Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 811 records · Page 45Linked to original sources

bioRxiv2022

Deep Learning-based Pseudo-Mass Spectrometry Imaging Analysis for Precision Medicine

Liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics provides systematic profiling of metabolic. Yet its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss, and low reproducibility. Here, we present the deepPseudoMSI project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.

bioinformatics↗

bioRxiv2022

Volitional generation of reproducible energy-efficient temporal patterns

One of the extraordinary characteristics of the biological brain is its low energy expense to implement a variety of biological functions and intelligence compared to the modern artificial intelligence (AI). Spike-based energy-efficient temporal codes have long been suggested as the contributor for the brain to run with a low energy expense. Despite this code having been largely reported in the sensory cortex, whether this code can be implemented in other brain areas to serve broader functions and how the brain learns to generate it have remained unaddressed. In this study, we designed a novel brain-machine interface (BMI) paradigm, by learning which two macaques could volitionally generate reproducible energy-efficient temporal patterns in the primary motor cortex (M1). Moreover, most neurons that were not directly assigned for controlling the BMI did not boost their excitability, demonstrating an overall energy-efficiency manner in performing the task. Over the course of learning, we found that the firing rates and temporal precision of selected neurons co-evolved to generate the energy-efficient temporal patterns, suggesting a cohesive rather than dissociable processing underlie the refinement of energy-efficient temporal patterns.

neuroscience↗

bioRxiv2022

Neural Implementation of Precise Temporal Patterns in Motor Cortex

One of the most concerned problems in neuroscience is how neurons communicate and convey information through spikes. There is abundant evidence in sensory systems to support the use of precise timing of spikes to encode information. However, it remains unknown whether precise temporal patterns could be generated to drive output in the primary motor cortex (M1), a brain area containing ample recurrent connections that may destroy temporal fidelity. Here, we used a novel brain-machine interface that mapped the temporal order and precision of motor cortex activity to the auditory cursor and reward to guide the generation of precise temporal patterns in M1. During the course of learning, rats performed the "temporal neuroprosthetics" in a goal-directed manner with increasing proficiency. Precisely timed spiking activity in M1 was volitionally and robustly produced under this "temporal neuroprosthetics", demonstrating the feasibility of M1 implementing temporal codes. Population analysis showed that the local network was coordinated in a fine time scale as the overall excitation heightened. Furthermore, we found that the directed connection between neurons assigned to directly control the output ("direct neurons") was strengthened throughout learning, as well as connections in the subnetwork that contains direct neurons. Network models revealed that excitatory gain and strengthening of subnetwork connectivity transitioned neural states to a more synchronous regime, which improved the sensitivity for coincidence detection and, thus, the precision of spike patterns. Therefore, our results suggested the recurrent connections facilitate the implementation of precise temporal patterns instead of impairing them, which provided new perspectives on the fine-timescale activity and dynamics of M1.

neuroscience↗

bioRxiv2022

NuRD independent Mi-2 activity represses ectopic gene expression during neuronal maturation.

During neuronal development, extensive changes to chromatin states occur to regulate lineage-specific gene expression. The molecular factors underlying the repression of non-neuronal genes in differentiated neurons are poorly characterised. The Mi2/NuRD complex is a multiprotein complex with nucleosome remodelling and histone deacetylase activity. Whilst NuRD has previously been implicated in the development of nervous system tissues the precise nature of the gene expression programmes that it coordinates are ill-defined. Furthermore, evidence from several species suggests that Mi-2 may be incorporated into multiple complexes that may not possess histone deacetylase activity. We show that Mi-2 activity is required for suppressing ectopic expression of germline genes in neurons independently of HDAC1/NuRD, whilst components of NuRD, including Mi-2, regulate neural gene expression to ensure proper development of the larval nervous system. We find that Mi-2 binding in the genome is dynamic during neuronal maturation and Mi-2 mediated repression of ectopic gene expression is restricted to the early stages of neuronal development, indicating that Mi-2/NuRD is required for establishing stable neuronal transcriptomes during the early stages of neuronal differentiation.

developmental biology↗

bioRxiv2022

Self-avoidance alone does not explain the function of Dscam1 in mushroom body axonal wiring

