DPEP2 suppresses hyperinflammation via metabolic reprogramming of macrophages in sepsis

Domain: chemistry
Published: 2026-03-09
Editorial outcome: minor_revision

Abstract
Sepsis-induced excessive inflammation contributes to mortality, but restricting hyperinflammation in sepsis remains challenging. Here, we identify dipeptidase 2 (DPEP2) as an immunotherapeutic target in sepsis by integrating single-cell and bulk RNA sequencing data from septic patients. In patients with sepsis, peripheral monocytes/macrophages have reduced DPEP2 expression, with DPEP2 levels negatively correlating with inflammation severity, disease progression, and clinical outcomes. In vitro, Dpep2 knockdown enhances macrophage-mediated inflammation, while in septic mice in vivo, macrophage-specific Dpep2 loss decreases survival by exacerbating inflammation and organ damage. Mechanistically, sepsis-induced EGR1 represses Dpep2 transcription, leading to reduced DPEP2-mediated enzymatic cleavage of leukotriene D4 (LTD4). Increased LTD4 redirects the metabolic flux toward prostaglandin E2 overproduction, amplifying NF-κB activation and lipopolysaccharide-induced inflammatory cytokine production. Lastly, lipid nanoparticle (LNP)-mediated delivery of Dpep2 mRNA expression to monocytes/macrophages mitigates inflammation and organ damage in septic mice. Our findings thus suggest a protective function for DPEP2 in sepsis-induced hyperinflammation via immunometabolic regulation, and also present LNP-mediated Dpep2 mRNA delivery as a potential therapy for septic hyperinflammation.

Introduction
Sepsis is a life-threatening organ dysfunction caused by dysregulated host immune responses to infection1,2, and is responsible for approximately 20% of global deaths annually3,4. Sepsis management relies on two pillars: early and appropriate antimicrobial therapy and organ support5. However, given the contribution of dysregulated immune responses to high mortality rates6,7, immunotherapy has emerged as the third pillar in sepsis treatment6. In particular, immunotherapy targeting excessive inflammatory responses during the early acute phase of sepsis has attracted considerable attention. Reliable biomarkers and immunotherapy targets are urgently needed to provide immunotherapeutic approaches for treating sepsis6.

In the early stage of sepsis, innate immune cells release pro-inflammatory cytokines, including tumor necrosis factor-α (TNF) , interleukin (IL)-1β, and IL-68, leading to a life-threatening cytokine storm and excessive inflammation9. This intense inflammatory response correlates with increased cellular damage, thereby elevating the risk of organ dysfunction9. As part of the first line of defense, monocytes and macrophages are critical in initiating inflammatory responses and mediating tissue injury10. It is well-documented that metabolic reprogramming in macrophages is a key requisite for Toll-like receptor (TLR)-mediated pro-inflammatory cytokine production11. Therefore, immunometabolism critically impacts macrophage function, thereby exerting a profound influence on the prognosis of sepsis11. Among the metabolic pathways, lipid metabolism is particularly important for maintaining host defense and regulating inflammation during sepsis12. Indeed, reprogramming lipid metabolism directs pro-inflammatory differentiation of macrophages13. Hence, targeting lipid metabolism-related genes in monocytes/macrophages may provide an immunotherapeutic strategy for sepsis.

RNA-based drugs, including messenger RNA (mRNA), can manipulate genes of interest, therefore significantly contributing to the development of immunotherapeutic approaches for treating sepsis14. Lipid nanoparticle (LNP)-mediated mRNA delivery systems are emerging as a therapeutic strategy due to their ability to transiently and precisely program immune cells with minimal adverse effects14,15. Thus, targeting immune cells with LNP-mediated mRNA delivery offers a potential treatment avenue for sepsis. However, there is a notable gap in the development of LNP-based mRNA delivery systems designed to target specific genes for the treatment of sepsis. We and others have profiled the immune cell landscapes in sepsis, revealing a substantial immune response reprogramming of monocytes/macrophages in sepsis. Unfortunately, the therapeutic targets and strategies remain elusive.

Here we combine state-of-the-art techniques, including single-cell and bulk RNA profiling, in vitro/in vivo modeling of sepsis, and LNP–based therapeutic delivery engineering, to shed light on potential metabolic targets in monocytes/macrophages for sepsis treatment. We identify DPEP2 as an immunoregulatory factor in sepsis, with markedly reduced expression in peripheral monocytes/macrophages, correlating with heightened inflammation, disease progression, and poor outcomes. Loss of DPEP2 drives excessive macrophage-mediated inflammation by disrupting leukotriene D4 (LTD4) metabolism and increasing prostaglandin E2 (PGE2) production. Notably, macrophage-targeted delivery of Dpep2 mRNA improves survival in septic mice, supporting the translational potential of DPEP2-based gene therapy for sepsis management. Collectively, these findings identify DPEP2 as a regulator of monocyte/macrophage-mediated immune homeostasis in sepsis, highlighting its potential as both a prognostic biomarker and a therapeutic target.

Results
scRNA-seq and RNA-seq profiling identify immunometabolic reprogramming of monocytes associated with DPEP2 downregulation during sepsis

To explore the cellular landscape and underlying mechanism of immune response during sepsis, peripheral blood mononuclear cells (PBMC) from five patients in the early acute phase of sepsis and five healthy controls were profiled with single-cell RNA sequencing (scRNA-seq) using the 10× Genomics 5′ sequencing platform (dataset from our previous study16) and reanalyzed in this study (Fig. 1a and Supplementary Table 1-2). After removing doublets and filtering low-quality cells, 80,063 high-quality cells were retained for further analysis, with an average of 1,656 genes detected per cell. Five distinct cell types were identified based on canonical lineage markers: T cells (CD3D, CD3E), monocytes (VCAN, FCN1, CD14, LYZ), natural killer (NK) cells (GNLY, KLRF1), B cells (CD79A, MS4A1) and megakaryocytes (PF4, PPBP) (Fig. 1b, c). By comparing differentially expressed genes (DEG) between patients with sepsis and controls, substantial reprogramming of gene expression levels was detected in monocytes during sepsis (Fig. 1d). Moreover, monocyte populations were consistently enriched in patients with sepsis (Supplementary Fig. 3a). DEG analysis in monocytes via Metascape web-based platform17 revealed that top gene ontology (GO) processes are primarily related to the regulation of immune and inflammatory responses in patients with sepsis (Supplementary Fig. 3b).

Unsupervised clustering of monocytes revealed four distinct subclusters (Mono1–4) with divergent abundance patterns between patients with sepsis and healthy controls (Fig. 1e). We found that, compared with Mono2–4, Mono1 constituted the largest monocyte subset in both groups, and exhibited the most substantial alteration in sepsis compared to controls (Fig. 1f). Among the four monocyte subclusters, only Mono1 was reduced in sepsis, in stark contrast to the expansion of Mono2–4. To determine the monocyte subcluster most closely linked to sepsis-associated immune dysregulation, we performed GO enrichment analysis. The results showed that immune response and inflammation regulation terms were most specifically associated with Mono1, but not consistently observed in other subclusters (Fig. 1g). Gene set enrichment analysis (GSEA) further indicated that metabolic pathways were also active in Mono1 (Fig. 1h). This unique pattern drew our attention, as it may reflect a specific impairment of immunoregulatory or inflammatory monocytes in the early stage of sepsis, which might shed light on the early detection and therapy for sepsis. Pseudotime analysis indicated a gene expression trajectory of monocytes, where Mono1 represented a unique cell state during monocyte reprogramming from the control group to the sepsis group (Supplementary Fig. 3c). Taken together, these findings highlight Mono1 as a downregulated yet immunometabolically active subcluster in sepsis, supporting its relevance for further mechanistic investigation.

To further characterize the Mono1 cluster signature identified by scRNA-seq and prioritize robust monocyte targets in sepsis, we performed bulk RNA sequencing (RNA-seq) analysis of PBMCs from five additional patients in the early acute phase of sepsis and five non-septic controls (Supplementary Table 3-4, RNA-seq1, original dataset). Within RNA-seq1, 1,375 upregulated and 553 downregulated DEGs were identified between patients with sepsis and controls (Supplementary Fig. 3d, e). The integrated analysis of these two datasets identified ten overlapping genes (Fig. 1i). Considering that Mono1 is closely associated with immunometabolic regulation, and DPEP2's established role in lipid metabolism18-20 along with its consistent downregulation in all patients with sepsis, we selected DPEP2 for further investigation (Fig. 1i). DPEP2 downregulation in patients with sepsis was also verified by quantitative PCR of PBMCs and scRNA-seq of monocytes (Fig. 1i). Additionally, scRNA-seq data and the Human Protein Atlas revealed enriched DPEP2 expression in monocytes/macrophages (Supplementary Fig. 3f, g), suggesting a unique role for DPEP2 downregulation in monocytes/macrophages response to sepsis. Given DPEP2’s function in lipid metabolism regulation, it was hypothesized that it might influence the response of Mono1 by regulating the monocytes/macrophages inflammatory responses through lipid metabolism in sepsis4.

DPEP2 downregulation in monocytes correlates with inflammation, disease severity and prognosis in patients with sepsis

To evaluate the clinical relevance of monocyte DPEP2 expression in sepsis, we further recruited 36 patients in the early acute phase of sepsis (abdominal infection origin was the most common type) and 36 volunteers free of infection (Supplementary Table 5-6, original dataset) and collected blood samples, followed by the detection of DPEP2 protein levels in monocytes using flow cytometry (Fig. 2a). DPEP2 expression was significantly lower in the CD11b+CD14+ monocytes, but not in CD3+ T cells, of patients with sepsis compared to controls (Fig. 2b). In patients with sepsis, the proportion of DPEP2+ monocytes negatively correlated with the Sequential Organ Failure Assessment (SOFA) score (r = -0.6825), which monitors acute morbidity of sepsis and is a key criterion in diagnosing sepsis syndrome21, and the Acute Physiology and Chronic Health Evaluation score version II (APACHE-II) (r = -0.6522), which is a gold standard for risk evaluation in critically ill patients22 (Fig. 2c, d). Additionally, the lower proportion of DPEP2+ monocytes correlated with higher serum concentrations of pro-inflammatory cytokines in patients, including IL-1β, IL-6, IL-8 and TNF (r = -0.4389, -0.6100, -0.5313 and -0.3900, respectively; Fig. 2e-h), indicating a higher inflammatory response in septic patients with DPEP2 downregulation.

To explore the relationship between monocyte DPEP2 expression and the prognosis of patients with sepsis, 36 patients were followed up for one month after discharge, 10 of whom died. Sepsis survivors had a higher proportion of DPEP2+ monocytes than sepsis non-survivors (Fig. 2i). Based on the median percentage of DPEP2+ monocytes in patients with sepsis, those expressing higher levels of DPEP2 had a higher survival probability than those with low levels (Fig. 2j). Multivariate logistic analysis identified the proportion of DPEP2+ monocytes as a potential predictor of sepsis outcome (Fig. 2k). The clinical relevance between DPEP2 expression and sepsis-related death was further confirmed using publicly available RNA-seq data from the peripheral blood of 26 sepsis survivors and 9 sepsis non-survivors (GSE54514, RNA-seq2). DPEP2 expression was higher in sepsis survivors than sepsis non-survivors (Fig. 2l), and the DPEP2High group had a higher survival probability than the DPEP2Low group (Fig. 2m). Further multivariate logistic analysis of the GSE54514 data set confirmed that DPEP2 expression serves as a prognostic marker of sepsis related death (Fig. 2n).

