Evidence map›Paper›PMID 42524672›Full record

ArticleInfectious diseases & immunity2026

Mapping cellular signaling pathways involved in SARS-CoV-2 spike protein-mediated syncytia formation.

Peng Qiu, Xiaolu Xu, Junnan Lu, Jinzhi Wu, Chikin Chan, Guoyi Wu, Xiaoben Pan

Abstract read
In one paragraph

Article in Infectious diseases & immunity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Peng QiuAntiviral Group, Department of Diseases Biology, Global Health Drug Discovery Institute, Beijing 100192. China.
Xiaolu XuAntiviral Group, Department of Diseases Biology, Global Health Drug Discovery Institute, Beijing 100192. China.
Junnan LuAntiviral Group, Department of Diseases Biology, Global Health Drug Discovery Institute, Beijing 100192. China.
Jinzhi WuHigh-throughput Screening Platform, Global Health Drug Discovery Institute, Beijing 100192, China.
Chikin ChanHigh-throughput Screening Platform, Global Health Drug Discovery Institute, Beijing 100192, China.
Guoyi WuShanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China.
Xiaoben PanAntiviral Group, Department of Diseases Biology, Global Health Drug Discovery Institute, Beijing 100192. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Severe coronavirus disease 2019 is associated with extensive syncytia formation, a process driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein-mediated cell-cell fusion in infected pneumocytes. This study aims to identify compounds to prevent S-mediated fusion, which may represent a potential therapeutic strategy to limit disease progression. Methods: We developed a bimolecular multicellular complementation assay using NanoLuc binary technology to quantitatively detect cell-cell fusion. A high-throughput screen was conducted against a compound library comprising 16,520 molecules. Candidate inhibitors were classified based on their bioactivities, and selected hits were further assessed for their ability to inhibit infection using pseudotyped virus and authentic SARS-CoV-2 infection in the cell cultures. Results: The screening identified 62 compounds that suppressed S protein-mediated fusion. These included inhibitors of cellular proteases involved in S protein cleavage, ATPase inhibitors linked to endosomal acidification, and modulators of hormone receptors, neurotransmitter receptors, calcium channels, and transmembrane protein 16F. Calcium ions emerged as a common regulatory element across these pathways. However, most hits did not show significant antiviral activity against pseudotyped or authentic SARS-CoV-2 infection. Interestingly, a natural compound trigothysoid N exhibited cell-type-dependent effects, inhibiting fusion in Vero-E6 (half maximal inhibitory concentration (IC Conclusion: The identification of fusion inhibitors helps delineate the complex network of events and signaling pathways involved in S-mediated fusion and supports the development of therapeutics targeting this process. Our findings provide valuable insights for the development of therapeutics targeting syncytia formation. Moreover, the opposite effects of trigothysoid N observed across different cell lines highlight the need for careful evaluation of host-targeted fusion inhibitors, as they may exhibit divergent efficacy among patients.

Indexed as

COVID-19FusionSARS-CoV-2Spike proteinSyncytia

Identifiers

PMID42524672
PMCPMC13412718

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.