Alternative splicing of Drosophila Dscam1 into 38,016 isoforms provides neurons with a unique molecular code for self-recognition and self-avoidance. A canonical model suggests that homophilic binding of identical Dscam1 isoforms on sister branches of mushroom body (MB) axons supports segregation with high fidelity, even when only a single isoform is expressed. Here we generated a series of mutant flies with a single exon 4, 6, or 9 variant, encoding 1,584, 396, or 576 potential isoforms, respectively. Surprisingly, most of the mutants in the latter two groups exhibited obvious defects in the growth, branching, and segregation of MB axonal sister branches. This demonstrates that repertoires of 396 and 576 Dscam1 isoforms were not sufficient for normal patterning of axonal branches. Moreover, reducing Dscam1 levels largely reversed the defects caused by reduced isoform diversity, suggesting a functional link between Dscam1 expression levels and isoform diversity. Taken together, these results indicate that canonical self-avoidance alone does not explain the function of Dscam1 in MB axonal wiring.

neuroscience↗

bioRxiv2022

Cis mutagenesis in vivo reveals extensive noncanonical functions of Dscam1 isoforms in neuronal wiring

Drosophila Dscam1 encodes ten thousand of cell recognition molecules via alternative splicing, which is required for nervous function. However, the underlying mechanism has not fully understood. Here we revealed extensive noncanonical functions of Dscam1 isoform diversity in neuronal wiring. We generated a series of allelic cis mutations in Dscam1, encoding normal number of isoforms albeit with altered isoform composition. Despite normal dendritic self-avoidance and self/non-self discrimination in da neurons, which is consistent with canonical self-avoidance model, these mutants exhibited strikingly distinct spectra of phenotypic defects in three classes of neurons: up to [~]60% defects in mushroom bodies, a significantly increased branching and growth in da neurons, and mild axonal branching defects in mechanosensory neurons. These data suggest that proper Dscam1 isoform composition is required for axon and dendrite growth in diverse neurons. This unprecedented splicing-tuned regulation unlocks new insights into the molecular mechanisms that Dscam1 isoform diversity is engaged in.

neuroscience↗

bioRxiv2022

A congenital hydrocephalus causing mutation in Trim71 results in stem cell differentiation defects through inhibiting Lsd1 mRNA translation

Congenital hydrocephalus (CH) is a major cause of childhood morbidity. Mono-allelic mutations in Trim71, a conserved stem-cell-specific RNA-binding protein, cause CH, however, molecular basis for pathogenesis mediated by these mutations remains unknown. Here, using mouse embryonic stem cells as a model, we reveal that the mouse R783H mutation (R796H in human) significantly alters Trim71s mRNA substrate specificity and leads to accelerated stem-cell differentiation and neural lineage commitment. The mutant Trim71, but not the wild-type Trim71, binds Lsd1 (Kdm1a) mRNA and represses its translation. Specific inhibition of this repression or a slight increase of Lsd1 in the mutant cells alleviates the defects in stem cell differentiation and neural lineage commitment. These results determine a functionally relevant target of the CH-causing Trim71 mutant that can potentially be a therapeutic target and provide molecular mechanistic insights into the pathogenesis of this disease.

molecular biology↗

bioRxiv2022

Broadly neutralizing and protective nanobodies against diverse sarbecoviruses

As SARS-CoV-2 Omicron and other variants of concern continue spreading around the world, development of antibodies and vaccines to confer broad and protective activity is a global priority. Here, we report on the identification of a special group of nanobodies from immunized alpaca with exceptional breadth and potency against diverse sarbecoviruses including SARS-CoV-1, Omicron BA.1, and BA.2. Crystal structure analysis of one representative nanobody, 3-2A2-4, revealed a highly conserved epitope between the cryptic and the outer face of the receptor binding domain (RBD). The epitope is readily accessible regardless of RBD in "up" or "down" conformation and distinctive from the receptor ACE2 binding site. Passive delivery of 3-2A2-4 protected K18-hACE2 mice from infection of authentic SARS-CoV-2 Delta and Omicron. This group of nanobodies and the epitope identified should provide invaluable reference for the development of next generation antibody therapies and vaccines against wide varieties of SARS-CoV-2 infection and beyond.

immunology↗

bioRxiv2022

Protein kinase C alpha is a central node for tumorigenic transcriptional networks in human prostate cancer