Cumulatively, these results suggest that DPEP2 expression is downregulated in the monocytes of patients with sepsis and is associated with inflammation, severity and prognosis.

DPEP2 negatively regulates pro-inflammatory cytokine production in macrophages

To validate the relationship between DPEP2 and inflammation, an in vitro cellular inflammatory model was developed using several TLR ligands. TLRs (excluding TLR3) can activate NF-κB upon recognizing the corresponding pathogen ligands during initiation and amplification of inflammation in macrophages23. All tested TLR ligands reduced Dpep2 mRNA expression (Pam3Csk4 for TLR1/2, lipopolysaccharide [LPS] for TLR4, R848 for TLR7/8, and poly(I:C) for TLR3) in RAW264.7 murine macrophage-like cell line. LPS elicited more pronounced alterations in Dpep2 expression relative to other TLR ligands (Fig. 3a). DPEP2 mRNA expression was also downregulated in human CD14+ monocytes derived from PBMCs of healthy individuals and mouse bone marrow-derived macrophages (BMDM) after LPS stimulation (Fig. 3b, c). Stimulating RAW264.7 cells with varying LPS doses for different durations revealed a consistent decrease in DPEP2 mRNA and protein levels (Fig. 3d, e).

To characterize the transcriptome programs regulated by DPEP2 downregulation in macrophages, RNA-seq analysis was performed in RAW264.7 cells with shRNA-mediated Dpep2 knockdown (shDpep2 RAW264.7 cells) (RNA-seq3, original dataset). DEG analysis revealed extensive transcriptomic reprogramming after Dpep2 knockdown in RAW264.7 cells, with 485 upregulated and 502 downregulated DEGs (Fig. 3f and Supplementary Fig. 4a), which included key pro-inflammatory mediators, such as Il1a, Il1b, Il6, Tnfaip3, Ccl2, Ccl7, Ccl9, Ccl12 and Cd80 (Fig. 3f and Supplementary Fig. 4b). GSEA revealed a comprehensive upregulation of inflammatory pathways, including the inflammatory response, TNF signaling via NF-κB, IL-6-JAK-STAT3 signaling, and cytokine response, in shDpep2 RAW264.7 cells (Fig. 3g and Supplementary Fig. 4c-h), suggesting that DPEP2 acts as a suppressor of macrophage inflammatory responses. Similarly, pro-inflammatory cytokines (IL-1β, IL-6 and TNF) mRNA expression and protein secretion were increased in shDpep2 BMDMs and BMDMs derived from Dpep2-knockout (KO) mice, compared with shNT BMDMs or BMDMs derived from wild-type (WT) mice (Fig. 3h-k). These findings imply that DPEP2 deficiency promotes macrophage inflammatory responses.

The effect of DPEP2 on LPS-induced pro-inflammatory cytokine production was then evaluated. Following in vitro LPS stimulation, higher levels of pro-inflammatory cytokines were observed in Dpep2 suppressed RAW264.7 cells and BMDMs using shRNA (Fig. 3l and Supplementary Fig. 5a-c), and BMDMs derived from Dpep2 KO mice (Fig. 3m and Supplementary Fig. 5d). Additionally, decreases in mRNA and protein secretion levels of pro-inflammatory cytokines were observed in Dpep2-overexpressing BMDMs and RAW264.7 cells after LPS stimulation (Fig. 3n, o; Supplementary Fig. 5e, f).

Cumulatively, these data suggest that DPEP2 exerts an inhibitory effect on macrophage inflammatory responses.

Dpep2 knockout contributes to heightened inflammatory response, exacerbated organ injury, and reduced survival in septic mice

To further investigate the role of DPEP2 in inflammation in vivo, a sepsis mouse model was established via cecal ligation and puncture (CLP) (polymicrobial sepsis model) procedure in C57BL/6 mice24. DPEP2 levels were downregulated in the heart, liver, spleen, lung and kidney tissues, as well as in the peripheral blood of septic mice (Fig. 4a). Additionally, the proportion of DPEP2+ macrophages was significantly lower in the spleen and lung of septic mice compared to sham mice (Fig. 4b and Supplementary Fig. 6). In peripheral blood, the proportions of both DPEP2+ macrophages and DPEP2+ monocytes were significantly reduced during sepsis (Fig. 4b and Supplementary Fig. 6).

To fully reveal the in vivo function of DPEP2 in sepsis development and prognosis, a CLP sepsis model was developed in Dpep2 KO mice (Supplementary Fig. 7a). The mortality was significantly higher in Dpep2 KO mice than in WT mice after CLP challenge (Fig. 4c). Compared to WT controls, Dpep2 KO septic mice exhibited higher levels of pro-inflammatory cytokines (IL-1β, IL-6, and TNF) in peripheral blood (Fig. 4d-f), greater inflammatory cell infiltration and structural lung damage (Fig. 4g), and elevated markers of hepatic and renal dysfunction, including alanine transaminase (ALT), blood urea nitrogen (BUN) and creatinine, compared to controls (Supplementary Fig. 8a). These data suggest that DPEP2 regulates the acute inflammatory response, organ dysfunction and survival in sepsis in vivo.

To specifically investigate the role of macrophage DPEP2 in sepsis, we generated macrophage-specific Dpep2 knockout mice (Dpep2f/fLyz2Cre) (Supplementary Fig. 7b, c) by crossing loxP-flanked Dpep2 exon mice (Dpep2f/f) with transgenic Lyz2Cre mice. Dpep2f/fLyz2Cre mice displayed higher mortality than Dpep2f/f controls after CLP challenge (Fig. 4h). Consistently, Dpep2f/fLyz2Cre septic mice displayed higher systemic levels of IL-1β, IL-6, and TNF in peripheral blood than Dpep2f/f septic mice (Fig. 4i-k). Enhanced inflammatory cell infiltration in lungs, more severe pulmonary structural damage, higher lung injury scores (Fig. 4l) and elevated pulmonary apoptosis (Supplementary Fig. 8b) were also observed in Dpep2f/fLyz2Cre septic mice compared with Dpep2f/f septic mice. Additionally, the levels of liver, kidney and heart injury indicators, including ALT, BUN, creatinine and creatinine kinase myocardial band (CK-MB), were higher in Dpep2f/fLyz2Cre septic mice (Supplementary Fig. 8c). Glial fibrillary acidic protein (GFAP) staining levels — marker of neuroinflammation — were higher in the hippocampus of Dpep2f/fLyz2Cre mice than Dpep2f/f mice after CLP challenge (Supplementary Fig. 8d), suggesting that Dpep2 downregulation induces astrocytosis-related neuroinflammation during sepsis25.

These findings suggest a protective role of macrophage DPEP2 in suppressing excessive inflammation and tissue and organ injury, thus improving survival rate of septic mice.

EGR1-mediated repression of DPEP2 disrupts LTD4 metabolism in LPS-induced macrophage inflammation

Given the consistent downregulation of DPEP2 mRNA in sepsis, we first examined its upstream transcriptional regulation. To this end, we analyzed the DPEP2 promoter region using the ENCODE database and identified a potential binding site for early growth response 1 (EGR1) (Fig. 5a), a key transcription factor implicated in macrophage-mediated inflammation. Notably, EGR1 expression is consistently upregulated in macrophages during sepsis and Macrophage Activation Syndrome26-28. Similarly, we found LPS exposure upregulated Egr1 expression in macrophages (Fig. 5c). EGR1 is known as a transcriptional activator or repressor according to the context of target genes29. LPS stimulation significantly reduced Dpep2 mRNA levels, while pharmacological inhibition or genetic ablation of EGR1 substantially attenuated this repression (Fig. 5d, e). These findings identify EGR1 as an inflammation-inducible transcriptional repressor responsible for suppressing DPEP2 expression in macrophages during sepsis (Fig. 5b).

To elucidate how DPEP2 modulates macrophage inflammation, we examined its role in arachidonic acid (AA)–derived eicosanoid metabolism, a core component of immunometabolic regulation. DPEP2 enzymatically converts LTD4 to leukotriene E4 (LTE4), thereby participating directly in cysteinyl leukotriene metabolism18-20. Cysteinyl leukotrienes (a class of eicosanoids), such as LTD4 and LTE4, are lipid inflammatory mediators synthesized from AA through the 5-lipoxygenase (5-LO) pathway. Upon activation of 5-LO, AA is converted to leukotriene A4 (LTA4) in monocytes and macrophages. LTA4 is converted by LTA4 hydrolase (LTA4H) into LTB4, or by LTC4 synthase (LTC4S) into Leukotriene C4 (LTC4). LTC4 can be converted to LTD4 and then metabolized by DPEP2 to produce LTE4 (Fig. 5f). Consistent with its established enzymatic role, shRNA-mediated Dpep2 knockdown in RAW264.7 cells significantly increased extracellular LTD4 levels (Fig. 5g) and decreased LTE4 (Fig. 5h), whereas overexpression of Dpep2 elicited the opposite effects (Fig. 5i, j). After LPS stimulation, we found similar results (Fig. 5g–j), indicating that DPEP2 retains its metabolic activity under infection conditions. Functionally, LTD4, not LTE4, increased the expression of pro-inflammatory cytokines (Il1b, Il6 and Tnf) in LPS-stimulated macrophages (Fig. 5k), indicating that the LTD4 to LTE4 conversion directly modulates inflammatory output. Thus, DPEP2 suppresses macrophage inflammation in part by metabolically directing LTD4 toward LTE4.

To confirm whether these effects require DPEP2 enzymatic activity, we identified the essential catalytic glutamate residue using structural conservation among mammalian DPEP enzymes. Human DPEP2 and DPEP1 possess a conserved dizinc catalytic center in their extracellular domains, coordinated by a glutamate residue (E188 in human DPEP2 and E141 in human DPEP1)30,31. Sequence alignment identified E190 as the corresponding residue in mouse DPEP2 (Supplementary Fig. 9). We therefore generated a Flag-tagged WT DPEP2 plasmid and the enzymatic mutant targeting this catalytic residue (E190Q) (Fig. 5l). Both constructs were expressed at comparable levels (Fig. 5m). However, while WT DPEP2 decreased LTD4 and increased LTE4 in the supernatant, the E190Q mutant completely abolished this metabolic activity (Fig. 5n, o), confirming that E190 is essential for the enzymatic conversion of LTD4 to LTE4. Functionally, overexpression of WT DPEP2 markedly suppressed inflammatory cytokine expression in RAW264.7 cells, whereas the catalytically inactive E190Q mutant failed to confer anti-inflammatory effects (Fig. 5p-r). These results provide direct mechanistic evidence that the immunoregulatory function of DPEP2 strictly depends on its enzymatic activity and its capacity to reprogram eicosanoid metabolism.

