[System]
You are a **scientific‐rigor auditor**. You will receive the parsed contents of a research paper. Your job is to identify **only** those errors or flaws that directly undermine the **scientific validity** of the paper’s methods, analyses, or conclusions.

Your sole focus is to identify flaws—such as errors in experimental design, data integrity, calculations, statistical inference, or reproducibility—that directly call into question the validity of a specific claim, paragraph, or the paper as a whole.

**Do not** report issues that are purely presentational, rhetorical, stylistic, or related to citation practices.

---

After you’ve done a **detailed walkthrough** of the paper, output exactly in this format—no extra keys or commentary:

```
<analysis>
{detailed walk‑through of how you checked each section/figure and why you flagged (or did not flag) any flaw}
</analysis>

<response>
{
  "has_error": <true|false>,
  "errors": [
    {
      "location": "Section 2.1",
      "description": "Claim that ‘all X are Y’ is contradicted by citation [5], which shows examples of non‑Y X."
    },
    {
      "location": "Figure 3",
      "description": "XAxis labeled ‘Time (s)’ but units appear to be milliseconds; scale bar mismatches caption."
    }
    // …more entries…
  ]
}
</response>
```

- **Do not** include any other keys or prose outside these two tagged blocks.
- **Do not** report stylistic or citation issues.
- Be as precise as possible about **where** (section ID or figure/table) and **what** the scientific flaw is.
- Each description for the errors must be rooted in a scientific rationale explaining why it is 'wrong' (not how it could be improved).

Begin your analysis now.

[User]
Abdominal aortic aneurysm (AAA) is a complex fatal vascular disease characterized by irreversible enlargement of the aortic diameter, accompanied by focal inflammation, turbulent proteolytic activities, and extracellular matrix (ECM) degradation¹–³. Inflammatory cell infiltration and intense inflammatory response play crucial roles in AAA initiation and progression¹,⁴. Recently, there has been an emphasis on the role of immune regulation in maintaining the inflammatory balance during AAA progression⁵. Clinical investigations have found that aneurysm size in patients with AAA is dependent on the imbalance of pro-inflammatory and anti-inflammatory responses⁶. Moreover, serum levels of the pro-inflammatory cytokine interleukin 6 (IL-6) were significantly increased in AAA patients, in contrast to a notable reduction

Imbalance of proinflammatory and anti-inflammatory responses plays a crucial role in the progression of abdominal aortic aneurysms. ILF3, a known modulator of the innate immune response, is involved in cardiovascular diseases. This study aims to investigate the role of ILF3 in abdominal aortic aneurysm formation. Here, we use multi-omics analyses, transgenic male mice, and multiplex immunohistochemistry to unravel the underlying involvement of ILF3 in abdominal aortic aneurysms. The results show that macrophage ILF3 deficiency attenuates abdominal aortic aneurysm progression, while elevated macrophage ILF3 exacerbates abdominal aortic aneurysm lesions. Mechanistically, we reveal that macrophagic ILF3 increases NF-κB activity by hastening the decay of p105 mRNA, leading to amplified inflammation in macrophages. Meanwhile, ILF3 represses the anti-inflammatory action by inhibiting the Keap1-Nrf2 signaling pathway through facilitating the ILF3/eIF4A1 complex-mediated enhancement of Keap1 translational efficiency. Moreover, Bardoxolone Methyl treatment alleviates the severity of abdominal aortic aneurysm lesions in the context of elevated ILF3 expression. Together, our findings underscore the significance of macrophage ILF3 in abdominal aortic aneurysm development and suggest its potential as a promising therapeutic target for abdominal aortic aneurysms.

Zhao-yang Wang¹²⁶, Jie Cheng¹⁶, Ying Wang¹⁶, Hai-tao Yuan²⁶, Shao-jie Bi³⁶, Shuang-xi Wang¹, Ya-min Hou¹, Xu Zhang¹, Bo-han Xu¹, Ze-ying Wang¹, Yun Zhang¹, Wen-jian Jiang⁴, Yu-guo Chen⁵, & Ming-xiang Zhang¹

in the anti-inflammatory cytokine Interleukin-10 (IL-10)^2. Similar findings have been observed in human AAA explant cultures and murine models^5,7. Recent microarray-based gene expression studies have confirmed that AAA is a chronic inflammatory disease, with the actions of activated macrophage subsets playing a significant role^7. Macrophage infiltration into the adventitia serves as a seminal event in the onset of AAA pathogenesis, which mediates inflammatory homeostasis by producing chemokines and cytokines that is critical to aneurysmal progression^8,9. However, the specific molecular mechanism of macrophage infiltration and inflammation in aneurysm dilatation are not yet fully delineated. Besides, no explicit therapy targeting the immunoinflammatory response is currently available to impede excessive inflammatory response and remodeling of the abdominal aortic wall.

Interleukin enhancer-binding factor 3 (ILF3) is an RNA-binding protein initially identified as a positive regulator of IL2 gene expression^10. Subsequent reports have indicated that ILF3 is also involved in dendritic cell maturation, innate immune response, and carcinoma progression^11,12. More broadly, there is evidence that ILF3 is associated with cardiovascular diseases, such as stroke and myocardial infarction^13,14. It has been reported that ILF3 is involved in the enhanced vascular smooth muscle cell apoptosis due to the overexpression of circACTA2 via reducing CDK4 mRNA stability^15. Nevertheless, no studies have been conducted to explore the role of ILF3 in AAA.

The current study identified ILF3 as the target gene because of its high expression and specificity with macrophages in AAA lesions, as determined through bioinformatic analyzes and immunoassays. By employing conditional macrophage ILF3-deficient and overexpression mouse strains, we revealed the detrimental role of macrophage ILF3 in AAA development, with macrophage ILF3 being positively correlated to the intensity of focal vascular inflammatory response. Mechanistic studies further demonstrated that macrophage ILF3 acts as a dual modulator in the inflammatory cascade, where it both accelerates p105 mRNA instability to potentiate NF-κB-driven pro-inflammatory cascades and enhances Keap1 translational efficiency via the ILF3/eIF4A1 complex to dampen the Keap1-Nrf2-mediated anti-inflammatory responses. Furthermore, interventions targeting ILF3 gene expression, as well as pharmacological interventions using BM, were found to effectively mitigate the exacerbated progression of AAA caused by high ILF3 expression. These findings thus help elucidate the mechanisms of aortic aneurysm formation and provide a framework to guide the future development of treatments for patients with AAA by targeting ILF3.

To pinpoint the candidate genes potentially associated with AAA, we downloaded publicly available data from the Gene Expression Omnibus (GEO) database (GSE57691 and GSE12591), and DEGs were acquired via GEO2R analysis. 715 genes displayed the same trend in human and mouse AAA samples compared with normal controls, among which 410 genes were downregulated and 305 were upregulated (Fig. 1A). Among the top differentially expressed genes shown in the heatmap, ILF3 expression was significantly elevated in patients with AAA (Fig. 1B, and Supplementary Fig. 1A) and Ang-II-induced AAA mice (Fig. 1C), indicating a vital role of ILF3 in AAA progression. Besides, western blot (Figs. 1D, E), quantitative PCR (Fig. 1F), and immunohistochemical staining results (Supplementary Fig. 1B) showed that the expression of ILF3 was augmented in human AAA (patients’ information in Supplementary Table 1). Similar results were also seen in Ang-II-induced mouse AAA compared with the control group (Figs. 1G–I; and Supplementary Fig. 1C).

To discern ILF3’s spatial role in AAA lesions, we utilized Seurat for analyzing human single-cell RNA sequencing data (GSE166676) from the GEO database. Through UMAP and canonical markers, we classified cells into seven types (Supplementary Figs. 1D, E); the representative genes in each cell type are shown in Supplementary Fig. 1F. GO analysis showed that the immune response, particularly mononuclear cell activation and focal adhesion, were elevated in ILF3+ cells compared with ILF3– clusters (Supplementary Fig. 1G). Furthermore, a substantial increase in ILF3-positive macrophages was observed in human AAA lesions (Figs. 1J, K; Supplementary Figs. 1H–K), underscoring the critical role of macrophage ILF3 in AAA progression. To further explore ILF3’s function in macrophages within AAA, we established AAA models at different pathological stages using Ang II induction over varied timeframes. Flow cytometry demonstrated a progressive rise in ILF3-positive macrophages with prolonged Ang II exposure (Fig. 1L; Supplementary Fig. 2A). Additionally, increased ILF3 expression in both human and mouse AAA samples was verified, with clear macrophage colocalization noted in immunofluorescence assays (Figs. 1M, N). Notably, ILF3 protein levels in bone marrow-derived and peritoneal macrophages from wild-type C57BL/6J mice showed a time- and dose-dependent increase upon Ang II treatment (Supplementary Figs. 2B–E). Collectively, these findings compellingly suggest that the marked elevation of ILF3 in macrophages plays a crucial role in AAA development.

Observing elevated ILF3 levels in AAA, particularly in macrophages, we investigated macrophage ILF3 overexpression’s impact on AAA using ILF3 conditional overexpression mice (ILF3^flox/floxLysMCre^+; hereafter referred to as ILF3^M–Tg) and its littermates (ILF3^flox/floxLysMCre^–; hereafter referred to as ILF3^M–WT) in an ApoE^–/– background (Supplementary Figs. 3A–3D). Following a 4-week saline infusion, no aneurysms were observed in either group (Fig. 2A). However, under Ang II induction, both groups experienced a significant rise in systolic blood pressure with Ang II, with no differences between the ILF3^M–TgApoE^–/– and ILF3^M–WT ApoE^–/– mice (Supplementary Table 2). Notably, ILF3^M–TgApoE^–/– mice displayed more pronounced aortic dilation compared with ILF3^M–WT ApoE^–/– mice (Fig. 2A). The incidence of AAA formation (Fig. 2B) and the rupture rate of AAA (Fig. 2C) in ILF3^M–Tg ApoE^–/– mice was markedly higher than that of ILF3^M–WT ApoE^–/– group through the 4-week Ang II exposure. Among those subjected to Ang II infusion, 68.75% (22/32) of ILF3^M–TgApoE^–/– mice died, apparently higher than the mortality rate of ILF3^M–WT ApoE^–/– (37.50%, 12/32; Fig. 2D). Vascular ultrasound imaging and maximal external aortic diameter detection further corroborated these findings, revealing more pronounced dilation in the ILF3^M–Tg ApoE^–/– mice post-Ang II infusion (Fig. 2E; Supplementary Fig. 3E). Histological analysis indicated no elastin disruption under baseline conditions; however, ILF3 overexpression in macrophages significantly exacerbated elastin degradation in Ang II-induced AAA models (Figs. 2F, G).

To further validate the role of macrophage ILF3 in AAA, we evaluated the effects of macrophage ILF3 overexpression in CaCl_2-induced AAA model mice. Consistent with the results observed in Ang-II-induced AAA model mice, ILF3^M–Tg mice were more susceptible to abdominal aortic dilation with increased maximal abdominal aortic diameter in CaCl_2-induced AAA model (Figs. 2H, I). Moreover, HE and VVG staining showed that elastic lamellae of ILF3^M–Tg mice were disrupted and degraded to a greater extent than that of ILF3^M–WT mice (Supplementary Figs. 3F, G). Collectively, these results underscore the pivotal role of macrophage-derived ILF3 in promoting AAA formation and progression in both CaCl_2- and Ang II-induced models.

