Long-term weight loss and lung pathology in P. roborovskii hamsters after SARS-CoV-2 infection

To investigate the long-term effects of SARS-CoV-2 infection and determine whether these effects are specific to SARS-CoV-2 infection, we conducted a comparative longitudinal study using P. roborovskii hamster¹⁰. A group of hamsters (n = 80) was infected with either the SARS-CoV-2 Delta variant (10^5.5 TCID₅₀/ml, 1 lethal dose, 50% (LD₅₀)) or IAV H1N1 (10^7.0 TCID₅₀/ml, 1 LD₅₀), while a control group received phosphate-buffered saline (CTRL) (Fig. 1a). Weight changes were monitored for 30 dpi, with animals euthanized at 5, 15 and 30 dpi for serological, histopathological and viral load analyses. The phosphate-buffered saline-treated control group gained weight throughout the study (Fig. 1b). By contrast, the SARS-CoV-2-infected group showed marked weight loss by 7 dpi, with a mortality rate of 46.25% within the first 2 weeks (Fig. 1c–e). Among the SARS-CoV-2-infected group, 40% of the survivors regained their preinfection body weight by 30 dpi (SARS-CoV-2_recovery group), whereas 13.75% failed to recover and were classified as non-recovery, showing persistent weight loss (SARS-CoV-2_non-recovery group) (Fig. 1d, e). By comparison, the IAV-infected group showed a similar mortality rate of 47.50% by 10 dpi but demonstrated full weight recovery in all survivors by 30 dpi (IAV_recovery group) (Fig. 1c, f, g).

a A schematic of the experiment and sample collection. Samples from each group were collected at 5, 15 and 30 dpi for serological analysis, histopathology and virus titration. Notably, tissue samples at 30 dpi were prepared for scRNA-seq analysis. Created with BioRender.com. b Body weight change data of the control group (CTRL, n = 20). c Survival rate of all three groups. d Body weight change data of the SARS-CoV-2 infection group (n = 80). e Proportions of the SARS-CoV-2 infection group based on body weight changes and mortality at 30 dpi. f Body weight change data of the IAV infection group (n = 80). g Proportions of the IAV infection group based on body weight changes and mortality at 30 dpi. h, i TCID₅₀ (h) and viral RNA copy number (i) of various tissues in the SARS-CoV-2 infection group at 5 dpi (n = 3). j, k TCID₅₀ (j) and viral RNA copy number (k) of various tissues in the IAV infection group at 5 dpi (n = 3). l, m Viral RNA copy number in nasal turbinates and lung tissues at 2-day intervals post-infection for SARS-CoV-2 (l) and IAV (m) (n = 3). n H&E, MT staining and RNAscope images. Enlarged images (scale bars, 2 mm) show whole lung images of H&E, with high-magnification images of corresponding areas stained with H&E, MT and RNAscope (scale bars, 500 μm). Light blue in MT-stained images indicates fibrosis, and red dots in RNAscope images represent detected viral RNA. o Graph showing the percentage of fibrosis area relative to total lung area based on MT staining results for each group (n = 4). Data for all graphs are presented as means ± s.d. Statistical significance is indicated as follows: *P P P P P > 0.05), one-way ANOVA. SARS2, SARS-CoV-2.

To investigate whether the observed differences in recovery dynamics were related to variations in viral titers and replication kinetics, respiratory tissues were collected at 5, 15 and 30 dpi. In the SARS-CoV-2 group, infectious virus was recovered at 5 dpi, with lung titers approximately 10 times higher than those in the nasal turbinates, despite similar viral RNA copy numbers in both tissues (Fig. 1h, i). Similarly, in the IAV group, both infectious virus and viral RNA were detected in nasal turbinates and lung tissues at 5 dpi (Fig. 1j, k). However, by 15 and 30 dpi, no infectious virus or viral RNA was detectable in either group (Supplementary Fig. 1a–h). Viral RNA levels in nasal turbinates and lungs were analyzed at two-day intervals from 3 to 15 dpi, revealing higher viral titers in the lungs, peaking at 3 dpi and declining until undetectable by 13 dpi (Fig. 1l, m and Supplementary Fig. 1a–h). These results indicate that the persistent weight loss in the SARS-CoV-2 group is unrelated to ongoing viral replication.