DPEP2 modulates CysLTR2 signaling and redirects AA metabolic flux toward PGE2 to regulate LPS-induced macrophage inflammation

Cysteinyl leukotrienes act on cysteinyl leukotriene receptor 1 (CysLTR1) or cysteinyl leukotriene receptor 2 (CysLTR2), promoting inflammatory responses32-34. Therefore, DPEP2 downregulation was hypothesized to upregulate cysteinyl leukotrienes activity, promoting inflammatory responses in macrophages through CysLTR1 or CysLTR2. Indeed, LPS stimulation downregulated Cysltr1 expression, while upregulating Cysltr2 (Fig. 6a, b). Pretreatment of RAW264.7 cells with Zafirlukast, an inhibitor of CysLTR1, failed to suppress the enhanced LPS-induced inflammatory responses caused by Dpep2 knockdown (Fig. 6c). In contrast, pretreatment with the CysLTR2 inhibitor (HAMI3379) abrogated the enhanced inflammatory responses caused by Dpep2 knockdown in LPS-stimulated macrophages (Fig. 6d). These results suggest that the exacerbation of LPS-induced macrophage inflammatory responses by Dpep2 knockdown is partially mediated by increased LTD4 levels via the CysLTR2 pathway.

In addition to cysteinyl leukotrienes metabolism, DPEP2 deficiency also reshaped the AA metabolic network by diverting flux toward PGE2 synthesis. PGE2 (one of eicosanoids), a key mediator of inflammation, is also synthesized from AA via cyclooxygenase (COX) enzymes and terminal prostaglandin E synthases (PTGES) (Fig. 5f). Upon 5-LO downregulation, AA increases shunting to PGE2 synthesis35-38. Within the RNA-seq3, Dpep2 knockdown decreased the expression of 5-LO-encoding gene Alox5 and LTC4S-encoding gene Ltc4s, while increasing the expression of COX-encoding gene Ptgs1 and PTGES-encoding gene Ptges (Fig. 6e), which was validated using quantitative PCR (Fig. 6f, g). Protein levels of 5-LO were also decreased in RAW264.7 cells with stable Dpep2 knockdown (Fig. 6h). Correspondingly, RAW264.7 cells secreted more PGE2 after Dpep2 knockdown (Fig. 6i), while Dpep2 overexpression decreased PGE2 secretion (Fig. 6j). Moreover, adding PGE2 to the culture medium increased LPS-induced pro-inflammatory cytokine expression, which was further amplified by combining PGE2 and LTD4 (Fig. 6k). Additionally, PGE2 and LTD4 abrogated the downregulation of pro-inflammatory cytokines in RAW264.7 cells overexpressing Dpep2 (Fig. 6l).

Together, these findings demonstrate that DPEP2 regulates macrophage inflammation through direct immunometabolic mechanisms, including (1) enzymatic conversion of LTD4 to LTE4, (2) modulation of CysLTR2-mediated signaling, and (3) reprogramming of AA metabolic flux toward PGE2 synthesis. Loss of DPEP2 reprograms these metabolic pathways, synergistically amplifying inflammation in LPS-induced macrophages.

DPEP2 suppresses LPS-induced inflammatory responses by mediating NF-κB signaling

GSEA analysis of RNA-seq3 data comparing shDpep2 and shNT RAW264.7 cells showed that PI3K/AKT, NF-κB, and MAPKs pathways were enriched in shDpep2 RAW264.7 cells (Fig. 7a-c). This finding was further confirmed by pathway enrichment analysis of DEGs after Dpep2 knockdown (Fig. 7d). LPS activates TLR4, which contributes to macrophage inflammatory cytokine production through activation of PI3K/AKT, NF-κB and MAPK pathways39,40. Therefore, the roles of these pathways in DPEP2-mediated inflammatory cytokine regulation were investigated in LPS-induced macrophages.

Cells were pretreated with chemical inhibitors specifically targeting PI3K/AKT (Lyz294002), NF-κB (JSH23), MAPKs/p38 (SB203580), MAPKs/ERK (U0126) and MAPKs/JNK (SP600125), followed by LPS stimulation. Treatment with NF-κB or MAPKs/p38 inhibitors abrogated the upregulation of pro-inflammatory cytokines induced by Dpep2 knockdown and LPS stimulation (Fig. 7e, f), whereas consistent effects were not induced by PI3K/AKT, MAPKs/ERK or MAPKs/JNK inhibitors (Supplementary Fig. 10a-c). Hence, DPEP2 may inhibit inflammatory cytokine production in LPS-induced macrophages through NF-κB and MAPKs/p38 signaling pathways.

The activities of the NF-κB and MAPKs/p38 pathways were then evaluated by detecting essential components in these pathways. The phosphorylation levels of p65 and IKKα/β were downregulated in RAW264.7 cells overexpressing Dpep2 (Fig. 7g) and increased in shDpep2 RAW264.7 cells after LPS stimulation (Fig. 7h). However, no significant differences were observed in the phosphorylation levels of p38 after Dpep2 knockdown or overexpression (Fig. 7g, h). Correspondingly, in a dual-luciferase reporter assay, NF-κB luciferase reporter activity increased in shDpep2 RAW264.7 cells (Fig. 7i) and decreased in RAW264.7 cells overexpressing Dpep2 after LPS stimulation (Fig. 7j).

Next, RAW264.7 cells were treated with LTD4 and PGE2 to mimic the effect of Dpep2 knockdown on eicosanoid metabolism. Consistent with Dpep2 knockdown, LTD4 and PGE2 treatment induced the activation of the NF-κB pathway, as evidenced by increased p65 and IKKα/β phosphorylation in RAW264.7 cells after LPS stimulation (Fig. 7k). In contrast, LTD4 and PGE2 treatment did not affect p38 phosphorylation levels (Fig. 7k). Additionally, LTD4 and PGE2 partially reversed the decreased phosphorylation level of NF-κB pathway induced by Dpep2 overexpression in RAW264.7 cells after LPS stimulation (Fig. 7l). These results suggest that in LPS-induced septic macrophages, DPEP2 reduction triggers the NF-κB pathway-mediated inflammatory response, partly via LTD4 and PGE2 metabolism.

LNP-mediated DPEP2 mRNA delivery to monocytes/macrophages alleviates sepsis

Considering the protective role of DPEP2 in monocytes/macrophages during sepsis, the therapeutic potential of DPEP2 was evaluated by restoring monocytes/macrophages DPEP2 expression and activity. To address the current lack of DPEP2-targeted therapeutics, we developed an LNP-mediated gene delivery system specifically for DPEP2 modulation. LNP technology has demonstrated broad therapeutic potential across multiple disease areas including oncology, genetic disorders, and infectious diseases41,42. LNPs were specifically developed to deliver DPEP2 mRNA into monocytes/macrophages as illustrated in Fig. 8a. To this end, both human and mouse DPEP2 mRNAs were constructed (Supplementary Fig. 11a). After camouflaging with the hybrid liposomal membrane, the hydrodynamic diameter of the nanoformulation increased from 66.8 ± 0.42 nm to 142.6 ± 0.50 nm (Supplementary Fig. 11b), while the zeta potential decreased from 60.26 ± 5.56 mV to 27.97 ± 0.74 mV (Supplementary Fig. 11c). Injecting CLP or Sham mice (mice underwent the same procedures of CLP surgery without cecal puncture) with the LNPs did not induce toxic side effects based on peripheral blood pro-inflammatory cytokines, lung injury score, and serum biochemical markers (ALT, AST, BUN, creatinine and CK-MB) (Supplementary Fig. 11d-l). The efficiency of LNP-mediated mRNA delivery was evaluated in mouse macrophages. Following tail vein injection of LNP-Dpep2 (LNP encapsulating 20 μg Dpep2-mRNA) to mice, the proportion of DPEP2+ macrophages in the spleen increased significantly 12 h post-injection and subsided after 36 h of injection (Supplementary Fig. 11m). To directly assess the immunocyte tropism of the LNPs, we performed an in vivo experiment using Cre-reporter mice, which carry a LoxP-flanked STOP cassette controlling tdTomato expression (Supplementary Fig. 12a). After intravenous delivery of Cre mRNA via LNPs, mice were euthanized at 24 h, and splenic and peripheral blood cells were analyzed by flow cytometry. The results clearly demonstrated that the LNPs efficiently transfected both macrophages and monocytes in both the spleen and peripheral blood (Supplementary Fig. 12b, c). Consistent with lentivirus-mediated Dpep2 overexpression data, ex vivo administration of LNP-DPEP2 to both THP-1 derived human macrophages and RAW264.7 cells suppressed LPS-induced pro-inflammatory cytokine levels (Fig. 8b and Supplementary Fig. 11n).

To evaluate the protective effect of LNP-Dpep2 in vivo, CLP-induced septic mice were treated with LNP-Dpep2 via tail vein injection. The analysis included measuring inflammatory responses, organ injury indicators, lung histopathology, and survival rate (Fig. 8c). Considering the effect of LNP-Dpep2 lasted for about 36 h and the mortality of CLP-induced septic mice was highest within 48 h after CLP, LNP-Dpep2 was administered to septic mice every 24 h for three times (Fig. 8c). LNP-Dpep2 treatment significantly improved the survival rates of CLP-induced septic mice (Fig. 8d). Significant reductions were observed in IL-1β, IL-6 and TNF levels in the peripheral blood of LNP-Dpep2 treated CLP septic mice (Fig. 8e-g). Blood biochemistry revealed lower aspartate aminotransferase (AST), BUN, creatinine and CK-MB levels in the peripheral blood of septic mice treated with LNP-Dpep2 compared to those treated with empty-loaded LNPs (Fig. 8h-l), suggesting that LNP-Dpep2 exerts a protective effect on heart, liver and kidney injury during sepsis. Additionally, LNP-Dpep2 treatment mitigated lung injury, pulmonary inflammatory cell infiltration (Fig. 8m), and cell apoptosis in the lungs in CLP septic mice (Supplementary Fig.13b).

These results suggest that DPEP2 in monocytes/macrophages alleviates systemic inflammation, protects mice against sepsis, and can be targeted therapeutically via LNP delivery to monocytes/macrophages.

Methods
Study approval
All experimental protocols were approved by the Ethics Committee of Zhongshan Hospital, Fudan University, China. The human cohort study was approved by the Ethics Committee of Zhongshan Hospital, Fudan University [Number B2024-374R], and registered in the Chinese Clinical Trial Registry (ChiCTR2400091373) (https://www.chictr.org.cn/index.html). Informed consents were obtained from all the patients or individuals authorized by the participants, including consent to publish clinical information potentially identifying individuals. All animal experiments were approved by the Animal Ethics Committee of Fudan University [Number 202412032Z] and complied with the Regulations for the Administration of Affairs Concerning Experimental Animals and the guidelines set by the Animal Review Committee of Fudan University.

Human participants
Patients in the early acute phase of sepsis were diagnosed with sepsis based on the Third International Consensus Definition for Sepsis72 and should be diagnosed within 24 h. Inclusion criteria for patients with sepsis were as following: 1) evidence of bacterial infection; 2) SOFA score ≥ 2; 3) written informed consent was obtainable. Eligible controls were individuals without evidence of infection, and from whom written informed consent was obtained. The exclusion criteria for patients and controls included: 1) participation in organ transplantation within one month; 2) active cancer; 3) human immunodeficiency virus infection/acquired immunodeficiency syndrome; 4) autoimmune diseases; 5) participation in other drug trials 30 days before enrollment; 6) parturient or breast-feeding women. The clinical characteristics of the participants are documented (Supplementary Table 3-6).