Aberrant expression of protein kinase C (PKC) isozymes is a hallmark of cancer. The different members of the PKC family control cellular events associated with cancer development and progression. Whereas the classical/conventional PKC isozyme has been linked to tumor suppression in most cancer types, here we demonstrate that this kinase is required for the mitogenic activity of aggressive human prostate cancer cells displaying aberrantly high PKC expression. Immunohistochemical analysis showed abnormal up-regulation of PKC in human primary prostate tumors. Interestingly, silencing PKC expression from aggressive prostate cancer cells impairs cell cycle progression, proliferation and invasion, as well as their tumorigenic activity in a mouse xenograft model. Mechanistic analysis revealed that PKC exerts a profound control of gene expression, particularly over genes and transcriptional networks associated with cell cycle progression and E2F transcription factors. PKC RNAi depletion from PC3 prostate cancer cells led to reduced expression of pro-inflammatory cytokine and epithelial-to-mesenchymal transition (EMT) genes, as well as a prominent down-regulation in the expression of the immune checkpoint ligand PD-L1. This PKC-dependent gene expression profile was corroborated in a human prostate cancer database. Our studies established PKC as a multifunctional kinase that plays pleiotropic roles in prostate cancer, particularly by controlling genetic networks associated with tumor growth and progression. The identification of PKC as a pro-tumorigenic kinase in human prostate cancer provides strong rationale for developing therapeutic approaches towards targeting PKC or its effectors. IMPLICATIONSOur findings implicate PKC as a major node for transcriptional regulation of tumorigenic pathways in prostate cancer.

cancer biology↗

bioRxiv2022

A full spectrum PNGase activity analysis of R328 mutations on NGLY1

In humans, N-glycanase 1 (NGLY1; Peptide: N-glycanase, PNGase) is responsible for the deglycosylation of misfolded glycoproteins. Pathogenic mutations in NGLY1 cause a clinical condition known as congenital disorder of deglycosylation (NGLY1-CDDG), a rare autosomal recessive disease first reported in 2012. Although NGLY1-CDDG was diagnosed through whole-exome or whole-genome sequencing and by evaluating the expression levels of NGLY1, the clinical relevance of a detected mutation in NGLY1 needs to be further confirmed. In this study, an in vitro enzymatic assay system was established to evaluate the thermal stability and substrate specificity of NGLY1, as well as the optimum reaction conditions for its activity. A panel of all mutations at the amino acid site R328 in NGLY1 was subjected to this assay. The results revealed that R328A, R328D, R328E, R328F, R328G, R328I, R328P, R328V, R328W, and R328Y were dysfunctional mutations (10/19); NGLY1 mutations with R328H and R328T exhibited similar activity as wild-type NGLY1 (2/19); and NGLY1 mutations with R328C, R328K, R328L, R328M, R328N, R328Q, and R328S showed decreased activity (7/19) compared to wild-type NGLY1. In addition, the effect of potential regulatory compounds, including N-acetyl-L-cysteine and dithiothreitol, on NGLY1 was examined. This in vitro assay may serve as a standard protocol to facilitate rapid diagnosis of all mutations on NGLY1 and a practical screening method for drugs and compounds with potential therapeutic value for NGLY1-CDDG patients.

biochemistry↗

bioRxiv2022

Maximizing the Anti-tumor Potential of Immune Checkpoint Blockade through Modulation of Myeloid-specific CXCL16 and STAT1 Signaling

Sensitivity to immune checkpoint blockades (ICB) depends on the overall balance of immunogenic and immunosuppressive signals in the tumor immune microenvironment (TIME). Chemotherapy as an immunostimulatory strategy showed potential in improving ICBs clinical efficacy. Yet, evolution of highly plastic tumor-associated myeloid cells hinders ICBs potential to reach its full therapeutic potential. In this study, we leveraged single-cell transcriptomic and trajectory analyses to delineate TIME dynamics after chemotherapy priming. We found that metronomic chemotherapy (MCT) treatment led to an accelerated T cell exhaustion through CXCL16-mediated recruitment of peripheral immature myeloid cells and expansion of STAT1-driven PD-L1 expressing myeloid cells. Inhibiting STAT1 signaling in MCT-primed breast cancer relieved T cell exhaustion and significantly enhanced the efficacy of anti-PD-1 ICB treatment. Our study leveraged single-cell analyses to dissect the dynamics of breast cancer TIME and provides a pre-clinical rationale to translate the anti-STAT1 plus anti-PD-1 combinatorial immunotherapy regimen to maximize ICBs efficacy. Manuscript SummarySingle-cell analyses on low dose chemotherapy primed breast tumor-associated immune cells demonstrates a parallel coexistence of immunogenic and immunosuppressive myeloid cell subsets. Modulating STAT1 signaling in the tumor microenvironment fine-tunes immunogenic and immunosuppressive balance and maximizes the anti-PD-1 immunotherapy efficacy in chemotherapy-primed breast cancer.

