Biology subjects

Gu, H.

Publications and source records attributed to Gu, H..

At least 91 records · Page 5Linked to original sources

bioRxiv2021

Identification of candidate therapeutics and signaling pathways for multiple myeloma

Multiple myeloma (MM), a plasma cell malignancy, is related to critical morbidity due to end-organ destruction. A number of factors affect the MM cell proliferation and functions. Though MM is not curable, novel targets and inhibitors have shown great effects on MM patients. Here, we aim to identify significant genes and signaling pathways of MM with SI2 treatment using a bioinformatics method. The GSE156871 dataset was originally produced by using the high-throughput BGISEQ-500. The KEGG and GO results suggested that biological pathways such as "the complement and coagulation cascades" and "the transcription activator activity" are mostly affected in the SI2 treatment of MM cells. Moreover, we identified several genes including SRC, KNG1, and PI3KCG were involved in the treatment of MM cells. Therefore, our study provides further insights into the treatment of MM.

bioinformatics↗

bioRxiv2021

Brain functional connectome defines a transdiagnostic dimension associated with cognition and psychopathology in youth

BackgroundCognitive dysfunction and high-order psychopathologic dimensions are two main classes of transdiagnostic factors related to psychiatric disorders. They may link to common or distinct core brain networks underlying developmental risk of psychiatric disorders. MethodThe current study is a longitudinal investigation with 11,875 youths aged 9-to 10-years-old at study onset, from the Adolescent Brain Cognitive Development study. A machine-learning approach based on canonical correlation analysis was used to identify latent dimensional associations of the resting-state functional connectome with multi-domain behavioral assessments of cognitive functions and psychopathological problems. For the latent rsFC factor showing a robust behavioral association, its ability to predict psychiatric disorders was assessed using two-year follow-up data and its genetic association was evaluated using twin data from the same cohort. ResultA latent functional connectome pattern was identified that showed a strong and generalizable association with the multi-domain behavioral assessments (5-fold cross validation: {rho} = 0.68~0.73, for the training set (N = 5096); {rho} = 0.56 ~ 0.58, for the test set (N = 1476)). This functional connectome pattern was highly heritable (h2 = 74.42%, 95% CI: 56.76%-85.42%), exhibited a dose-response relationship with cumulative number of psychiatric disorders assessed concurrently and 2-years post-MRI-scan, and predicted the transition of diagnosis across disorders over the 2-year follow-up period. ConclusionThese findings provide preliminary evidence for a transdiagnostic connectome-based measure that underlies individual differences in developing psychiatric disorders in early adolescence.

neuroscience↗

bioRxiv2021

Fine-mapping of nuclear compartments using ultra-deep Hi-C shows that active promoter and enhancer elements localize in the active A compartment even when adjacent sequences do not

Megabase-scale intervals of active, gene-rich and inactive, gene-poor chromatin are known to segregate, forming the A and B compartments. Fine mapping of the contents of these A and B compartments has been hitherto impossible, owing to the extraordinary sequencing depths required to distinguish between the long-range contact patterns of individual loci, and to the computational complexity of the associated calculations. Here, we generate the largest published in situ Hi-C map to date, spanning 33 billion contacts. We also develop a computational method, dubbed PCA of Sparse, SUper Massive Matrices (POSSUMM), that is capable of efficiently calculating eigenvectors for sparse matrices with millions of rows and columns. Applying POSSUMM to our Hi-C dataset makes it possible to assign loci to the A and B compartment at 500 bp resolution. We find that loci frequently alternate between compartments as one moves along the contour of the genome, such that the median compartment interval is only 12.5 kb long. Contrary to the findings in coarse-resolution compartment profiles, we find that individual genes are not uniformly positioned in either the A compartment or the B compartment. Instead, essentially all (95%) active gene promoters localize in the A compartment, but the likelihood of localizing in the A compartment declines along the body of active genes, such that the transcriptional termini of long genes (>60 kb) tend to localize in the B compartment. Similarly, nearly all active enhancers elements (95%) localize in the A compartment, even when the flanking sequences are comprised entirely of inactive chromatin and localize in the B compartment. These results are consistent with a model in which DNA-bound regulatory complexes give rise to phase separation at the scale of individual DNA elements.