Isolation PBMCs from whole blood
Human PBMCs were extracted from anticoagulated whole blood using density-gradient centrifugation73. Whole blood was diluted 1:1 with 1 × phosphate buffer saline (PBS; C10010500BT, Thermo Fisher Scientific, Waltham, MA, USA), layered onto Lymphoprep (07851, Stem Cell, Vancouver, Canada), and centrifuged at 800 × g for 30 min. The PBMC layer was isolated and erythrocytes were removed using red blood cell lysis buffer (00-4333-57, Thermo Fisher Scientific) on ice. PBMCs were washed and resuspended in PBS for subsequent assays.

scRNA-seq data and analysis
The scRNA-seq dataset for the PBMCs of patients in the early acute phase of sepsis and healthy controls was obtained from our previous study16 (Supplementary Table 1-2) and downloaded from the Gene Expression Omnibus (GSE217906). The scRNA-seq and V(D)J libraries were constructed using the 10 × Genomics Chromium Controller Instrument, Chromium Single Cell 5′ library and gel bead kit and the V(D)J enrichment kit (10 × Genomics, Pleasanton, CA, USA). Cells were concentrated to approximately 1,000 cells/µL and introduced into each channel to produce single-cell gel bead-in-emulsions (GEM). Following reverse transcription, the GEMs were disrupted, and the barcoded cDNA was isolated and amplified. The final libraries were quantified utilizing a Qubit High-Sensitivity DNA assay (Q33230, Thermo Fisher Scientific); their size distribution was evaluated using a High-Sensitivity DNA chip on a Bioanalyzer 2200 (Agilent). All libraries were sequenced on an Illumina sequencer (Illumina, San Diego, CA, USA) with a 150-bp paired-end run. Fastp74 was employed with default settings to filter the adaptor sequences and remove low-quality reads, yielding clean data. Feature-barcode matrices were generated by aligning the reads to the human genome (GRCh38 Ensemble: version 91) using CellRanger v3.1.0. For cell normalization and regression, the Seurat package (version 3.1.4, https://satijalab.org/seurat/ ) was utilized to scale the data based on unique molecular identifier (UMI) counts and mitochondrial read percentages. Marker genes were identified using the graph-based clustering method and FindAllMarkers function, using the Wilcoxon rank-sum test algorithm and the following criteria: P adjusted < 0.05. To identify DEGs, the FindMarkers Seurat function with the Wilcox rank sum test algorithm was used under the following criteria: lnFC > 0.25; P adjusted < 0.05; min.pct > 0.1.

A single-cell trajectory analysis was performed utilizing Monocle2 (http://cole-trapnell-lab.github.io/monocle-release) with DDR-Tree and default parameters. Based on the pseudo-time analysis, branch expression analysis modeling (BEAM) was applied for branch fate-determined gene analysis. GO analysis elucidated the biological implications of the marker genes and DEGs. GO annotations were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/), UniProt (http://www.uniprot.org/), and Gene Ontology (http://www.geneontology.org/) databases. GSEA was performed on a preranked gene list, with gene expression fold change as the ranking metric, using the GSEA desktop application75. Fisher’s exact test was used to identify significant GO categories, and false discovery rate (FDR) was used to correct the P-values. P adjusted < 0.05 was considered significantly different.

Bulk RNA-seq data and analysis
We performed RNA-seq on PBMCs from five patients with sepsis and five controls (original dataset, RNA-seq1) (Supplementary Table 3-4) and analyzed publicly available bulk RNA-seq data from whole blood of sepsis survivors and non-survivors (GSE54514, RNA-seq2). Additionally, total RNA from RAW264.7 cells with DPEP2 deficiency by shRNA-mediated Dpep2 knockdown (shDpep2 RAW264.7 cells) and control RAW264.7 cells was also obtained for RNA-seq analysis (original dataset, RNA-seq3). Total RNA was extracted to construct libraries with the Illumina TruSeq Stranded Total RNA Library Prep Kit. The library was sequenced using paired-end 150-bp reads. Raw FASTQ reads were processed using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and transcript quantification was conducted using Salmon software in quasi-mapping mode76. Salmon “quant” files were subsequently converted using the Tximport function. DEG analysis was performed between two groups using log2FoldChange values in the RNA-seq analysis, with a significance threshold set at P adjusted < 0.05. The results were visualized using heatmaps and volcano plots, and subjected to GSEA.

Mice and treatment
Eight- to ten-week-old mice of both sexes were used for all animal experiments, except for six-week-old mice that were used for BMDM isolation. All mice were bred on a C57BL/6 background and housed in a specific pathogen-free environment with access to food and water at an environmental temperature of 20–26 °C and a 12 h light/12 h dark cycle. Relative humidity was around 40%– 70%. All experimental and control animals were co-housed. C57BL/6 mice were purchased from Charles River (Nanjing, China, Stock number: 213). Dpep2 knockout mice (Dpep2 KO), Dpep2f/f mice, macrophage-specific Dpep2-deficient mice (Dpep2f/fLyz2Cre) and Cre-reporter mice were purchased from GemPharmatech Limited Liability Company (Nanjing, China, Stock number: T002363, T008962, T066441 and T003822 respectively). Dpep2 KO and Dpep2f/fLyz2Cre mice were generated from the same strain as the C57BL/6J lineage. Dpep2f/fLyz2Cre mice were generated by crossing Dpep2f/f mice with Lyz2Cre mice.

Mice were randomly assigned to groups, and a CLP model was established45. Mice were anesthetized with isoflurane (4% for induction and 2% for maintenance). The abdominal cavity was opened under sterile conditions, the cecum was carefully exposed, and approximately 75% of the cecum was ligated using a 5-0 suture and punctured twice with a 20-gauge needle to expel a small amount of fecal matter into the abdominal cavity. The cecum was then repositioned, and the incision was closed with two layers of sutures. The sham-operated mice underwent the same procedures without cecal puncture. Mice intraperitoneally received 1 mL of 0.9% saline for fluid resuscitation and buprenorphine (0.05 mg/kg) for analgesia after the surgery.

For macrophage DPEP2 activation in vivo, an initial dose of LNP-Dpep2 (LNP encapsulating 20 μg Dpep2-mRNA) was injected intravenously via the tail vein 2 h after sepsis model induction, followed by one injection every 24 h for a total of three times. The control mice received the same volume of LNP without Dpep2-mRNA.

At specified time points, mice were euthanized via CO2 inhalation until respiration came to a complete stop and reflexes in the animals’ paws could no longer be triggered. Then, samples were collected and fixed in 4% paraformaldehyde (E672002, Sangon Biotech, Shanghai, China), or stored at -80 °C. Whole blood samples were collected in serum separator tubes, allowed to coagulate for 2 h at room temperature, and then centrifuged at 1000 ×g for 20 min to collect sera.

BMDMs and human monocyte isolation
Murine BMDMs were generated as follows45: bone marrow cells were harvested by flushing the femurs of six-week-old WT or Dpep2 KO mice. After erythrocyte lysis with red blood cell lysis buffer (00-4333-57, Thermo Fisher Scientific), the bone marrow cells were cultured in BMDM differentiation medium (Dulbecco’s modified eagle medium [DMEM, C11995500BT, Gibco, CA, USA] containing 10% fetal bovine serum [FBS, FBSSR-01021-500, OriCell, Guangzhou, China] and 30% L929-conditioned supernatant) for 7 days. BMDMs were obtained, with a purity of > 90%, as confirmed using flow cytometry. Human monocytes were isolated from the PBMCs of healthy participants using the Human Monocyte Isolation Kit (19359, Stem Cell) following the manufacturer's protocol. The presence of purified monocytes was confirmed using flow cytometry before further use.

THP-1 derived macrophages
THP-1 cells (TIB-202, ATCC) were differentiated into macrophages by treatment with 100 nM PMA (P1585, Sigma, St. Louis, MO, USA) for 48 h, followed by 24 h rest in fresh Roswell Park Memorial Institute (RPMI)-1640 complete medium (C11875500BT, Thermo Fisher Scientific). Flow cytometry confirmed macrophage purity >90%.

Plasmid construction
The shRNA oligos of Dpep2 and Egr1 were ordered from GENEWIZ (China), and their sequences are listed in Supplementary Table 7. Oligo annealing was performed by mixing 5 μL of 100 μM forward and reverse oligos, followed by PCR: 95 °C for 10 min using PCR machine, and then slowly cooling to 4 °C. Next, 2 μg of the pLKO.1-puro plasmid77 (Addgene, 8453) was digested with 1 μL AgeI (R3552S, New England Biolabs, Ipwsich, MA, USA), 1 μL EcoRI (R3101V, New England Biolabs), 2 μL CutSmart Buffer (B6004V, New England Biolabs) and ddH2O (total volume is 20 μL) at room temperature for 2 h. The digested pLKO.1-puro fragments were analyzed via agarose gel electrophoresis and purified using a DNA Purification Kit (DP103-03, Tiangen Biotech, Beijing, China). The annealed oligos and pLKO.1-puro fragments were ligated with T4 ligase (M0202S, New England Biolabs) at room temperature for 2 h. Next, 5 μL of the ligation mix was introduced into 50 μL of competent Stbl3 cells (TSC-C06, Tsingke Biotech, Beijing, China), incubated on ice for 30 min, heat-shocked at 42 °C for 50 s, and returned to ice for 2 min. Subsequently, 700 μL of SOC medium (B540119-0100, Sangon Biotech) was added and the mixture was incubated at 37 °C for 60 min. Successfully ligated plasmids were verified by sequencing (Sangon Biotech) and purified using a DNA Purification Kit (DP118, Tiangen Biotech). The Flag-tagged plasmids overexpressing Dpep2 and DPEP2 point mutants were constructed as follows: The full-length coding sequence (CDS) of mouse DPEP2 (NM_001301204.1) was amplified from RAW264.7 cell cDNA by PCR using PrimeSTAR Max DNA Polymerase Ver.2 (R047A, TakaraBio, Shiga, Japan), following the manufacturer’s instructions. To generate an N-terminally tagged construct, a Kozak–Dpep2–2×FLAG fragment was assembled by PCR fusion, ensuring the inclusion of a consensus Kozak sequence to enhance translational efficiency. The resulting fragment and the pLVX-puro lentiviral expression vector78 (Addgene, 125839) were digested with XbaI (R0145S, New England Biolabs) and XhoI (R0146S, New England Biolabs), purified, and ligated using T4 DNA ligase. Recombinant plasmids were transformed into Stbl3 competent cells selected on LB agar plates containing ampicillin (B540119-0500, Sangon Biotech), and verified by colony PCR and Sanger sequencing. Dpep2 point mutants were generated by overlapping-extension PCR using mutation-specific primers. The mutant PCR products were digested with XbaI and XhoI and cloned into the pLVX-puro vector using the same procedure as for the wild-type construct. All mutant plasmids were sequence-validated to confirm the presence of the intended nucleotide substitutions and to exclude unwanted secondary mutations.

