scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure.

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://github.com/ailabstw/scDrugplus.

Macrophages as determinants and regulators of systemic sclerosis-related interstitial lung disease.

BACKGROUND: Interstitial lung disease (ILD) is the primary cause of mortality in systemic sclerosis (SSc), an autoimmune disease characterized by tissue fibrosis. SSc-related ILD (SSc-ILD) occurs more frequently in females aged 30-55 years, whereas idiopathic pulmonary fibrosis (IPF) is more prevalent in males aged 60-75 years. SSc-ILD occurs earlier than IPF and progresses rapidly. FCN1, FABP4, and SPP1 macrophages are involved in the pathogenesis of lung fibrosis; SPP1 macrophages demonstrate upregulated expression in both SSc-ILD and IPF. To identify the differences between SSc-ILD and IPF using single-cell analysis, clarify their distinct pathogeneses, and propose directions for prevention and treatment. METHODS: We performed single-cell RNA sequencing on NCBI Gene Expression Omnibus (GEO) databases GSE159354 and GSE212109, and analyzed lung tissue samples across healthy controls, IPF, and SSc-ILD. The primary measures were the filtered genes integrated with batch correction and annotated cell types for distinguishing patients with SSc-ILD from healthy controls. We proposed an SSc-ILD pathogenesis using cell-cell interaction inferences, and predicted transcription factors regulating target genes using SCENIC. Drug target prediction of the TF gene was performed using Drug Bank Online. RESULTS: A subset of macrophages activates the MAPK signaling pathway under oxidative stress. Owing to the lack of inhibitory feedback from ANNEXIN and the autoimmune characteristics, this leads to an earlier onset of lung fibrosis compared to IPF. During initial lung injury, fibroblasts begin to activate the IL6 pathway under the influence of SPP1 alveolar macrophages, but IL6 appears unrelated to other inflammatory and immune cells. This may explain why tocilizumab (an anti-IL6-receptor antibody) only preserves lung function in patients with early SSc-ILD. Finally, we identified BCLAF1 and NFE2L2 as influencers of MAPK activation in macrophages. Metformin downregulates NFE2L2 and could serve as a repurposed drug candidate. CONCLUSIONS: SPP1 alveolar macrophages play a role in the profibrotic activity of IPF and SSc-ILD. However, SSc-ILD is influenced by autoimmunity and oxidative stress, leading to the continuous activation of MAPK in macrophages. This may result in an earlier onset of lung fibrosis than in IPF. Such differences could serve as potential research directions for early prevention and treatment.

DUSP22 inhibits lung tumorigenesis by suppression of EGFR/c-Met signaling.

DUSP22, an atypical dual-specificity phosphatase enzyme, plays a significant role in regulating multiple kinase signaling pathways by dephosphorylation. Our study demonstrated that decreased DUSP22 expression is associated with shorter disease-free survival, advanced TNM (tumor, lymph nodes, and metastasis), cancer stage, and higher tumor grade in lung adenocarcinoma (LUAD) patients. Exogenous DUSP22 expression reduces the colony-forming capacity of lung cancer cells and inhibits xenograft tumor growth primarily by targeting EGFR and suppressing its activity through dephosphorylation. Knockdown of DUSP22 using shRNA enhances EGFR dependency in HCC827 lung cancer cells and increases sensitivity to gefitinib, an EGFR inhibitor. Consistently, genetic deletion of DUSP22 enhances EGFRdel (exon 19 deletion)-driven lung tumorigenesis and elevates EGFR activity. Pharmacological inhibition of DUSP22 activates EGFR, ERK1/2, and upregulates downstream PD-L1 expression. Additionally, lentiviral deletion of DUSP22 by shRNA enhances lung cancer cell migration through EGFR/c-Met and PD-L1-dependent pathways. Gefitinib, an EGFR inhibitor, mechanistically suppresses migration induced by DUSP22 deletion and inhibits c-Met activity. Furthermore, cabozantinib, a c-Met inhibitor, reduces migration and attenuates EGFR activation caused by DUSP22 deletion. Collectively, our findings support the hypothesis that loss of DUSP22 function in lung cancer cells confers a survival advantage by augmenting EGFR signaling, leading to increased activation of downstream c-Met, ERK1/2, and PD-L1 axis, ultimately contributing to the progression of advanced lung cancer.