genomics↗

bioRxiv2021

A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex

Identification of the structural connections between neurons is a prerequisite to understanding brain function. We developed a pipeline to systematically map brain-wide monosynaptic inputs to specific neuronal populations using Cre-driver mouse lines and the recombinant rabies tracing system. We first improved the rabies virus tracing strategy to accurately identify starter cells and to efficiently quantify presynaptic inputs. We then mapped brain-wide presynaptic inputs to different excitatory and inhibitory neuron subclasses in the primary visual cortex and seven higher visual areas. Our results reveal quantitative target-, layer- and cell-class-specific differences in the retrograde connectomes, despite similar global input patterns to different neuronal populations in the same anatomical area. The retrograde connectivity we define is consistent with the presence of the ventral and dorsal visual information processing streams and reveals further subnetworks within the dorsal stream. The hierarchical organization of the entire visual cortex can be derived from intracortical feedforward and feedback pathways mediated by upper- and lower-layer input neurons, respectively. This study expands our knowledge of the brain-wide inputs regulating visual areas and demonstrates that our improved rabies virus tracing strategy can be used to scale up the effort in dissecting connectivity of genetically defined cell populations in the whole mouse brain.

neuroscience↗

bioRxiv2021

Bile Acid Regulates the Colonization and Dissemination of Candida albicans from the Gastrointestinal Tract by Controlling Host Defense System and Microbiota

Candida albicans (CA), a commensal and opportunistic eukaryotic organism, frequently inhabits the gastrointestinal (GI) tract and causes life-threatening infections. Antibiotic-induced gut dysbiosis is a major risk factor for increased CA colonization and dissemination from the GI tract. We identified a significant increase of taurocholic acid (TCA), a major bile acid in antibiotic-treated mice susceptible to CA infection. In vivo findings indicate that administration of TCA through drinking water is sufficient to induce colonization and dissemination of CA in wild type and immunosuppressed mice. Treatment with TCA significantly reduced mRNA expression of immune genes ang4 and Cxcr3 in the colon. In addition, TCA significantly decreased the relative abundance of three culturable species of commensal bacteria, Turicibacter sanguinis, Lactobacillus johnsonii, and Clostridium celatum, in both cecal contents and mucosal scrapings from colon. Taken together, our results indicate that TCA promotes fungal colonization and dissemination of CA from the GI tract by controlling host defense system and intestinal microbiota that play a critical role in regulating CA in the intestine. ImportanceBroad-spectrum antibiotics, FDA-approved bile acid drugs, and probiotics used to control metabolic and infectious diseases profoundly alter the level of TCA in the gut. Furthermore, TCA level is highly altered in a subset of cancer, colitis and surgery patients who are highly susceptible to CA infection. Inadvertently, these therapies and disease conditions could be either promoting CA colonization and dissemination. Our findings indicate that TCA alone can induce fungal colonization and dissemination from the intestine. Results from this study will have a significant impact in understanding how bile acids interact with the microbiota and host in regulating invasive fungal infections that originate from the intestine and to develop potential new antifungal therapeutics.

microbiology↗

bioRxiv2021

Combined free energy calculation and machine learning methods for understanding ligand unbinding kinetics

The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive, and time-consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free energy profiles of the ligand unbinding process, focusing on the free energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (IC) that form contacts between the protein and the ligand, it then iteratively updates these interactions during a series of biased molecular-dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite temperature string simulations to obtain the free energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased "downhill" trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is Cyclin Dependent Kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for potential further development of these compounds. We compared the free energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates, and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery.

