Single-Cell Network Analysis Identifies CLEC4E as a Key Mediator of Proinflammatory mDC Responses in Influenza Infection

Background:

Influenza is a major global health issue, causing hundreds of thousands of deaths worldwide each year [1]. While antiviral drugs and vaccines provide partial protection, the state of the illness is frequently determined by the host’s dysregulated immune response, rather than by viral replication [2][3][4]. Severe inflammation, especially from myeloid cells, can cause tissue damage and worsen outcomes [5].

Traditional single-cell RNA sequencing (scRNA-seq) analyses of influenza patients have shown many differentially expressed genes (DEGs) across immune cell types [6][7][8]. While this information is insightful, DEGs typically show individual gene changes without the coordinated regulatory networks and upstream drivers that control these responses. However, identifying these regulators is crucial, as it drives researchers closer to developing host-directed therapies.

To address this gap, the Cho et al. re-analysed publicly available peripheral blood mononuclear cell (PBMC) scRNA-seq datasets, consisting of patients infected with influenza and with SARS-CoV-2, and healthy patients. To analyze this data, the authors used multiscale co-expression networks (MEGENA) combined with key driver analysis, which allowed them to go beyond DEG lists and identify genes that are the center of regulatory modules, or potential “control nodes” in immune-cell behavior. By integrating pseudotime, cross-dataset validation, and experimental work in a mouse infection model, they were able to locate novel regulators of influenza immunopathology and investigate whether blocking them could improve the state of the disease.

Key Highlights

Monocyte and myeloid dendritic cell populations are key contributors to pro-inflammatory signaling

The authors compared immune cell responses in influenza, COVID-19, and healthy donors using cell cluster-specific differential gene expression analysis (DEA). They found that myeloid cells, especially monocytes and myeloid dendritic cells (mDCs), had the largest number of DEGs, implying that they are highly activated and functionally altered. However, lymphoid populations are relatively unresponsive, with much fewer DEGs. Classical and non-classical monocytes both exhibited pro-inflammatory, innate immune responses. Influenza showed upregulation of genes such as TLR2, CXCL8, and ADM. COVID-19 showed an upregulation of genes IL-10, CD82, TNFAIP6, CCRL2, and CD55. All of these genes point to active inflammatory signaling. Both diseases showed an upregulation of the chemokine CXCL8, which suggests a strong immune activation.

To do more investigation beyond individual genes, the authors also built co-expression networks for monocytes and mDCs, then used key-driver analysis to find strongly connected genes that likely control the modules in the networks. In monocytes, these networks were enriched for inflammasome and interferon-stimulated gene (ISG) pathways. In mDCs, influenza induced an especially strong pro-inflammatory response, with upregulation of CLEC4E, CXCL8, and TLR2. The authors conducted a network analysis and identified module M50, which is highly enriched for inflammasome genes (see Figure 3G from the preprint). They found CLEC4E and CD14 to be central hubs.

CLEC4E is identified as a pro-inflammatory hub across multi-layered analyses

The authors applied an integrated, multi-layered analysis to uncover new therapeutic targets driving harmful inflammation in monocytes and mDCs. They identified ten key driver genes using a ranked composite score combining multiple factors: differential expression in single-cell data, status as a key driver in co-expression networks, changes along pseudotime trajectories, correlation with clinical traits in bulk RNA-seq, and novelty based on influenza-related literature mentions. Cross-validation with bulk mouse lung RNA-seq confirmed that several genes were consistently upregulated during infection, with CLEC4E showing the strongest induction in severe strains and increasing over time post-infection.

To confirm reproducibility, the authors re-clustered mDCs in the original human influenza dataset and identified CLEC4E expression restricted to two infection-specific subclusters. They replicated this finding in an independent influenza PBMC dataset, again finding CLEC4E specifically upregulated in a defined mDC subcluster. A gene signature derived from CLEC4E-expressing cells in the first dataset successfully projected onto the same CLEC4E-expressing population in the second dataset, confirming this subset is a reproducible and influenza-specific mDC subset.