Lentivirus packaging and infection
HEK-293T cells (CRL-11268, ATCC) were seeded in 100-mm dishes and incubated at 37 °C with 5% CO2 for 1-2 days. Transfection was performed when the cells reached 100% confluence. The medium was replaced with serum-free DMEM 0.5–1 h before transfection. The transfection mixture was prepared by combining 5 μg of shRNA plasmid/pLVX-DPEP2-puro plasmid, 3.75 μg of psPAX2 packaging plasmid (Addgene, 12260), and 1.25 μg of pMD2.G envelope plasmid (Addgene, 12259) in 400 μL of serum-free DMEM. Next, 20 μL of LipoD293 In Vitro DNA Transfection Reagent (SL100668, SignaGen Laboratories, Rockville, MD, USA) diluted in 400 μL of serum-free DMEM was added to the mixture. After 10 min of incubation at room temperature, the mixture was added dropwise to the cells, and incubated at 37 °C with 5% CO2. After 12 h, the medium was replaced with DMEM supplemented with 10% FBS without antibiotics, and the viral supernatants were harvested 48 h post-transfection, and passed through a 0.45-μm filter to remove cells. For the lentivirus infection, 2 mL of viral supernatant was used to transduce 5 × 106 RAW264.7 cells or BMDMs in the presence of 8 μg/mL polybrene (H8761, SignaGen Laboratories) for 24 h. At 48 h post-infection, RAW264.7 cells or BMDMs were selected using 5 μg/mL puromycin (sc-108071, Santa Cruz Biotechnology, Damascus, MD, USA) for 7 days.

Cell culture and cell treatment
RAW264.7 murine macrophage-like cells (TIB-71, ATCC), BMDMs and HEK-293T cells (CRL-11268, ATCC) were cultured in DMEM supplemented with 10% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin (15140122, Thermo Fisher Scientific) at 37 °C with 5% CO2. Human monocyte, THP-1 (TIB-202, ATCC) and L929 cells (CCL-1, ATCC) were cultured in RPMI-1640 medium supplemented with 10% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin.

RAW264.7 cells were treated with TLR1/2 ligands (1 μg/mL Pam3Csk4; HY-P1180A, MedChemExpress, Monmouth Junction, NJ, USA), TLR7/8 ligands (1 μg/mL R848; HY-13740, MedChemExpress) and TLR3 ligands (1 μg/mL poly(I:C); HY-107202, MedChemExpress) for 12 h. RAW264.7 cells, human monocytes and BMDMs were stimulated with LPS (l4391, Sigma) to establish in vitro inflammatory model. RAW264.7 cells were treated with LTD4 (1μM; 20310, Cayman, Ann Arbor, MI, USA), LTE4 (1 μM; 20410, Cayman) or PGE2 (1 μM; HY-101952, MedChemExpress) in vitro. RAW264.7 cells were treated with a EGR1 inhibitor (5 μM EGR-1-IN-1 (IT25); HY-163731, MedChemExpress), CysLTR1 inhibitor (5 μM Zafrilukast; HY-17492, MedChemExpress), CysLTR2 inhibitor (1 μM HAMI3379; HY-112248A MedChemExpress), NF-κB inhibitor (30 μM JSH-23; HY-13982, MedChemExpress), ERK inhibitor (1 μM U0126; HY-12031A, MedChemExpress), JNK inhibitor (25 μM SP600125; HY-12041, MedChemExpress), p38 MAPK inhibitor (30 μM SB203580; HY-10256, MedChemExpress), or AKT inhibitor (30 μM Ly294002; HY-101002, MedChemExpress), for 1 h and stimulated with LPS (1 μg/mL) for 12 h.

Hematoxylin-eosin (H&E) staining and histological analysis
Mice were euthanized, and their lungs were removed and fixed in 4% paraformaldehyde at room temperature for 24 h. Tissues were dehydrated, embedded in paraffin, and stained with H&E. Slides were evaluated under a light microscope to assess the severity of lung injury79. Five random high-power fields were blindly scored independently for each slice. The lung injury score was based on leukocyte infiltration, alveolar edema, hyaline membranes, proteinaceous debris in the alveolar space and the thickness of the alveolar septa79.

Immunofluorescent staining
Immunofluorescence staining was performed as follows25: 24 h after surgery, mice were anesthetized and subjected to transcardial perfusion with 4% paraformaldehyde. The brains were harvested, fixed in 4% paraformaldehyde at 4 °C for 24 h, embedded in paraffin, and sliced into 5 μm-thick sections. The paraffin-embedded tissue sections were deparaffinized and rehydrated for antigen retrieval. The primary antibodies were: rabbit anti-ionized calcium-binding adapter molecule 1 (Iba-1) antibody (1:1000; 019-19741, FUJIFILM Wako Pure Chemical, Japan) and glial fibrillary acidic protein (GFAP) Polyclonal antibody (1:200; 16825-1-AP, Proteintech, Chicago, IL, USA). Images were captured using a fluorescence microscope (Olympus, Japan).

TUNEL staining
Paraffin-embedded lung tissue sections were subjected to TUNEL staining to assess cell apoptosis according to the manufacturer’s protocol (A111-01, Vazyme Biotech, Nanjing, China).

RNA extraction and quantitative real-time PCR
Total RNA was isolated using the SteadyPure Universal RNA Extraction Kit (AG21017, Accurate Biotechnology, Guangzhou, China) following the manufacturer’s instructions. The concentration of RNA was determined using a Thermo Scientific NanoDrop One/One C Microvolume UV-Vis Spectrophotometer (Thermo Fisher Scientific). Reverse transcription of RNA was performed using the High-Capacity cDNA Reverse Transcription Kit (4368814, Thermo Fisher Scientific) with 1 μg of total RNA. The mRNA levels of the target genes were assessed using the PowerUp SYBR Green Master Mix (4309155, Thermo Fisher Scientific) on a Bio-Rad CFX96 real-time system (Life Technologies). mRNA expression was quantified using the comparative Ct (2-ΔΔCt) method and normalized to β-actin expression. Primer sequences used in this study are listed in Supplementary Table 8.

Enzyme-linked immunosorbent assay (ELISA)
Cell culture media and mouse sera were collected and cytokine production was measured using mouse TNF (EK282, MultiSciences, Hangzhou, China), IL-1β (EK201B, MultiSciences), IL-6 (EK206, MultiSciences), LTD4 (mI037877, mlbio, Shanghai, China), LTE4 (ml495212, mlbio) and PGE2 (mI037542, mlbio) ELISA kits according to the manufacturers' instructions.

Protein extraction and western blot
Cells and lung tissues were lysed with RIPA Buffer (R10010, Solarbio, Beijing, China) supplemented with a proteinase inhibitor cocktail (4693124001, Sigma). Equal amounts of protein (determined using BCA assay) were loaded onto 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gels, separated by electrophoresis, and transferred to polyvinylidene fluoride (PVDF) membranes. The membranes were blocked with 5% nonfat milk in Tris-buffered saline (pH 7.4) (PS105S, Epizyme Biomedical Technology, Shanghai, China) containing 0.1% Tween-20 (HY-Y1891, MedChemExpress) for 2 h and incubated with primary antibodies overnight against DPEP2 (1:1000; A15502, ABclonal, Wuhan, China), NF-κB p65 (1:1000; sc8008, Santa Cruz Biotechnology), Phospho-NF-κB p65 (1:3000; 3033T, Cell Signaling Technology, Beverly, MA, USA), IKKα (1:1000; sc7606, Santa Cruz Biotechnology), Phospho-IKKα/β (1:3000; 2697T, Cell Signaling Technology), p38 MAPK (1:1000; sc7972, Santa Cruz Biotechnology), Phospho-p38 MAPK (1:3000; 4511T, Cell Signaling Technology), 5-LO (1:1000; sc136195, Santa Cruz Biotechnology), Flag (1:3000; F1804, Sigma), β-Actin (1:3000; GB15003-100, Servicebio, Woburn, MA, USA). The membranes were washed the next day, and incubated with Goat Anti-Rabbit IgG horseradish peroxidase (HRP) (1:3000; M21002S, Abmart, Shanghai, China) or Goat Anti-Mouse IgG HRP (1:3000; M21001S, Abmart) for 1.5 h at room temperature. An ECL chemiluminescence kit (SB-WB004, Share-bio, Shanghai, China) and a ChemiDoc XRS+ chemiluminescence gel imaging system (BioRad, Hercules, CA, USA) were used to visualize the target protein bands.

Flow cytometry
Single-cell suspensions were prepared and stained in PBS with the following antibodies for 40 min on ice in the dark: APC anti-mouse/human CD11b Antibody (1:100, 101211, Biolegend, San Diego, CA, USA), APC anti-human CD14 Antibody (1:20, 399205, Biolegend), PE anti-human CD11b Antibody (1:20, 393111, Biolegend), Pacific Blue anti-human CD3 Antibody (1:20, 300329, Biolegend), PE anti-mouse F4/80 Antibody (1:20, 123109, Biolegend), DPEP2 antibody (FITC) (1:40, orb187504, Biorbyt, Cambridge, UK). Flow cytometry was performed using a Thermo Attune NxT Flow Cytometer, and data were analyzed by Flowjo software.

Dual-luciferase reporter assay
RAW264.7 cells were seeded in 24-well plates (5 × 104 cells/well) and transfected when they reached 60–80% confluence. The cells were transfected with 500 μL of penicillin-streptomycin-free DMEM, 2 μL Advanced DNA RNA Transfection reagent (AD600025, Zeta Life, Menlo Park, CA, USA), 1 μg shDpep2 plasmid/Dpep2 overexpression plasmid/control vector plasmid, 0.75 μg pNFκB-luciferase reporter plasmid (pNFκB-luc) (D2206, Beyotime Biotechnology, Shanghai, China), and 0.25 μg internal control plasmid phRL-TK (D2760, Beyotime Biotechnology) per well. After 24 h of transfection, the medium was replaced with 200 μL of fresh medium, and the cells were incubated for an additional 48 h. The cells were then treated with 1 μg/mL LPS for 24 h, harvested, and lysed, luciferase activity was measured using the Dual-Luciferase Reporter Assay Kit (FR201, TransGen, Beijing, China) on a Tecan Spark multimode microplate reader (Tecan, Switzerland). Firefly luciferase activity was normalized to the Renilla luciferase activity.

Assessment of organ injury markers
Plasma levels of ALT, AST, creatinine, BUN and CK-MB were measured using an automatic chemical analyzer (BS-240 Chemistry Analyzer; Mindray, Shenzhen, China).

Mouse and human DPEP2 mRNA construction
The mouse Dpep2 sequences were amplified by primer Dpep2_F (5′-AGAGAACCCGCCACCATGGCCTGGCATGGAGAGACATACT-3′) and Dpep2_R (5′-CGAGGCTCCAGCTCATCACACAATAAGGCCTAATAAG-3′). Amplified DNA sequences were inserted into the in vitro transcription (IVT) vector (6143, TakaraBio) with EcoRI/BamHI cutting sites using the Takara IVTpro mRNA synthesis system (638947, TakaraBio). The recombinant plasmid overexpressing Dpep2 was treated with the Hind III restriction enzyme (R0104S, New England Biolabs) at 37 °C for 1 h, and the linearized template plasmid was purified using a PCR Purification Kit (19106ES50, Yeasen, Shanghai, China). Linearized template plasmids were used for IVT of mRNA. Dpep2 mRNA was produced through an IVT process employing T7 RNA Polymerase (EP0111, Thermo Fisher Scientific), pyrophosphatase (EF0221, Thermo Fisher Scientific), RNase (R1253, Thermo Fisher Scientific), GTP (R0461, Thermo Fisher Scientific), CTP (R0451, Thermo Fisher Scientific), ATP (R0441, Thermo Fisher Scientific), and N1-Methylpseudouridine (N1-Mep-UTP, R0491SKB012, Thermo Fisher Scientific). Following transcription, the reaction mixture was treated with DNase I (18047019, Thermo Fisher Scientific), precipitated, and purified using lithium chloride (A10531-22, Thermo Fisher Scientific). Oligo dT purification was employed to further isolate mRNA using Oligo dT (K04N2901, Nanomicrotech Co., Ltd., Suzhou, China). The human DPEP2 mRNA was purchased from Tsingke Biotech (China). The purity was detected using an Agilent 4200 Fragment Analyzer system.

