Epoxy-oxylipins direct monocyte fate in inflammatory resolution in humans

Domain: biology
Published: 2026-01-16
Editorial outcome: major_revision

Abstract
The role of cytochrome P450-derived epoxy-oxylipins and their metabolites in human inflammation and resolution is unknown. We report that epoxy-oxylipins are present in blood of healthy, male volunteers at baseline and following intradermal injection of UV-killed Escherichia coli, an experimental model of acute resolving inflammation. At the site of inflammation, cytochrome P450s and epoxide hydrolase (EH) isoforms, which catabolise oxylipins to corresponding diols, are differentially upregulated throughout the inflammatory response, as is the biosynthesis of epoxy-oxylipins. GSK2256294, a selective sEH inhibitor specifically elevates 12,13-EpOME and 14,15-EET. While inhibition of sEH hastens pain resolution, it has no effect on tissue heat, redness and swelling. GSK2256294, however, significantly reduces numbers of circulating intermediate monocytes that expand during inflammation. We find that 12,13-EpOME blocks the transition of classical to intermediate monocytes in a p38 MAPK-dependent manner, results that are recapitulated when blocking p38 MAPK in vitro and when administering the p38 MAPK inhibitor losmapimod in vivo to healthy volunteers. Furthermore, fewer intermediate monocytes are observed at the site of inflammation, accompanied by reduced tissue CD4 T cells. Hence, we have mapped the expression, activity and function of epoxy-oxylipins in human inflammation revealing new mechanisms of monocyte differentiation and resolution biology.

Introduction
There is much to learn about the regulatory mechanisms that control inflammatory responses as well as those that bring about their resolution. Gaining a better understanding of these protective pathways and how they are dysregulated by factors such as genetic abnormalities1,2, ageing3 or the environment4, will inform on why inflammation can change from being our greatest ally against infection and injury, to a serious threat as a result of failed resolution resulting in chronicity of disease.

While many soluble mediators and cellular processes necessary for effective resolution have been described5, the lack of curative therapeutics calls for more research into understanding the complexity of inflammatory responses. One under investigated pathway in human immunology is that of the cytochrome P450 (CYP450) epoxygenases. The CYP450 epoxygenases convert polyunsaturated fatty acids (PUFAs) into epoxy-oxylipins. Linoleic acid (LA) is metabolised into epoxy-octadecenoic acids (EpOMEs), arachidonic acid (AA) into epoxy-eicosatetraenoic acids (EETs), docosahexaenoic acid (DHA) into epoxy-docosapentanoic acids (EpDPA/EpDPE) and eicosapentaenoic acid (EPA) into epoxy-eicosatetraenoic acids (EpETEs)6,7. In humans, there are 41 protein-coding subfamilies comprising 57 genes, while the murine genome contains 102 full-length putatively functional genes8. The CYP450 enzymes most associated with epoxy-oxylipin production in humans are from the CYP2C family, namely CYP2C8 and CYP2C9, and CYP2J29. After production, the epoxy-oxylipins are catabolised into more stable diols by a group of enzymes known as the epoxide hydrolases (EH). The primary epoxide hydrolase enzymes are microsomal EH (mEH) and soluble epoxide hydrolase (sEH), although a further two have been identified in the human genome: Epoxide Hydrolase 3 (EH3) and Epoxide Hydrolase 4 (EH4)10–12. mEH is a major metaboliser of xenobiotic compounds and, thus its therapeutic inhibition is likely to result in side effects13. However, inhibiting sEH is proven safe and effective14. Hence, inhibiting sEH elevates levels of epoxy-oxylipins and represents a useful tool to understand their role in health and disease.

The immuno-modulatory properties of the epoxy-oxylipins include inhibiting NF-κB and blocking p38 MAPK phosphorylation, resulting in reduced expression of pro-inflammatory cytokines and cell adhesion molecules15,16. Epoxy-oxylipins have also been shown to induce angiogenesis via the production of VEGF17, while in murine models, we have shown that sEH depletion enhances resolution of acute peritonitis18. There is a large body of evidence from others showing sEH inhibition reduces inflammation in mouse models of Alzheimer’s disease19, diabetic retinopathy20, sepsis21 and autoimmunity22; a body of literature also exists on the analgesic effects of the epoxy-oxylipins both in rodents and in larger mammals, including horses and dogs23–26. However, little is known about how epoxy-oxylipins impact inflammation and resolution in humans.

GSK2256294 is a potent and reversible inhibitor of sEH27–30. It is safe and well tolerated in humans with maximum systemic concentrations achieved within 1–2 h of dosing, with the average inhibition of sEH being 99.8% and a half-life of 20–30 h31. Using GSK2256294 in our model of UV-killed E. coli (UV-KEc)-induced dermal inflammation32–35, we report on the epoxy-oxylipins elevated following sEH inhibition and how they modulate the salient, molecular and cellular aspects of local and systemic inflammation and its resolution.

In this work, we show that sEH inhibition significantly elevates both plasma and tissue levels of the LA-derived epoxy-oxylipin, 12,13-EpOME, which results in a significant reduction of intermediate monocytes during inflammation, both in peripheral blood and in the skin. Furthermore, we show that 12,13-EpOME significantly inhibits the phosphorylation of p38 MAPK, which, when inhibited in vitro and in vivo using losmapimod, an oral inhibitor for p38 MAPK, significantly reduces the presence of intermediate monocytes.

Results
CYP/EH isoform profiles and lipid metabolites in resolution
The expression of key enzymes in the synthesis and metabolism of epoxy-oxylipins, namely CYP2J2, the CYP2C family and the epoxide hydrolase enzymes, was characterised in UVKEc-elicited dermal inflammation. Under baseline conditions, CYP2J2 was found in the epidermis, hair follicles and sweat glands, but not in the reticular dermis (Fig. 1A). Following injection of UVKEc, CYP2J2 was observed in the infiltrating immune cells at 4 and 24 h, primarily in the reticular dermis (Fig. 1A, C). A similar profile of expression was found for the CYP2C family of enzymes, Supplementary Fig. 1 and Fig. 1C.

sEH was detected in the epidermis, hair follicles and sweat glands at baseline (Fig. 1B). As expected, sEH increased following onset of inflammation and was maintained at twice the level of baseline up to 24 h (Fig. 1B, C). mEH was found within the epidermis and dermis at all time points with the highest level of expression found in the papillary layer (Supplementary Fig. 1). Levels of mEH remained unchanged from baseline to the early phase of inflammation (8 h) but increased ×1.6 fold at 24 h (Fig. 1C). In contrast, EH3 was expressed in the epidermis, hair follicles and sweat glands and decreased during acute inflammation (4 and 8 h post intradermal UV-KEc injection), returning to baseline by 24 h (Supplementary Fig. 1).

Using negative pressure to elicit a blister over the site of inflammation to collect the inflammatory exudate, we quantified the levels of the PUFAs AA, LA and DHA, as well as their respective metabolites by sEH, namely 14,15-DHET, 12,13-DiHOME, 19,20-DiHDPA during inflammation and its resolution (Fig. 1D). Additionally, we quantified the LA CYP450 metabolite, 12,13-EpOME (Fig. 1D). It is worth noting that the levels of 12,13-DiHOME are nearly 100 times higher at 14 h compared to 14,15-DHET and 19,20-DiHDPA. Although we could not detect the PUFA EPA, we were able to detect its sEH metabolite 17,18-DiHETE. The CYP450 products peak at 14 h post UV-KEc injection, implying they play a role in the resolution of inflammation, whereas the COX products peak at 4 h, during the acute phase of the inflammatory response (Fig. 1D).