Histopathological examination revealed that virus-induced lung lesions, including fibrosis, persisted until 30 dpi only in the SARS-CoV-2_non-recovery group (Fig. 1n, o). By contrast, the IAV_recovery and SARS-CoV-2_recovery groups showed significant improvement in lung pathology by 30 dpi, as confirmed by H&E and MT staining. The IAV_recovery group showed fibrosis at 5 dpi, which resolved over time, while fibrosis persisted in the SARS-CoV-2_non-recovery group (Fig. 1n, o). RNA in situ hybridization (RNAscope) confirmed viral RNA presence only until 5 dpi in both groups, consistent with TCID₅₀ and qRT–PCR results (Fig. 1h–k, n and Supplementary Fig. 1a–h). These findings suggest that persistent weight loss and severe lung pathology in the SARS-CoV-2_non-recovery group are driven by sustained lung damage rather than prolonged viral replication, highlighting a distinct pathophysiological feature of long COVID in this animal model²⁷.

Transcriptional profiling of SARS-CoV-2 non-recovery group reveals hallmark PASC genes

To elucidate the pathophysiological differences among the IAV_recovery, SARS-CoV-2_recovery and SARS-CoV-2_non-recovery groups, we performed scRNA-seq with the BALF, lung and spleen collected at 30 dpi, a stage where distinct group-specific features are evident (Fig. 2a). At first, we investigated whether the SARS-CoV-2_non-recovery group exhibited characteristics similar to human cases of PASC. Interestingly, the SARS-CoV-2_non-recovery group showed significant upregulation of hallmark PASC-associated genes^28,29,30,31, with over 65.00% (26/40) of these genes elevated across all three tissues, including S100A8, S100A9, MMP8, IL1B, TNF and others (Fig. 2b, c and Supplementary Fig. 2a). This transcriptional signature, absent in the recovery groups (‘SARS2_rec’ and ‘IAV_rec’), aligns with human PASC profiles, validating the SARS-CoV-2_non-recovery group as a model for PASC. Consequently, we refer to this group as ‘SARS2_PASC,’ while the remaining groups are designated as ‘CTRL’, ‘SARS2_rec’ and ‘IAV_rec’ for simplicity.

a A schematic overview of the scRNA-seq workflow from sample preparation to analysis. Created with BioRender.com. b Module scores calculated from the expression value of human PASC marker genes across the merged tissue (BALF, lung and spleen combined) as well as individual BALF, lung and spleen tissues. The boxes display the interquartile range (IQR = Q3–Q1; the 25th (Q1) to the 75th percentiles (Q3)), with the centerline denoting the median and the yellow dot representing the mean. c Heatmaps depicting the expression of representative human PASC hallmark genes in the merged tissue and individual tissues. The top 20 genes are only described, based on the log₂ fold-change (log₂FC) of their gene expression compared to the control. d UMAP plot showing the colored visualization of 15 cell types generated from scRNA-seq analysis of BALF, lung and spleen tissues. e Dot plot annotation of the 15 distinct cell types based on 30 marker genes. Dot size and color indicate the percentage (pct.1) of cells expressing each marker and the average log₂ fold-change (avg_log₂FC) of marker expression, respectively. f–h UMAP plots depicting the distribution of cell populations in BALF (f), lung (g) and spleen (h) tissues. Small insets illustrate group-specific distributions within each tissue. i–k Proportions of each cell type in control and diseased groups, identified in BALF (i), lung (j) and spleen (k) tissues. Some significance was determined using the Wilcoxon rank-sum test, and all P c). Other statistical significances are indicated as follows: *P P P P P > 0.05). The P values were estimated using Wilcoxon rank-sum test. CTRL, control group; IAV_rec, IAV_recovery group; SARS2_rec, SARS-CoV-2_recovery group; SARS2_PASC, SARS-CoV-2_non-recovery group; T, T cells; NK, natural killer cells; B, B cells; PC, plasma cells; DC, dendritic cells; HSC, hematopoietic stem cells; Myeloid_prog, myeloid progenitor cells; AT, pulmonary alveolar type I and type II cells; Ciliated, ciliated cells; Endothelial, endothelial cells; MgK, megakaryocytes.

Next, we conducted scRNA-seq analysis of 158,116 individual cell transcriptomes from 36 tissue samples, including lung, BALF and spleen (Supplementary Table 2). Unsupervised clustering and differential gene expression analysis identified 15 major cell types, including immune, progenitor, epithelial and endothelial cells (Figs. 2d–h and Supplementary Table 3). Cell-type quantification revealed that neutrophil and macrophage (MQ) compositions in both recovery groups and CTRL were comparable across BALF and lung tissues (neutrophils 3.04–23.08%, MQ 6.12–80.05%) (Fig. 2i, j, Supplementary Fig. 2b, c and Supplementary Table 4). In stark contrast, the SARS2_PASC group exhibited a marked increase in neutrophils (61.88–78.45%) and a decrease in MQ (4.16–16.02%) in both tissues. In addition, myeloid progenitor cells notably accumulated in the spleen of the SARS2_PASC group, whereas they were scarce in the recovery groups (Fig. 2k, Supplementary Fig. 2d and Supplementary Table 4).