cancer biology↗

bioRxiv2022

Drug design and repurposing with a sequence-to-drug paradigm

Drug development based on target proteins has been a successful approach in recent decades. A conventional structure-based drug design pipeline is a complex, human-engineered pipeline with multiple independently optimized steps. Advances in end-to-end differentiable learning suggest the potential benefits of similarly reformulating drug design. Here, we proposed a new sequence-to-drug paradigm that discovers drug-like small-molecule modulators directly from protein sequences and validated this concept for the first time in three stages. First, we designed TransformerCPI2.0 as a core tool for the sequence-to-drug paradigm, which exhibited competitive performance with conventional structure-based drug design approaches. Second, we validated the binding knowledge that TransformerCPI2.0 has learned. Third, we applied a sequence-to-drug paradigm to discover new hits for E3 ubiquitin-protein ligases: speckle-type POZ protein (SPOP), ring finger protein 130 (RNF130) which does not have a 3D structure, and repurposed proton pump inhibitors (PPIs) for ADP-ribosylation factor 1 (ARF1). This first proof of concept shows that the sequence-to-drug paradigm is a promising direction for drug development.

molecular biology↗

bioRxiv2022

Global Spatial Transcriptome of Macaque Brain at Single-Cell Resolution

Global profile of gene expression at single-cell resolution remains to be determined for primates. Using a recently developed technology ("Stereo-seq"), we have obtained a comprehensive single-cell spatial transcriptome map at the whole-brain level for cynomolgus monkeys, with [~]600 genes per cell for 10 m-thick coronal sections (up to 15 cm2 in size). Large-scale single-nucleus RNA-seq analysis for [~]1 million cells helped to identify cell types corresponding to Stereo-seq gene expression profiles, providing a 3-D cell type atlas of the monkey brain. Quantitative analysis of Stereo-seq data revealed molecular fingerprints that mark distinct neocortical layers and subregions, as well as domains within subcortical structures including hippocampus, thalamus, striatum, cerebellum, hypothalamus and claustrum. Striking whole-brain topography and coordinated patterns were found in the expression of genes encoding receptors and transporters for neurotransmitters and neuromodulators. These results pave the way for cellular and molecular understanding of organizing principles of the primate brain.

cell biology↗

bioRxiv2022

Characterization of Quantum Dots with Hyperspectral Fluorescence Microscopy for Multiplexed Optical Imaging of Biomolecules

The optical properties of quantum dots were extensively characterized using a hyperspectral fluorescence microscopy system. The system provides a single excitation wavelength in the ultraviolet and 311 emission wavelength channels in the visible. This allows detection of multiple fluorophores (e.g. different quantum dots) with a high spectral resolution in one go which is not achievable with a conventional fluorescence microscope where different filter sets have to be used. A spectral library was established based on the spectral profiles of six types of quantum dots. Notably, a slight spectral shift was observed for all samples while the sample was drying. Subsequently, two quantum dot mixture samples were studied. Using the spectral unmixing approach, the relative proportions of each quantum dot within a homogeneous mixture and the spatial distribution of each quantum dot within a heterogeneous mixture were calculated. The calculated values match well with the theoretical predictions. Hence, the analysis method presented here can be used for simultaneous imaging of multiple fluorophores using hyperspectral imaging technology. The results provide valuable information for the realization of real-time multi-channel in vivo fluorescent imaging of biomolecules.

bioengineering↗

bioRxiv2022

HCLC-FC: a novel statistical method for phenome-wide association studies

The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC. Author summaryAs a complementary approach to genome-wide association studies, phenome-wide association studies (PheWAS) have been an efficient tool for testing associations between genetic variations and a wide range of phenotypes utilizing all available phenotypic information. For instance, the first PheWAS has demonstrated that rs3135388 on HLA-DRB1 associated with atrial fibrillation and multiple sclerosis. A challenging step in performing large-scale multiple testing of PheWAS is to control the false discovery rate (FDR). In this work, we propose a novel and powerful multivariate method, HCLC-FC, to test the association between a genetic variant with a large number of phenotypes simultaneously controlling FDR. Within each phenotypic category, a newly proposed method clusters phenotypes into different groups and the combined test statistic within each category based on the phenotypic clusters has an asymptotic distribution which avoids the computational burden of simulation. Furthermore, the newly developed FDR controlling process is based on p-values and does not depend on test statistics. Therefore, it is more general and can be applied to other multiple testing procedures to control FDR.

bioinformatics↗

bioRxiv2022

Gene selection by incorporating genetic networks into case-control association studies