biophysics↗

bioRxiv2021

Using hypertemporal Sentinel-1 data to predict forest growing stock volume

In this study, we assess the potential of long time series of Sentinel-1 SAR data to predict forest growing stock volume and evaluate the temporal dynamics of the predictions. The boreal coniferous forests study site is located near the Hyytiala forest station in central Finland and covers an area of 2,500 km2 with nearly 17,000 stands. We considered several prediction approaches (linear, support vector and random forests regression) and fine-tuned them to predict growing stock volume in several evaluation scenarios. The analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate considerable decrease in RMSEs of growing stock volume as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE (relative RMSE 50-53%), RMSE with combined images decreased to 75.6 m3/ha (relative RMSE 44%). Feature extraction and dimension reduction techniques facilitated achieving the near-optimal prediction accuracy using only 8-10 images. When using assemblages of eight consecutive images, the GSV was predicted with the greatest accuracy when initial acquisitions started between September and January. HighlightsO_LITime series of 96 Sentinel-1 images is analysed over study area with 17,762 forest stands. C_LIO_LIRigorous evaluation of tools for SAR feature selection and GSV prediction. C_LIO_LIImproved periodic seasonality using assemblages of consecutive Sentinel-1 images. C_LIO_LIAnalysis of combining images acquired in "frozen" and "dry summer" conditions. C_LIO_LICompetitive estimates using calculation of prediction errors with stand-area weighting. C_LI

ecology↗

bioRxiv2021

RPnet: A Reverse Projection Based Neural Network for Coarse-graining Metastable Conformational States for Protein Dynamics

Markov State Model (MSM) is a powerful tool for modeling the long timescale dynamics based on numerous short molecular dynamics (MD) simulation trajectories, which makes it a useful tool for elucidating the conformational changes of biological macromolecules. By partitioning the phase space into discretized states and estimate the probabilities of inter-state transitions based on short MD trajectories, one can construct a kinetic network model that could be used to extrapolate long time kinetics if the Markovian condition is met. However, meeting the Markovian condition often requires hundreds or even thousands of states (microstates), which greatly hinders the comprehension of conformational dynamics of complex biomolecules. Kinetic lumping algorithms can coarse grain numerous microstates into a handful of metastable states (macrostates), which would greatly facilitate the elucidation of biological mechanisms. In this work, we have developed a reverse projection based neural network (RPnet) method to lump microstates into macrostates, by making use of a physics-based loss function based on the projection operator framework of conformational dynamics. By recognizing that microstate and macrostate transition modes can be related through a projection process, we have developed a reverse projection scheme to directly compare the microstate and macrostate dynamics. Based on this reverse projection scheme, we designed a loss function that allows effectively assess the quality of a given kinetic lumping. We then make use of a neural network to efficiently minimize this loss function to obtain an optimized set of macrostates. We have demonstrated the power of our RPnet in analyzing the dynamics of a numerical 2D potential, alanine dipeptide, and the clamp opening of an RNA polymerase. In all these systems, we have illustrated that our method could yield comparable or better results than competing methods in terms of state partitioning and reproduction of slow dynamics. We expect that our RPnet holds promise in analyzing conformational dynamics of biological macromolecules.

biophysics↗

bioRxiv2021

An Efficient Method to Quantify Structural Distributions in Heterogeneous cryo-EM Datasets