Construction and characterization of LNPs
Ethanol solutions of ionizable lipids (A500737-0005, Sangon Biotech), cholesterol (57-88-5, Adamas, Shanghai, China), 1,2-distearoyl-sn-glycero-3-phosphorylcholine (DSPC, S01005, AVT, Shanghai, China), and 1,2-dimyristoyl-rac-glycero-3-methoxypolyethylene glycol-2000 (DMG-PEG2000, O02005, AVT) were mixed at a molar ratio of 50:19.25:5:0.75. The lipid mixture was added dropwise to sodium acetate buffer (25 mM, pH 5.2) to prepare blank LNPs. The LNPs were dialyzed using a Slide-A-Lyzer MINI Dialysis Device (3.5 K molecular weight cutoff, Thermo Fisher Scientific). LNP/mRNA formulations were prepared by mixing blank LNPs and mRNA at a mass ratio of 10:1 (ionizable lipid/mRNA). The size, polydispersity index (PDI), and surface zeta potential of the LNPs were characterized using a Zetasizer Nano (Malvern Instruments, Malvern). The in vivo efficiency of LNP-Dpep2 was determined by flow cytometry. After tail injection of LNP-Dpep2, mice were euthanized at the indicated time, and the spleens were removed for flow cytometry to detect the proportion of DPEP2+F4/80+ macrophages14.