Effect of sEH inhibition on the core features of inflammation
Given that epoxy-oxylipins were elevated in inflammatory exudates at the transition from onset to resolution (Fig. 1), we felt that a prophylactic and a separate therapeutic inhibition of sEH was needed to distinguish constitutive epoxy-oxylipins from those elicited by intradermal UVKEc. Therefore, we followed the prophylactic and therapeutic dosing regimens outlined in Fig. 2A, B. A table of characteristics for the participants recruited into this study for the prophylactic and therapeutic arms can be found in Tables 1 and 2, respectively. In the prophylactic arm of the study, participants were dosed with 15 mg GSK2256294 2 h prior to the onset of inflammation. In the therapeutic arm of the study, participants were dosed with 15 mg GSK2256294 4 h after the injection of UV-KEc.

Perception of pain peaked between 4 and 24 h in this model, with GSK2256294 having no analgesic effect during this phase, Fig. 2C, D. However, at 24 h in prophylactic participants and between 24 and 48 h in therapeutic participants, the resolution of pain was hastened with sEH inhibition (Fig. 2C, D). Local temperature peaked at 24 h with inhibition of sEH having no effect on this inflammatory parameter (Fig. 2C, D). Furthermore, GSK2256294 did not affect oedema, measured indirectly by quantifying blister fluid volume (Fig. 2C, D). Injection of UVKEc results in increased microvascular reactivity quantified by laser doppler analysis, peaking at 24 h (Fig. 2E), although inhibition of sEH did not overtly affect local redness (Fig. 2E).

Taken together, except for pain, inhibition of sEH with GSK2256294 did not significantly alter the clinical features associated with UVKEc injection.

Effect of sEH inhibition on plasma epoxy-oxylipins
To determine which epoxy-oxylipins were altered following sEH inhibition, we performed detailed lipidomic analysis. Plasma levels of the four PUFAs from which the epoxy-oxylipins are derived are unchanged with the induction of inflammation and, as expected, are unaffected by dosing participants with GSK2256294 (Supplementary Fig. 2).

Following the pre-dosing approach described in Fig. 2A, GSK2256294 significantly elevated plasma 14,15-EET and 12,13-EpOME within 2 h, which resulted in an increase in the ratio of 12,13-EpOME:12,13-DiHOME and 14:15-EET:14,15-DHET (Fig. 3A), a reliable index of sEH inhibition. Following induction of inflammation, prophylactic sEH inhibition resulted in a significant increase in the levels of 14,15-EET and 12,13-EpOME, concomitant with a significant increase in the ratio of 14,15-EET:14,15-DHET and 12,13-EpOME:12,13-DiHOME at 4, 24 and 48 h (Fig. 3B). In the therapeutic dosing regime, GSK2256294 caused an increase in 14,15-EET at 24 and 48 h and a corresponding increase in the ratio of 14,15-EET:14,15-DHET (Fig. 3C). The ratio of 12,13-EpOME:12,13-DiHOME was also significantly increased at 24 and 48 h following therapeutic dosing compared to untreated participants (Fig. 3C). Although we focused here on 12,13-EpOME and 14,15-EET, we quantified all other CYP450 derived lipids, a table of which can be found in Supplementary Fig. 3.

Hence, these data demonstrate efficient inhibition of sEH by GSK2256294 in this model.

Inhibiting sEH blocks the expansion of intermediate monocytes
Employing an antibody panel designed to capture changes in all blood immune cells, using manual gating in FlowJo, we noted that GSK2256294 had a striking effect on blood monocyte populations. Specifically, in both interventional arms of the study, sEH inhibition blocked the expansion of intermediate and non-classical monocytes between 4 and 24 h (Fig. 4A); with a significant difference in the number of intermediate monocytes at 24 h between treated and untreated groups in the prophylactic arm. The gating strategy for the monocyte subsets can be found in Supplementary Fig. 4. These data show, unexpectedly, that sEH inhibition blocks the expansion of intermediate and non-classical monocytes in humans during inflammation.

Data were also processed by unbiased multiparametric analysis using the CATALYST package in R where we identified six groups of monocytes—two sub-categories of classical monocytes (CM (1) and CM (2)), one population that is transitioning between classical-intermediate monocytes (CM-IM), one distinct intermediate population (IM), a population transitioning from intermediate to non-classical monocytes (IM-NCM) and, finally, non classical monocytes (NCM); all presented as Uniform Manifold Approximation and Projection (UMAP) (Fig. 4B) with the gating strategy for the monocyte input shown in Supplementary Fig. 4. Expression markers charted the differentiation of these populations with CD14, CCR2, CD64 and CD205 typically declining as classical monocytes differentiate with a corresponding increase in CD16, HLA-DR, CD86 and CD11c as cells acquire a non-classical phenotype33, (Fig. 4B). These data support analysis using manual gating (Fig. 4A and Supplementary Fig. 4)—that GSK2256294 inhibits the expansion of intermediate monocytes between 4 and 24 h following UVKEc injection (Fig. 4B). To exclude margination on the microvascular endothelium as an explanation for reduced intermediate numbers, we quantified expression of ICAM-1, PSGL-1, VCAM-1, CCR2 and CX3CR1 on these cells and found no difference between the control and GSK2256294 treated group (Fig. 4C). Therefore, given the current paradigm of CM-IM-NCM linear differentiation33,36,37, we suggest that sEH inhibition blocks the expansion of intermediate monocytes in blood of volunteers injected with intradermal UV-KEc.

For the effects of GSK2256294 on other peripheral blood cell numbers, see Supplementary Fig. 5.

12,13-EpOME blocks monocyte differentiation by inhibiting p38 MAPK
To understand how sEH inhibition blocks the expansion of intermediate monocytes in blood, we incubated 14,15-EET and 12,13-EpOME with PBMCs and monitored monocyte differentiation in vitro. 12,13-EpOME, but not 12,13-DiHOME, partially blocked classical to intermediate monocyte differentiation with both 14,15-EET and 14,15-DHET being without effect (Fig. 5A and Supplementary Fig. 6). Representative flow plots from these experiments can be found in Supplementary Fig. 7. Investigating the molecular basis of this inhibition, it is reported that 12,13-EpOME blocks the phosphorylation of p38 MAPK15. Phospho-p38 MAPK was enriched in classical monocytes, with phosphorylation levels declining as monocytes differentiate towards the non-classical phenotype (Fig. 5B and Supplementary Fig. 8). 12,13-EpOME significantly blocked phosphorylation of p38 MAPK, in monocytes (Fig. 5C). By corollary, losmapimod, the selective p38 MAPK inhibitor, also blocked monocyte differentiation in vitro (Fig. 5D). Furthermore, participants dosed orally with losmapimod for four days resulted in a significant decrease in the percent of intermediate monocytes in peripheral blood (Fig. 5E). Collectively, these data suggest that 12,13-EpOME and its target p38 MAPK controls the differentiation of classical to intermediate monocytes in humans.