Cryogenic Electron Microscopy (cryo-EM) preserves the ensemble of protein conformations in solution and thus provide a promising way to characterize conformational changes underlying protein functions. However, it remains challenging for existing software to elucidate distributions of multiple conformations from a heterogeneous cryo-EM dataset. We developed a new algorithm: Linear Combinations of Template Conformations (LCTC) to obtain distributions of multiple conformations from cryo-EM datasets. LCTC assigns 2D images to the template 3D structures obtained by Multi-body Re-finement of RELION via a novel two-stage matching algorithm. Specifically, an initial rapid assignment of experimental 2D images to template 2D images was applied based on auto-correlation functions of image contours that can efficiently remove the majority of irrelevant 2D images. This is followed by pixel-pixel matching of images with fewer number of 2D images, which can accurately assign the 2D images to the template images. We validate the LCTC method by demonstrating that it can accurately reproduce the distributions of 3 Thermus aquaticus (Taq) RNA polymerase (RNAP) structures with different degrees of clamp opening from a simulated cryo-EM dataset, in which the correct distributions are known. For this dataset, we also show that LCTC greatly outperforms clustering-based Manifold Embedding and Maximum Likelihood-based Multi-body Re-finement algorithms in terms of reproducing the structural distributions. Lastly, we also successfully applied LCTC to reveal the populations of various clamp-opening conformations from an experimental Escherichia coli RNAP cryo-EM dataset. Source code is available at https://github.com/ghl1995/LCTC.

biophysics↗

bioRxiv2021

Aplp1 and the Aplp1-Lag3 Complex facilitates transmission of pathologic alpha-synuclein

Pathologic -synuclein (-syn) spreads from cell-to-cell, in part, through binding to the lymphocyte-activation gene 3 (Lag3). Here we report that amyloid {beta} precursor-like protein 1 (Aplp1) forms a complex with Lag3 that facilitates the binding, internalization, transmission, and toxicity of pathologic -syn. Deletion of both Aplp1 and Lag3 eliminates the loss of dopaminergic neurons and the accompanying behavioral deficits induced by -syn preformed fibrils (PFF). Anti-Lag3 prevents the internalization of -syn PFF by disrupting the interaction of Aplp1 and Lag3, and blocks the neurodegeneration induced by -syn PFF in vivo. The identification of Aplp1 and the interplay with Lag3 for -syn PFF induced pathology advances our understanding of the molecular mechanism of cell-to-cell transmission of pathologic -syn and provides additional targets for therapeutic strategies aimed at preventing neurodegeneration in Parkinsons disease and related -synucleinopathies. One Sentence SummaryAplp1 forms a complex with Lag3 that facilitates the binding, internalization, transmission, and toxicity of pathologic -synuclein. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/442157v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1d746bforg.highwire.dtl.DTLVardef@65acb2org.highwire.dtl.DTLVardef@12cf2f0org.highwire.dtl.DTLVardef@19d6e_HPS_FORMAT_FIGEXP M_FIG Aplp1 and the Aplp1-Lag3 complex facilitates transmission of pathologic -synuclein. Aplp1 is a receptor that drives pathologic -syn transmission, and genetic depletion of Aplp1 can significantly reduce the -synuclein pathogenesis. Aplp1 and Lag3 forms an Aplp1-Lag3 complex that accounts for substantial binding of pathologic -syn to cortical neurons. Together Aplp1 and Lag3 play a major role in pathologic -syn internalization, transmission and toxicity. Double knockout of Aplp1 and Lag3 and or a Lag3 antibody that disrupts the Aplp1 and Lag3 complex almost completely blocks -syn PFF-induced neurodegeneration. C_FIG

neuroscience↗

bioRxiv2021

Transcriptomic encoding of sensorimotor transformation in the midbrain

Sensorimotor transformation, a process that converts sensory stimuli into motor actions, is critical for the brain to initiate behaviors. Although the circuitry involved in sensorimotor transformation has been well delineated, the molecular logic behind this process remains poorly understood. Here, we performed high-throughput and circuit-specific single-cell transcriptomic analyses of neurons in the superior colliculus (SC), a midbrain structure implicated in early sensorimotor transformation. We found that SC neurons in distinct laminae express discrete marker genes. Of particular interest, Cbln2 and Pitx2 are key markers that define glutamatergic projection neurons in the optic nerve (Op) and intermediate gray (InG) layers, respectively. The Cbln2+ neurons responded to visual stimuli mimicking cruising predators, while the Pitx2+ neurons encoded prey-derived vibrissal tactile cues. By forming distinct input and output connections with other brain areas, these neuronal subtypes independently mediate behaviors of predator avoidance and prey capture. Our results reveal that, in the midbrain, sensorimotor transformation for different behaviors may be performed by separate circuit modules that are molecularly defined by distinct transcriptomic codes.