Multiomics Reveals Induction of Neuroblastoma SK-N-BE(2)C Cell Death by Mitochondrial Division Inhibitor 1 through Multiple Effects.

Mitochondrial division inhibitor 1 (Mdivi-1) is a well-known synthetic compound aimed at inhibiting dynamin-related protein 1 (Drp1) to suppress mitochondrial fission, making it a valuable tool for studying mitochondrial dynamics. However, its specific effects beyond Drp1 inhibition remain to be confirmed. In this study, we employed integrative proteomics and phosphoproteomics to delve into the molecular responses induced by Mdivi-1 in SK-N-BE(2)C cells. A total of 3070 proteins and 1945 phosphorylation sites were identified, with 880 of them represented as phosphoproteins. Among these, 266 proteins and 97 phosphorylation sites were found to be sensitive to the Mdivi-1 treatment. Functional enrichment analysis unveiled their involvement in serine biosynthesis and extrinsic apoptotic signaling pathways. Through targeted metabolomics, we observed that Mdivi-1 enhanced intracellular serine biosynthesis while reducing the production of C24:1-ceramide. Within these regulated phosphoproteins, dynamic dephosphorylation of proteasome subunit alpha type 3 serine 250 (PSMA3-S250) occurred after Mdivi-1 treatment. Further site-directed mutagenesis experiments revealed that the dephosphorylation-deficient mutant PSMA3-S250A exhibited a decreased cell survival. This research confirms that Mdivi-1's inhibition of mitochondrial division leads to various side effects, ultimately influencing cell survival, rather than solely targeting Drp1 inhibition.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment.

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma.

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma.

Ectopic ATP synthase stimulates the secretion of extracellular vesicles in cancer cells.

ABSTARCT: Ectopic ATP synthase on the plasma membrane (eATP synthase) has been found in various cancer types and is a potential target for cancer therapy. However, whether it provides a functional role in tumor progression remains unclear. Here, quantitative proteomics reveals that cancer cells under starvation stress express higher eATP synthase and enhance the production of extracellular vesicles (EVs), which are vital regulators within the tumor microenvironment. Further results show that eATP synthase generates extracellular ATP to stimulate EV secretion by enhancing P2X7 receptor-triggered Ca2+ influx. Surprisingly, eATP synthase is also located on the surface of tumor-secreted EVs. The EVs-surface eATP synthase increases the uptake of tumor-secreted EVs in Jurkat T-cells via association with Fyn, a plasma membrane protein found in immune cells. The eATP synthase-coated EVs uptake subsequently represses the proliferation and cytokine secretion of Jurkat T-cells. This study clarifies the role of eATP synthase on EV secretion and its influence on immune cells.

Spatial and temporal dynamics of ATP synthase from mitochondria toward the cell surface.

Ectopic ATP synthase complex (eATP synthase), located on cancer cell surface, has been reported to possess catalytic activity that facilitates the generation of ATP in the extracellular environment to establish a suitable microenvironment and to be a potential target for cancer therapy. However, the mechanism of intracellular ATP synthase complex transport remains unclear. Using a combination of spatial proteomics, interaction proteomics, and transcriptomics analyses, we find ATP synthase complex is first assembled in the mitochondria and subsequently delivered to the cell surface along the microtubule via the interplay of dynamin-related protein 1 (DRP1) and kinesin family member 5B (KIF5B). We further demonstrate that the mitochondrial membrane fuses to the plasma membrane in turn to anchor ATP syntheses on the cell surface using super-resolution imaging and real-time fusion assay in live cells. Our results provide a blueprint of eATP synthase trafficking and contribute to the understanding of the dynamics of tumor progression.

Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts.