Large-scale genome-wide association studies (GWAS) have been successfully applied to a wide range of genetic variants underlying complex diseases. The network-based penalized regression approach has been developed to overcome the challenges caused by the computational efficiency for analyzing high-dimensional genomic data by incorporating a biological genetic network. In this paper, we propose a gene selection approach by incorporating genetic networks into case-control association studies for DNA sequence data or DNA methylation data. Instead of using traditional dimension reduction techniques such as principal component analyses and supervised principal component analyses, we use a linear combination of genotypes at SNPs or methylation values at CpG sites in each gene to capture gene-level signals. We develop three approaches for the linear combination: optimally weighted sum (OWS), LD-adjusted polygenic risk score (LD-PRS), and beta-based weighted sum (BWS). OWS and LD-PRS are supervised approaches that depend on the effect of each SNP or CpG site on the case-control status, while BWS can be extracted without using the case-control status. After using one of the linear combinations of genotypes or methylation values in each gene to capture gene-level signals, we regularize them to perform gene selection based on the biological network. Simulation studies show that the proposed approaches have higher true positive rates than using traditional dimension reduction techniques. We also apply our approaches to DNA methylation data and UK Biobank DNA sequence data for analyzing rheumatoid arthritis. The results show that the proposed methods can select potentially rheumatoid arthritis related genes that are missed by existing methods. Author SummaryThere is strong evidence showing that when genes are functionally related to each other in a genetic network, statistical methods utilizing prior biological network knowledge can outperform other methods that ignore genetic network structures. Therefore, statistical methods that can incorporate genetic network information into association analysis in human genetic association studies have been widely used since 2008. Here, we take advantage of recently developed methods to capture the gene-level signals in network-based penalized regression of high-dimensional genetic data. We have shown that the selection performance of our proposed methods can outperform three traditional principal component-based dimension reduction techniques in several simulation scenarios in terms of true positive rates. Meanwhile, by applying our methods in both DNA methylation data and DNA sequence data, the genes identified by our proposed methods can be significantly enriched into the rheumatoid arthritis pathway, such as genes HLA-DMA, HLA-DPB1, and HLA-DQA2 in the HLA region.

bioinformatics↗

bioRxiv2022

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive domain. Furthermore, all modalities were able to predict cognition better than other behavioral domains. Combining all modalities improved prediction of cognition, but not other behavioral domains. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC. HighlightsO_LIFC predicts behavior better than anatomical and diffusion features C_LIO_LICognition is predicted better than other behavioral domains regardless of modality C_LIO_LICombining resting & task FC improves prediction as much as combining all modalities C_LIO_LIFindings were replicated over 3 regression models and 2 datasets C_LI

neuroscience↗

bioRxiv2022

Low expression of EXOSC2 protects against clinical COVID-19 and impedes SARS-CoV-2 replication

New therapeutic targets are a valuable resource in the struggle to reduce the morbidity and mortality associated with the COVID-19 pandemic, caused by the SARS-CoV-2 virus. Genome-wide association studies (GWAS) have identified risk loci, but some loci are associated with co-morbidities and are not specific to host-virus interactions. Here, we identify and experimentally validate a link between reduced expression of EXOSC2 and reduced SARS-CoV-2 replication. EXOSC2 was one of 332 host proteins examined, all of which interact directly with SARS-CoV-2 proteins; EXOSC2 interacts with Nsp8 which forms part of the viral RNA polymerase. Lung-specific eQTLs were identified from GTEx (v7) for each of the 332 host proteins. Aggregating COVID-19 GWAS statistics for gene-specific eQTLs revealed an association between increased expression of EXOSC2 and higher risk of clinical COVID-19 which survived stringent multiple testing correction. EXOSC2 is a component of the RNA exosome and indeed, LC-MS/MS analysis of protein pulldowns demonstrated an interaction between the SARS-CoV-2 RNA polymerase and the majority of human RNA exosome components. CRISPR/Cas9 introduction of nonsense mutations within EXOSC2 in Calu-3 cells reduced EXOSC2 protein expression, impeded SARS-CoV-2 replication and upregulated oligoadenylate synthase (OAS) genes, which have been linked to a successful immune response against SARS-CoV-2. Reduced EXOSC2 expression did not reduce cellular viability. OAS gene expression changes occurred independent of infection and in the absence of significant upregulation of other interferon-stimulated genes (ISGs). Targeted depletion or functional inhibition of EXOSC2 may be a safe and effective strategy to protect at-risk individuals against clinical COVID-19.

genetics↗