Statistical analysis
Normally distributed continuous outcomes determined using the Shapiro‒Wilk test were reported as mean ± standard deviation (SD) and assessed using two-sided Student’s t-test for two groups, and one-way analysis of variance (ANOVA) followed by the Bonferroni multiple comparisons or two-way repeated measures ANOVA with Tukey's multiple comparisons test for three or more groups. Non-normally distributed data were reported as median with interquartile ranges (IQR) and assessed using the two-sided Wilcoxon rank-sum test or Kruskal‒Wallis test followed by the Dunn post hoc test. Categorical outcomes were compared using two-sided Fisher’s exact test with low expected cell counts. Multivariate logistic regression analysis was used to analyze the effect of DPEP2 expression on the risk of sepsis-related death. Pearson’s correlation coefficient was used to assess the relationship between the percentage of DPEP2+ monocytes and clinical laboratory indicators. The log-rank (Mantel Cox) test was used for survival analysis. The datasets were analyzed using GraphPad Prism 9 (GraphPad, La Jolla, CA, USA) or R software (version 4.2.1, R Core Team, Vienna, Austria). A two-tailed P-value < 0.05 was considered to indicate statistical significance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_2": "The proposed manuscript by Luo et al. provides valuable and timely insights into the role of DPEP2 in regulating macrophage-mediated inflammatory responses during sepsis. The authors present a range of experimental evidence supporting the involvement of DPEP2 in the pathophysiology of sepsis, contributing to a better understanding of the underlying molecular mechanisms. This work has the potential to advance our knowledge of immune regulation and may open new perspectives for therapeutic targeting. Nevertheless, despite the substantial evidence provided regarding DPEP2 biology, the manuscript would benefit from addressing several important comments to improve clarity, strengthen the interpretation of findings, and enhance the overall impact of the study.\n\nMajor points to be addressed:\n1. The proposed manuscript currently lacks a clearly stated rationale and hypothesis. Instead of presenting a well-defined research hypothesis in the introduction, the authors include a paragraph that merely summarizes data from the results section. This approach weakens the overall narrative and does not provide the reader with a clear understanding of the study's objectives. It is strongly recommended to omit the summarizing paragraph and replace it with a concise and well-formulated hypothesis that outlines the central research question and the expected role of DPEP2 in macrophage-mediated inflammatory responses during sepsis.\n2. The manuscript almost lacks the characterization of sepsis patients - in the text it is just stated “in early acute phase of sepsis”. Further explanation and characterization of enrolled patients are needed in the main text of the manuscript, since sepsis is a very heterogenous syndrome, which can have very dynamic development. Also both Supplementary Tables 1 and 2 needs to have similar level of detail - biochemical and blood parameters, site of infection, diagnosis, etc., Also both tables need statistical evaluation comparisons between compared groups.\n3. Septic cohort presented in manuscript very small - for scRNAseq was used 5 patients with different sepsis origin, for validation authors analysed samples from 20 patients. Regarding known heterogeneity of sepsis, it is very tricky to evaluate valuability of marker.\n4. It is very hard to orient in the text, which datasets were original, and which were reused. For example, line 110, there is mentioned Bulk RNAseq data without further detail. What is this data set? I am also not sure about comparison between scRNASeq dataset (compared PBMC of 5 healthy vs 5 septic patients) together with dataset GSE54514, where was compared with peripheral blood of group, where was compared survivors vs non-survivors. There is need for more detailed explanation within manuscript, eventually validation with a third dataset, which will provide more accurate comparison.\n5. Data coming from sequencing are associated with inflammation and regulation immune response, when compared sepsis vs healthy not surprising. I am also wondering why the whole analysis was done on cluster Mono 1, which is the most abundant in healthy donors, whereas clusters Mono 2-4 are more abundant sepsis. Please provide further details about frequency / quantification of cells within all clusters in healthy as well as in sepsis. Whole paragraph also need to be rephrased to improve clarity of text.\n6. The manuscript suggested that monocyte reprogramming is controlled by immunometabolism, but the paper did not provide any evidence about metabolic regulation of processes - regulation is on transcription level. Similarly in the sentence on line 114 - “immunometabolism related gene DPEP2, which was robustly downregulated. There is no direct evidence about immunometabolism, just gene expression. Please improve all parts, where you are referring to immunometabolism or provide more evidence on immunometabolic level to prove real connection to immunometabolism. Some figures provide these details, but are not commented in results and not even mentioned in discussion.\n7. Many of the data are generated using monocytes, yet subsequent discussions often interpret these findings in the context of mouse macrophages. However, direct evidence linking human monocyte-derived macrophages (MDMs) to the observed phenotypes remains limited, particularly given known differences between mouse and human macrophage biology. Can you provide data, e.g. using 3D organoid models to investigate the fate and functional responses of MDMs especially in the context of Macrophage Activation Syndrome (MAS), a hyperinflammatory condition characterized by excessive macrophage activation and cytokine storm. This model can also offer a platform to test interventions, such as LNP-mediated DPEP2 mRNA delivery, to evaluate whether restoring DPEP2 mitigates MAS-like inflammation. Integrating human organoids thus provides translationally relevant evidence linking monocyte-derived macrophages to MAS and complements existing in vitro and mouse studies.\n8. In Figure 2b, there is evident, that healthy donors have to subsets based on frequency of DPEP2 monocytes. Can you provide further evidence, if these subgroups also differs in other aspects? Which data was used for this gating strategy - according completely missing population of LDN / PMN-MDSC cells, it seems that it was healthy donor? Can you provide data / gating strategy/ies with sepsis patients data to allow the reader have much clear impression how does data look like? Please also improve gating strategy - please interchange the axes of CD11b and CD14 (Fig 2a and also Suppl. Fig. 1 to have same axes).\n9. Section discussion did not really discuss data presented within this manuscript. This section need to be improved and much more reflecting proposed results of manuscript.\n10. The manuscript is currently missing a dedicated section discussing the study’s limitations. Including such a section is essential to provide readers with a broader context for interpreting the findings and to ensure a balanced presentation of the results. It is therefore recommended to add a concise discussion of the study’s key limitations, such as potential methodological constraints, sample size considerations, and any factors that may affect the generalizability or interpretation of the conclusions.\n\nMinor points to be addressed:\n- “Monocyte population are constantly enriched in patients with sepsis” Please provide more meaningful figure / data, where is possible to evaluate frequency of all cell populations. Provided supplementary figure does not make much sense - percentages do not reflect 100% as maximum.\n- Paragraph on lines 204 to 217 need to be rephrased to improve clarity of text.\n- Line 386 - referring to different paper than is in the list of references\n- Graphical abstract / graphical summarization will help readers to navigate through all information", "reviewer_3": "The authors investigated the proinflammatory role of macrophage DPEP2 in sepsis. The experimental design and storyline are well constructed, and the study provides insights into the field. The following are my comments.\n\nThe similar inflammatory role of macrophage DPEP2 in sepsis has already been published (PMC12283963), which could decrease the novelty of the present study, even though the authors cited the paper in the Discussion section.\n\nI’m not sure the eicosanoid-related studies are focused enough in the overall flow to serve as the title. Currently, it could rather give the impression that it’s just a side mechanistic study.\n\nFig. 1. Why did the authors first investigate PBMCs, which barely contain neutrophils, the major human leukocytes known to a play significant role in sepsis? Did they predetermine to focus on monocytes? That should also be acceptable.\n\nWhat is the mechanism of the DPEP2 decrease in sepsis? How are its levels maintained at a baseline condition?\n\nFig 4a. Macrophages/monocytes are minor populations in some of these compartments. Do these expressions/changes really reflect that of macrophages/monocytes?\n\nFig. 4b. The dot plot shows that ~90% of macrophages (F4/80+ cells, y-axis) express DPEP2 in sham and ~40% even in CLP. Are they supposed to express DPEP2 at such high levels? This is a huge difference from human monocyte data in Fig. 2a (20% and 10%, respectively).\n\nFig. 4b. How was the actual blood count? The decrease in percentage might be due to an increase in other cell populations, as 1-4% can easily be affected by changes in other populations.\n\nFig. 7a. Can the authors explain more about how LNP specifically targets macrophages? Does it also target monocytes?", "reviewer_4": "This is a large study on the role of dipeptidase 2, an enzyme mostly expressed in monocytes and involved in immunometabolism, in sepsis. The authors provide a comprehensive analysis of the gene expression, function and potential therapeutic targeting of the dipeptidase. The experimental design is multidisciplinary, which involves clinical studies in patients with sepsis, in vitro-cell models in monocytes/macrophages and in vivo pre-clinical mouse models of sepsis and adopts novel technologies including single cell RNA seq analysis. The molecular analysis of mechanisms is appropriately conducted using loss-of-function studies and highlights the important role of leukotrienes and prostaglandins in inflammation. However, while the data are supportive of the conclusions, additional information on the patient population and clarification of certain methodologies are needed to fully interpret the findings for clinical translation.\n\n1. The data of the sc-RNA analysis (Table 1) were from a previous published study and were integrated with data of bulk RNA seq-analysis (Figure 1). It is not clear whether the same patient and control samples were used across both datasets or whether a different cohort of patients was used for the bulk RNA-seq analysis. This should be specified.\n2. In all studies involving involving patients with sepsis, the timing of blood sampling should be specified (i.e., at admission or days after sepsis diagnosis)). This information is important to interpret the role of dipeptidase 2 in the context of the acute phase of sepsis.\n3. The patient population (n=20) of Table 3 (used for the correlative analysis of the inflammatory status) share a similar etiology of sepsis (gastrointestinal tract), which may limit the generalizability of the findings. Also, this study focuses on a quite elderly population. These issues of etiology and age should be discussed.\n4. The authors should also comment on the animal model of sepsis, which is a juvenile model, and may not fully reflect the pathophysiology of the adult/elderly population considered in the clinical studies."}, "author_response": "We thank the reviewer for the positive and constructive feedback on our manuscript. In response to the reviewer comments, we have thoroughly revised the manuscript to enhance clarity, strengthen the interpretation of our findings, and increase the study's overall impact. Our point-by-point responses to the specific comments are detailed below.\n\nMajor points to be addressed:\n1. The proposed manuscript currently lacks a clearly stated rationale and hypothesis. Instead of presenting a well-defined research hypothesis in the introduction, the authors include a paragraph that merely summarizes data from the results section. This approach weakens the overall narrative and does not provide the reader with a clear understanding of the study's objectives. It is strongly recommended to omit the summarizing paragraph and replace it with a concise and well-formulated hypothesis that outlines the central research question and the expected role of DPEP2 in macrophage-mediated inflammatory responses during sepsis.\nRESPONSE: We thank the reviewer for pointing out this issue. We have omitted the summarizing paragraph and replaced it with a concise, well-formulated hypothesis in the introduction, per your suggestion. (Page 4, Line 65-70).\n\n2. The manuscript almost lacks the characterization of sepsis patients - in the text it is just stated “in early acute phase of sepsis”. Further explanation and characterization of enrolled patients are needed in the main text of the manuscript, since sepsis is a very heterogenous syndrome, which can have very dynamic development. Also both Supplementary Tables 1 and 2 needs to have similar level of detail - biochemical and blood parameters, site of infection, diagnosis, etc., Also both tables need statistical evaluation comparisons between compared groups.\nRESPONSE: We sincerely thank the reviewer for this valuable suggestion. The explanation and characterization of the enrolled patients with sepsis have been detailed in our team’s previously published work1. We apologize for not explaining patient information clearly in this study. All the enrolled patients were required to meet the Sepsis 3.0 diagnostic criteria and to be diagnosed within 24 hours, in order to define the “early acute phase of sepsis.” This definition has been clarified in the Methods sections “human subjects” in the revised manuscript (Page 24, Line 515-516).\nIn addition, we have now provided detailed biochemical and blood parameters of human subjects, as well as information such as the site of infection in Revised Manuscript Supplementary Table 1-6. All data have been statistically analyzed and are presented in the Revised Manuscript Supplementary Tables, with group comparisons included. We believe that these additions will strengthen the characterization of our sepsis cohort, as suggested.\n\n3. Septic cohort presented in manuscript very small - for scRNAseq was used 5 patients with different sepsis origin, for validation authors analysed samples from 20 patients. Regarding known heterogeneity of sepsis, it is very tricky to evaluate valuability of marker.\nRESPONSE: We thank the reviewer for this valuable comment. To increase robustness, we have augmented the validation cohort with additional peripheral blood samples from both sepsis patients and controls, with each group now totaling 36 subjects (Response Fig. 1a-k) (Revised Manuscript Supplementary Table 5-6). Sepsis of abdominal origin was the most common type. The gender and age between groups were balanced (Revised Manuscript Supplementary Table 5). Peripheral blood samples were collected from all patients in early acute phase of sepsis in both the single-cell RNA sequencing (scRNA-seq) and validation cohort within 24 hours of sepsis diagnosis. This uniform sampling strategy ensures that variability related to disease stage and infection source is minimized, thus reducing the influence of clinical heterogeneity on evaluating valuability of maker. The results show that DPEP2 expression is lower in the monocytes of patients with sepsis compared to controls, and DPEP2 downregulation in monocytes correlates with inflammation, disease severity and prognosis in patients with sepsis. The inclusion of this expanded cohort provides enhanced statistical power, thereby strengthening the reliability of the proposed marker.\nTo further strengthen our findings, we have validated the clinical relevance of DPEP2 using a public RNA sequencing (RNA-seq) dataset (GSE54514) (Response Fig. 1l-n). The consistency between our cohort and external datasets increases confidence in the robustness of the observed associations. Taken together, the convergence of evidence from a relatively homogeneous early-stage cohort, an external public dataset, and our expanded validation cohort collectively strengthens the reliability of our biomarker. We have included this information on Page 6-7, Line 124-126.\n\n4. It is very hard to orient in the text, which datasets were original, and which were reused. For example, line 110, there is mentioned Bulk RNAseq data without further detail. What is this data set? I am also not sure about comparison between scRNASeq dataset (compared PBMC of 5 healthy vs 5 septic patients) together with dataset GSE54514, where was compared with peripheral blood of group, where was compared survivors vs non-survivors. There is need for more detailed explanation within manuscript, eventually validation with a third dataset, which will provide more accurate comparison.\nRESPONSE: We apologize for the confusion caused by the insufficient description of datasets in the original version of the manuscript. In the revised manuscript, we have clarified the provenance of all datasets, specifying whether they were generated originally or obtained from public sources (Page 4, Line 77; Page 6, Line 106-109; Page 6-7, Line 124-126; Page 7, Line 146; Page 24, Line 534-536; Page 26, Line 566-568). We have also summarized the patient information in supplementary tables for better readability (Revised Manuscript Supplementary Table 1-6). The datasets used in our study are as follows:\n1) Single-cell RNA-seq dataset (scRNA-seq) (dataset from our previous study1 and was reanalyzed in this study) (Revised Manuscript Fig. 1 and Supplementary Fig. 3a-c, g): This dataset includes peripheral blood mononuclear cells (PBMCs) samples from 5 patients with sepsis and 5 healthy controls (Revised Manuscript Supplementary Table 1-2). The dataset was mainly used to identify candidate genes and characterize immune cell heterogeneity.\n2) Original RNA-seq dataset (RNA-seq1) (Revised Manuscript Fig. 1i and Supplementary Fig. 3d-e): This dataset was newly generated in the current study, including PBMCs samples from 5 patients with sepsis and 5 controls (Revised Manuscript Supplementary Table 3-4). These samples were from independent patients, compared to scRNA-seq dataset. This dataset was combined with the scRNA-seq dataset to explore immune cell changes in sepsis, leading to the identification of DPEP2 as a potential target gene.