Beyond these PASC-specific alterations, recovery groups exhibited distinct immune cell dynamics. For example, the SARS2_rec group displayed increased B cell proportions in BALF and spleen compared with CTRL and other disease groups (Fig. 2i, k). Conversely, the IAV_rec group showed a twofold increase in T cell composition in the lung relative to CTRL, a change absent in the SARS2_PASC and SARS2_rec groups (Fig. 2j). These findings highlight unique transcriptional profiles and cellular changes linked to SARS-CoV-2 and IAV infections, emphasizing the distinct immune dysregulation associated with SARS2_PASC.

Sustained neutrophil differentiation and accumulation in the SARS2_PASC group

scRNA-seq analysis revealed a marked increase in neutrophil levels and a corresponding decrease in MQ levels in the BALF and lung tissues of the SARS2_PASC group (Fig. 3a, b). To validate the distinct cell populations observed in the scRNA-seq data, we performed multiplex immunofluorescence on hamster lung tissues (Fig. 3c). Analysis of the percentage of MPO-positive cells (neutrophil marker) and CD68-positive cells (monocyte/MQ marker) revealed that the SARS2_PASC group exhibited more than a fivefold increase in neutrophils compared with the recovery groups, along with a significant decrease in CD68-positive cells (Fig. 3d). These results corroborate the scRNA-seq findings, establishing elevated neutrophil levels as a hallmark feature of the SARS2_PASC group.

a UMAP plot showing myeloid cell populations. b Proportions of MQ, monocyte and neutrophil populations across different groups. c Multiplex immunofluorescence image of 30 dpi lung tissues. Each group of tissues was stained with DAPI (nuclei, blue), MPO (green) and CD68 (red) (scale bars, 200 μm). d Quantification of the percentage of MPO⁺ and CD68⁺ cells in 30 dpi lung tissues (n = 3). e Trajectory analysis depicting the differentiation of myeloid progenitor cells into either monocytes/MQ or neutrophils with Monocle3. The UMAP is colored by pseudotime. Arrows 1 and 2 indicate distinct differentiation paths. f, g Feature plots showing CEBPE (f) and IRF8 (g) gene expression in each group, respectively. h Representative images showing IHC results of viral antigens in lung tissue. Arrowheads indicate antigen-positive cells (scale bars, 20 μm). i–k Quantification of the percentage of SARS-CoV-2 (SARS2) S1-positive (i), N-positive (j) and IAV NP-positive (k) cells in lung tissue at different dpi. l Magnified images (scale bars, 20 μm) comparing the presence of antigens and immune cells at 30 dpi. Arrowheads indicate monocytes/MQs, arrows indicate neutrophils and asterisks highlight areas of inflammation. Data are presented as means ± s.d. (d and i–k). Statistical significance is indicated as follows: *P P P P P > 0.05). The P values were estimated using one-way ANOVA. Myeloid_prog, myeloid progenitor cells; CTRL, control group; IAV_rec, IAV_recovery group; SARS2_rec, SARS-CoV-2_recovery group; SARS2_PASC, SARS-CoV-2_non-recovery group; S1, spike protein S1 subunit; N, nucleocapsid; NP, nucleoprotein.

To investigate the mechanisms underlying the increased neutrophil levels and decreased MQ levels in the SARS2_PASC group, we conducted a trajectory analysis of myeloid cell populations in the UMAP plots to investigate their compositional differences (Fig. 3e). To this end, myeloid progenitors were classified into granulocyte–monocyte progenitor (GMP), neutrophil progenitor (Neu_prog) and monocyte progenitor (Mono_prog) subtypes using eight marker genes (Supplementary Fig. 3a,b). Our trajectory analysis identified two distinct differentiation pathways: GMP to neutrophils via Neu_prog (path1) and GMP to monocytes/MQs via Mono_prog (path2) (Fig. 3e and Supplementary Fig. 3c). Accordingly, neutrophil-specific transcription factors (TFs) were highly expressed in Neu_prog populations, while monocyte/MQ-specific TFs were more prominent in Mono_prog populations (Supplementary Fig. 3d). Notably, in the SARS2_PASC group, the GMP, Neu_prog and neutrophil populations exhibited increased expression of neutrophil-specific TFs, particularly CEBPE, which is critical for neutrophil maturation, along with other key TFs such as KLF5 and GFI1^32,33,34,35 (Fig. 3f and Supplementary Fig. 3e, f). Conversely, TFs associated with monocyte/MQ differentiation, such as IRF8³⁶, were substantially downregulated in the SARS2_PASC group compared with recovery groups, which had higher monocyte and MQ ratios (Fig. 3g). These findings suggest that the skewed neutrophil-to-monocyte/MQ ratios observed in the SARS2_PASC group are driven by disrupted myeloid cell differentiation pathways.