neuroscience↗

bioRxiv2021

A bacterial "shield and sword": A previously uncharacterized two-component system protects uropathogenic Escherichia coli from host-derived oxidative insults and promotes hemolysin-mediated host cell pyroptosis

Uropathogenic Escherichia coli (UPEC) deploys an array of virulence factors to successfully establish urinary tract infections. Coordinated expression of these various virulence factors is critical for UPECs overall fitness in the host. Two-component signaling systems (TCSs) are a major mechanism by which bacteria sense environmental cues and initiate adaptive responses. Here, we report a previously uncharacterized TCS encoded on a pathogenicity island in UPEC that directly activates the expression of a putative methionine sulfoxide reductase system (C3566/C3567) and a pore-forming -hemolysin in response to host-derived hydrogen peroxide (H2O2) exposure. The TCS increases UPEC resistance to H2O2 in vitro and survival in macrophages in tissue culture via C3566/C3567. Additionally, the TCS mediates -hemolysin-induced renal epithelial cell and macrophage death via a pyroptosis pathway. Taken together, our data suggest a paradigm in which this signal transduction system coordinates both bacterial pathogen defensive and offensive traits in the presence of host-derived signals.

microbiology↗

bioRxiv2021

Identification of biomarkers and pathways of mouse embryonic fibroblasts with the dysfunction of mitochondrial DNA

Mitochondrial diseases are clinically heterogeneous which involve multiple systems such as organs that are highly dependent on metabolism. Dysfunction of mtDNA is the main cause of mitochondrial diseases that trigger inflammation and immune responses. Here, we aim to identify the biological function and pathways of MEFs with the dysfunction of mtDNA through deletion of YME1L. The gene expression profiles of GSE161735 dataset were originally created by the Illumina NovaSeq 6000 (Mus musculus) for gene biogenesis and function panel. The biological and functional pathways were analyzed by the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), Gene Ontology (GO), and Reactom visual map. KEGG and GO results showed the metabolism and immune responses were mostly affected by the loss of mtDNA. Moreover, we discovered several interacting genes including POLR2F, HIST1H2BJ, PPP1CC, HOXB4, ARG1, APITD1, BUB1B, POLR2K, HOXC4, and HOXB3 were involved in the regulation of metabolic or cancer diseases. Further, we predicted several regulators that had the ability to affect mitochondria during the dysfunction of mtDNA by L1000fwd analysis. Thus, this study provides further insights into the mechanism of mtDNA in metabolic diseases.

bioinformatics↗

bioRxiv2021

An aging clock using metabolomic CSF

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid from healthy human subjects, and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimers and Parkinsons disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small dataset (n = 85), and discuss techniques for drift correction, missing data imputation, and regularized regression which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of cerebrospinal fluid.

systems biology↗

bioRxiv2021

Identification of biomarkers and candidate regulators for multiple myeloma under the knockout of AURKA

Multiple myeloma (MM) is a plasma cell malignancy that is characterized by the overabundance of monoclonal paraprotein. Aurora kinase A (AURKA) was upregulated in patients with high-risk MM. AURKA inhibitors were used to inhibit MM cell proliferation by inducing cell apoptosis and injury. In our study, we aim to identify biological processes and pathways of MM cells under the knockout of AURKA (AURKA KO) by using a bioinformatics method to elucidate their potential pathogenesis. The gene expression profiles of the GSE163589 dataset were originally produced by using the high-throughput BGISEQ-500 (Homo sapiens). The biological categories and pathways were analyzed by the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), Gene Ontology (GO), and Reactom enrichment. KEGG and GO results indicated the biological pathways related to the immune responses and cancer activities were mostly affected in the development of MM with AURKA KO. Moreover, we identified several genes including GNG5, UBE2D1, and BUB1B were involved in the regulation of cancer genesis. We further predicted novel regulators that had the ability to affect the progression of MM with AURKA KO based on the L1000fwd analysis. Therefore, this study provides further insights into the mechanism of MM under AURKA inhibitor treatments.

