteins are overrepresented in the potential drug targets set in pathogenic microbe, but not in the commensal microbe. The systematic studies of polypharmacological landscape of the microbiome network may open a new avenue for the small-molecule drug discovery of microbiome. Author SummaryAs one of the most abundant components in human bodies, the microbiome has an extensive impact on human health. Pathogenic-microbes have become emerging potential therapeutic targets. Small-molecule drugs that only intervene in the growth of a specific pathogenic microbe without considering the interacting dynamics of the microbiome community may disrupt the ecosystem homeostasis, thus can cause drug side effect or prompt drug resistance. To discover novel drugs for safe and effective microbe-targeting therapeutics, a systematic approach is needed to fine-tune the microbiome ecosystem. To this end, we built a disease-centric signed microbe-microbe interaction network which accurately predicts the pathogenic or commensal effect of microbe on human health. Based on annotated and predicted pathogens and commensal species, we performed a systematic survey on therapeutic space and target landscape of existing drugs for modulating the microbiome ecosystem. Enrichment analysis on potential microbe-targeting drugs shows that drugs for diabetes could be repurposed to maintain the healthy state of microbiome. Furthermore, periplasmic and cellular outer membrane proteins are overrepresented in the potential drug targets of pathogenic-microbes, but not in proteins that perturb commensal-microbes. Our study may open a new avenue for the small molecule drug discovery of microbiome.

bioinformatics↗

bioRxiv2020

SARS-CoV-2 and SARS-CoV Spike-RBD Structure and Receptor Binding Comparison and Potential Implications on Neutralizing Antibody and Vaccine Development

SARS-CoV-2 and SARS-CoV share a common human receptor ACE2. Protein-protein interaction structure modeling indicates that spike-RBD of the two viruses also has similar overall binding conformation and binding free energy to ACE2. In vitro assays using recombinant ACE2 proteins and ACE2 expressing cells confirmed the two coronaviruses similar binding affinities to ACE2. The above studies provide experimental supporting evidences and possible explanation for the high transmissibility observed in the SARS-CoV-2 outbreak. Potent ACE2-blocking SARS-CoV neutralizing antibodies showed limited cross-binding and neutralizing activities to SARS-CoV-2. ACE2-non-blocking SARS-CoV RBD antibodies, though with weaker neutralizing activities against SARS-CoV, showed positive cross-neutralizing activities to SARS-CoV-2 with an unknown mechanism. These findings suggest a trade-off between the efficacy and spectrum for therapeutic antibodies to different coronaviruses, and hence highlight the possibilities and challenges in developing broadly protecting antibodies and vaccines against SARS-CoV-2 and its future mutants.

microbiology↗