Persistent neutrophil accumulation in the SARS2_PASC group: the role of residual S1 antigen

Although both SARS-CoV-2 and IAV infection groups effectively suppressed viral replication by 13 dpi, the persistent neutrophil accumulation and reduced MQ population observed exclusively in the SARS2_PASC group remain unexplained. We hypothesized that, despite the absence of detectable viral RNA, other factors might drive sustained tissue inflammation in this group. Recent studies have suggested that the SARS-CoV-2 spike protein, particularly the S1 subunit, can persist in tissues and contribute to prolonged sequelae, even in the absence of viral RNA^37,38. To test this hypothesis, we investigated the presence of viral antigens, specifically the S1 subunit, which is known to modulate key TFs involved in immune response regulation^39,40. IHC revealed that S1 antigens remained detectable in the SARS2_PASC group up to 30 dpi but were absent in the SARS2_rec group (Fig. 3h, i and Supplementary Fig. 3g). By contrast, SARS-CoV-2 nucleocapsid (N) and IAV nucleoprotein (NP) were detectable only up to 5 dpi and completely cleared by 15 dpi (Fig. 3h, j, k and Supplementary Fig. 3g). In the SARS2_PASC group, the S1 antigens were distributed within interstitial pneumonia lesions, accompanied by inflammatory cell infiltration, indicative of ongoing inflammation (Fig. 3h, l and Supplementary Fig. 3g). Notably, monocytes/MQs were observed to internalize S1 antigens, with neutrophils infiltrating the surrounding areas (Fig. 3l, arrowheads and asterisks). These findings highlight a distinct pattern of chronic inflammation in SARS-CoV-2-infected tissues, driven by the prolonged presence of S1 antigens, a phenomenon not observed in IAV-infected tissues, where NP was cleared much earlier. This finding underscores a critical pathophysiological difference between SARS-CoV-2 and IAV infections. While both viruses achieve similar replication suppression timelines, the residual S1 antigens in SARS-CoV-2-infected tissues appear to sustain inflammatory responses, consistent with previous findings^38,41. Moreover, the prolonged presence of S1 antigens may influence TF expression, skewing immune responses and contributing to the non-recovery phenotype observed in the SARS2_PASC group. These results provide key insights into the mechanisms underlying PASC and suggest that persistent viral antigens, rather than active replication, drive chronic inflammation in this condition.

Distinct myeloid subpopulations and their contribution to inflammation and fibrosis in PASC

Consistent with histopathological findings of persistent fibrosis in the SARS2_PASC group (Fig. 1n,o), we observed dense infiltration of MPO- and CD68-positive cells in fibrotic lung regions (Supplementary Fig. 4a). Transcriptional profiling revealed that neutrophils, monocytes and MQs exhibited significantly elevated module scores for inflammation and fibrosis-related gene sets (Supplementary Fig. 4b). Key mediators such as S100A8, S100A9, IL1B, MMP9, CSF1, TGFA and TGFB1 were strongly upregulated in these cell types (Supplementary Fig. 4c). CellChat analysis further identified myeloid populations as major contributors to TGF-β signaling^25,42, with enriched interactions both within the myeloid compartment and between myeloid and alveolar epithelial cells^43,44,45 (Supplementary Fig. 4d). These findings suggest that enhanced cross-talk among myeloid cells and their stromal environment promotes fibrotic remodeling in SARS2_PASC lungs.

a UMAP plot and distribution ratios of monocyte and MQ subpopulations across tissues. b, c Bar plots displaying −log₁₀(P value) from EnrichR analysis with MSigDB Hallmark, GOBP, KEGG and Reactome databases. The analysis focuses on specifically elevated genes in the SARS2_PASC group compared with control (CTRL) or the IAV_ and SARS-CoV-2_ recovery (recovery) group in monocytes and MQ subpopulations. Data are presented for lung (b) and spleen (c) tissues. d, e Heatmap displaying the log₂FC values of fibrosis-related pathway and inflammation genes in monocyte and MQ subpopulations of the lung (d) or spleen (e) tissue. log₂FC values represent the average expression levels of each gene in the diseased groups compared to the control and are depicted by color intensity. Significance was determined using the Wilcoxon rank-sum test, and all P 