Inhibiting sEH reduces the number of IMs at the inflammatory site
As in blood, prophylactic dosing caused a significant increase in 12,13-EpOME compared to untreated controls and a concomitant reduction in 12,13-DiHOME at 4 h, resulting in a significant increase in the ratio of 12,13-EpOME:12,13-DiHOME, similar to the effect seen in blood, Fig. 6A. Therapeutically, GSK2256294 increased the ratio of 12,13-EpOME:12,13-DiHOME at both 24 and 48 h as a result of the significant reduction of 12,13-DiHOME following sEH inhibition (Fig. 6A).

We applied a similar unbiased analytical approach as in Fig. 4 to identify mononuclear phagocytes at the site of inflammation with the gating strategy shown in Supplementary Fig. 4. Here, we found nine separate populations of mononuclear phagocytes in the tissue. CD163hiTIM-4lo, CD163hiTIM-4hi, CD206hi, classical monocytes (CM), classical-intermediate monocytes (CM-IM), intermediate monocytes (IM), intermediate to non classical (IM-NCM) monocyte transitioning cells, non-classical monocytes (NCM) and finally CD205hi Monocyte-DC (Mo-DC) (Fig. 6B) with expression markers for each in Fig. 6C. Monocyte marker expression changes between peripheral blood and tissue, hence the difference in monocyte populations between Figs. 4B and 6C (Supplementary Fig. 9). Boxplots for marker expression in the tissue can be found in Supplementary Fig. 9. Faceting the UMAP of mononuclear phagocyte populations by time and treatment indicated a reduction in intermediate monocytes at 24 and 48 h following GSK2256294 (Fig. 6D). This was confirmed using traditional gating revealing a trend towards a decrease at 24 h in the prophylactic participants and a significant reduction in intermediate monocytes at 48 h in therapeutic participants, which is mirrored in the supervised clustering analysis (Fig. 6E). Classical and non-classical monocyte numbers were not significantly altered in response to dosing with GSK2256294 in both prophylactic and therapeutic dosing regimens (Supplementary Fig. 10).

Compared to untreated controls, prophylactic and therapeutic GSK2256294 had no effect on typical inflammatory cytokines/chemokines including TNFα, IL-10, IL-1β and IL-8 as well as the chemokines involved in monocyte trafficking namely MCP-1, IP-10 (CXCL10) and fractalkine (CX3CL1) (Supplementary Table 1).

Intermediate monocytes, a double-edged sword in inflammation
With a reduction in tissue intermediate monocyte-derived macrophages came the opportunity to investigate the role these cells play during resolving inflammation. Other cells that increase during the inflammatory response are CD4 and CD8 T cells, as previously published35 with GSK2256294 causing a trend towards a reduction in numbers of CD4 T cells and regulatory T cells, but not CD8 cells (Fig. 7A). This reduction was associated with an increase in numbers of total T cells acquiring a vital stain indicating death (Fig. 7A). This occurred alongside increased levels of IL-1α, which is an alarmin liberated into the extracellular space following cell death38 (Fig. 7B). With intermediate monocytes being the most abundant mononuclear phagocyte at 24 and 48 h (Fig. 7C) co-existing with infiltrating CD4 and CD8 T cells (Fig. 7D), we incubated intermediate monocytes with blood CD4 T lymphocytes finding that intermediate monocytes drive a phenotype reminiscent of activated, proliferating, tissue-resident, T cells with regulatory features (Fig. 7E and Supplementary Fig. 11). Representative flow plots can be found in Supplementary Fig. 12.

In contrast, incubating intermediate monocytes with CD8 T lymphocytes in a CD8 cytotoxicity assay against K562 cells, an MHC-deplete target cell line, caused increased cell death (Fig. 7F). With considerable evidence for expanded intermediate monocytes in diverse chronic inflammatory conditions39–45 implicated in tissue damage, these data highlight that IMs increase the cytotoxic capacity of CD8 T cells, but also drive CD4 T cell viability and differentiation, on the other.

Methods
Ethical statement
All studies were approved by the UCL Research Ethics Committee (Project ID number: 1309/004). All procedures performed were in line with the ethical standards of the UCL Institutional Committee and according to the principles of the Declaration of Helsinki. Written informed consent was obtained from all volunteers prior to their participation.

Volunteer recruitment
In line with our ethical approval, we recruited young, healthy, male volunteers who were non-smokers between the ages of 18–50 for this study. Sex of participants was determined based on self-reporting. Exclusion criteria included vaccination in the previous three months, allergies, diagnosis of chronic inflammatory disease, taking regular prescribed medication, or taking over-the-counter medication within 7 days prior to their participation in the study. Prior to the study commencing all volunteers were given a participant information sheet and required to attend a screening session to assess eligibility where written consent was obtained. At the screening session, bloods were taken and sent to The Doctor’s Laboratory (TDL, London, UK) to record full blood count and biochemistry. For the 48-h duration of the study, participants were asked to abstain from alcohol and caffeine to control for the effect these drugs have on inflammatory processes. Participants were compensated for their time in accordance with ethical approval. No statistical methods were used to predetermine sample sizes. As this experimental study was designed to test a hypothesis in humans in vivo, and not to determine clinical benefit, no randomisation was performed. No adverse events were observed in our study group.

The human intradermal UV-killed E. coli inflammatory model
The human intradermal UV-KEc model of inflammation was developed in the host laboratory to study resolving inflammation in response to a localised bacterial insult32–35. Briefly, 1.5×107 UV-KEc (Strain: NCTC10418, Public Health England, UK) in 100 µL saline was administered intradermally into the forearms of participants. This results in localised inflammation characterised by pain, heat, redness, and swelling. Using negative pressure blister suction machines (Electronic Diversities Ltd.), two blisters (one on each forearm) were raised over the inflammatory site to acquire inflammatory cells and exudate for the determination of local immune cell profiles as well as cytokine, chemokine and lipid mediator levels. Blister exudate is collected into 50 μL sodium citrate (3% in PBS). The UV-KEc model also results in systemic changes in peripheral blood, which is collected at each time point for plasma analysis and cellular characterisation.

sEH inhibitor study design
Prophylactic. The initial visit (−24 h) for individuals allocated to the sEH inhibited group included pre-dose recording of baseline inflammatory symptoms and blood sample collection. Prior (−2 h) to intradermal injection of UV-killed E. coli, participants were orally dosed with 15 mg of the sEH inhibitor GSK2256294. Untreated participants did not receive a placebo control. At the zero hour time point, dermal inflammation was induced. The protocol for the subsequent visits for the sEH-inhibited volunteers was identical to the control group; at the beginning of every visit (0, 4, 24 and 48 h post-induction of inflammation) inflammatory symptoms were evaluated (discussed below), and peripheral blood was collected at each time point for plasma, cellular analysis by flow cytometry, and storing PBMCs. At the 4 and 24 h time points, the inflammatory site was sampled by the creation of suction blisters.

Therapeutic. Participants attended sessions in the lab four times over the course of the experiment: at 0 h to induce inflammation, 4, 24 and 48 h after the onset of inflammation. Participants in the drug arm of the study received a single 15 mg dose of GSK2256294 at the 4 h time point after the UV-KEc injection. Untreated participants did not receive a placebo control. The study was divided into two flow cytometry panels. Panel 1 was for the investigation of monocytes and dendritic cells at the site of inflammation and panel 2 was for investigating T-cell subsets. Studies using panel 1 involved participants receiving one blister at 24 h and the second blister at 48 h. Studies using Panel 2 involved participants receiving two blisters at 48 h, one on each forearm. Peripheral blood was collected at each time point for plasma, cellular analysis by flow cytometry, and storing PBMCs.