\n3) Publicly available dataset (RNA-seq2, GSE54514) (Revised Manuscript Fig. 2l-n): This RNA-seq dataset includes whole blood samples from 26 sepsis survivors and 9 non-survivors. This dataset provides additional independent data to validate the correlation between DPEP2 and sepsis-related patient survival.\n4) Original prospective patient cohort (Revised Manuscript Fig. 2a-k): We enrolled 36 patients with sepsis (including 10 non-survivors and 26 survivors) and 36 controls. PBMCs were collected for flow cytometry analysis. Detailed clinical characteristics are provided in the Revised Manuscript Supplementary Table 5-6.\nTaken together, results of these 4 datasets consistently suggested that DPEP2 expression in monocytes is robustly associated with inflammation, disease severity, and outcome in septic patients. We have made these datasets clearly stated in the manuscript.\n\n5. Data coming from sequencing are associated with inflammation and regulation immune response, when compared sepsis vs healthy not surprising. I am also wondering why the whole analysis was done on cluster Mono 1, which is the most abundant in healthy donors, whereas clusters Mono 2-4 are more abundant sepsis. Please provide further details about frequency / quantification of cells within all clusters in healthy as well as in sepsis. Whole paragraph also need to be rephrased to improve clarity of text.\nRESPONSE: We thank the reviewer for this insightful comment. We would like to further clarify the rationale for focusing our analysis on the Mono 1 cluster.\nQuantification and cluster frequency: In the revised manuscript, we have added detailed quantification of all monocyte clusters (Mono 1–4) in both healthy donors and patients with sepsis, highlighting the relative abundance of each cluster and their changes in sepsis (Response Fig. 2a). The results show that, compared with Mono 2–4, Mono 1 constitutes the largest monocyte subset in both groups, and exhibits the most substantial alteration in sepsis compared to controls.\nFunctionally, while clusters Mono 2–4 are more abundant in patients with sepsis, their transcriptional programs are less specifically enriched for the inflammatory and immunoregulatory pathways of interest (Response Fig. 2b). In contrast, Mono 1 cells are closely associated with inflammatory and immunoregulatory functions with high specificity, making it particularly relevant for understanding sepsis-associated immune dysregulation. This unique pattern drew our attention, as it may reflect a specific impairment of immunoregulatory or inflammatory monocytes in the early stage of sepsis, which might shed light on the early detection and therapy for sepsis.\nTaken together, our analysis emphasizes Mono 1 because of its functional specificity, unique downregulation in sepsis, and potential mechanistic relevance. We have rephrased the entire paragraph in the revised manuscript to improve clarity, explicitly describing the functional relevance, frequency, and transcriptional changes of all monocyte clusters. (Page 5-6, Line 89-105)\n\n6. The manuscript suggested that monocyte reprogramming is controlled by immunometabolism, but the paper did not provide any evidence about metabolic regulation of processes - regulation is on transcription level. Similarly in the sentence on line 114 - “immunometabolism related gene DPEP2, which was robustly downregulated. There is no direct evidence about immunometabolism, just gene expression. Please improve all parts, where you are referring to immunometabolism or provide more evidence on immunometabolic level to prove real connection to immunometabolism. Some figures provide these details, but are not commented in results and not even mentioned in discussion.\nRESPONSE: We sincerely thank the reviewer for this insightful comment on the need for direct evidence connecting DPEP2 to immunometabolic regulation. In response, we have conducted a series of new functional assays (Response Fig. 3-4) to move beyond transcriptional associations. The manuscript has been revised extensively to clarify the immunometabolic implications of all data.\nStructurally, DPEP1 and DPEP2 extracellular domains share a conserved, dimeric TIM (β/α)₈-barrel fold containing a dizinc (Zn) metal center, where the zinc ions are bridged by a conserved glutamate residue essential for catalytic activity1,2. In human DPEP2 and DPEP1, this key glutamate is located at positions E188 and E141, respectively2. Based on sequence alignment with these human enzymes, we identified the corresponding catalytic glutamate residue (E190) in mouse DPEP2, which is predicted to be critical for the enzymatic conversion of the LTD4 to LTE4 (Response Fig. 3). To test this, we constructed Flag-tagged wild-type (WT) DPEP2 plasmid and the catalytic site mutant (E190Q) plasmid (Response Fig. 4a). While both constructs were expressed at comparable levels (Response Fig. 4b), only WT DPEP2 significantly reduced extracellular LTD4 and increased LTE4 in the culture supernatant (Response Fig. 4c-d). In contrast, the E190Q mutant completely abolished this activity, confirming E190 is essential for enzymatic function (Response Fig. 4c-d).\nWe further demonstrated the functional consequence of this enzymatic activity in macrophage inflammation. Overexpression of WT DPEP2 significantly suppressed the inflammatory response in RAW264.7 cells, whereas the catalytically inactive E190Q mutant failed to do so (Response Fig. 4e-g). This finding provides mechanistic evidence implying that the anti-inflammatory role of DPEP2 depends on its enzymatic activity, thereby establishing a validated link between DPEP2-mediated metabolism and immunoregulation. Together, these additions provide solid mechanistic evidence for the involvement of DPEP2 enzyme activity in metabolic and inflammatory regulation.\nFollowing the reviewer’s suggestion, we have revised the text to avoid overstating the immunometabolic role and describe DPEP2 as a gene implicated in lipid metabolic processes and markedly downregulated in patients with sepsis (Page 6, Line 112-114). We further explore the potential immunometabolic role of DPEP2 in the Discussion section (Pages 20–22, Lines 435–475), explicitly linking these insights to the metabolic experiments presented in the Results (Pages 11–14, Lines 237–301). We believe these additions considerably strengthen the scientific rigor and interpretability of our study.\n\n7. Many of the data are generated using monocytes, yet subsequent discussions often interpret these findings in the context of mouse macrophages. However, direct evidence linking human monocyte-derived macrophages (MDMs) to the observed phenotypes remains limited, particularly given known differences between mouse and human macrophage biology. Can you provide data, e.g. using 3D organoid models to investigate the fate and functional responses of MDMs especially in the context of Macrophage Activation Syndrome (MAS), a hyperinflammatory condition characterized by excessive macrophage activation and cytokine storm. This model can also offer a platform to test interventions, such as LNP-mediated DPEP2 mRNA delivery, to evaluate whether restoring DPEP2 mitigates MAS-like inflammation. Integrating human organoids thus provides translationally relevant evidence linking monocyte-derived macrophages to MAS and complements existing in vitro and mouse studies.\nRESPONSE: We sincerely thank the reviewer for this insightful suggestion. We fully agree that incorporating human monocyte-derived macrophages (MDMs) and human organoid models would provide valuable translational evidence in the human context. To address the reviewer’s concern on human macrophage biology, we conducted additional therapeutic experiments using a human monocyte-derived macrophage model in the context of Macrophage Activation Syndrome (MAS). Specifically, we employed a human THP-1 monocyte model, which was differentiated into macrophage-like cells using PMA1 (Response Fig. 5) and stimulated with LPS to establish a MAS-like hyperinflammatory condition. Using this MDMs model, we found that lipid nanoparticle (LNP)-mediated human DPEP2 mRNA delivery significantly attenuated MAS-like inflammation in MDMs (Response Fig. 6). Following the editor’s suggestion and the scope of this work, we have acknowledged the potential contribution of human 3D organoid models and discussed this point in the Discussion (Page 22, Line 485-490). These findings confirm that DPEP2 restoration mitigates hyperinflammatory responses in human macrophages, supporting the translational relevance of our conclusions. We have added these new data and corresponding descriptions to the revised manuscript. (Page 16, Line 341-343; Page 17, Line 359-362)\n\n8. In Figure 2b, there is evident, that healthy donors have two subsets based on frequency of DPEP2 monocytes. Can you provide further evidence, if these subgroups also differs in other aspects? Which data was used for this gating strategy - according completely missing population of LDN / PMN-MDSC cells, it seems that it was healthy donor? Can you provide data / gating strategy/ies with sepsis patients data to allow the reader have much clear impression how does data look like？ Please also improve gating strategy - please interchange the axes of CD11b and CD14 (Fig 2a and also Suppl. Fig. 1 to have same axes).\nRESPONSE: We sincerely thank the reviewer for these valuable and detailed comments. We appreciate the observation regarding the presence of two subgroups of healthy donors in Fig. 2b based on the frequency of DPEP2⁺ monocytes. As noted, healthy individuals with a DPEP2⁺ monocyte frequency above 20% were classified as the DPEP2 group 1 (19% of donors), whereas those below 20% were classified as DPEP2 group 2. To address this point, we performed additional analyses to determine whether these subgroups differ in other aspects. Our analysis revealed no significant differences in sex distribution or in biochemical and hematological parameters between the two subgroups of healthy donors (Response Table 1). Interestingly, the group 1 among healthy donors tended to be older, which may reflect age-related changes in the immune system. Importantly, this does not indicate pathological change but rather represents normal inter-individual variation. Overall, these results suggest that the variation in DPEP2⁺ monocyte frequencies observed across healthy donors most likely reflects natural inter-individual variability, rather than reflecting biologically meaningful subgroup differences\nRegarding the reviewer’s question about the dataset used for the gating strategy, we confirm that the example shown in Supplementary Fig. 1 before was indeed derived from a healthy donor. As the reviewer correctly noted, LDN/PMN-MDSC populations are nearly absent in healthy individual. We have now added the full gating strategy using samples from patients with sepsis (Response Fig. 7), which enables readers to directly compare the gating pattern between healthy controls and patients with sepsis.\nIn accordance with the reviewer’s suggestion, we have also standardized and improved the gating strategy presentation. Specifically, we have interchanged the axes of CD11b and CD14 in Revised Manuscript Supplementary Fig. 1 (Response Fig. 7), so that all flow cytometry panels now display markers consistently. This modification enhances the readability and uniformity of the figures.\n\n9. Section discussion did not really discuss data presented within this manuscript. This section need to be improved and much more reflecting proposed results of manuscript.\nRESPONSE: We sincerely thank the reviewer for this critical and constructive comment. In response to this comment, we have completely restructured and rewritten the Discussion section to ensure it robustly and explicitly centers on the data presented in this manuscript, rather than overgeneralizing or over-relying on previous literature (Page 18-23, Line 381-503). The revised Discussion now synthesizes the clinical, mechanistic, metabolic, and therapeutic findings presented in this study into a coherent narrative. Specifically, we clearly discuss that scRNA-seq, bulk RNA-seq, and patient cohort analyses consistently demonstrate marked downregulation of DPEP2 in monocytes from patients during acute sepsis and this reduction correlates with systemic inflammation and mortality. These human observations are discussed together with our murine cecal ligation and puncture (CLP) data, demonstrating that DPEP2 suppression is a conserved feature across species.\nFurthermore, the revised Discussion now explicitly emphasized that selective restoration of DPEP2 in monocytes/macrophages attenuates inflammation, protects against organ injury, and improves survival in septic mice, establishing a clear causal relationship between DPEP2 activity and inflammatory regulation. In addition, we expanded the Discussion to contextualize our therapeutic findings, clearly explaining the innovation and translational relevance of the monocytes/macrophages-targeted LNP-mRNA delivery system developed in this study. The revised section compares our approach with existing anti-inflammatory or mRNA-based strategies for sepsis and clarifies the advantages of our therapeutic system.\nWe also substantially expanded the mechanistic discussion to explicitly highlight the immunometabolic nature of DPEP2. The revised text now explains how DPEP2 suppression regulating inflammation by reshaping eicosanoid metabolism—promoting LTD4 accumulation, enhancing CysLTR2 and NF-κB activation, shifting arachidonic acid metabolism toward PGE2 production, and thereby amplifying macrophage inflammatory responses. Importantly, the new Discussion highlights that catalytic-site mutations abolish both enzymatic function and immunoregulatory activity, firmly establishing DPEP2 as a bona fide enzymatic immunometabolic regulator. These mechanistic insights are now explicitly discussed, thereby addressing the reviewer’s concern regarding insufficient interpretation of the presented results.\nFinally, we added a detailed limitations paragraph and strengthened the concluding remarks to more accurately summarize the contributions of the work based on the data we generated. We believe the revised Discussion is now substantially improved, providing a deeper and more focused interpretation of our data, and we are grateful for the reviewer's guidance in strengthening this crucial part of our manuscript.\n\n10. The manuscript is currently missing a dedicated section discussing the study’s limitations. Including such a section is essential to provide readers with a broader context for interpreting the findings and to ensure a balanced presentation of the results. It is therefore recommended to add a concise discussion of the study’s key limitations, such as potential methodological constraints, sample size considerations, and any factors that may affect the generalizability or interpretation of the conclusions.\nRESPONSE: Following this suggestion, we added limitations of the study within the Discussion section (Page 22-23, Line 484-497).:\nFirst, our experimental strategy primarily relied on in vitro human and murine macrophage systems, as well as a mouse model of sepsis. While these models provide valuable mechanistic insights, they remain inherently limited in capturing the full complexity and heterogeneity of human sepsis pathophysiology. Future studies incorporating human 3D organoid systems will enable a more physiologically faithful exploration of macrophage fate, functional responses, and the therapeutic potential of LNP-DPEP2. Second, the clinical cohort in this study, while providing consistent and biologically meaningful results, constitutes an initial dataset. Validation in larger and more diverse cohorts will be critical to robustly establish DPEP2 as a biomarker and to confirm the generalizability of its prognostic utility across broader clinical settings. Finally, our in vivo experiments were performed in juvenile mice, which do not fully recapitulate the immune, metabolic, and physiological landscape of older adults who most commonly develop sepsis. Future work employing adult or aged animal models will be essential to establish the translational robustness of DPEP2 as both a biomarker and a therapeutic target.\nWe believe that the addition of this section significantly strengthens the manuscript by providing readers with a transparent framework to interpret our findings and by highlighting important avenues for future research. We are grateful to the reviewer for this insightful recommendation, which has undoubtedly improved the overall quality and credibility of our work.\n\n11. “Monocyte population are constantly enriched in patients with sepsis” Please provide more meaningful figure / data, where is possible to evaluate frequency of all cell populations. Provided supplementary figure does not make much sense - percentages do not reflect 100% as maximum.\nRESPONSE: We thank the reviewer for this valuable comment. We have now included an updated figure (Response Fig. 8) that displays the full composition of PBMCs, allowing direct comparison of the frequencies of all major immune cell populations (T cells, B cells, NK cells, monocytes, and dendritic cells) between patients with sepsis and controls.\n\n12. Paragraph on lines 204 to 217 need to be rephrased to improve clarity of text.\nRESPONSE: We thank the reviewer for this constructive feedback. We agree that the paragraph in question could be phrased with greater clarity. We have thoroughly rephrased the text to improve its flow and readability. (Page 10-11, Line 208-221)\n\n13. Line 386 - referring to different paper than is in the list of references\nRESPONSE: We thank the reviewer for pointing out this discrepancy. We have carefully reviewed the manuscript and corrected the citation and description. (Page 20, Line 426; Page 22, Line 480)\n\n14. Graphical abstract / graphical summarization will help readers to navigate through all information\nRESPONSE: We have designed a new graphical summary that visually encapsulates the principal findings of our work. This graphical abstract has now been included in the revised manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Minor points:\n- Fig. 1 - title - metabolic regulation not metabolism\n- Fig. 4 - some small issue with colours and columns on graph (moved)", "reviewer_3": "The authors sufficiently addressed my comments.", "reviewer_4": "The authors have addressed my concerns. No further comments."}, "author_response": "Reviewer #2 (Remarks to the Author):\nMinor points:\n1. Fig. 1 - title - metabolic regulation not metabolism\nRESPONSE: We have revised the title of Figure 1.\n2. Fig. 4 - some small issue with colours and columns on graph (moved)\nRESPONSE: Thank you for pointing this out. We have adjusted the colours and columns positions in Fig. 4.\n\nReviewer #3 (Remarks to the Author):\nThe authors sufficiently addressed my comments.\nRESPONSE: We thank the reviewer for the positive feedback. We are pleased that the revisions have sufficiently addressed all the comments.\n\nReviewer #4 (Remarks to the Author):\nThe authors have addressed my concerns. No further comments.\nRESPONSE: We sincerely thank the reviewer for the time and for acknowledging that the comments have been adequately addressed."}]

Decision letter