Given the important roles of myeloid populations in lung fibrosis, to further investigate the characteristics of MQ differentiation from myeloid cells during SARS-CoV-2 infection, we classified MQs into three distinct subpopulations: M1 MQs (M1_MQ), M2 MQs (M2_MQ) and alveolar MQs (Alveolar_MQ) (Fig. 4a and Supplementary Fig. 4e). In respiratory tissues such as BALF and lung, alveolar MQs dominated the MQ population, comprising 75.30–91.30% of total MQs. Conversely, monocytes, M1_MQ and M2_MQ were more prevalent in the spleen, highlighting tissue-specific distribution patterns of MQ subsets (Fig. 4a). Comparative functional analyses revealed substantially heightened inflammatory responses in monocytes and MQs of the SARS2_PASC group compared with recovery (SARS2_rec, IAV_rec) and CTRL groups (Fig. 4b, c and Supplementary Table 5). Notably, monocytes, M2_MQ and alveolar MQs in the lungs of the SARS2_PASC group exhibited upregulation of genes associated with TGF-β, VEGF and inflammation pathways, indicating their active involvement in localized inflammation and fibrosis. By contrast, M1_MQ populations in the spleen displayed more pronounced engagement in these pathways, reflecting tissue-specific functional divergence (Fig. 4c).

Importantly, monocytes, M2_MQ and alveolar MQs in the lungs of the SARS2_PASC group persistently overexpressed key extracellular matrix (ECM) genes, including FN1, VCAN, TIMP1 and TIMP2, as well as TGFB1, a critical profibrotic cytokine that stimulates ECM production^42,46 (Fig. 4d and Supplementary Fig. 4f). These genes are closely linked to fibrosis and tissue remodeling, suggesting that these MQ subpopulations are central to sustained ECM deposition and fibrotic progression. Furthermore, fibrosis-associated pathways were highly upregulated in monocytes and M2_MQ populations of the SARS2_PASC group compared with the CTRL group, emphasizing their role in perpetuating lung fibrosis during PASC. In addition, monocytes and M2_MQs in the lungs and spleen of the SARS2_PASC group showed elevated expression of inflammation-related genes, such S100A8, S100A9 and FPR1 (Fig. 4d, e and Supplementary Fig. 4f, g). This suggests that these subpopulations actively contribute to the inflammatory milieu observed in PASC, further differentiating the SARS2_PASC group from the recovery and control groups.

Hyperactivated neutrophil subpopulations in the SARS2_PASC group

To investigate the mechanisms underlying the dramatic increase in neutrophils observed in the SARS2_PASC group, we analyzed genes that were specifically upregulated or downregulated in each group compared to CTRL (Fig. 5a). IPA revealed that neutrophils in the lung and spleen of the SARS2_PASC group exhibited high activation of pathways such as ‘formyl-methionyl-leucyl-phenylalanine (fMLP) signaling’, ‘neutrophil extracellular trap signaling’ and ‘interleukin-1 family signaling’ pathways (Fig. 5b). In the BALF, additional pathways including ‘S100 family signaling’ and ‘pathogen-induced cytokine storm signaling’ were notably upregulated. Consistent upregulation of the ‘neutrophil degranulation’ pathway across all three tissues further indicated a state of heightened inflammatory response and immune activation mediated by neutrophils in the SARS2_PASC group. By contrast, the recovery groups showed minimal changes in inflammation-related genes (Fig. 5a, b), indicating resolved inflammation. Interestingly, the IAV_rec group showed reduced activation of ‘leukocyte extravasation signaling’ and ‘chemokine signaling’ in the lungs and spleens compared with both the SARS2_rec and SARS2_PASC groups, underscoring shared yet distinct immune responses between IAV and SARS-CoV-2 infections.