To identify whether macrophage ILF3 deficiency is protective against AAA formation, we constructed macrophage-ILF3 knockout mice (ILF3^flox/floxLysMCre^+; hereafter referred to as ILF3^M–KO) in ApoE^–/– mice (Supplementary Figs. 4A, B). Upon four weeks of Ang II or saline infusion, ILF3^M–KO ApoE^–/– mice exhibited reduced aortic dilation in

response to Ang II, while saline infusion had no discernible effect on either group (Fig. 3A). Importantly, blood pressure measurements remained consistent between the two genotypes, both pre- and post-Ang II infusion (Supplementary Table 3). Upon Ang II treatment, ILF3^M-KO^ApoE^-/-^ mice showed a decreased incidence of AAA (Fig. 3B) and a reduced rupture rate (Fig. 3C) compared to ILF3^M-WT^ApoE^-/-^ mice. And the mortality rate of ILF3^M-WT^ApoE^-/-^ mice post-Ang II infusion was 40.63% (13/32), evidently higher than the 15.79% (6/38) observed in ILF3^M-KO^ApoE^-/-^ (Fig. 3D). Moreover, after Ang II infusion, ILF3^M-KO^ApoE^-/-^ mice exhibited a discernible reduction in aortic dilation relative to ILF3^M-WT^ApoE^-/-^ mice (Supplementary Fig. 4C; Fig. 3E). Histological assessments using HE and VVG staining revealed that ILF3-deficient mice presented with moderate elastic degradation (Fig. 3F, G). Using CaCl₂-induced AAA model, we also found that ILF3^M-KO^ mice were less susceptible to abdominal aortic dilation with mitigated elastic degradation (Figs. 3H, I; Supplementary Figs. 4D, E).

Fig. 1 | ILF3 expression is elevated in patients and mice with abdominal aortic aneurysm (AAA). A Venn diagram showing the overlap of differentially expressed genes (DEGs) between human and mouse samples. B Heatmap showing gene expression in human control and AAA aorta tissues (p < 0.05, Foldchange>1.2). C Heatmap showing gene expression in mouse control and angiotensin II–induced AAA aorta tissues (p < 0.05, Foldchange>1.2). D and E Western blot analysis of ILF3 levels in aortas of controls and patients with AAA (n = 10 per group). F Relative mRNA levels of ILF3 in aorta tissues from controls and patients with AAA (n = 10 per group). The control sample was set as 1. G–H Western blot analysis of ILF3 levels in aortas from mouse control and angiotensin II–induced AAA samples (n = 7 per group). I RT-PCR of ILF3 mRNA in aorta tissues from control and angiotensin II–induced AAA mouse samples (n = 7 per group). The control sample was set as 1. J ILF3⁺ macrophages from human AAA and non-aneurysmal samples were projected onto the t-SNE plot. The color intensity of each dot corresponds to the average gene expression across all cells. K Dot plot representing the ILF3 expression of macrophage clusters in human AAA and non-aneurysmal samples. The color of the dot represents the ILF3 expression level (average expression), and the size of the dot corresponds to the percentage of cells expressing ILF3 (percent expressed) across the human AAA and non-aneurysmal samples. L FACS analysis quantified the proportion of ILF3⁺ macrophages within lesions of ApoE–/– mice subjected to Ang II infusion at specified time (n = 4 per group). M Representative confocal images of ILF3 (green) colocalized with CD68 (a marker of macrophage, red) in aorta tissues from controls and AAA lesions of humans. Nuclei were stained with DAPI (blue), and yellow represents merged images (Scale bar = 25 µm; n = 8 per group). N Representative confocal images of ILF3 (green) colocalized with CD68 (marker of macrophage, red) in aorta tissues from ApoE–/– mice treated with Ang II for different intervals (Scale bar = 25 µm; n = 8 per group). Data are represented as mean ± SD. For comparisons between two groups, a Student’s t-test with two-tailed analysis (1B–1C, 1E–1F, 1H–1I). For comparisons between more than two groups, one-way ANOVA followed by Tukey’s post hoc multiple comparisons test was used (1L). Boxplots were created using the first and third quartiles to define the bounds of the box. The minima are defined as the first quartile minus 1.5 times the inter-quartile range (IQR), and the maxima are defined as the third quartile plus 1.5 times the IQR. Outliers are displayed as individual points (1E–1F, 1H–1I and 1L).

To uncover the mechanism underlying the potential role of macrophage-derived ILF3 on AAA, we analyzed the enrichment results based on the scRNA-seq data. As shown by the Gene Ontology (GO, Fig. 4A) and KEGG analysis (Fig. 4B), macrophage ILF3 was significantly associated with immune response and pathways related to immune response (such as NF-κB, TNF, and HIF1 signaling pathway were apparently enriched with macrophage ILF3). Macrophages play a pivotal role in AAA pathogenesis, where they mainly accumulate in the adventitial layer, serving as essential producers and responsive entities to inflammatory mediators, thereby orchestrating the intricate inflammatory dynamics within the aortic wall.

In vitro, we treated the isolated peritoneal macrophages (Fig. 4E) and bone marrow-derived macrophages (Supplementary Fig. 5C) from the macrophage ILF3 deficiency and wild-type mice with Ang II. Immunoblotting analysis demonstrated that macrophages deficient in ILF3 showed decreased expression of MCP-1 and inflammatory cytokines (IL-6 and TNF-α). IL-10 levels were, however, intensified due to the lack of ILF3. The above results showed that deficiency of macrophage-derived ILF3 attenuates Ang-II-induced inflammatory cell infiltration and concomitant inflammation, while an elevation in ILF3 expression exerts the opposite effect.

Utilizing immunofluorescent double staining techniques, we found the number of CD68⁺ macrophages was reduced in ILF3ᴹᴷᴼApoE–/– mice (Fig. 4C) and increased in ILF3ᴹᵀᵍApoE–/– mice compared with control mice (Supplementary Fig. 5A). Meanwhile, the results revealed the expression of several inflammatory cytokines and chemokines, including monocyte chemoattractant protein-1 (MCP-1), IL-6, TNF-α, and IL-10 exhibited a pronounced decrement in the ILF3ᴹᴷᴼApoE–/– mice (Fig. 4C). Conversely, these were significantly upregulated in the ILF3ᴹᵀᵍApoE–/– group (Supplementary Fig. 5A). Notably, the macrophage-produced immunosuppressive cytokine IL-10 expression was diametrically opposed to the aforementioned pro-inflammatory mediators (Fig. 4C; Supplementary Fig. 5A). Moreover, similar results were further validated in the AAA mouse model by assessing serum samples through Elisa (Fig. 4D; Supplementary Fig. 5B).

To investigate the machinery between ILF3 and subsequent physiobiologic features of macrophages, we conducted proteomics using peritoneal macrophages from mice of ILF3ᴹᵂᵗ and ILF3ᴹᴷᴼ. Our findings revealed 1233 uniquely differentially expressed proteins attributable to ILF3 deficiency (Supplementary Fig. 6A; and Supplementary Data 1), although a small amount of residual expression of ILF3 was observed after its knockout. GO enrichment analysis, aligning with our scRNA-seq results, demonstrated ILF3’s strong association with inflammatory response (Supplementary Fig. 6B). KEGG pathway analysis highlighted ILF3’s role in key pathways such as Fluid Shear Stress and Atherosclerosis and NF-κB pathway (Fig. 5A), with significant alterations in p105 (nfkb1) and Keap1 (Fig. 5B). These factors are known to regulate various inflammatory cytokines and chemokines via NF-κB and Keap1-Nrf2 signaling pathway. Western blotting validated these findings, showing decreased Keap1 and increased p105 and p50 in ILF3ᴹᴷᴼ mice across both peritoneal and bone marrow-derived macrophages (Supplementary Figs. 6C–6F). Additionally, ILF3 deficiency could inhibit the Ang II-induced p65 nuclear translocation and increased cytoplasmic expression of p105 and p50 (Fig. 5C–E; Supplementary Figs. 6G, H), and moderated impaired Nrf2 nuclear translocation while lowering Keap1 expression levels (Fig. 5C, D, F; and Supplementary Fig. 6J, H). Furthermore, Co-IP analysis showed more prevalence of p50:p105 and p50:p50 complexes, while less p50:p65 complexes in ILF3ᴹᴷᴼ peritoneal macrophages (Figs. 5G, H). Instead, both p65 and Nrf2 became essential transcription factors in the inflammatory network. Electrophoretic Mobility Shift Assay (EMSA) showed decreased NF-κB DNA binding and elevated Nrf2-ARE DNA binding in ILF3-deficient macrophages, in particular increased Nrf2 binding to the ARE sequence of key inflammatory genes (e.g., MCP1; Fig. 5I; Supplementary Fig. 6I, J). Collectively, these findings indicate that ILF3 deficiency modulates inflammatory balance by simultaneously inhibiting the NF-κB and Keap1-Nrf2 signaling pathways.

To elucidate whether ILF3’s regulation of NF-κB and Keap1-Nrf2 signaling depends on p105 and Keap1 expression, we used p105-overexpressing adenovirus (AdV-p105, Supplementary Figs. 7A–B) and Keap1 small interfering RNA (si-Keap1, Supplementary Figs. 7C–D). Western blotting and immunofluorescence revealed that p105 overexpression limited p65 translocation in ILF3ᴹᵀᵍ mice, without altering Keap1/Nrf2 expression (Supplementary Fig. 8A–D). EMSA confirmed that ectopic p105 expression reduced NF-κB activation, leaving the Nrf2 pathway unaffected caused by overexpression of macrophage ILF3 (Supplementary Figs. 8E, F). Additionally, Keap1 knockdown restored nuclear Nrf2 expression (Supplementary Figs. 8C, D; Supplementary Figs. 8G, H) and Nrf2 activation (Supplementary Fig. 8I–J) under ILF3 overexpression, without inhibiting NF-κB pathway (Supplementary Fig. 8L). And both si-Keap1 and AdV-p105 significantly reduced ILF3 overexpression-induced inflammation in macrophages (Figs. 5K, L; Supplementary Fig. 9). In summary, our data demonstrate that ILF3 deficiency modulates inflammatory balance through dual mechanisms: inhibiting NF-κB via p105 upregulation and activating Keap1-Nrf2 pathway due to reduced Keap1 expression.

Overall, these results showed that macrophage ILF3 deficiency attenuated AAA formation, thereby highlighting its promise as a therapeutic target for AAA management.