Gene regulatory networks govern complex gene expression programs in various biological phenomena, including embryonic development, cell fate decisions and oncogenesis. Single-cell techniques are increasingly being used to study gene expression, providing higher resolution than traditional approaches. However, inferring a comprehensive gene regulatory network across different cell types remains a challenge. Here, we propose to construct context-dependent gene regulatory networks (CDGRNs) from single-cell RNA sequencing data utilizing both spliced and unspliced transcript expression levels. A gene regulatory network is decomposed into subnetworks corresponding to different transcriptomic contexts. Each subnetwork comprises the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells, inferred by a Gaussian mixture model. We find that the union of gene regulation pairs in all contexts is sufficient to reconstruct differentiation trajectories. Functions specific to the cell cycle, cell differentiation or tissue-specific functions are enriched throughout the developmental process in each context. Surprisingly, we also observe that the network entropy of CDGRNs decreases along differentiation trajectories, indicating directionality in differentiation. Overall, CDGRN allows us to establish the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level.

scDrug: From single-cell RNA-seq to drug response prediction.

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.

BIC: a database for the transcriptional landscape of bacteria in cancer.

Microbial communities are massively resident in the human body, yet dysbiosis has been reported to correlate with many diseases, including various cancers. Most studies focus on the gut microbiome, while the bacteria that participate in tumor microenvironments on site remain unclear. Previous studies have acquired the bacteria expression profiles from RNA-seq, whole genome sequencing, and whole exon sequencing in The Cancer Genome Atlas (TCGA). However, small-RNA sequencing data were rarely used. Using TCGA miRNA sequencing data, we evaluated bacterial abundance in 32 types of cancer. To uncover the bacteria involved in cancer, we applied an analytical process to align unmapped human reads to bacterial references and developed the BIC database for the transcriptional landscape of bacteria in cancer. BIC provides cancer-associated bacterial information, including the relative abundance of bacteria, bacterial diversity, associations with clinical relevance, the co-expression network of bacteria and human genes, and their associated biological functions. These results can complement previously published databases. Users can easily download the result plots and tables, or download the bacterial abundance matrix for further analyses. In summary, BIC can provide information on cancer microenvironments related to microbial communities. BIC is available at: http://bic.jhlab.tw/.

Secretory autophagy promotes RAB37-mediated insulin secretion under glucose stimulation both in vitro and in vivo.

High blood glucose is one of the risk factors for metabolic disease and INS (insulin) is the key regulatory hormone for glucose homeostasis. Hypoinsulinemia accompanied with hyperglycemia was diagnosed in mice with pancreatic ß-cells exhibiting autophagy deficiency; however, the underlying mechanism remains elusive. The role of secretory autophagy in the regulation of metabolic syndrome is gaining more attention. Our data demonstrated that increased macroautophagic/autophagic activity leads to induction of insulin secretion in ß-cells both in vivo and in vitro under high-glucose conditions. Moreover, proteomic analysis of purified autophagosomes from ß-cells identified a group of vesicular transport proteins participating in insulin secretion, implying that secretory autophagy regulates insulin exocytosis. RAB37, a small GTPase, regulates vesicle biogenesis, trafficking, and cargo release. We demonstrated that the active form of RAB37 increased MAP1LC3/LC3 lipidation (LC3-II) and is essential for the promotion of insulin secretion by autophagy, but these phenomena were not observed in rab37 knockout (rab37-/-) cells and mice. Unbalanced insulin and glucose concentration in the blood was improved by manipulating autophagic activity using a novel autophagy inducer niclosamide (an antihelminthic drug) in a high-fat diet (HFD)-obesity mouse model. In summary, we reveal that secretory autophagy promotes RAB37-mediated insulin secretion to maintain the homeostasis of insulin and glucose both in vitro and in vivo.

LncRNA SNHG1 regulates neuroblastoma cell fate via interactions with HDAC1/2.