Non-invasive inflammatory symptom recordings
Core temperature, ambient temperature, and temperature at the injection site were monitored at each time point. Participants were asked for a pain score at the inflammatory site between 0 and 10. Additionally, participants were asked for a tenderness score. For this, a 100 g weight was placed onto the forearm at the centre of the inflammatory site, and participants were asked to give a tenderness score of between 0 and 10. Finally, changes to vascular hyperaemia were investigated through the use of a Laser Doppler Imager (Moor LDI-HIR, Moor instrument). The laser emitted by the scanner is scattered by red blood cells present at the site of inflammation. The change in the frequency of light in the laser beam is detected by a photodetector. The signal is measured in arbitrary perfusion units; the ‘intensity’ of which is dependent on the velocity and concentration of the red blood cells in the fixed area being scanned. Images were analysed using the LDI software v5.2. To exclude background noise, the threshold of the perfusion unit was set to >300.

Flow cytometry
To isolate leucocytes for flow cytometry analysis, 1 ml of whole blood, collected in a EDTA vacutainer tube (4 ml; Griener Bio-One), was added to 9 ml of ammonium-chloride-potassium lysis buffer (ACK; Lonza). Blister cells were obtained via centrifugation of blister exudate. All samples were stained in 100 μL for 30 min at 4 °C. Antibody cocktails were prepared with 50 μL of Brilliant Stain Buffer (BD Biosciences, 566349), antibodies (Supplementary Table 2), with the remainder of the volume made up with FACS buffer (PBS, 5% Foetal Calf Serum, 2 mM EDTA). All panels were compensated appropriately, either using cells as vehicles for single stains, UltraComp eBeads™ compensation beads (Thermo Fisher, 01-2222-41), or ArC™ Amine Reactive compensation bead kit (Thermo Fisher, A10346). Unstained sample and Fluorescence minus-one (FMO) and, where appropriate, isotype control antibodies were used as a control. Data were acquired on a BD Fortessa UV X20 flow cytometer using BD FACSDIVA software or a Sony ID7000 spectral flow cytometer. Data analysis was performed using FlowJo software v10.10 or using the R package ‘CATALYST’ (v1.26.1)56. Blister cell numbers were quantified by acquiring the entire sample on the flow cytometer. Peripheral blood samples were quantified either by manual haemocytometer or CountBright cell counting beads (Invitrogen).

Isolation of peripheral blood mononuclear cells (PBMCs)
Peripheral blood was collected in EDTA. Peripheral blood mononuclear cells were isolated by mixing 1:1 with Hanks Buffer Salt Solution (HBSS) (Gibco, 14170). The 1:1 peripheral blood/HBSS solution was layered onto Ficoll-Paque PLUS (17-1440-03) in a 2:1 ratio. Samples were spun at 1000×g for 30 min with the brakes off. The layer of PBMCs was collected and washed in HBSS, followed by a spin at 400×g for 10 min. Red blood cell contamination was lysed using 1 ml Ack lysis buffer for 5 min at RT. Samples were topped up to 30 ml with PBS, cells were enumerated and then spun for a further 5 min at 400×g.

In vitro monocyte differentiation assay
Following isolation of PBMCs, samples were plated or immediately stained for analysis by flow cytometry. Samples were plated in RPMI complete medium (RPMI (31870-025), 10% AB human serum (H4522), 1% pen/strep (15140-122), 1% L-glutamine (15140-122)) at a cell density of 2 million cells/well in a 6 well untreated plate (Corning, 3736) or 1 million cells/well in a 24 well untreated plate (Corning, 3738) for 24 h at 37 °C incubator either alone or with different treatments. To test the effect of epoxy-oxylipins on monocyte conversion, 12,13-EpOME (Cayman Chemical, 52450), 12,13-DiHOME (Cayman Chemical, 10009832), 14,15-EET (Cayman Chemical, 50651) and 14,15-DHET (Cayman Chemical, 51651) were added to PBMCs at a concentration of 1 μM. Lipids had their respective vehicles (Methyl Acetate (MA) for EpOMEs/DiHOMEs and ethanol (EtOH) for EETs/DHETs) evaporated under nitrogen gas in glass vials and were immediately reconstituted in RMPI complete medium prior to addition to the plate. Vehicle controls were prepared by adding the respective amount of MA or EtOH into a glass vial, which was then evaporated under nitrogen gas, followed by the addition of RMPI complete medium to control for any residual vehicle. Losmapimod was reconstituted in DMSO and diluted in RMPI complete medium and added to wells at a final concentration of 3 μM. A 0.03% DMSO vehicle was used. After a 24 h incubation period PBMCs were lifted from the plate and pelleted at 400×g for 5 min. Dissociation buffer (EDTA + Lidnocaine) was added to the wells and incubated for 5 min at RT. Pellets were resuspended in dissociation buffer (containing residual cells) and spun for a further 5 min at 400 g. Monocyte subsets were stained and visualised by flow cytometry as CD3−CD19−CD20−CD56-HLA-DR+CD14+CD16+.

Phosphorylation assays
To examine the phosphorylation status of proteins in monocytes, PBMCs were isolated and seeded into 24-well plates at a density of 1×106 cells per well. PBMCs were cultured with 1 μM 12,13-EpOME, MA or losmapimod overnight. After the 24 h incubation period, lipids or losmapimod were added again and incubated with 100 ng/LPS for 30 min. After 30 min, 4% PFA was added directly to wells at a 1:1 ratio and incubated for 15 min at 37 °C to preserve phosphorylation status. Cells were spun at 400 g for 5 min, the supernatant removed and then washed in 300 μL perm buffer and spun again. Samples were resuspended in 200 μL fixation buffer (Thermofisher, 00-5523-00) and incubated for 10 min at RT in the dark. Samples were spun at 400×g for 5 min and subsequently washed in perm buffer and then stained for AF647 phospho-p38 (1:10, 526066).

Immunohistochemistry
To analyse the temporal profile of both the CYP and EH enzymes, skin biopsies were obtained for immunohistochemistry analysis. Acu-Punch 3-millimetre kits (Schuco) were used post anaesthetisation (with 1% lignocaine hydrochloride (Hameln)) to collect skin biopsies at baseline or post UV-KEc injection. The biopsies were washed with saline (Hameln) and fixed for 24 h in 10% neutral buffered formalin (Roche) at 4 °C. Samples were embedded in paraffin, and 4 μm-thick sections were cut and placed on glass slides. Antibodies used for immunohistochemistry can be found in Supplementary Table 3.