To unravel the underlying mechanisms behind the altered expression of Keap1 and p105 in ILF3-deficient macrophages, we initially assessed protein degradation pathways as contributing factors. This was achieved by employing MG132, a proteasome inhibitor, and hydroxychloroquine (HCQ), a classical lysosomal inhibitor. Neither of these inhibitors altered the changes observed in peritoneal macrophages (Supplementary Fig. 10A) and bone marrow-derived macrophages (Supplementary Fig. 10B) from ILF3ᴹ⁻ᴷᴼ mice.

established role in RNA metabolism, a fact substantiated by both our enrichment analysis and previous studies¹⁰, we next conducted RNA Immunoprecipitation sequencing (RIP-seq). The results revealed that 83.2% of ILF3-bound RNAs were involved in protein coding, highlighting ILF3's critical role in biological functions via RNA metabolism (Fig. 6A). Furthermore, RIP-seq showed ILF3 was able to bind to both p105 and Keap1 RNA (Figs. 6B, C; Supplementary Fig. 10C, D). RT-PCR analysis indicated that ILF3 deficiency increased p105 mRNA expression without affecting Keap1 RNA levels in peritoneal and bone marrow-derived macrophages (Fig. 6D; Supplementary Fig. 10E)

| A                   | B                 |
| ------------------- | ----------------- |
| Gene Ontology terms | KEGG pathway data |

| C                      |
| ---------------------- |
| Immuno-staining images |

| D                   |
| ------------------- |
| Plasma levels graph |

| E                                        |
| ---------------------------------------- |
| Western blot analysis and quantification |

Fig. 4 | Macrophage ILF3 contributes to Ang-II-induced inflammatory cell infiltration and the inflammatory response. A Representative Gene Ontology terms of differentially expressed genes (DEGs) enriched in ILF3⁺ macrophage clusters based on functional enrichment analysis. B Representative Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway data for macrophage DEGs enriched in ILF3⁺ vs ILF3⁻. C Immuno-staining of macrophage-derived MCP-1, TNF-α, IL-6, and IL-10 levels in AAA lesions of ILF3ᴹ⁻ᵂᵗ ApoE⁻/⁻ and ILF3ᴹ⁻ᴷᴼ ApoE⁻/⁻ mice treated with Ang II for four weeks (n = 6 per group; Scale bar = 25 μm). D Plasma IL-6, TNF-α, MCP-1, IL10 levels in ILF3ᴹ⁻ᵂᵗ ApoE⁻/⁻ and ILF3ᴹ⁻ᴷᴼ ApoE⁻/⁻ mice treated with Ang II for four weeks (n = 10 per group). E Western blot analysis and quantification of ILF3, MCP1, TNF-α, IL-6, and IL-10 expression in peritoneal macrophages from the ILF3 deficiency and control groups with or without Ang II treatment (1 μM; n = 6 per group). Data are represented as mean ± SD. P values were calculated by one-sided Fisher's exact test in (4A and 4B). For comparisons between two groups, an unpaired Student’s t-test with two-tailed analysis was performed if the data met the assumptions of normality and equal variances; otherwise, the Wilcoxon rank-sum test was used (4D). For comparisons among four groups, one-way ANOVA followed by Tukey's post hoc multiple comparisons test was used (4E).

Nature Communications | (2024) 15:7249 7

Assessment of mRNA stability showed that deficiency of ILF3 slowed the degradation of p105 mRNA but had no impact on Keap1 mRNA (Fig. 6E; Supplementary Figs. 10F–H). To further explore ILF3’s influence on Keap1 protein expression, we performed IP-MS and found no interaction between ILF3 and Keap1. However, eIF4A1 emerged as a potential mediator, given its role in initiating cap-dependent eukaryotic protein translation (Fig. 6F). Subsequent Co-IP confirmed the interaction between ILF3 and eIF4A1 (Fig. 6G, H). Moreover, we employed the catRAPID method to estimate the binding potential of eIF4A1 protein to Keap1 mRNA21. The catRAPID identified a direct interaction between eIF4A1 protein and Keap1 mRNA, with a Global Score of 1.00 (scaling from 0 to 1, where 0 indicates no RNA-binding ability and 1 indicates strong affinity). And it revealed that the predominant binding site for eIF4A1 is located in the 5′ UTR of Keap1

The overarching findings of our study demonstrated that ILF3 is upregulated in aneurysmal macrophages. This upregulation of macrophage ILF3 appears to accelerate the formation of AAA, as corroborated by human and mouse AAA tissue data. Importantly, we found that the activation of macrophage ILF3 disrupts the inflammatory balance within localized AAA lesions. This disruption occurs through two primary mechanisms: enhancing the NF-κB pathway by destabilizing p105 mRNA and inhibiting the Keap1-Nrf2 pathway by increasing Keap1 translational efficiency via the ILF3/eIF4A1 complex. Moreover, our study highlights the therapeutic potential of BM in mitigating the adverse effects of AAA exacerbated by macrophage ILF3 activation.

Prior studies have shown that ILF3 is involved in the progression of cardiovascular diseases such as myocardial infarction^26,27. Our previous work demonstrated that elevated ILF3 expression could promote arteriosclerotic calcification by acting on the promoter regions of BMP2 and STAT1^28. A recent study revealed that ILF3 participates in the process of circACTA2 reducing CDK4 mRNA stability and protein expression via competing for association with CDK4 mRNA, thus leading to Ang II-induced VSMC death^15. However, the specific role of ILF3 in AAA development is still unclear. Here, using bioinformatic and immunological methods, we have identified markedly elevated levels of ILF3 in AAA tissues of humans and mice. Notably, this increase was closely associated with a heightened accumulation of vascular macrophages, denoting the potential role of macrophage ILF3 in AAA progression.

The adventitial remodeling and inflammation are the main pathological features of AAA^9,29. Macrophages are key phenotypes of immune cells in the adventitia, where they secrete pro-inflammatory mediators, including TNF-α, IL-6, and MCP-1 to initiate a positive feedback loop to extensive remodeling of the aortic wall and eventual aortic dissection^30,31. Mellak et al. found that macrophage depletion inhibits the development of AAA using bone marrow transplantation in ApoE–/– mice^32. However, the underlying molecular mechanisms that regulate macrophagic inflammatory homeostasis in situ are still to be comprehensively understood. Our study addresses this gap through a comprehensive approach involving bioinformatic and proteomic analyzes. We discovered a robust association between ILF3 and inflammatory responses, specifically implicating the NF-κB and Keap1-Nrf2 pathways. Our gene silencing experiments showed that inhibiting macrophage ILF3 led to a marked reduction in macrophage infiltration. The dampened inflammatory response was directly associated with a pronounced reduction in aortic remodeling and dissection in mouse models of AAA, thereby unequivocally establishing a causal link between ILF3 activity and AAA pathology. Furthermore, our in vitro and in vivo analyzes added another layer of understanding. We found that ILF3 deficiency mitigates the release of the chemokine MCP-1 and reduces the levels of pro-inflammatory cytokines such as IL-6 and TNF-α. Given that MCP-1 is a potent chemotactic factor for macrophage recruitment^33 and that IL-6 and TNF-α are critical players in vascular inflammation^34,35, our findings have broad implications. It suggests that mRNA (Supplementary Fig. 10I), which was further validated by an in vitro RIP assay (Fig. 6I). These findings propose the assembly of an ILF3/eIF4A1/Keap1 mRNA complex, which might account for the observed reduction in Keap1 protein levels. Ribosomal profiling confirmed our suspicions, and similar results were acquired using RT-PCR, which showed that macrophage ILF3 deficiency inhibits the translation efficiency of Keap1 (Figs. 6J, K; and Supplementary Data 2). Besides, results showed knockdown of eIF4A1 via si-eIF4A1 suppressed the Keap1 translation efficiency (Supplementary Fig. 10J) and inhibited the elevated translation of Keap1 due to ILF3 overexpression (Supplementary Fig. 10K). In summary, our findings prove that ILF3 deficiency in macrophages enhances p105 mRNA stability and inhibits Keap1 translation, thereby modulating the inflammatory response through the NF-κB and Keap1-Nrf2 pathways.

Bardoxolone methyl (BM, CDDO-Me) is an oral oleanolic acid-derived semi-synthetic triterpenoids and has been proven as an effective inducer of Keap1-Nrf2 (induce Keap1-Nrf2 disassociation, promotes Nrf2 protein stabilization and nuclear translocation^22) as well as an inhibitor of the NF-κB pathway (inhibit NF-κB p65 to the nucleus^23), which is undergoing clinical trials to explore the potential to treat chronic kidney diseases^24,25. Given the shared targets and mechanisms between BM and ILF3, we aim to explore the potential of BM in treating AAA with high ILF3 expression, while simultaneously further validating the underlying mechanisms of ILF3's action. Initially, we administered BM to ILF3^WT ApoE–/– mice and observed that BM treatment resulted in a reduction of AAA incidence, suggesting a protective effect of BM against the development of AAA (Supplementary Fig. 11). Furthermore, the in vitro research indicated that BM could restrain the inflammatory response induced by ILF3 overexpression via hindering NF-κB pathway and enhancing the Keap1-Nrf2 pathway (Supplementary Figs. 12A–12F). While no significant changes in blood pressure were observed across different treatment groups (Supplementary Table 4), BM potently attenuated exacerbated AAA progression in Ang II-treated ILF3^Tg ApoE–/– mice (Fig. 7A). This was evidenced by reduced incidence of aneurysms (Fig. 7B), rupture rate (Fig. 7C), and mortality (Fig. 7D) attributed to ectopic ILF3 expression. Vascular ultrasound imaging and maximal external aortic diameter measurement further corroborate BM’s inhibitory effects on vessel dilation in ILF3^Tg ApoE–/– mice (Figs. 7E, F). Furthermore, BM treatment significantly ameliorated Ang II-induced elastin degradation (Figs. 7G, H). The CaCl2-induced and elastase-induced experimental AAA trials attained consistent results, with BM intervention causing additional obvious improvements (Figs. 7I, J) and in other different disease models (Figs. 12G and 12H). In total, our results revealed the regulatory role of ILF3 in promoting AAA through the NF-κB and Keap1-Nrf2 inflammatory pathways, and demonstrated that BM administration could suppress the negative effects of AAA triggered by macrophage ILF3 overactivation.

Fig. 5 | Macrophage ILF3 influences inflammation via the NF-κB and Keap1-Nrf2 pathways. A Bubble plot showing Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes (DEGs) in macrophages of ILF3^MWT vs. ILF3^MKO mice (one-sided Fisher’s exact test). The top 10 significantly enriched KEGG pathways. B KEGG pathway analysis for DEGs in significant KEGG pathway terms was visualized as a chord dendrogram composed of ribbons. C–D Western blot analysis and quantification of cytoplasmic and nuclear p105, p50, p65, Keap1, and Nrf2 expression in peritoneal macrophages of ILF3 deficiency and control groups with or without Ang II treatment (1 μM; n = 6 per group). E–F Immunofluorescence staining for p65 (red, E) and Nrf2 (green, F) in peritoneal macrophages from ILF3 deficiency and control mice with or without Ang II treatment (1 μM); nuclei were stained with DAPI (blue, Scale bar, 20 μm; n = 6 per group). G–H Co-immunoprecipitation (Co-IP) assay and visualization of interactions between p50, p105, and p65 with an anti-p50 monoclonal antibody in peritoneal macrophages from ILF3 deficiency and control mice after Ang II stimulus (1 μm, 24 h, n = 3 per group). I–J, EMSA analysis showing the DNA binding activity of both NF-κB (I) and Nrf2 (J) in nuclear extracts of peritoneal macrophages from ILF3 deficiency and control mice after Ang II stimulus (1 μm, 24 h, n = 6 per group). K–L Western blot analysis and quantification of MCP-1, TNF-α, IL-6, and IL-10 expression in peritoneal macrophages from ILF3^MWT and ILF3^MTg mice treated with si-Keap1 and AdV-p105 inhibitor under the stimulus without or with Ang II (1 μM; n = 5 per group). Data are represented as mean ± SD. For comparisons between more than two groups, one-way ANOVA followed by Tukey's post hoc multiple comparisons test was used (5D, 5L). For comparisons between two groups, an unpaired Student’s t-test with two-tailed analysis (5H).