The small nucleolar RNA host gene 1 (SNHG1) is a novel oncogenic long non-coding RNA (lncRNA) aberrantly expressed in different tumor types. We previously found highly expressed SNHG1 was associated with poor prognosis and MYCN status in neuroblastoma (NB). However, the molecular mechanisms of SNHG1 in NB are still unclear. Here, we disrupted endogenous SNHG1 in the MYCN-amplified NB cell line SK-N-BE(2)C using the CRISPR/Cas9 system and demonstrated the proliferation and colony formation ability of SNHG1-knowndown cells were suppressed. The transcriptome analysis and functional assays of SNHG1-knockdown cells revealed SNHG1 was involved in various biological processes including cell growth, migration, apoptosis, cell cycle, and reactive oxygen species (ROS). Interestingly, the expression of core regulatory circuitry (CRC) transcription factors in MYCN-amplified NB, including PHOX2B, HAND2, GATA3, ISL1, TBX1, and MYCN, were decreased in SNHG1-knockdown cells. The chromatin-immunoprecipitation sequencing (ChIP-seq) and transposase-accessible chromatin using sequencing (ATAC-seq) analyses showed that chromatin status of these CRC members was altered, which might stem from interactions between SNHG1 and HDAC1/2. These findings demonstrate that SNHG1 plays a crucial role in maintaining NB identity via chromatin regulation and reveal the function of the lncRNA SNHG1 in NB.

A Single-Cell Network-Based Drug Repositioning Strategy for Post-COVID-19 Pulmonary Fibrosis.

Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients' transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interactome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF.

pubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature.

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

Stratification of lncRNA modulation networks in breast cancer.

BACKGROUND: Recently, non-coding RNAs are of growing interest, and more scientists attach importance to research on their functions. Long non-coding RNAs (lncRNAs) are defined as non-protein coding transcripts longer than 200 nucleotides. We already knew that lncRNAs are related to cancers and will be dysregulated in them. But most of their functions are still left to further study. A mechanism of RNA regulation, known as competing endogenous RNAs (ceRNAs), has been proposed to explain the complex relationships among mRNAs and lncRNAs by competing for binding with shared microRNAs (miRNAs). METHODS: We proposed an analysis framework to construct the association networks among lncRNA, mRNA, and miRNAs based on their expression patterns and decipher their network modules. RESULTS: We collected a large-scale gene expression dataset of 1,061 samples from breast invasive carcinoma (BRCA) patients, each consisted of the expression profiles of 4,359 lncRNAs, 16,517 mRNAs, and 534 miRNAs, and applied the proposed analysis approach to interrogate them. We have uncovered the underlying ceRNA modules and the key modulatory lncRNAs for different subtypes of breast cancer. CONCLUSIONS: We proposed a modulatory analysis to infer the ceRNA effects among mRNAs and lncRNAs and performed functional analysis to reveal the plausible mechanisms of lncRNA modulation in the four breast cancer subtypes. Our results might provide new directions for breast cancer therapeutics and the proposed method could be readily applied to other diseases.

Quantitative phosphoproteomics reveals ectopic ATP synthase on mesenchymal stem cells to promote tumor progression via ERK/c-Fos pathway activation.

The tumor microenvironment (TME), which comprises cellular and noncellular components, is involved in the complex process of cancer development. Emerging evidence suggests that mesenchymal stem cells (MSCs), one of the vital regulators of the TME, foster tumor progression through paracrine secretion. However, the comprehensive phosphosignaling pathways that are mediated by MSC-secreting factors have not yet been fully established. In this study, we attempt to dissect the MSC-triggered mechanism in lung cancer using quantitative phosphoproteomics. A total of 1958 phosphorylation sites are identified in lung cancer cells stimulated with MSC-conditioned medium. Integrative analysis of the identified phosphoproteins and predicted kinases demonstrates that MSC-conditioned medium functionally promotes the proliferation and migration of lung cancer via the ERK/phospho-c-Fos-S374 pathway. Recent studies have reported that extracellular ATP accumulates in the TME and stimulates the P2X7R on the cancer cell membrane via purinergic signaling. We observe that ectopic ATP synthase is located on the surface of MSCs and excreted extracellular ATP into the lung cancer microenvironment to trigger the ERK/phospho-c-Fos-S374 pathway, which is consistent with these previous findings. Our results suggest that ectopic ATP synthase on the surface of MSCs releases extracellular ATP into the TME, which promotes cancer progression via activation of the ERK/phospho-c-Fos-S374 pathway.