a Heat maps displaying notable upregulated or downregulated genes in neutrophils compared with the control group. log₂FC values of average expression per gene are represented with colors ranging from high (yellow) to low (purple). b Dot plot of the IPA canonical pathways analysis showing the −log₁₀(P value) (dot size) and the Z score (color), in diseased groups compared with control. c UMAP and pie chart depicting the total neutrophil subcluster population. d Multiple violin plot illustrating the expression levels of specific marker genes in the different neutrophil subclusters. e UMAPs and pie charts (left) displaying the population distribution of each neutrophil subcluster for each group. Bar plots (right) showing relative population changes are noted in all diseased groups compared with the control group. f–h Heat maps of neutrophil function-related genes in BALF (f), lung (g) and spleen (h). The log₂FC expression values of each gene in the diseased groups compared with the control group is represented by color. i GM-CSF and G-CSF levels measured via ELISA in hamster serum at 30 dpi across all groups (n = 6). Data are presented as means ± s.d. j Heat maps of relative gene expression levels of CSF2RA and CSF3R in lung or spleen tissues. log₂FC values of gene expression are calculated for GMP, Neu_prog and neutrophil subclusters, comparing the diseased groups with control group. k Trajectory analysis of myeloid progenitors and neutrophil subclusters showing two differentiation paths from GMP to neutrophil subclusters. l Box plots displaying the pseudotime distribution of each cell in the subclusters along the two differentiation paths. m, n Box plots displaying module scores for aging and apoptosis-related gene expression (m), and leukocyte adhesion to vascular endothelial cell (n) in neutrophil subclusters; aging (de Magalhaes et al. [24]), apoptosis (M5902) and leukocyte adhesion to vascular endothelial cells (M14170, GO:0061756). o Violin plots showing the expression levels of SELL and ITGB2, key genes involved in leukocyte adhesion to vascular endothelial cells. p Box plots showing module scores of lung fibrosis (WP3624) and TNFA signaling via NFKB (M5890) in neutrophil subclusters. Some significance was determined using the Wilcoxon rank-sum test, and all P f–h and j). Other statistical significances are indicated as follows: *P P P P P > 0.05). The P values were calculated using Wilcoxon rank-sum test (f–h, j and m–p) and one-way ANOVA (i). CTRL, control group; IAV_rec, IAV_recovery group; SARS2_rec, SARS-CoV-2_recovery group; SARS2_PASC, SARS-CoV-2_non-recovery group.

To explore functional differences among neutrophils, we further categorized them into five subclusters (Neu1–Neu5) based on marker genes associated with neutrophil maturation and function (Fig. 5c, d). Cell distribution analysis revealed that neutrophil numbers decreased in both the IAV_rec and SARS2_rec groups compared with the CTRL group (Fig. 5e). Conversely, all neutrophil subclusters increased markedly in the SARS2_PASC group. Unlike their counterparts in the recovery groups, neutrophils in the SARS2_PASC group (Neu1–Neu5) displayed high expression of genes associated with ‘leukocyte migration’, ‘granule production’ and ‘inflammatory response’, including S100A8, S100A9, FPR1, FPR2, MMP8 and MMP9 (Fig. 5f–h and Supplementary Fig. 5a–c). ELISA confirmed elevated levels of GM-CSF and G-CSF, critical for neutrophil development, survival and function, in the SARS2_PASC group^47,48 (Fig. 5i). These increases corresponded with the upregulation of their receptors, CSF2RA and CSF3R, in all Neu1–Neu5 subsets (Fig. 5j). Collectively, these findings indicate that the coordinated upregulation of receptor-cytokine genes supports sustained neutrophil activation and inflammation in the SARS2_PASC group.

Neutrophil trajectory analysis revealed two differentiation pathways: Neu4 via Neu1 and Neu2 (path1) and Neu5 via Neu1 and Neu3 (path2) (Fig. 5k, l and Supplementary Fig. 5d). Neu4, expanding across all tissues, exhibited mature neutrophil characteristics⁴⁹ (Fig. 5d and Supplementary Fig. 5e). By contrast, Neu5, predominantly in BALF, showed higher module scores associated with aging and cell death than Neu1-4 subsets (Fig. 5m and Supplementary Fig. 5f, g). Notably, Neu5 also had lower module scores for ‘leukocyte adhesion to vascular endothelial cells’, with reduced expression of key genes⁵⁰ (SELL and ITGB2) (Fig. 5n, o). To further define the functional contribution of neutrophil subsets to chronic lung pathology in PASC, we compared inflammatory and fibrotic gene signatures across Neu1–Neu5 populations. Among these, Neu5 exhibited the highest enrichment for gene modules associated with lung fibrosis, TNF-α/NF-κB signaling, and inflammatory responses (Fig. 5p and Supplementary Fig. 5h). Expression of fibrosis-associated genes, including TGFB1, HGF, IL1B, CCL3, CSF1 and CEBPB, was strongly elevated in Neu5 relative to other subsets (Supplementary Fig. 5i). Notably, Neu5 also showed features of cellular senescence and reduced leukocyte adhesion, suggesting impaired clearance and selective retention in BALF. These findings support Neu5 as a functionally distinct, aged neutrophil population that plays a predominant role in driving tissue damage and chronic inflammation in SARS2_PASC. These findings highlight the critical role of neutrophil dynamics in PASC pathogenesis and the therapeutic potential of targeting neutrophil-driven inflammation.

Targeted neutrophil inhibitors effectively mitigate PASC progression in SARS-CoV-2-Infected hamsters

Neutrophil-mediated inflammation has been implicated as a central driver of PASC. Recent studies have identified key proinflammatory mediators, such as S100A8, S100A9 and their heterodimer S100A8/A9 (calprotectin)⁵¹, as well as FPR2, a chemotaxis regulator crucial for neutrophil migration and infiltration at infection sites^52,53. In addition, neutrophil elastase plays a pivotal role in acute lung injury by promoting neutrophil-mediated tissue damage⁵⁴. Based on these findings, we hypothesized that targeting these pathways could attenuate PASC progression.