Compelling evidence underscores the pivotal role of NF-κB p65-mediated transactivation in the etiology of various chronic inflammatory diseases. Achieving targeted inhibition of p65, tailored to specific tissues and contexts, remains a significant therapeutic objective. In our research, the absence of macrophage ILF3 did not alter p65 levels, yet we observed an upregulation of p105 expression as confirmed by proteomic and immunoblotting analyzes. Previous studies have reported that p105 acts as an essential suppressor of inflammation, with p105 deficiency leading to spontaneous

Fig. 6 | Macrophage ILF3 influences p105 mRNA stability and Keap1 translation efficiency. A. Distribution of ILF3-binding regions on the genome relative to RIP-Sequence. B–C. Representative ILF3 RIP-seq peaks were shown as track signals in an integrative genomic viewer of p105 (Nfkb1) (B) and Keap1 (C). Peak area is highlighted in light blue and peak region is denoted as red rectangle. D. Relative mRNA levels of Keap1 and p105 in peritoneal macrophages from ILF3M–WT and ILF3M–KO mice (n = 6 per group). E. Relative p105 remaining RNA levels at different time points after Actinomycin-D (Act-D) treatment in peritoneal macrophages from ILF3M–WT and ILF3M–KO mice (n = 6 per group). F. Venn diagrams showing the proteins immunoprecipitated with anti-IgG and anti-ILF3 antibodies in wild-type peritoneal macrophages. Blue represents proteins immunoprecipitated with anti-ILF3 under basal conditions (NC). Pink represents proteins immunoprecipitated with anti-IgG.
G–H. Co-IP assay showing interactions between ILF3 and eIF4A1 with an anti-ILF3 antibody (G) and anti-eIF4A1 (H) antibody in peritoneal macrophages from control mice (n = 3 per group). I. RIP assay for enrichment of Keap1 mRNA with eIF4A1 in wild-type peritoneal macrophages; IgG served as an internal control (n = 3). J. Translation profiles for Keap1 computed from Ribo-seq data without induction. Positions of the genetic parts and gene are shown above the profiles. The Y axis indicates sequence depth. K. Box plot showing the translation efficiency (TE) of Keap1 mRNAs in peritoneal macrophages from ILF3M–WT and ILF3M–KO mice (n = 3 per group). Data are represented as mean ± SD. For comparisons between two groups an unpaired Student’s t-test with two-tailed analysis (6D and 6K). For 6E, statistical analysis was performed using Two-way ANOVA and Sidak’s multiple comparison post-hoc test (***p < 0.001).

Human AAA tissues from ten male patients undergoing surgical repair for AAA in the Beijing Anzhen Hospital (N = 10) were donated by Dr. Wen-jian Jiang. The detailed information on the ten patients is listed in Extended Data Table S1. The protocol was approved by local Ethics Committee of Beijing Anzhen Hospital Capital Medical University (#2018004). Patient consent was obtained for the use of this tissue. Age-matched non-AAA segments acquired from organ donors were harvested during explantation (N = 10) of Shandong Provincial Hospital. The protocol was endorsed by the Ethics Committee of Shandong First Medical University (#2022-303).

Animal models and pharmacological therapy. ILF3^Flox/Flox^ mice, which were generated by inserting the mouse ILF3-201 transcript between the first and second exons of the mouse ROSA26 locus. Specifically, the

targeting vector used for this purpose was engineered to include a splice acceptor (SA), a loxP-flanked STOP cassette containing three polyadenylation signals, and the ILF3 cDNA, followed by an additional polyadenylation signal. This vector was introduced into C57BL/6J zygotes via microinjection, along with Cas9 mRNA and sgRNA targeting the first intron of the Gt(ROSA)26Sor locus (ROSA26). The procedure was conducted by Viewsolid Biotech Co, Ltd, Beijing, China. These ILF3^Flox/Flox mice were hybridized with C57BL/6 LysMCre mice to generate macrophage-ILF3 conditional overexpression mice (ILF3^Flox/Flox;LysMCre⁺; hereafter referred to as ILF3^M-Tg) and its littermates (ILF3^Flox/Flox;LysMCre⁻; hereafter referred to as ILF3^M-WT). ILF3^Flox/Flox mice, possessing two loxP sites flanking exon 3 of the ILF3 gene (Viewsolid Biotech Co, Ltd; Beijing, China), were crossed with C57BL/6 LysMCre mice to generate macrophage-ILF3 knockout mice (ILF3^Flox/Flox;LysMCre⁺; hereafter referred to as ILF3^M-KO) and controls (ILF3^Flox/Flox;LysMCre⁻). ApoE⁻/⁻ mice were purchased from Beijing Viewsolid Biotechnology (Beijing, China) and crossed with ILF3^M-KO, ILF3^M-Tg, and their littermates to generate macrophage ILF3 genetic-engineering mice in ApoE⁻/⁻ background. To avoid potential confounding factors related to sex-specific differences, male mice aged eight weeks old were selected in this study⁴⁴,⁴⁵. The mice were housed at 40 ± 5% humidity and 24 ± 2°C under a 12 h light/dark cycle with access to water and food ad libitum.

To establish an angiotensin II (Ang II)-induced AAA mouse model, a mini osmotic pump (Alzet Model 2004, Alza Corp, Palo Alto, CA, USA) loaded with Ang II (1000 ng/kg/min, Sigma-Aldrich, Saint Louis, MO, USA) or saline (0.9% NaCl) was implanted subcutaneously at the dorsum of the neck for Ang-II or saline perfusion, and with concurrent high-fat feeding. Four weeks later, the abdominal aorta was collected for further analysis. Mouse aortic diameter was measured after cleaning the perivascular tissue using the microscope eyepiece reticle micrometer ruler, as mentioned in previous studies⁴⁶.

To establish a CaCl₂-induced AAA mouse model, infrarenal abdominal aortas were isolated and wrapped with fertile cotton balls presoaked in 0.5 mol/L calcium chloride for 15 min under general anesthesia using isoflurane. After four weeks, the mice were sacrificed for sample collection.

BM (CDDO-Me, Selleck, Shanghai, China) was dissolved in dimethyl sulfoxide (DMSO) and further diluted in PEG300 and PBS. Ang II-infused and CaCl₂-treated mice were intraperitoneally injected with BM (1.25 mg/kg/day) or saline on the day after Ang II or CaCl₂.

treatment⁴⁷. Four weeks later, the mice were sacrificed for sample collection.

This study was conducted in strict compliance with the proposals outlined in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All animal experiments have been reviewed and approved by the Institutional Animal Care and Use Committee of Shandong University (KYLL2020-454).

Blood pressures were measured before and four weeks after Ang-II or saline perfusion using the pulse-based tail-cuff method with a photoelectric device (Softron, BP-2010). Mice were sacrificed four weeks after Ang-II or saline perfusion, and body weights were obtained.

Pulse wave velocity and aortic diameter were assessed using an ultrasound machine (Visualsonics, Vevo2100). Before positioning the collection probe, the mice anesthetized by inhalation of isoflurane were placed on the heated 37°C plastic pillow, scratched with water ultrasonic transmission gel and coated on the mouse abdomen. Basal temperature and heart rate were observed throughout the ultrasound imaging procedure. Vascular ultrasonography was conducted to measure the maximal luminal diameter, with assessments independently carried out by two observers blinded to the experimental groups. Measurements were taken both the day before Ang II infusion and on day 28 post-infusion, as previously described⁴⁸,⁴⁹. B-mode imaging was used to determine the diameter of the suprarenal aorta and quantify the incidence of aneurysms according to the definition of AAA, which is defined by an enlargement of the aortic diameter by ≥50% compared to its baseline⁵⁰,⁵¹. Furthermore, any dissection resulting in an intramural hematoma in the AAA mouse model should be considered, even if the dilation is just 110%⁵⁰.

Peritoneal macrophages were isolated through peritoneal lavage three days after the intraperitoneal injection of sterile suspension of 6% starch. Peritoneal macrophages were harvested, washed in phosphate-buffered saline (PBS), and cultured in DMEM medium with 1% penicillin/streptomycin and 10% bovine serum.

Bone marrow-derived macrophages were isolated from mouse femurs and tibiae, as described previously⁵². PBS was used to flush out the bone marrows of femurs and tibiae with a syringe with a 27 G needle. Cell suspension under 4°C to 1500 rpm centrifuge for 5 min. Cells were then cultured with complete DMEM containing 30 ng/ml recombinant macrophage colony-stimulating factor (M-CSF) (R&D, Minnesota, USA) for macrophage differentiation. After 7 days, bone marrow-derived macrophages were acquired and used for further study.

Protein samples were electrophoresed on 10% sodium dodecyl sulfate-polyacrylamide gels and then transferred to a PVDF membrane. The membranes were blocked with 5% nonfat milk at room temperature for 1 h and then incubated with a specific diluted primary antibody overnight. After being washed 3 times with TBST, membranes were incubated with secondary antibodies at room temperature for 1 h. Chemiluminescence (Millipore, Billerica, MA, USA) identified the bound primary antibodies and measured using Image J (NIH, Bethesda, MD, USA). Antibodies used are as follows: β-actin, RRID: AB_2223172, Cell Signaling Technology, Catalog number: 4970; NF-κB1, RRID: AB_2282895, Cell Signaling Technology, Catalog number: 4717; NF-kappa B, RRID: AB_10859369, Cell Signaling Technology, Catalog number: 8242; NRF2, RRID: AB_2715528, Cell Signaling Technology, Catalog number: 12721; eIF4A1, RRID: AB_, Abcam, Catalog number: ab185946; IL-6, RRID: AB_2927381, Abcam, Catalog number: ab259341; MCP1, RRID: AB_448636, Abcam, Catalog number: ab25124; TNF-α, RRID: AB_2935774, Abcam, Catalog number: ab215188; Keap1, RRID: AB_, Abcam, Catalog number: ab227828; IL-10, RRID: AB_2847946, Abcam, Catalog number: ab189392; ILF3, RRID: AB_2049804, Abcam, Catalog number: ab92355; Lamin B1, RRID: AB_443298, Abcam, Catalog number: ab16048. Expression was normalized relative to the control.

An EMSA was conducted using Light Shift Chemiluminescence EMSA kit (Thermo Fisher Scientific, Rockford, USA) to assess Ang II-induced NF-κB and Nrf2 activation in peritoneal macrophages. Nuclear extracts isolated from cultured peritoneal macrophages (5 × 10⁶/ml) using the NE-PER Nuclear and Cytoplasmic Extract Reagents (Thermo Fisher Scientific, Rockford, USA). The NF-κB and Nrf2 binding assay to predicted sites was conducted using annealed biotin labeled oligonucleotide probes. The labeled oligonucleotide probe designed for NF-κB is 5′(Biotin)-AGTTGAGGGGACTTTCCCAGGC-3′, and the probe for Nrf2 is 5′(Biotin)-TGGGGAACCTGTGCTGAGTCACTGGAG-3′.

The RIP assay involved using the Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore, Billerica, MA, USA) in strict accordance with the manufacturer's recommendations. Anti-ILF3 (19887-1-AP, Proteintech) and anti-IgG antibodies were used to immunoprecipitate RNAs. The final analysis was performed with qRT-PCR, showing fold enrichment of the target RNAs between the immunoprecipitated and input fractions.

The cell lysates of macrophages were collected and centrifuged, followed by incubation with anti-P50 (SC-8414, Santa), anti-eIF4A1 (ab185946, Abcam), and anti-ILF3 (19887-1-AP, Proteintech) antibodies at 4°C overnight. A lysate immunoprecipitated with anti-IgG (ab172730, Abcam) antibody was used as a negative control. The immunocomplexes were then purified using 30 μl of protein G magnetic beads (Bimake, Houston, USA) for 2 hours at 4°C, centrifuged, and washed with Pierce™ IP Lysis Buffer (Thermo Fisher Scientific, Waltham, MA, USA) four to five times. The immunoprecipitated protein was further analyzed by western blot with target antibodies.