Initially, the majority of paraffin was removed from paraffin-embedded biopsy sections by melting at 75 °C for 15 min. The remainder was removed by using an Autostainer (Leica); the biopsy sections were washed in xylene (a solvent) for 20 min. To remove the xylene, sections were washed with 100% ethanol for 10 min. To hydrate the sections, 5-min graded washes of ethanol (100, 70 and 30% ethanol) to water were performed. Antigen retrieval was achieved by breaking the formalin cross-links with a sodium citrate solution (0.3% sodium citrate (Sigma-Aldrich), 0.05% TWEEN 20 (Sigma-Aldrich), pH 6) at 95 °C in the water bath for 13 min. Following antigen retrieval, sections were washed with PBS and permeabilised/blocked for endogenous peroxidase activity by incubation with 3% hydrogen peroxide (VWR Chemicals) in methanol (VWR Chemicals) for 10 min at room temperature. The primary polyclonal antibody (supplementary) dilutions were prepared with antibody diluent PBS + 0.01% sodium azide (Sigma-Aldrich) + 0.1% bovine serum albumin (Sigma-Aldrich) + 4% horse serum (Thermo Fischer Scientific) and the sections were incubated with the primary antibody overnight at 4 °C. Subsequently, the sections were washed with PBS and incubated with a horseradish peroxidase anti-rabbit immunoglobulin G (IgG) polymer reagent biotinylated secondary antibody; ImmPRESS™ horseradish peroxidase Anti-Rabbit IgG (Polymer Detection Kit) for 30 min at room temperature. To visualise staining, the ImmPACT® NovaRED™ Peroxidase Substrate kit was used. Finally, sections were incubated in haematoxylin for 6 s for counterstaining. Excess haematoxylin staining was removed by incubation with acidified (hydrochloric acid; Thermo Fischer Scientific) 70% ethanol for 10 s. The sections were washed with water for 2 min and dehydrated by incubation with 70% and 100% ethanol for 2 and 4 min, respectively. To protect the biopsies, cover slips were applied using xylene and an automated Coverslipper machine (Leica). To analyse, sections were scanned using the Nanozoomer (Hamamatsu). The magnified images were taken using a 100x lens on a brightfield microscope (Euromex Novex 86025-LED). The Leica LAS AF Lite software v4.0, NDP.view v2.2 and imageJ v2 were used to qualitatively analyse images.

Lipidomics
Peripheral blood was collected by venepuncture using an aseptic technique. Plasma was isolated by centrifugation of sodium heparin vacutainer tubes (10 ml; Griener Bio-One) at 20,000×g for 10 min (room temperature). Both plasma and blister fluid tubes were frozen at −80 °C, anonymized and sent for lipid analysis at the Zeldin laboratory (National Institute of Environmental Health Sciences). A total of 297 biologically independent samples were analysed. Plasma samples (200 µL) were thawed and diluted 1:1 with (5 % methanol and 0.1% acetic acid). To prevent epoxy-oxylipin catabolism by sEH during extraction, 1 μL of t-AUCB in methanol (sEH-I; 10 μM final concentration) was added to each sample. After the addition of internal standards (11,12-DHET-d11 (150 ng), 11,12-EET-d11 (300 ng), 15-HETE-d8 (300 ng)), extraction was performed by liquid:liquid extraction using 1 ml ethyl acetate. The samples were dried and reconstituted in 50 µL of 30% glycerol. 10 μL injections were assayed on an Ultimate 3000 UHPLC equipped with an Xselect CSH C18, 2.1×50 mm, 3.5 μm particle column (Waters, Wilford, MA) and a TSQ Quantiva tandem mass spectrometer (Thermo Fisher Scientific, Waltham, MA), as previously described57. Relative response ratios of analytes were compared to linear standard curves generated with purchased oxylipins (Cayman Chemical, Ann Arbor, MI). Analyte retention times, ionisations, internal standards used and the range and r2 of the standard curves are shown in Supplementary Table 4. Representative chromatograms for 14,15-EET, 14,15-DHET, 12,13-EpOME and 12,13-DiHOME from a plasma sample are represented in Supplementary Fig. 13.

Luminex cytokine/chemokine analysis
Cytokine and chemokine profiles in blister fluid were quantified using the Luminex Performance Human XL Cytokine Panel (FCSTM18). Blister fluid was applied neat to the plate.

Losmapimod study design
The losmapimod experimental design has been previously described58. Peripheral blood samples were collected, and monocyte subsets were visualised using flow cytometry.

CD4 T cell—monocyte co-culture
Intermediate monocytes and total CD4 T cells were isolated using a BD S8 Spectral cell sorter (BD Biosciences) with the gating strategy shown in Supplementary Fig. 14, using a panel including Zombie NIR (Biolegend), BUV615 HLA-DR (BD Biosciences), BUV395 CD4 (BD Biosciences), AF700 CD14 (Biolegend), BV650 CD16 (BD Biosciences), APC-H7 CD3 (BD Biosciences). CD4 T cells were cultured with each monocyte subset at a 5:1 ratio for 48 h in complete RPMI medium incubated with cytokines 5 ng/ml IL-6 (Thermofisher, A42541), 20 ng/ml IL-8 (Thermofisher, PHC0084), 10 ng/ml IL-1β (Thermofisher, A42509), 20 ng/ml IL-18 (R&D, 9124-IL-050/CF) and 10 ng/ml IL-15 (Thermofisher, 200-15). Following incubation, samples were lifted from the plate, and the phenotype of CD4 T cells was analysed using a Sony ID7000 spectral flow cytometer with a panel containing antibodies for CD25, CD39, CD69, CD103, CLA, CTLA-4, HLA-DR, Ki67, CD45RO, FOXP3 and CD127 (Supplementary Table 2).

CD8 cytotoxicity assays
NK, CD8 T cells and monocyte subsets were isolated using a BD S8 Spectral cell sorter (BD Biosciences) using a panel including CD14 BUV805 (BD Biosciences), CD16 BV711 (BD Biosciences), HLA-DR PE (Biolegend), CD56 BUV395 (BD Biosciences), TCRαβ BUV615 (BD Biosciences), CD8 APC-Cy7 (Biolegend), CD19/CD20 FITC (Biolegend) and Live/Dead Zombie UV (Biolegend), plated and incubated with and without cytokines 5 ng/ml IL-6 (Thermofisher, A42541), 20 ng/ml IL-8 (Thermofisher, PHC0084), 10 ng/ml IL-1β (Thermofisher, A42509), 20 ng/ml IL-18 (R&D, 9124-IL-050/CF) and 10 ng/ml IL-15 (Thermofisher, 200-15) for 4 days. CD8 T cells and monocytes were culture data 5:1 ratio.

K562 target cells (ATCC, CCL-243) were loaded with 15 µM Calcein-AM (Thermofisher) for 30 min at 37 °C. Cells were washed and seeded at a 20:1 effector to target ratio in a 96-well U-bottom plate. NK cells were used as a positive control (maximum Lysis). K562 cells alone were used as a negative control (spontaneous release). Co-culture plates were spun at 400 g for 1 min and subsequently incubated for 6 h at 37 °C. Following incubation, samples were spun at 400×g for 5 min and 100 µL of supernatant was transferred to a black flat-bottom 96-well plate. Fluorescence was quantified using an Infinite 200 Pro plate reader (Ex: 488 nm, Em: 517 nm). Specific lysis was calculated as: (Sample − Spontaneous release)/(Maximum Lysis − Spontaneous release) × 100.

Fluorescent activated cell sorting (FACS)
Cell sorting was performed on the BD FACSDiscover S8 cell sorter. PBMCs were isolated, enumerated and stained in 0.2 µL antibody per 1 million cells for 30 min at 4 °C. Samples were washed and resuspended in PBS prior to sorting. Sorted cells were collected into PBS supplemented with 50% FBS.