To test this hypothesis, we selected three inhibitors: Paquinimod (S100A inhibitor), WRW4 (FPR2 antagonist) and Sivelestat (NE inhibitor), each targeting key inflammatory pathways associated with PASC. SARS-CoV-2-infected P. roborovskii hamsters (n = 50 per group) were treated with these drugs from 5 to 11 dpi, during the peak pathogenic phase (Fig. 6a). Then, the recovery patterns were monitored for up to 30 dpi. The nontreated control (SARS-CoV-2 virus only) and solvent control (2% DMSO/saline) groups exhibited approximately 14.00% and 16.00% PASC incidence with over 45% mortality (Fig. 6b, c, g). By contrast, the WRW4- and Paquinimod-treated groups showed reduced PASC incidence of 10.00% and 6.00%, respectively, although mortality rates remained comparable to controls (Fig. 6d, e, g). Remarkably, the Sivelestat-treated group exhibited a 30.0% increase in recovery rate (with only an 8.0% PASC incidence) and more than a 50% reduction in mortality (22.0% mortality) (Fig. 6f, g). By contrast, delayed treatment initiated after 15 dpi (15–21 dpi) failed to enhance recovery or prevent weight loss (Supplementary Fig. 6a–c). This result suggests that irreversible pathological changes had begun to take place in a subset of animals at 15 dpi, and a clear divergence between recovering and nonrecovering individuals had already emerged. Consequently, delayed administration did not alter disease outcomes, underscoring the critical importance of timely therapeutic intervention to prevent long-term lung damage (Supplementary Fig. 6b, c).

a A schematic overview of the animal experiment. The diagram reflects each group of the inhibitor treatment schedule. Created with BioRender.com. b–f Body weight change data of ‘Non-treat’ (SARS2, SARS-CoV-2 infection only) (b), 2% DMSO in a saline treatment (2% DMSO) (c), WRW4/2% DMSO in a saline treatment (WRW4) (d), Paquinimod/2% DMSO in a saline treatment (Paquinimod) (e) and Sivelestat/2% DMSO in a saline treatment (f) (n = 50). g Proportion of recovery (blue), non-recovery (yellow) and death (red) of each group. h, i Relative expression levels (indicated as delta–delta cycle threshold (Ct) values) of IL1B, NFKB1 and IFNG at 15 (h) and 30 (i) dpi in the lung. The gene expressions were normalized by glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as a housekeeping gene. Statistical significances are indicated as follows: *P P P P P > 0.05), one-way ANOVA. CTRL, control group; Non-treat_rec, recovery group without treatment; Non-treat_PASC, non-recovery group without treatment; 2% DMSO_rec, recovery group treated with 2% DMSO in saline; 2% DMSO_PASC, non-recovery group treated with 2% DMSO in saline.

To evaluate the anti-inflammatory effects of these inhibitors, we measured transcript levels of inflammation-related cytokines (IL1B, NFKB1 and IFNG) in lung and spleen tissues using qRT–PCR. At 15 dpi, 4 days post-treatment, Paquinimod and Sivelestat significantly reduced cytokine expression in the lungs, with Sivelestat showing the greatest reduction, correlating with reduced early mortality (Fig. 6h). This suppression persisted, with cytokine levels remaining low at 30 dpi, 19 days after treatment discontinuation (Fig. 6i). In the spleen, all three drugs reduced IL1B expression at 15 dpi, while Paquinimod uniquely reduced IFNG expression (Supplementary Fig. 6d). By 30 dpi, inflammatory cytokine levels were significantly reduced across all treatment groups in the spleen (Supplementary Fig. 6e). Drug treatment also suppressed neutrophil-associated inflammatory genes (FPR2, S100A9 and ELANE) across tissues, although lung-specific reductions were observed earlier, between 15 dpi and 30 dpi (Supplementary Fig. 6f–i). This difference probably reflects the localized accumulation of neutrophils in the lung, which was absent in the spleen. Among the inhibitors, Sivelestat exhibited the most robust anti-inflammatory effects in both lung and spleen, with suppression of inflammatory gene expression persisting up to 19 days post-treatment. This sustained efficacy underscores Sivelestat’s potential as a therapeutic strategy for mitigating PASC, providing a foundation for targeting neutrophil-driven inflammation to alleviate long-term complications of SARS-CoV-2 infection.