Abdominal aortas were harvested, perfused with saline, and then fixed with 4% paraformaldehyde, followed by embedding in paraffin and serial sectioning. Paraffin-embedded sections (5 μm thick) were prepared for hematoxylin and eosin (HE) staining to observe general morphology and Verhoff-Van Gieson (VVG) staining to evaluate elastin degradation. Elastin degradation was graded using a quantification method by counting the number of breaks per vessel³.

The paraffin embedded arterial sections were dewaxed and stained with primary antibodies, followed by a biotin-conjugated secondary antibody (1:500) and horseradish peroxidase-conjugated streptavidin (Dianova, Rodeo, CA).

For immunofluorescence staining, sections were stained overnight with specific antibodies at 4°C and then added with fluorescence-labeled secondary antibodies. Nuclei were stained with 4,6-diamidino-2-phenylindole (DAPI). Specific fluorescence imaging was obtained by laser-scanning confocal microscopy (LSM710, Carl Zeiss, Germany).

IP-MS was conducted as previously described⁵³. Briefly, peritoneal macrophages were obtained from C57BL/6 mice and separated. IP was performed using an immunoprecipitation kit (Abcam, Cambridge,

To identify subsets of inflammatory cells in murine models, single-cell suspensions were isolated from mouse aortas following established protocols⁵⁴. The enzymatically digested suspensions were filtered through a 70 µm sterile mesh and centrifuged at 300 g for 5 minutes at 4 °C. The resulting pellet was resuspended in a buffer containing 0.5% BSA. Cell viability and count were determined using trypan blue exclusion and an automated cell counter (Countess, Life Technologies). Anti-CD16/CD32 (101302, biolegend) was added and incubated for 10 minutes at room temperature to minimize nonspecific binding. Subsequently, cells were stained with specific marker antibodies APC anti-mouse CD83 (121509, biolegend), Percpcy5.5 anti-mouse CD45 (103132, biolegend), FITC anti-mouse CD11b (101206, biolegend), PE anti-mouse F4/80 (12-4801-82, ebioscience), Pacific Blue anti-mouse Ly-6G/Ly-6C (Gr-1) (108429, biolegend), PE anti-mouse CD19 (115507, biolegend), APC anti-mouse NK1.1 (108709, biolegend) and FITC anti-mouse CD3 (100203, biolegend). Data were further analyzed by NovoExpress® Software.

Total cellular protein was extracted from isolated peritoneal macrophages derived from both ILF3ᴹ⁻ᴷᴼ mice (n = 3) and wild-type counterparts (n = 3) for analysis through 4D label-free quantification (4D-LFQ) proteomics, conducted by PTM Biolabs (Hangzhou, China). The analysis employed a false discovery rate (FDR) of 5% for both protein identification and peptide spectral matching (PSM), establishing a threshold for differential expression: proteins with fold changes exceeding 1.2 and adjusted P values below 0.05 were classified as differentially expressed proteins (DEPs). Functional enrichment analysis of these DEPs was carried out using a two-tailed Fisher's exact test. Significance was attributed to Gene Ontology (GO) terms, KEGG pathways, and protein domains that achieved a p-value of less than 0.05.

Ribosome profiling was performed on isolated peritoneal macrophages from both ILF3ᴹ⁻ᴷᴼ (n = 6) and ILF3ᴹ⁻ᵂᵀ (n = 6) tissues by Gene Denovo (Guangzhou, China). In brief, cycloheximide (Sigma, St Louis, MO, USA; 100 µg/ml) was added to the peritoneal macrophage medium for 2 min to block translational elongation. The supernatant was collected after trituration and centrifuged at 20000 g for 10 min at 4 °C. To prepare RFs, 10 µL of RNase I (NEB, Ipswich, MA, USA) and 6 µL of DNase I (NEB, Ipswich, MA, USA) were added to 400 µL of lysate, mixed and incubated for 45 min at room temperature. Nuclease digestion was halted via adding 10 µL of SUPERase, an RNase Inhibitor (Ambion, Austin, TX, USA). Then 100 µL of digested RFs was added to the prepared Size exclusion columns (Illustra MicroSpin S-400 HR Columns; GE Healthcare; catalog no. 27-5140-01) and centrifuged (600 g, 2 min). Subsequently, 10 µL of 10% (w/v) SDS was added up to the elution, and RFs (size > 17nt) were separated via RNA Clean and Concentrator-25 kit (Zymo Research; R1017). Further, rRNA and residual DNA probes were removed via RNase H (NEB, Ipswich, MA, USA) and DNase I (NEB, Ipswich, MA, USA). Lastly, the RFs were refined by magnet beads (Vazyme, Nanjing, Jiangsu, China).

Ribo-seq libraries were framed by the NEBNext® Multiple Small RNA Library Prep Set for Illumina® (catalog no. E7300S, E7300L). In brief, adapters were added up to RFs, succeeded by reverse transcription and PCR amplification. The PCR products (140-160 bp size) were enriched to construct cDNA library, sequenced using Illumina HiSeq™ X10. Moreover, we used FASTP to filter the raw data and remove low-quality bases (over 50%) or N bases (over 10%). After trimming the adapter sequences, reads with length between 20–40 bp were kept for analysis. Raw reads were filtered and mapped to the mouse ribosome RNA database using bowtie2. The reads mapped to rRNAs, transfer RNAs (tRNA), small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), and miRNAs were eliminated. Processed RNA-seq reads were mapped to the rn6 genome using whole-genome alignment by STAR with a 2-pass setting. Ribo-seq data analysis was performed as previously described.[23] Differential translated genes between groups were defined as |log2(FoldChange)| > 0.3 and p-value < 0.05.

After modeling, blood samples were collected and stored at −80 °C until further analysis. Following the manufacturers' guidelines, plasma levels of IL-6 (Cat#88-7064, Invitrogen), IL-10 (Cat#88-7105, Invitrogen), TNF-α (Cat#88-7324, Invitrogen), and MCP-1 (Cat#BMS6005, Invitrogen) were quantified using Elisa kits.

UK). In brief, cells were collected, resuspended, and lysed in lysis buffer containing the protease inhibitor cocktail. The resulting cell lysates and anti-ILF3 antibody (19887-1-AP, Proteintech) were mixed overnight in a rotary mixer at 4 °C. Next, 40 µL protein A sepharose bead slurry was added into each tube and incubated for 1 h at 4 °C, followed by 2000 g centrifugation for 2 min. After washing three times with 1 mL 1× wash buffer, 40 µL 2× SDS-PAGE loading buffer added to the beads and boiled for 5 min to elute the complex.

Western blot analysis was used for the validation of IP samples. The gels were then excised and subjected to MS. UPLC-MS/MS involved using nano ACQUITY UPLC Columns (Waters) with Orbitrap Elite Mass Spectrometers (Thermo Fisher Scientific, Waltham, MA, USA). The resulting MS/MS data were handled with Proteome Discoverer 1.3 (high peptide confidence, peptide ion score > 20).

For RNA-seq, total RNA of peritoneal macrophages from both ILF3ᴹ⁻ᴷᴼ (n = 6) and ILF3ᴹ⁻ᵂᵀ (n = 6) tissues was isolated utilizing the RNeasy Mini Kit (Qiagen, Germany). Subsequently, strand-specific libraries were constructed employing the TruSeq® Stranded Total RNA Sample Preparation kit (Illumina, USA) following the manufacturer's instructions. Library quantification was conducted using a Qubit® 2.0 Fluorometer (Life Technologies, USA) and validated by Agilent 2100 bioanalyzer (Agilent Technologies, USA) to confirm the insert size and calculate the mole concentration. Library clustering was performed on a cBot system at a concentration of 10 pM, followed by sequencing on the HiSeq X Ten platform (Illumina, USA) (Illumina, USA). The sequencing raw reads were preprocessed to remove rRNA sequences, sequencing adapters, short fragments, and other low-quality reads. We employed Tophat v2.0.9⁵⁵ for mapping the filtered reads to the mouse mm10 reference genome, allowing for a maximum of two mismatches. Following genomic mapping, Cufflinks v2.1.1⁵⁶ was utilized with a reference annotation to calculate FPKM values for known gene models. Differential expression of genes between the groups was ascertained using the DESeq R package, with stringent criteria set at FoldChange > 2 and p-value < 0.05. Translational efficiency was quantified by the ratio of translating mRNAs to total mRNAs, defined as: TE = (FPKM in Ribo-seq) / (FPKM in RNA-seq). Furthermore, to elucidate the functional similarity among identified genes, we employed the Friends analysis, a method executed using the GOSemSim package⁵⁷. The raw data from the ribosome sequencing and associated mRNA sequencing conducted in this study are publicly available, which have been deposited in the NCBI Gene Expression Omnibus (GEO) and can be accessed through the GEO Series accession numbers GSE241911, GSE242024, GSE242088, and GSE242185.

Macrophages were suspended in TRIzol Reagent (Invitrogen, Waltham, MA, USA) after treatment with actinomycin D (Act-D, 10 mg/mL) for 0, 2, 4, 8, and 12 h. Total RNA was purified using the RNeasy Mini Kit (Qiagen, Hilden, Germany) based on the manufacturer's recommendations. The total RNA was reverse transcribed by using the PrimeScript RT Reagent Kit with gDNA Eraser (Perfect Real Time) (Takara,

Sure, here are the accurately transcribed and corrected texts from the provided crops:

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**Crop 1:**
Japan), and qRT-PCR was conducted using the LightCycler 480 SYBR Green I Master (Roche Life Science, NSW, Australia). The level of each transcript was normalized to the control gene β-actin and shown relative to the average expression of the control sample (n = 5 under each experimental condition).

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**Crop 2:**
The ggplot2 package of the interesting plots in the finding of the R foundation and statistic in R v4.2.1 (R Foundation for Statistical Computing, Vienna, Austria)^{58,59}, and Prism 9.0 (GraphPad Inc) were used for statistical analyzes. Continuous parameters are presented as (mean ± SD), and categorical variables are shown as percentages. After verification of the normal distribution of the data via the Shapiro-Wilk test and variance equality via the Levene test, the Student’s t test was used when the variance of the two groups was equal, and the unequal variance t test was used for data with unequal variances. Otherwise, the Mann-Whitney U test is for data sets that are not normally distributed. Brown Forsythe test was used to evaluate the homogeneity of variance in multiple groups (> 2 groups). After One-way ANOVA analysis, a Tukey post-hoc analysis was used if the data passed the equal variance test; otherwise, a Welch ANOVA test was performed, followed by a post hoc analysis. Two-way ANOVA with a Bonferroni post hoc test was used to test the difference among groups with multiple factors. The survival curve analysis was analyzed using the Kaplan–Meier product-limit approach and compared by the log-rank test. We downloaded publicly available data from the Gene Expression Synthesis (GEO) database (GSE57691 and GSE12591) and obtained DEGs by GEO2R analysis.

---

**Crop 3:**
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the iProX partner repository with the dataset identifier PXD047695. IP-MS datasets are available via ProteomeXchange with identifiers PXD047694. All sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive under the BioProject ID PRJNA1050581 for Ribo-seq, PRJNA1050631 for RIP-seq. All other data are available in the article and its Supplementary files or from the corresponding author upon request. Source data are provided with this paper.

---

Z.W., J.C., Y.W., H.Y., and S.B. contributed equally to this work. Z.W., M.Z., and Y.C. conceived the project. Z.W. and M.Z. wrote the manuscript. Y.Z., W.J., and M.Z. supervised the study. Z.W., J.C., H.Y., S.B., S.W., and Ze.W. performed human studies and carried out analyses. Z.W., J.C., Y.W., B.X., X.Z., and Y.H. performed animal experiments and prepared figures. Z.W., J.C., and M.Z. interpreted the results. All authors reviewed the results and approved the final version of the manuscript.