Piceatannol Inhibits CLEC4E and Lessens Severity of Influenza

After identifying CLEC4E as highly enriched in influenza, researchers evaluated CLEC4E as a therapeutic target in vivo using a mouse model of influenza A. Mice treated with piceatannol, an inhibitor of CLEC4E signaling, showed a reduced severity of influenza, less weight loss, and lower lung viral titers (see Figure 5C from the preprint). Bulk RNA-seq analysis of four mice groups (uninfected untreated, uninfected treated, infected untreated, and infected treated) after treatment revealed that piceatannol reversed gene expression changes caused by infection, particularly the genes within CLEC4E’s network. The authors mapped these genes onto the mDC co-expression network and identified 20 key genes whose upregulation during infection was suppressed by treatment. The authors then confirmed these findings with quantitative polymerase chain reaction (qPCR).

Overall, Cho et al., used a cell cluster-specific differential gene expression analysis on PBMCs from COVID-19 patients, influenza patients, and healthy donors to identify monocytes and mDCs as drivers of pro-inflammatory response. They then used MEGENA and key-driver analysis to identify CLEC4E as a major hub for pro-inflammatory activation in mDCs. Using a multi-layered composite ranking system and in silico validation, the authors discovered that CLEC4E was upregulated in multiple mDC subpopulations across independent datasets. Finally, in a mouse influenza model, pharmacological inhibition of CLEC4E signaling with piceatannol reduced disease severity, highlighting the CLEC4E as a potential therapeutic target for influenza infections.

Why this paper is important

One notable strength of this paper is the authors’ inventive approach with scRNA-seq and differential expression analysis that led them to the discovery of CLEC4E overexpression in mDCs and its link to pro-inflammatory pathways. Their use of co-expression networks and key driver analysis is very unique, as typical single-cell analysis does not look into coordinated changes between the individual genes identified with DEG analysis. I thought this new method of enriched gene analysis was important to further the single-cell analysis pipeline, and pave the way for a better understanding of the disease studied at a molecular level. 

The authors’ in vivo validation was also important to the research. These experiments provided in vivo confirmation of the authors’ findings, which were initially established in vitro. In vivo validation allows researchers to see how a treatment affects the entire organism, which can reveal side effects or complex interactions that might not be apparent in a controlled lab environment outside of the organism.

References:

[1] Paget, J., Spreeuwenberg, P., Charu, V., Taylor, R. J., Iuliano, A. D., Bresee, J., Simonsen, L., Viboud, C., & Global Seasonal Influenza-associated Mortality Collaborator Network and GLaMOR Collaborating Teams* (2019). Global mortality associated with seasonal influenza epidemics: New burden estimates and predictors from the GLaMOR Project. Journal of global health, 9(2), 020421. https://doi.org/10.7189/jogh.09.020421

[2] Nguyen, T. H. O., Rowntree, L. C., Chua, B. Y., Thwaites, R. S. & Kedzierska, K. Defining the balance between optimal immunity and immunopathology in influenza virus infection. Nat. Rev. Immunol. 24, 720–735 (2024).

[3] Hulme, K. D., Noye, E. C., Short, K. R. & Labzin, L. I. Dysregulated Inflammation During Obesity: Driving Disease Severity in Influenza Virus and SARS-CoV-2 Infections. Front. Immunol. 12, 770066 (2021).

[4] Speaks, S. et al. Gasdermin D promotes influenza virus-induced mortality through neutrophil amplification of inflammation. Nat. Commun. 15, 2751 (2024).

[5] Klomp, M., Ghosh, S., Mohammed, S., & Nadeem Khan, M. (2021). From virus to inflammation, how influenza promotes lung damage. Journal of leukocyte biology, 110(1), 115–122. https://doi.org/10.1002/JLB.4RU0820-232R

[6] Ghanem, M. H. et al. Proteomic and Single-Cell Transcriptomic Dissection of Human Plasmacytoid Dendritic Cell Response to Influenza Virus. Front. Immunol. 13, 814627 (2022).

[7] Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).

[8] Guo, C. et al. Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm. Nat. Commun. 11, 3924 (2020).

Tags: influenza, scrna-seq

doi: https://doi.org/10.1242/prelights.41486

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