Identification of Entry Inhibitors against Delta and Omicron Variants of SARS-CoV-2.

Entry inhibitors against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently needed to control the outbreak of coronavirus disease 2019 (COVID-19). This study developed a robust and straightforward assay that detected the molecular interaction between the receptor-binding domain (RBD) of viral spike protein and the angiotensin-converting enzyme 2 (ACE2) receptor in just 10 min. A drug library of 1068 approved compounds was used to screen for SARS-CoV2 entry inhibition, and 9 active drugs were identified as specific pseudovirus entry inhibitors. A plaque reduction neutralization test using authentic SARS-CoV-2 virus in Vero E6 cells confirmed that 2 of these drugs (Etravirine and Dolutegravir) significantly inhibited the infection of SARS-CoV-2. With molecular docking, we showed that both Etravirine and Dolutegravir are preferentially bound to primary ACE2-interacting residues on the RBD domain, implying that these two drug blocks may prohibit the viral attachment of SARS-CoV-2. We compared the neutralizing activities of these entry inhibitors against different pseudoviruses carrying spike proteins from alpha, beta, gamma, and delta variants. Both Etravirine and Dolutegravir showed similar neutralizing activities against different variants, with EC50 values between 4.5 to 5.8 nM for Etravirine and 10.2 to 22.9 nM for Dolutegravir. These data implied that Etravirine and Dolutegravir may serve as general spike inhibitors against dominant viral variants of SARS-CoV-2.

Single-cell RNA sequencing uncovers the individual alteration of intestinal mucosal immunocytes in Dusp6 knockout mice.

Single-cell RNA sequencing (scRNA-seq) approach can broadly and specifically evaluate the individual cells with minimum detection bias. To explore the individual compositional and transcriptional alteration of intestinal leukocytes in the Dual Specificity Phosphatase six knockout (D6KO) mice, we performed a scRNA-seq followed by the cell type annotation based on ImmGen database. Composition assessments found that D6KO-derived intestinal leukocytes tend to stay inactivate or immature status. The enrichment analysis showed that D6KO-derived intestinal leukocytes are less sensitive to microbes. The mod PhEA phenotypic analysis showed that the D6KO leukocytes may link to not only immune-associated but also diverse previously non-immune-related diseases. Integrating our dataset with the published dataset GSE124880 generated a comprehensive dataset for exploring intestinal immunity. Down-regulation of Ccl17 gene was found in the D6KO-derived dendritic cells. Our results demonstrated the advantage of applying scRNA-seq for dissecting the individual alteration of intestinal leukocytes, particularly in the D6KO mice at a naive state.

Drug Repurposing for the Identification of Compounds with Anti-SARS-CoV-2 Capability via Multiple Targets.

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been rapidly spreading worldwide, causing hundreds of millions of infections. Despite the development of vaccines, insufficient protection remains a concern. Therefore, the screening of drugs for the treatment of coronavirus disease 2019 (COVID-19) is reasonable and necessary. This study utilized bioinformatics for the selection of compounds approved by the U.S. Food and Drug Administration with therapeutic potential in this setting. In addition, the inhibitory effect of these compounds on the enzyme activity of transmembrane protease serine 2 (TMPRSS2), papain-like protease (PLpro), and 3C-like protease (3CLpro) was evaluated. Furthermore, the capability of compounds to attach to the spike-receptor-binding domain (RBD) was considered an important factor in the present assessment. Finally, the antiviral potency of compounds was validated using a plaque reduction assay. Our funnel strategy revealed that tamoxifen possesses an anti-SARS-CoV-2 property owing to its inhibitory performance in multiple assays. The proposed time-saving and feasible strategy may accelerate drug screening for COVID-19 and other diseases.