This work was supported by the National Natural Science Foundation of China (No. 82370455 and 82200466), the National Key R & D Program of China (No.2017YFC0908700, 2017YFC0908703 and 2017YFC1308000), the Taishan Scholar Project of Shandong Province of China (No.ts20190972, No.tsqn202306377), Natural Science Foundation of Shandong Province (No.ZR2022QH296), Key Clinical Research project of Clinical Research Center of Shandong University (No.2020SDUCRCA016), and Academic promotion program of Shandong First Medical University (No.2021QL021).

The authors declare no competing interests.

Correspondence and requests for materials should be addressed to Yun Zhang, Wen-jian Jiang, Yu-guo Chen or Ming-xiang Zhang.

Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41467-024-51030-4.

Peer review information Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

http://www.nature.com/reprints

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.


[System]
You are a **scientific‐rigor auditor**. You will receive the parsed contents of a research paper. Your job is to identify **only** those errors or flaws that directly undermine the **scientific validity** of the paper’s methods, analyses, or conclusions.

Your sole focus is to identify flaws—such as errors in experimental design, data integrity, calculations, statistical inference, or reproducibility—that directly call into question the validity of a specific claim, paragraph, or the paper as a whole.

**Do not** report issues that are purely presentational, rhetorical, stylistic, or related to citation practices.

---

After you’ve done a **detailed walkthrough** of the paper, output exactly in this format—no extra keys or commentary:

```
<analysis>
{detailed walk‑through of how you checked each section/figure and why you flagged (or did not flag) any flaw}
</analysis>

<response>
{
  "has_error": <true|false>,
  "errors": [
    {
      "location": "Section 2.1",
      "description": "Claim that ‘all X are Y’ is contradicted by citation [5], which shows examples of non‑Y X."
    },
    {
      "location": "Figure 3",
      "description": "XAxis labeled ‘Time (s)’ but units appear to be milliseconds; scale bar mismatches caption."
    }
    // …more entries…
  ]
}
</response>
```

- **Do not** include any other keys or prose outside these two tagged blocks.
- **Do not** report stylistic or citation issues.
- Be as precise as possible about **where** (section ID or figure/table) and **what** the scientific flaw is.
- Each description for the errors must be rooted in a scientific rationale explaining why it is 'wrong' (not how it could be improved).

Begin your analysis now.

[User]
The impact understanding of exosome therapy in COVID-19 and preparations for the future approaches in dealing with infectious diseases and inflammation

Zeynab Nasiri¹, Hoorieh Soleimanjahi¹*, Nafiseh Baheiraei², Seyed Mahmoud Hashemi³ & Mahmoud Reza Pourkarim⁴

Cytokine storms, which result from an abrupt, acute surge in the circulating levels of different pro-inflammatory cytokines, are one of the complications associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This study aimed to assess the effect of exosomes on the release of pro-inflammatory cytokines in patients with coronavirus disease 2019 (COVID-19) and compare it with a control group. The cytokines evaluated in this study were TNF-α, IL-6, IL-17, and IFN-γ. The study compared the levels of these pro-inflammatory cytokines in the peripheral blood mononuclear cells (PBMCs) of five COVID-19 patients in the intensive care unit, who were subjected to both inactivated SARS-CoV-2 and exosome therapy, with those of five healthy controls. The cytokine levels were quantified using the ELISA method. The collected data was analyzed in SPSS Version 26.0 and GraphPad Prism Version 9. According to the study findings, when PBMCs were exposed to inactivated SARS-CoV-2, pro-inflammatory cytokines increased in both patients and healthy controls. Notably, the cytokine levels were significantly elevated in the COVID-19 patients compared to the control group P-values were <0.001, 0.001, 0.008, and 0.008 for TNF-α, IL-6, IL-17, and IFN-γ, respectively. Conversely, when both groups were exposed to exosomes, there was a marked reduction in the levels of pro-inflammatory cytokines. This suggests that exosome administration can effectively mitigate the hyperinflammation induced by COVID-19 by suppressing the production of pro-inflammatory cytokines in patients. These findings underscore the potential safety and efficacy of exosomes as a therapeutic strategy for COVID-19.

Keywords Cytokine storm, SARS-CoV-2, Exosome, COVID-19, PBMC

Abbreviations

|              |                                                        |
|--------------|--------------------------------------------------------|
| SARS-CoV-2   | Severe acute respiratory syndrome coronavirus 2         |
| COVID-19     | Coronavirus disease 2019                                |
| IL           | Interleukin                                             |
| IFN-γ        | Interferon gamma                                        |
| TNF-α        | Tumor necrosis factor alpha                             |
| hUC-MSC      | Human umbilical cord mesenchymal stem cell              |
| PBMC         | Peripheral blood mononuclear cells                      |
| SD           | Standard deviations                                     |

¹Department of Virology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. ²Department of Anatomical Science, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. ³Department of Immunology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ⁴Laboratory for Clinical and Epidemiological Virology, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, KU Leuven, 3000 Leuven, Belgium. *email: soleim_h@modares.ac.ir

Scientific Reports | (2024) 14:5724 | https://doi.org/10.1038/s41598-024-56334-5

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| Abbreviation | Full Term                             |
| ------------ | ------------------------------------- |
| SE           | Standard error                        |
| CI           | Confidence interval                   |
| IQR          | Interquartile range                   |
| PE           | Phycoerythrin                         |
| FITC         | Fluorescein isothiocyanate            |
| Per CP       | Peridinin chlorophyll protein complex |

The severe acute respiratory syndrome coronavirus 2, which originated in Wuhan, China, in late 2019, soon escalated into a global pandemic, posing a significant threat to public health worldwide. This virus causes a type of pneumonia infection known as coronavirus disease 2019, which became a major global health concern¹. COVID-19 triggers intense immune responses and promotes uncontrolled cytokine release, resulting in cytokine storms in the lungs². Current evidence suggests that lymphopenia (a condition characterized by low levels of lymphocytes in the blood) and increased levels of circulating cytokines are key factors in the progression of the disease or recovery from it³⁻⁶. As such, strategies aimed at controlling cytokine release and implementing anti-inflammatory therapy could potentially serve as effective approaches for treating or mitigating pneumonia in COVID-19 patients⁷.

According to the World Health Organization (WHO), the majority of COVID-19 patients exhibit mild (40%) to moderate (40%) symptoms. Conversely, about 15% of patients experience severe symptoms that necessitate oxygen therapy, and 5% develop extremely severe symptoms. These severe symptoms are characterized by serious complications, such as acute respiratory distress syndrome (ARDS), respiratory failure, sepsis, septic shock, and multiorgan failure, which can include cardiac and critical kidney injuries. ARDS is primarily caused by the host's antiviral inflammatory responses, often triggered by cytokine storm syndrome. This can ultimately lead to multiorgan failure or even death in severe COVID-19 cases⁸,⁹. Cytokine storms, which significantly contribute to the progression of COVID-19 infection, are sustained and intensified by several concurrent processes, including the activation of antigen-presenting cells (e.g., macrophages), alerting lymphocytes to the presence of the virus, replication of viral RNA within host cells, production of pro-inflammatory factors, and invasion of lymphocytes by the virus, which triggers lymphocyte apoptosis and thus facilitates continuous immune evasion¹⁰.

Clinical data suggests that cytokine storms induced by COVID-19 involve a self-perpetuating cycle of inflammatory responses, characterized by the continuous release of pro-inflammatory cytokines, such as interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-12 (IL-12), interferon gamma (IFN-γ), and tumor necrosis factor-alpha (TNF-α), which adversely impact lung tissues¹¹⁻¹³. Stem cell-based therapies have been recently recognized as innovative treatment strategies for managing patients with COVID-19¹⁴. A study by Leng et al. was the first to showcase the therapeutic potential of transplanted mesenchymal stem cells (MSCs) in enhancing the pulmonary function of COVID-19 patients¹⁵. Over the past few decades, the potential mechanisms of MSCs in treating various stages of respiratory diseases have been elucidated¹⁶. For instance, in multiple ARDS models, the anti-inflammatory and anti-apoptotic properties of MSCs have been shown to improve lung function by restoring epithelial and endothelial cells and facilitating the clearance of alveolar edema fluid. Generally, MSCs are known for their ability to secrete anti-inflammatory cytokines, such as IL-10 and transforming growth factor-β (TGF-β), leading to a reduction in the recruitment of neutrophils into damaged organs and a decrease in the levels of pro-inflammatory cytokines, such as TNF-α, IL-8, and IL-6¹⁷.

Human umbilical cord-derived (hUC-MSCs) are the primary source of MSCs. According to previous studies, MSC therapies can effectively treat ARDS¹⁸ and sepsis¹⁹, which are often severe pathological symptoms in COVID-19 patients. Patients may experience symptoms, such as edema, intrapulmonary shunting, and hypoxemia. However, by regulating the inflammatory activity and preventing apoptosis, MSCs can help halt the progression of the disease²⁰. A study by Johnson et al.²¹ demonstrated that human MSC therapy can ameliorate ARDS and sepsis in rats and mice. Furthermore, according to a phase I trial conducted by Zheng et al.²² and Wilson et al.²³, the administration of MSCs did not lead to any adverse side effects. Guo et al. discovered that treating patients with severe COVID-19 symptoms using hUC-MSCs can enhance clinical outcomes by increasing the oxygenation levels and mitigating the incidents of cytokine storms²⁴.

Similarly, Liang et al. reported that the allogeneic use of hUC-MSCs can alleviate inflammatory symptoms in patients with COVID-19 pneumonia²⁵. Additionally, in their respective clinical studies, both Lanzoni et al.²⁶ and Feng et al.²⁷ reported encouraging results in COVID-19 patients treated with hUC-MSCs. MSCs utilize three primary mechanisms to manage the symptoms of COVID-19, including immunomodulatory effects, reparative and recovery effects, and antimicrobial effects, particularly in cases of ARDS and sepsis²⁸. Despite significant progress, stem cell-based therapies face several obstacles that limit their clinical application, such as immunogenicity, restricted sources of derivation, and ethical considerations. Furthermore, it is widely recognized that the success and effectiveness of stem cell-based therapies in treating COVID-19 largely depend on their paracrine effects and their ability to regulate cytokine storms¹⁴,²⁹,³⁰.

Exosomes, which are secreted by MSCs, are among the most crucial paracrine effectors. They carry biological cargos similar to their parent cells and possess healing properties, making them appealing substitutes for MSCs in treating various diseases³¹. Exosomes offer several benefits over their parent cells, including their small size, non-toxic nature, low immunogenicity (lacking MHC class I–II), high stability, and ease of storage. Furthermore, exosomes can be easily engineered and manipulated, and they can be produced as readily available products¹⁰,³². These advantages have contributed to the growing use of exosomes in clinical applications as new therapeutic alternatives³³.

Exosomes, which are produced by cells infected with SARS-CoV-2, contain viral RNA, key viral proteins, and host cell proteins, such as ACE-2. These components are essential for the virus to enter cells and propagate the infection. This unique characteristic enables exosomes to stimulate both innate and adaptive immune responses,

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Alipoor et al. provided compelling experimental evidence that exosomes, derived from stem cells, have the ability to inhibit signaling pathways associated with hypoxia38. This could potentially help reduce inflammation and hypertension as symptoms that are particularly prominent in respiratory diseases. Studies have demonstrated that exosomes, derived from MSCs, produce miRNA. This miRNA acts as a silencing complex and alters the expression of cellular receptors through epigenetic modifications. This process inhibits various RNA viruses, including coronavirus, influenza, and hepatitis C, from invading the body35,39. The present study aimed to explore the use of exosomes derived from hUC-MSCs to decrease inflammation in COVID-19 patients in a laboratory setting. This approach has potential applications for other infectious and inflammatory diseases.