Therapeutic targeting of neutrophil-mediated inflammation attenuates pulmonary fibrosis and chronic lung pathology

To evaluate the impact of drug treatment on lung pathology, lung tissues were collected at 15 and 30 dpi and subjected to histological analysis using H&E and MT staining to assess tissue morphology and fibrosis. In the nontreated and DMSO-treated groups, fibrosis was evident as early as 15 dpi and persisted through 30 dpi, indicating sustained lung damage in these groups (Fig. 7a, b). By contrast, all drug-treated groups showed significant improvements in lung pathology, with Sivelestat demonstrating the most pronounced reduction in fibrosis—exceeding 50% in cellular lesions and overall fibrotic changes at 15 dpi (Fig. 7c). By 30 dpi, fibrosis was markedly reduced across all drug-treated groups compared with untreated controls, underscoring the therapeutic efficacy of the treatments (Fig. 7d). In addition, we examined whether targeting neutrophil-associated cytokines could reduce neutrophil infiltration in the lungs. Multiplex immunofluorescence assays revealed significantly lower neutrophil levels in drug-treated groups at 15 dpi compared with the nontreated group (Fig. 7e, g). This reduction was even more pronounced by 30 dpi, with neutrophil levels decreasing by approximately 10-fold in the drug-treated groups (Fig. 7f, h). These findings confirm that targeting neutrophil-mediated inflammation with inhibitors such as Sivelestat effectively reduces neutrophil infiltration in the lungs.

a, b H&E and MT staining image of lung tissue at 15 (a), 30 (b) dpi. Enlarged images (scale bars, 2 mm) represent total lung images of H&E, with the high-magnification section showing corresponding areas stained with H&E and MT (scale bars, 500 μm). Light-blue coloration in the MT-stained images indicates areas of fibrosis. c, d The percentage of fibrosis area relative to total lung area based on MT staining results at 15 (c) and 30 (d) dpi (n = 4). e, f Quantification of the percentage of MPO⁺ cells in lung tissues at 15 (e) and 30 (f) dpi (n = 4). g, h Multiplex immunofluorescence images of lung tissues at 15 (g) and 30 (h) dpi, stained for nuclei (DAPI, blue), MPO (green) and CD68 (red) (scale bars, 200 μm). Data for all graphs are presented as means ± s.d. Statistical significances are indicated as follows: *P P P P P > 0.05), one-way ANOVA. CTRL, control group; Non-treat_rec, recovery group without treatment; Non-treat_PASC, non-recovery group without treatment; 2% DMSO_rec, recovery group treated with 2% DMSO in saline; 2% DMSO_PASC, non-recovery group treated with 2% DMSO in saline.

Interestingly, all treatment groups, Sivelestat, Paquinimod and WRW4, not only reduced the overall incidence of PASC but also decreased the detection rate of the SARS-CoV-2 S1 subunit antigen in lung tissues (Supplementary Fig. 7a, b). To further characterize the immunomodulatory effects of treatment, an additional infection study was conducted using Sivelestat, the most effective compound in reducing PASC incidence in our model, and flow cytometric analysis was performed in the lung and spleen at 5, 15 and 30 dpi (Fig. 8a and Supplementary Fig. 7c). The observed changes in body weight, recovery rates and mortality were consistent with those in the initial drug treatment experiment (Fig. 8b–d). Flow cytometry analysis revealed a marked reduction in neutrophil populations in both the lung and spleen following Sivelestat treatment, along with a significant decrease in splenic monocyte/MQ populations (Fig. 8e, f), consistent with previous reports⁵⁵. By contrast, T and B cell frequencies remained unchanged in both organs, supporting the notion that Sivelestat exerts a selective anti-inflammatory effect primarily on the innate immune compartment.

a A schematic overview of drug administration and flow cytometry analysis. Created with BioRender.com. b, c Body weight change data of ‘Non-treat’ (SARS2, SARS-CoV-2 infection only, n = 40) (b) and Sivelestat/2% DMSO in a saline treatment (n = 50) (c). d Proportion of recovery (blue), non-recovery (yellow) and death (red) of each group. e, f Percentage of B cells (CD79⁺), T cells (CD3⁺), monocytes/MQs (CD11b⁺CD14⁺) and neutrophils (CD11b⁺Ly6G⁺) in the lung (e) and spleen (f). Data for all graphs are presented as means ± s.d. Statistical significances are indicated as follows: *P P P P P > 0.05), Mann–Whitney test. CTRL, control group.

Overall, these results highlight that therapeutic strategies targeting neutrophil activity not only reduce inflammation-associated gene expression but also markedly attenuate chronic lung pathology, including fibrosis, induced by SARS-CoV-2 infection. This approach could prove essential for managing lung damage and mitigating long-term complications associated with viral infections.