immunology↗

bioRxiv2021

Identification of biomarkers and candidate inhibitors for multiple myeloma

Multiple myeloma (MM) is a plasma cell malignancy that causes the overabundance of monoclonal paraprotein (M protein) and organ damages. In our study, we aim to identify biological markers and processes of MM using a bioinformatics method to elucidate their potential pathogenesis. The gene expression profiles of the GSE153626 datasets were originally produced by using the high-throughput Illumina HiSeq 4000 (Mus musculus). The functional categories and biochemical pathways were identified and analyzed by the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG), Gene Ontology (GO), and Reactom enrichment. KEGG and GO results showed the biological pathways related to immune dysfunction and signal transduction are mostly affected in the development of MM. Moreover, we identified several genes including Gngt2, Foxp3, and Cd3g were involved in the regulation of immune cells. We further predicted new inhibitors that have the ability to block the progression of MM based on the L1000fwd analysis. Therefore, this study provides further insights into the underlying pathogenesis of MM.

bioinformatics↗

bioRxiv2020

MLX balances metabolism and stress to suppress apoptosis and maintain spermatogenesis

Male germ cell production is a metabolically-driven and apoptosis-prone process. Here we show that the glucose-sensing transcription factor MLX, and its binding partner MondoA, are both required for male fertility in the mouse, as well as survival of human tumor cells derived from the male germ line. Loss of Mlx results in altered metabolism as well as activation of multiple stress pathways and germ cell apoptosis in the testes. This is concomitant with dysregulation of the expression of male-specific germ cell transcripts and proteins, manifesting as oligoasthenoteratozoospermia (OAT). Our genomic and functional analyses identify loci directly bound by MLX involved in these processes, including metabolic targets, apoptotic effectors and obligate components of male-specific germ cell development. These in vivo and in vitro studies implicate MLX and other members of the proximal MYC network, such as MNT, in regulation of metabolism and differentiation, as well as in suppression of intrinsic and extrinsic death signaling pathways in both spermatogenesis and male germ cell tumors. Highlights- The MAX-like bHLHLZ protein MLX is required for male fertility, but not embryonic development. - MLX and its heterodimeric partner MondoA are each required for both male fertility and survival of male germ cell tumors. - Genomic analysis identifies direct MLX targets associated with metabolism, stress and male germ cell development. - Loss of MLX alters MYC network genome occupancy and transcriptional output.

developmental biology↗

bioRxiv2020

Recombinant Fc-fusion vaccine of RBD induced protection against SARS-CoV-2 in non-human primate and mice

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to infect people globally. The increased COVID-19 cases and no licensed vaccines highlight the need to develop safe and effective vaccines against SARS-CoV-2 infection. Multiple vaccines candidates are under pre-clinical or clinical trails with different strengths and weaknesses. Here we developed a pilot scale production of a recombinant subunit vaccine (RBD-Fc Vacc) with the Receptor Binding Domain of SARS-CoV-2 S protein fused with the Fc domain of human IgG1. RBD-Fc Vacc induced SARS-CoV-2 specific neutralizing antibodies in non-human primates and human ACE2 transgenic mice. The antibodies induced in macaca fascicularis neutralized three divergent SARS-CoV2 strains, suggesting a broader neutralizing ability. Three times immunizations protected Macaca fascicularis (20ug or 40ug per dose) and mice (10ug or 20ug per dose) from SARS-CoV-2 infection respectively. These data support clinical development of SARS-CoV-2 vaccines for humans. RBD-Fc Vacc is currently being assessed in randomized controlled phase 1/II human clinical trails. SummaryThis study confirms protective efficacy of a SARS-CoV-2 RBD-Fc subunit vaccine.

immunology↗