The morphology of hUC-MSCs remained consistent when cultured in fetal bovine serum (FBS)-free conditions for exosome generation (Fig. 1). As observed under an inverted microscope, hUC-MSCs are elongated and spindle-shaped, forming compact clusters of uniform size. We investigated the immunophenotypes of MSCs using flow cytometry, a crucial biochemical method for understanding cell types. As depicted in Fig. 2, very high expression levels of CD44 and CD90 were detected, while very low expression levels were observed for CD34 and CD45. These findings align with the criteria set by the International Society for Cellular Therapy for the definition of MSCs.

...making them potential candidates for vaccines. Additionally, exosomes could serve as effective drug carriers in the treatment of COVID-1934. Moreover, exosomes increase the count of anti-inflammatory signaling mediators, which could potentially reduce lung damage by enhancing the functional characteristics and permeability of the alveolar epithelium35,36. As a result, the exchange of oxygen-rich air is significantly improved. In addition to their effects observed in preclinical models of acute lung disorders, exosomes derived from MSCs were found to directly inhibit viral replication35,37.

Images obtained through field emission scanning electron microscopy (FESEM) confirmed that the exosomes derived from MSCs were small, spherical, and less than 100 nm in size. The morphology of these exosomes was further analyzed using transmission electron microscopy (TEM) with negative staining. The TEM images offered a more intricate view of the exosomes derived from MSCs. These images revealed that the particle pellets were vesicles resembling a cup shape, with membranes attached (Fig. 3a, b).

The Western blot analysis detected the presence of CD9, a surface marker typically found on exosomes derived from MSCs. The protein content of the exosomes can be assessed by flow cytometry and Western blot, and the combination of these two methods results in an investigation of both the membrane-bound (CD9, CD63, and CD81) and internalized proteins (Tsg101 and Alix) of the exosomes. Detection of proteins enriched in exosomes, such as CD9, Tsg101, and Alix, and the absence of proteins, such as the endoplasmic reticulum protein calnexin, is an indication that the exosome-enriched pellet is indeed exosomes and not contaminating vesicles from other...

The size distribution of the isolated exosomes was determined using the DLS. The DLS analysis revealed that the average size of the exosomes was 89.65 nm, with measurements taken at a constant temperature of 25°C (Fig. 5).

Additionally, the protein concentration was quantified using the BCA method. The resulting value, determined from the protein concentration standard curve depicted in Fig. 4, was measured to be 3482 μg/mL.

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Figure 2. Histogram of a flow cytometric analysis: hUC-MSCs were positive for CD44 and CD90 with percentages of 99.8% and 97.3% of total cells, respectively, and negative for CD34 and CD45 with percentages of 1.85% and 2.05% of total cells, respectively.

| Marker       | Positive (%) | Negative (%) |
| ------------ | ------------ | ------------ |
| CD44 (FL1-H) | 99.8%        | 0.20%        |
| CD90 (FL3-H) | 97.3%        | 2.67%        |
| CD45 (FL1-H) | 2.05%        | 97.9%        |
| CD34 (FL2-H) | 1.85%        | 98.2%        |

Figure 3. (a) FE-SEM images of the isolated exosomes. (b) TEM micrographs of isolated exosomes. FE-SEM, Field emission scanning electron microscopy. TEM, Transmission electron microscopy.

compartments of the cell as is presented. The exosomes harvested from hUC-MSCs are devoid of any cellular components, as evidenced by the absence of the endoplasmic reticulum chaperone protein, calnexin (Fig. 6).

Determination of the dose
The assay was employed to identify the optimal dose of the isolated exosomes after 72 h (Fig. 7). Various doses of 10 µg/ml, 20 µg/ml, 40 µg/ml, and 60 µg/ml were evaluated for this purpose. Despite the differences in the

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Five patients were admitted to the hospital's intensive care unit (ICU), all of whom tested positive for COVID-19 via the PCR test, with cycle threshold (CT) values ranging between 15 and 25. The healthy controls consisted of five individuals who neither contracted the virus during the epidemic, nor received a vaccine for prevention. In the patient group, three individuals (60%) were men, and two (40%) were women. The healthy group also comprised five individuals, including one (20%) man and four (80%) women.

The levels of four cytokines were measured in the culture supernatants of PBMCs. These conditions include 1. exposure of PBMCs to the RPMI 1640 medium as a negative control, 2. exposure of PBMCs to the inactivated virus, 3. treatment with exosomes after exposure to the negative control (RPMI 1640 medium), and 4. treatment with exosomes after exposure to the inactivated virus in both patients and healthy participants. To begin with, due to our small sample size, the Shapiro–Wilk test was used to assess the distribution of cytokines' levels in each group in patients and healthy participants. An independent T-test was performed for variables with normal distribution, and for others Mann–Whitney was used to compare the cytokine levels. Results are shown in Tables 2 and 3. All our results demonstrated a significant difference between patient and healthy participants for each condition except for TNF-α, IFN-γ and IL-17 levels in PBMCs exposed to inactivated virus and treated by exosome P-values were 0.055, 0.327, and 0.627 respectively. We used paired T-test and Wilcoxon Signed Ranks Test to compare cytokines' levels before and after PBMCs exposure to the inactivated virus and before and after PBMCs treatment with exosome. We observed a notable decrease in the levels of cytokines among the patient

calculated viability of the samples not being statistically significant, a dose of 40 µg/mL (the highest dose) was selected as the optimal dose for subsequent experiments.

Upon admission to the ICU, the patients were subjected to laboratory tests to determine their white blood cell (WBC) and platelet (PLT) counts, as well as their blood erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and D-dimer levels (Table 1).

group. Additionally, we found a significant reduction in cytokine levels among healthy controls whose PBMCs were stimulated with inactivated virus. P-values and effect sizes for these observations are available in Tables 4 and 5. The findings suggested that when PBMCs were stimulated with the inactivated virus for 24 h, there was a significant increase in the release of pro-inflammatory cytokines, such as IL-6, IFN-γ, TNF-α, and IL-17. Infection with SARS-CoV-2 resulted in the production of pro-inflammatory cytokines. Individuals with symptomatic COVID-19 had higher levels of four cytokines (IL-6, IFN-γ, TNF-α, and IL17) than healthy controls. In Fig. 8, the expression levels of cytokines (IL-6, IFN-γ, TNF-α, and IL17) were displayed for patients and healthy individuals under four different conditions mentioned above.

Table 4. Comparing cytokine levels after exosome treatment using paired T-test. SD, Standard deviation; 95% CI, 95% Confidence interval; t, test statistic, Cohen’s d: effect size (0.2 = small effect, 0.5 = medium, 0.8 = large, and 1.3 = very large), α = 0.05, P-values < 0.05 are significant, n = 5.

| Cytokine | Intervention                 | HEALTHY (N = 5)
Median ± IQR | HEALTHY (N = 5)
Z | HEALTHY (N = 5)
P-value | HEALTHY (N = 5)
r | PATIENT (N = 5)
Median ± IQR | PATIENT (N = 5)
Z | PATIENT (N = 5)
P-value | PATIENT (N = 5)
r |
| -------- | ---------------------------- | -------------------------------- | --------------------- | --------------------------- | --------------------- | -------------------------------- | --------------------- | --------------------------- | --------------------- |
| IL-6     | Negative Control             | 2.041 ± 2.083c                   | 0.000                 | 1                           | 0                     | 105.976 ± 48.481d                | -2.023                | 0.043                       | 0.64                  |
|          | Exosomes +                   | 2.998 ± 2.998                    |                       |                             |                       | 9.521 ± 9.521                    |                       |                             |                       |
|          | Inactivated Virus            | 65.038 ± 21.040d                 | -2.023                | 0.043                       | 0.64                  | 297.933 ± 112.825b               | -2.023                | 0.043                       | 0.64                  |
|          | Exosomes + Inactivated Virus | 35.810 ± 11.506                  |                       |                             |                       | 18.380 ± 4.660                   |                       |                             |                       |
| IFN-γ    | Negative Control             | 0.000 ± 0.958b                   | -2.023                | 0.043                       | 0.64                  | 80.715 ± 37.273d                 | -2.023                | 0.043                       | 0.64                  |
|          | Exosomes +                   | 2.665 ± 2.665                    |                       |                             |                       | 10.446 ± 10.446                  |                       |                             |                       |
|          | Inactivated Virus            | 66.350 ± 16.433d                 | -2.023                | 0.043                       | 0.64                  | 221.607 ± 96.459d                | -2.023                | 0.043                       | 0.64                  |
|          | Exosomes + Inactivated Virus | 28.032 ± 26.908                  |                       |                             |                       | 27.200 ± 11.522                  |                       |                             |                       |
| IL-17    | Negative Control             | 0.000 ± 0.000d                   | 0.000                 | 1                           | 0                     | 233.721 ± 73.502b                | -2.023                | 0.043                       | 0.64                  |
|          | Exosomes +                   | 0.000 ± 0.000                    |                       |                             |                       | 60.503 ± 60.503                  |                       |                             |                       |
|          | Inactivated Virus            | 191.348 ± 103.731b               | -2.023                | 0.043                       | 0.64                  | 412.712 ± 183.304b               | -2.023                | 0.043                       | 0.64                  |
|          | Exosomes + Inactivated Virus | 83.990 ± 47.362                  |                       |                             |                       | 89.564 ± 63.960                  |                       |                             |                       |

Table 5. Comparing cytokine levels after exosome treatment using Wilcoxon. IQR, Interquartile range; aWilcoxon Signed Ranks Test, based on negative ranks; The sum of negative ranks equals the sum of positive ranks; bBased on negative ranks; cBased on positive ranks; dBased on positive ranks; Z: z-score; r: effect size (it is calculated via Z/√N; r figures are interpreted based on the following definition: 0.1 = small effect, 0.3 = medium effect, 0.5 = large effect); α = 0.05; P-values < 0.05 are significant; n = 5.

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This study aimed to investigate the impact of exosomes on the secretion of pro-inflammatory cytokines. Based on our findings, a therapeutic strategy for SARS-CoV-2 infection could effectively involve immunomodulatory treatment to regulate cytokine responses, in conjunction with antiviral treatment. Current evidence suggests that administering exosomes is beneficial for patients with hyperinflammation induced by COVID-19. This is due to the inhibition of the production of several cytokines, specifically IFN-γ and TNF-α, by activated T lymphocytes.

The results revealed that the levels of these cytokines were elevated in the culture supernatants of PBMCs from COVID-19 patients compared to those from healthy controls; this suggests that these cytokines could contribute to the pathogenesis of the disease. Furthermore, increased levels of IFN-γ and TNF-α have been linked to the severity of COVID-19, reinforcing their potential role in disease progression. Several clinical studies have found a correlation between the severity and mortality of COVID-19 and hyperinflammation. This hyperinflammation is marked by increased serum levels of pro-inflammatory cytokines and chemokines. Postmortem analyses have shown that high concentrations of these pro-inflammatory cytokines are associated with the infiltration of cells into organs, such as the kidneys, heart, and lungs⁴⁰⁻⁴². Furthermore, Han et al. conducted a study where they observed cytokine storms, characterized by elevated serum levels of TNF-α and IL-6⁴³. These storms were suggested to be indicators of the severity of the disease. In a similar vein, a retrospective observational study involving hospitalized COVID-19 patients revealed that if the serum level of IL-6 exceeded 30 pg/mL, it was a predictor of the need for invasive mechanical ventilation⁴⁴. Notably, a comparable pattern of cytokine storms

was observed in previous outbreaks, such as MERS-CoV and SARS-CoV. The pharmacological adjustment of cytokine hypersecretion in coronavirus infection calls for more research, given the fatalities associated with multiorgan damage45–48. Recent clinical studies have indicated that the application of hUC-MSCs yielded favorable outcomes in patients with COVID-1915,25,49.