Western blotting
Monocyte subsets were isolated using FACS. Cell pellets with lysed with RIPA buffer (Sigma-Aldrich), supplemented with phosphatase and protease inhibitor (Cell Signalling), vortexed and left on ice for 30 min. Samples were spun at 13,000×g for 20 min at 4 °C. The supernatant was combined with reducing agents (Life Technologies) and denatured for 5 min at 95 °C. Extracts were separated by protein electrophoresis using a 10% Bis-Tris pre-cast gel (NuPage) and transferred overnight at 4 °C onto Hybond PVDF membrane (GE Healthcare). Membranes were blocked in ECL blocking reagent (GE Healthcare) for 1 h prior to probing with phospho-p38 MAPK (T180/Y182; 9211) overnight at 4 °C. The membrane was washed and incubated with HRP-conjugated secondary antibodies (GE Healthcare, 1:4000 dilution) for 1 h at room temperature. Antibodies were detected using the ECL detection kit (GE Healthcare).

Statistical analysis
All statistical analysis was carried out using GraphPad Prism. Normalisation tests to determine normal or lognormal distribution were carried out on each data set. Normalisation tests used were the D’Agostino and Pearson test and the Shapiro–Wilk test and data were visualised using a QQ plot. Data sets with a normal distribution are presented as mean ± standard deviation (SD). Non-parametric data are presented as median ± 95% confidence interval (CI). During the course of the human study, there were instances where blood collection was not possible due to participant refusal. Furthermore, some blister samples were unable to be analysed due to technical issues. Therefore, then numbers between study groups and time points vary. For peripheral blood analysis n numbers are between 8–12 in each group. For blister cell analysis n numbers are between 5–12 in each group. For statistical analysis where there are two dependent variables, in this case time point and treatment, and one independent variable (e.g. cell count or lipid concentration) we used a two-way ANOVA mixed effect model. A large proportion of the data presented here is lognormally distributed, rather than normally distributed. In this case, we decided to proceed with a two-way ANOVA mixed-effect model (to account for missing values). In all other cases, where data were normally distributed we used two-way ANOVA, one-way ANOVA or a parametric t-test, as appropriate. With non-parametric data we used a Kruskal–Wallis test or Mann–Whitney non-parametric t-test. A p value below 0.05 was considered significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript investigates the effect of acute inflammation on epoxy-oxylipin metabolism and describes a novel finding that these epoxy-oxylipins could potentially modulate monocyte differentiation in humans. The findings could have a significant impact on the field of oxylipins and the inflammation process. This study represents the first human study that reveals how acute inflammation alters the epoxy-oxylipin metabolic enzyme expression and associated metabolite levels in both plasma (systemic) and inflamed tissues. In addition, because multiple clinical candidates of soluble epoxide hydrolase inhibitors, including the one used in this study, have been developed and the target diseases are highly related to inflammation, this study may shine a light on the molecular mechanism of how the inhibitors work which remains understudied. Besides, this study represents the first study that connects epoxy-oxylipin to monocyte differentiation. All of these represent a novel, original study. However, there are several key weaknesses in experimental design, statistical analysis, and discussions that require corrections as follows:\n1) The author claimed that inhibition of sEH preferentially elevated 12,13-EpOME and 14,15-EET only; however, the oxylipin analytical method used in the study does not cover all epoxy-oxylipin and corresponding dihydroxy-oxylipin.\n2) The lipidomic analysis method was under-described, there are a lot of missing details that prohibit one from evaluating if the method is appropriate, for example, what is the limit of detection/ quantifications of each metabolite, what are the instruments that were used in this method, was t-AUCB added after the blood was collected or after plasma separation? How was the t-AUCB was added? Was antioxidant added for the analysis? These lipids are highly sensitive to oxidation. What is the linear range of the analysis?\n3) The author should, at the very least, include the 12,13-EpOME level in Fig 1 to discuss how an elevation of CYP450 and sEH modulates the epoxy and dihydroxy-oxylipins.\n4) When referring to the data in the discussion, the author should reference the figures to make it easier for the reader to read.\n5) There are multiple occasions in which the author claimed inflammation increases the epoxy-oxylipin’s levels without the actual data.\n6) The authors also suggested that blocking inflammation-elevated epoxy-oxylipins by sEH inhibition is beneficial, but the outcome of sEH inhibition should increase / at least maintain epoxy-oxylipins. Therefore, the discussion is contradict to the main theme of this study.\n7) One of the key findings of this study is the discovery of epoxy-oxylipin’s effects on monocyte differentiation, affecting inflammation. The author suggests that sEH inhibitors reduce intermediate monocytes and increase T cell death. However, at most, there was a couple of % increase in T cell death with large variability.\n8) The author has repeatedly stated that the sEH inhibitor treatment alleviated UV-KEc-induced effects. However, statistical analyses between the treatment and control groups are lacking.\n9) Most experimental procedures in the method section are very brief. The author is strongly encouraged to provide more details to help others to reproduce the data.\nThe following are some minor comments that could improve this manuscript.\n1) Several new clinical studies suggest that sEH inhibitor treatment is safe. The author should cite the updated references.\n2) The author should include a detailed breakdown regarding the population of the recruited patients in this study.", "reviewer_2": "This study examines the effects of the selective sEH inhibitor GSK2256294 on inflammation using a transient skin inflammation model in humans. The analysis of lipid products in blood and skin provides valuable insights into real-time changes during localized inflammation. However, the authors attempt to infer new functional roles for intermediate monocytes, which the current data do not support. As a result, the paper’s conclusions are overstated and not fully backed by the experiments conducted.\nMajor Concerns: The flow cytometric analysis of monocytes in Figures 4 and 6 lacks clarity and supporting data. The authors should:\n1. Clearly explain the rationale for investigating blood monocytes in Figure 4, following findings from Figures 1–3, and the experimental approach. This is not explained in this part of the results section.\n2. Provide flow cytometric plots to illustrate gating strategies and confirm robustness of the UMAP. Also to provide context to the populations gated on for Figures 4C and D.\n3. Verify that Figure 4B is correctly labeled, as the CM-IM population appears inconsistent with the UMAP data. CM-IM look more like CM(1) than CM (2) while the CM(2) population is integrated into the CM(1) population in the UMAP.\n4. Justify the choice of cell identity assignments and antibody panel, as markers like CD206, CD163, and CD64 are more typical of differentiated macrophages than blood monocytes.\n5. Improve clarity in Figure 4D, where the suggested changes to IM are not clear, and seem to be more prevalent at 4 hours than 24—quantification would help.\n6. Clarify why the UMAP in Figure 4E (referred to as 4F in the text) has a different shape.\n7. Include flow cytometric plots for Figure 6 to confirm cell gating and demonstrate that there are sufficient cell numbers per gate. The complete separation of CM and IM populations in Figure 6’s UMAP is inconsistent with the transition from CM to IM shown in Figure 4 and needs explanation.\n7. Standardize cluster labeling between Figures 4 and 6—e.g., why are CD205+ cells considered moDCs in skin but CM in blood?\nAdditional Comments:\n1. Figure 5 requires flow cytometry data to validate the CM-to-NCM differentiation model and demonstrate that differentiation of CM occurs in vitro.\n2. The claim that in vitro conversion to IM is blocked (Figure 5A) is overstated—there is only a slight decrease in IM numbers.\n3. Figure 5B labeling should be consistent with they rest of the paper (e.g., CM instead of CD14+).\n4. Representative histograms would strengthen Figure 5C, where gMFI changes are minimal.\n5. The increase/stabilization of NCMs with Losmapimod in Figure 5E is striking but unexplained. How does this fit with the decrease in IMs?\n6. The conclusions from Figure 7 are speculative.For example the statement that “Neutrophil numbers were unaltered with the reduction in tissue intermediate monocyte-derived macrophages indicating that the removal of neutrophils was carried out by classical monocyte-derived macrophages earlier in the response” is not supported by the data. The authors have also not referred to monocyte-derived macrophages before this section. Other experiments are needed to demonstrate this interaction.\n7. More experiments are needed to directly demonstrate that IMs support CD4 T cells in the skin. This is derived from a trend towards a decrease in CD4 T cells in the context of fewer IMs which is not statistically significant ,and the authors cannot infer a causal link between the 2 observations. The statement “This was confirmed by showing that intermediate monocytes directly activate CD4 T cells in the absence of any co-stimulation” does not support the conclusion.\n8. The discussion over interprets the data and is very long, particularly in linking findings to opioid use—this section should be removed.\n9. Figures need better organization: e.g. graph sizes are inconsistent, axis labels are too small to read clearly, p-value notations vary (use of “ns” or the exact value is given), and legends lack clarity on n values and replicates."}, "author_response": "Comment 1. The author claimed that inhibition of sEH preferentially elevated 12,13-EpOME and 14,15-EET only; however, the oxylipin analytical method used in the study does not cover all epoxy-oxylipin and corresponding dihydroxy-oxylipin.\nAuthor response: A comprehensive list of major metabolites of PUFAs by CYP450s following prophylactic and therapeutic studies is provided in supplementary figure 10.\nComment 2. The lipidomic analysis method was under-described, there are a lot of missing details that prohibit one from evaluating if the method is appropriate, for example, what is the limit of detection/ quantifications of each metabolite, what are the instruments that were used in this method, was t-AUCB added after the blood was collected or after plasma separation? How was the t-AUCB was added? Was antioxidant added for the analysis? These lipids are highly sensitive to oxidation. What is the linear range of the analysis?\nAuthor response: We apologize for the brief description of the lipidomic methods and reliance on previous references. We have expanded the description in methods as cited below and added 2 supplemental figures to address these issues (Supplementary Figure 16 and Supplementary Table 4). The table includes transitions, retention times, ISTDs employed as well as the low and high limit of the standard curves (in pg/ul injected). These standard curves were all linear within the range, as evidenced by visual inspection and indicated by the high r2 values. All the minimum standards serve as LLOQ as all these peaks for these analytes exceed S/N>10. Supplementary figure 10 shows values for all analytes where most or all samples had peak responses with S/N>10. tAUCB was added by the Zeldin lab during or after thawing immediately prior to extraction. We observe substantial oxidation of fatty acids and oxylipins when using SPE; however, we do not have similar issues with liquid:liquid extractions. In the past we have seen no benefit of the addition of antioxidants in this assay and none were used in this study.\n“Plasma samples (200 ul) were thawed and diluted 1:1 with (5 % methanol and 0.1 % acetic acid). To prevent epoxy-oxylipin catabolism by sEH during extraction, 1 ul of t-AUCB in methanol (sEH-I; 10 μM final concentration) was added to each sample. 1 After addition of internal standards (11,12-DHET-d11 (150ng), 11,12-EET-d11 (300ng), 15-HETE-d8 (300ng)), extraction was performed by liquid:liquid extraction using 1 ml ethyl acetate. The samples were dried and reconstituted in 50 µl of 30 % glycerol. 10 ul injections were assayed on an Ultimate 3000 UHPLC equipped with an Xselect CSH C18, 2.1 × 50 mm, 3.5 μm particle column (Waters, Wilford, MA) and a TSQ Quantiva tandem mass spectrometer (Thermo Fisher Scientific, Waltham, MA) as previously described61. Relative response ratios of analytes were compared to linear standard curves generated with purchased oxylipins (Cayman Chemical, Ann Arbor, MI). Analyte retention times, ionizations, internal standards used and the range and r2 of the standard curves is shown in Supplemental Table 3. Representative chromatograms for 14,15-EET, 14,15-DHET, 12,13-EpOME and 12,13-DiHOME from a plasma sample are presented in Supplementary Figure 16”\nComment 3. The author should, at the very least, include the 12,13-EpOME level in Fig 1 to discuss how an elevation of CYP450 and sEH modulates the epoxy and dihydroxy-oxylipins.\nAuthor response: Included as suggested in Figure 1D vi.\nComment 4. When referring to the data in the discussion, the author should reference the figures to make it easier for the reader to read.\nAuthor response: We have made these additions, but, as this is unusual practice, we leave the final decision to the editor.\nComment 5. There are multiple occasions in which the author claimed inflammation increases the epoxy-oxylipin’s levels without the actual data.\nAuthor response: See figure 1, panel D (vi and vii) where 12,13-EpOME and total products, for instance, are elevated in the inflammatory exudates after intradermal injection of UVKEc in humans.\nComment 6. The authors also suggested that blocking inflammation-elevated epoxy-oxylipins by sEH inhibition is beneficial, but the outcome of sEH inhibition should increase / at least maintain epoxy-oxylipins. Therefore, the discussion is contradicted to the main theme of this study.\nAuthor response: We fear that there is some confusion on this point. To clarify our data, we indeed show that sEH inhibition elevates epoxy oxylipins above untreated control, see Figures 3 and 6, an expected outcome. This is associated with greater resolution of pain and a block in the expansion of intermediate monocytes, the implication of which, in chronic inflammation, are discussed.\nComment 7. One of the key findings of this study is the discovery of epoxy-oxylipin’s effects on monocyte differentiation, affecting inflammation. The author suggests that sEH inhibitors reduce intermediate monocytes and increase T cell death. However, at most, there was a couple of % increase in T cell death with large variability.\nAuthor response: sEH inhibition caused an increase in T cell death from 1.5% to 5.1%, with a variable SD as noted. This, in turn resulted in a reduction in CD4 T cells 2 from 672 cells/blister to 105 cells/blister at the site of inflammation, see Figure 7A. The question is – to what extent could this increase in percentage of cell death account for the corresponding reduced cell numbers. As stated by this reviewer, variability is an issue. But unlike rodents, human inflammation biology can be very variable in its responses.\nNow, we can certainly play with a hypothetical number pertinent to the current scenario: assume we have an initial number of CD4 T cells of 600 with a doubling time of 72 hours, a concomitant death rate of 5%/hour (i.e., survival = 95% per hour) over 24h. This would result in approximately 221 cells remaining after 24h. However, with limited information this is only conjecture. We are proposing that intermediate monocyte-derived macrophages keep tissue CD4 T cells alive, which is supported by the outcome of in vitro co-culture experiments presented in Figure 7E. However, there are inherent difficulties in predicting the precise extent to which this happens in vivo. Specifically, the impact of reducing the presence of a viability factor (intermediate monocyte-derived macrophages in this case) on numbers of surviving cells in a complex tissue microenvironment as we don’t know (1) the precise rate of CD4 T cell proliferation, (2) the rate of cell death, (3) ongoing infiltration alongside (4) efflux to draining lymph nodes.\nTherefore, we reasonably suggest stating that “However, numbers of CD4 T cells were reduced at 48 hrs following GSK2256294 coincident with an increase in T cells acquiring a live dead stain and remnants of immune debris. These findings are consistent with intermediate monocytes being able to maintain T cell viability creating a tentative link with helping to imprint long-term tissue immunity following infection”. This can be found in line 378 of the manuscript.