Bone marrow-derived exosomes are novel, multi-targeted, next-generation biological agents, secreted by bone marrow-MSCs50. These exosomes are a complex blend of signaling nanovesicles capable of suppressing the cytokine storm and the host's antiviral defenses, both of which are key features of COVID-1951.

Patients with elevated IL-6 levels appear to derive greater benefits from hUC-MSC infusion. This suggests that a more intense inflammatory environment may stimulate the immunomodulatory response of MSCs52. Both preclinical and clinical studies have demonstrated that exosomes can mitigate complications arising from cytokine storms in inflammatory diseases, such as ARDS, asthma, chronic obstructive pulmonary disease, and acute lung injury. They achieve this by diminishing alveolar inflammation and edema, while fostering the regeneration of epithelial tissue10,53–59.

This study had certain limitations. First, the number of patients included in the sample was limited because of the strict inclusion criteria during the sampling period. Second, this was an in vitro study, and further appropriate trials need to be conducted to test the efficacy of exosomes. Therefore, additional trials are required to investigate the potential of exosomes in improving COVID-19 outcomes in clinical settings.

It is known that SARS-CoV-2 triggers an overactive immune response, leading to cytokine storms and respiratory distress syndrome, which are significant contributors to COVID-19 morbidity and mortality. MSC-derived exosomes reduce pro-inflammatory cytokines, which are responsible for cytokine storms and strengthen the host's viral defenses against COVID-19. The ability of exosomes to modulate the immune system and facilitate tissue repair, coupled with their small size and targeted transfer capability, render them a promising treatment alternative for COVID-19.

All participants in the study had the option to participate voluntarily, and their privacy was highly valued. Prior to entering the survey, all participants provided their informed consent for research participation. The participants were guaranteed that their personal information would be kept confidential and not be disclosed. All methods used in the study were in compliance with relevant guidelines and regulations. All methods were conducted in accordance with the Declaration of Helsinki and relevant guidelines and regulations. All experimental protocols were approved by an institutional and/or licensing committee. All experiments followed the guidelines of the Laboratory Ethical Commission of the Faculty of Medical Sciences, Tarbiat Modares University. After receiving Approval No. IR.TMU.REC.1400.017, the study complies with the rules and regulations. Ethical standards were strictly adhered to during all phases of this research.

In this study, umbilical cords were obtained from the Private Royan Umbilical Cord Blood Bank and transferred to the laboratory in phosphate-buffered saline (PBS, Gibco, Germany), containing 100 mg/mL of penicillin and streptomycin antibiotics (Gibco, Germany) under sterile conditions. After being washed, the cords were sectioned into 5-cm pieces each. The blood vessels, which included one large vein and two smaller arteries, were removed, and the Wharton's jelly was subsequently extracted. The cells were isolated using the explant method. For this purpose, the Wharton's jelly was formed into small spheres measuring 3–5 mm. They were then cultured in T-25 flasks filled with Dulbecco's modified eagle medium (DMEM, Gibco, Germany), 20% FBS, and 1% Pen-Strep. The cultures were then placed in a CO2 incubator at a temperature of 37 °C. The culture media were renewed every three days. After observing tiny clumps of cells around the pieces on days 5–7, the culture medium was renewed every other day for up to seven days23. The passage was iterated three times to achieve a uniform cell population. It was then sent to the laboratory to confirm the presence of CD34, CD44, CD45, and CD90 surface markers. Cell surface marker expression in hUC-MSCs was determined from the third passage using a FACSCalibur flow cytometer (BD Biosciences, USA). The anti-human antibodies used for staining included CD34, CD44, CD45, and CD90 (all from eBioscience).

After reaching 80–90% confluency, MSCs at passage 2 were adapted to serum-free culture by gradually reducing serum concentrations over two weeks. After 48 h, the cell supernatants were collected and filtered through 0.22 μm filters. Exosomes were then extracted using an extraction kit (Exosib, Iran) with two reagents (A and B), according to the manufacturer’s instructions. The culture supernatant was mixed with reagent A at a ratio of 5:1, vortexed for five minutes, and incubated at 4 °C overnight. It was then centrifuged at 3500 rpm for 40 min, and the resulting supernatant was discarded. The exosome sediment was mixed with 100 μL of reagent B and stored at −80 °C for future studies.

Electron microscopy

The morphology and size of the exosomes were evaluated via FESEM (MIRA3 TESCAN) and TEM (Zeiss, EM10C). For FESEM imaging, 1 μg/mL of exosome solution was dried on a glass slide for 24 h and covered with a thin layer of gold. For TEM, exosomes derived from MSCs were fixed in paraformaldehyde and glutaraldehyde.

Subsequently, they were loaded on a formvar/carbon-covered mesh and contrasted with 2% uranyl acetate. Multiple fields of view were examined for both FESEM and TEM imaging. Representative images were selected based on their ability to accurately allocate the overall characteristics observed in the various fields of view.

The total exosome content was extracted for quantification with a Bicinchoninic (BCA) Acid Protein Assay Kit (DNA Biotech, Iran), consisting of a standard solution, copper, and BCA reagents. The standard curve was plotted at different levels (50–250 µg/mL) against bovine serum albumin (BSA) as the standard solution. The exosomes and standard solutions were separately mixed with a mixture of copper and BCA reagents at a ratio of 1:50 and incubated at 60 °C for 60 min. Finally, the absorbance of the samples was read with a spectrophotometer at 562 nm (MPR4+; Hyperion, Roeder mark, Germany).

The size of the exosomes was determined using DLS with a Zeta device (Malvern Instruments, UK). PBS, with a refractive index of 33.1 and viscosity of 1.08, was used as the solvent. These parameters are essential for the device software to analyze the data. This simple, rapid, and nondestructive method, can measure particles ranging from a few nanometers to micrometers. The exosomes were diluted five times, and the sample was then read and analyzed using the Zetasizer APS.

The protein production was confirmed using the Western blotting analysis. For this purpose, the samples were centrifuged at 14,000 rpm for 20 min at 4 °C to eliminate the lysate. The protein concentration was measured using the BCA Protein Quantification Kit, following the instructions provided by the manufacturer. Equal volumes of lysate and Laemmli 2X sample buffer were combined, and then, 20 µg of this mixture was boiled for five minutes. Subsequently, it was subjected to SDS-PAGE and transferred to a 0.2-µm membrane (Immun-BlotTM, PVDF). Next, the membranes were blocked with 5% BSA in 0.1% Tween 20 for one hour and incubated with anti-CD9 (Cat. No.: ab223052, Abcam) and anti-calnexin control antibodies (Cat. No.: ab133615, Abcam) to show the purity of the extracted exosome from contaminated cellular components, at room temperature for one hour. The membranes were washed with TBST three times and incubated with goat anti-rabbit IgG (H&L) secondary antibody. They were then incubated with enhanced chemiluminescence immunoassay for 1–2 min.

The cytotoxicity of exosomes was evaluated using the colorimetric MTT assay. For this purpose, 100 µL of DMEM culture medium containing 10^6 PBMCs was added to each well of a 96-well plate. The MTT assay was performed after 72 h by adding 100 µL of MTT solution (5 mg/mL in PBS) into each well. The cells were then incubated for four hours. The MTT solution was removed, and 100 µL of dimethyl sulfoxide (DMSO; Sigma–Aldrich, USA) was added to each well to dissolve the purple formazan crystals. The cytotoxic activity of exosomes was then evaluated by a standard MTT assay. The optical absorbance at 570 nm was measured using a microplate reader (ELISA reader, ELX808, BioTek). The results were reported as the rate of viability based on the concentration curve. All tests were performed in three iterations. The relative cell viability was calculated as follows: Relative cell viability (%) = (ODs/ODc) × 100.

The relative cell viability was calculated as follows: Blood samples were collected from five COVID-19 patients admitted to the ICU, who were selected between July and October 2021 in Tehran, Iran. The inclusion criteria were a positive real-time PCR result with a CT value of 15–25, an age range of 20–40 years, and no history of any underlying diseases. Tests for CBC, CRP, ESR, D-dimer, and IL-6 were conducted to ensure that the patients were in similar conditions in terms of disease severity. The exclusion criteria were any dissatisfaction with sampling on the part of the patients and any deterioration in the patient’s condition.

Three milliliters of blood, treated with heparin, was collected from the patients and then diluted with an equal volume of PBS. This mixture was then added to 2 mL of Ficoll® (Innotriane, Germany) and centrifuged at 2400 rpm for 20 min. The PBMCs, which appeared as a cloudy layer between the Ficoll® and the diluted blood, were carefully collected using a Pasteur pipette. The collected cells were suspended in 2 mL of PBS and centrifuged at 2000 rpm for 10 min to remove Ficoll®; this step was repeated at 1000 rpm to remove platelets.

Following a 3-hour incubation period, the PBMCs from each patient were cultivated in six wells. The titer of SARS-CoV-2 was determined to be 10^7.66 ID50/mL. Three of the PBMC culture wells were exposed to 0.3 µL of inactivated SARS-CoV-2, while the remaining three wells, which contained PBMCs but were not exposed to the virus, served as negative controls (RPMI 1640 medium).

After 24 h, the cell supernatant was collected, centrifuged, and stored at −80 °C until further evaluation. Subsequently, 40 µg/mL of exosomes was added to the wells containing the control sample from a healthy individual, as well as to the other wells containing patient samples. They were then incubated for 72 h.

Following exposure to the inactivated SARS-CoV-2 virus and exosomes, the supernatant from the cell culture was used to measure the levels of IL-6, IFN-γ, IL-17, and TNF-α. This was accomplished using the ELISA method, with the Human Cytokines Measurement Kit.

The gathered data was analyzed using SPSS Version 26.0 and GraphPad Prism 9. An independent sample t-test was employed to compare the mean values between patient and healthy participants. For data with a nonparametric distribution, the Mann–Whitney U test was used for analysis. A P-value of less than 0.05 was considered statistically significant. For data with normal distribution and for non-parametrical data Paired t-tests and Wilcoxon were respectively performed to evaluate effects of exosome treatment in cytokines levels variations. Continuous variables were expressed as either the mean with standard deviation or the median with interquartile range. Categorical variables were represented as frequency percentages.

The corresponding author will provide supporting data via email upon request by editors or referees. All data generated or analyzed during this study are included in the main body of this article.

Received: 31 October 2023; Accepted: 5 March 2024
Published online: 08 March 2024

We want to thank the deputy of research, Tarbiat Modares University, for their financial support and assistance. The results described in this manuscript were part of the Ph.D. student thesis, which was supported by Grant Number Med-80650 from the Research Deputy of Tarbiat Modares University, Faculty of Medical Sciences.

H.S. and N.B. conceived and designed the research. Z.N. carried out the experiments and acquired the data. H.S., S.M.H., and M.R.P. interpreted the data and carried out data analysis and statistical analysis. Z.N. wrote the original draft of the manuscript. H.S. and N.B. modified and revised the paper. All the authors have read and approved the final manuscript.

This study was performed as part of a Ph.D. thesis and financially supported by the deputy of Tarbiat Modares University, Faculty of Medical Sciences.

The authors declare no competing interests.