\nComment 8. The author has repeatedly stated that the sEH inhibitor treatment alleviated UV-KEc-induced effects. However, statistical analyses between the treatment and control groups are lacking.\nAuthor response. These analyses have now been included in Figure 3, Figure 4 and Figure 6 where we have included comparisons between the drug and control groups for lipidomic analysis, IMs at 24 hrs in peripheral blood and IMs at 24 hrs in tissue\nComment 9. Most experimental procedures in the method section are very brief. The author is strongly encouraged to provide more details to help others to reproduce the data.\nAuthor response. These have been expanded as suggested including western blotting, PBMC isolation, flow cytometry, immunohistochemistry and an expanded on lipidomics analysis.\nMinor comments\nComment 1. Several new clinical studies suggest that sEH inhibitor treatment is safe. The author should cite the updated references.\nAuthor response: We have included references in the paper (page 14, line 319) that confirm the safety of sEH inhibitors for use in humans.\nComment 2. The author should include a detailed breakdown regarding the population of the recruited patients in this study.\nAuthor response: We included a table of characteristics on page 19, line 442, but as this is not a clinical trial we didn’t record weight/height/ethnicity.\nComment: Major Concerns: The flow cytometric analysis of monocytes in Figures 4 and 6 lacks clarity and supporting data. The authors should:\nAuthor response: We have addressed these flow cytometric issues in points 2-9 below.\nComment 1. Clearly explain the rationale for investigating blood monocytes in Figure 4, following findings from Figures 1–3, and the experimental approach. This is not explained in this part of the results section.\nAuthor response: When conducting interventional experiments for the first time especially in humans (it’s easy to re-order more mice from Charles River etc, but recruiting humans is less easy), an exploratory approach is best – shake the tree and see what falls out, as it were, and design your experiments to capture this knowledge based on extensive experience. A hypothesis driven approach runs the risk of being too prescriptive and missing something fundamentally important. This is precisely the approach we took to these studies. Certainly, we had prior knowledge of the model (PMID: 27499924) and data showing the CYP450 products are elevated in tissues 14hrs after UVKEc injection (Figure 1 vii) mirroring mononuclear 4 phagocyte infiltration. Explaining why a particular trail of investigation was followed, will only lengthen an already long manuscript and add little to the overall take home point.\nComment 2. Provide flow cytometric plots to illustrate gating strategies and confirm robustness of the UMAP. Also to provide context to the populations gated on for Figures 4C and D.\nAuthor response: Gating strategies have been included in Supplementary Figure 11 to show the gating strategies for the manual gating in Figure 4A and to show which population was used in the generation of the UMAPs in 4B.\nComment 3. Verify that Figure 4B is correctly labelled, as the CM-IM population appears inconsistent with the UMAP data. CM-IM look more like CM(1) than CM (2) while the CM(2) population is integrated into the CM(1) population in the UMAP.\nAuthor response: We confirm that the UMAP has been labelled correctly as the CM-IM monocyte population is gaining the expression of CD16, which is mirrored in the manual gating.\nComment 4. Justify the choice of cell identity assignments and antibody panel, as markers like CD206, CD163, and CD64 are more typical of differentiated macrophages than blood monocytes.\nAuthor response: In this study we are dealing with two compartments - peripheral blood and inflamed tissue with cells migrating from blood into tissue in a dynamic continuum. Hence, for direct comparison purposes, we designed the panel of archetypical markers to capture mononuclear phagocytes in both compartments bearing in mind that some blood monocytes also share markers with tissue macrophages. Therefore, it was important to include both monocyte markers and markers more typical of tissue macrophages with CD64, for instance, being an important monocyte marker, as well as being expressed on macrophages. These markers also inform on the phenotype of tissue mononuclear phagocytes.\nComment 5. Improve clarity in Figure 4D, where the suggested changes to IM are not clear, and seem to be more prevalent at 4 hours than 24—quantification would help.\nAuthor response: We have updated this figure and have quantified the UMAP population of IMs in Figure 4B.\nComment 6. Clarify why the UMAP in Figure 4E (referred to as 4F in the text) has a different shape.\nAuthor response: Monocyte marker expression changes when monocytes enter the tissue, hence the change in shape of the UMAP from peripheral blood monocytes to mononuclear phagocytes that have been isolated from inflamed tissue. Below, we have provided graphs that show how the MFIs of the markers we have used differ between blood monocytes at 24 hrs and tissue monocytes at 24 hrs for the reviewers in the figure below:\nMonocyte phenotype in blood vs. blister 24 hrs after inflammatory onset.\nThe forearms of participants were intradermally injected with UV-killed E. coli (UVKEc) resulting in a local and peripheral inflammatory response. Peripheral bloods and local inflammatory exudate were collected and analysed at baseline and 4 hrs, 24 hrs and 48 hrs post UVKEc injection. Data shows monocyte expression for (A) HLA-DR, (B) CCR2, (C) CD64, (D) CD86, (E) CD14, (F) CD16, (G) CD11c and (H) CD163 on (i) peripheral blood or (ii) blister monocyte subsets at 24 hrs post UVKEc. Data are presented as mean ± SD for n = 11 or n = 6 for both groups. All graphs were analysed using one-way-ANOVA mixed effect analysis with Sidak’s multiple comparisons test, *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001.\nFurthermore, we know that there is a linear differentiation pathway for classical monocytes to intermediate monocytes, and then non-classical monocytes in blood (PMID: 28606987). However, we cannot say with certainty that there is a similar linear differentiation process in inflamed tissues, or whether respective blood monocyte populations migrate into inflamed tissue in a sequential manner. The latter is supported by the fact that sEH inhibition reduced numbers of blood intermediate monocytes (Figure 4), and consequently fewer of these cells were found in the 6 inflamed skin, Figure 6. Indeed, given the differing shape of the UMAP we would hypothesise that differentiation is not occurring in the tissue.\nComment 7. Include flow cytometric plots for Figure 6 to confirm cell gating and demonstrate that there are sufficient cell numbers per gate. The complete separation of CM and IM populations in Figure 6’s UMAP is inconsistent with the transition from CM to IM shown in Figure 4 and needs explanation.\nAuthor response: Flow cytometry plots representative of a 24h blister sample have been included in Supplementary Figure 11. There are several reasons for the differences between UMAPs in Figure 4 versus Figure 6. In peripheral blood, classical monocytes are the predominant population making up over 80% of the total monocyte population followed by 10% each of intermediate and non-classical. However, in the inflamed skin, mononuclear phagocytes bearing an intermediate phenotype are quantitatively the predominate cells type (Figure 7C), arising from infiltrating blood intermediate monocytes as argued above. Moreover, in blood, monocytes are in a relatively low state of activation which changes dramatically when they transmigrate across the microvascular endothelium and into inflamed tissue niches. Therefore, this difference in UMAP between Figure 4 and Figure 6 is a facet of relative proportions, quantities and activation status.\nWe have clarified in the text that we expect that linear differentiation is not occurring in tissue, see page 15 line 333.\nComment 8. Standardize cluster labeling between Figures 4 and 6—e.g., why are CD205+ cells considered moDCs in skin but CM in blood?\nAuthor response: In humans, classic monocytes are identified using standard markers including HLA-DR-, CD14+ and CD16-. As stated above, we used the same markers for blood monocytes as for tissue mononuclear phagocytes. Given that we would not expect to see monocyte-derived DCs in blood, CD205 is simply higher in blood classical monocytes as is consistent with the literature. In contrast, in the inflamed tissue, mononuclear phagocytes positive for CD205 are likely to be monocyte-derived DCs. Hence, the differences between blood monocytes and tissue mononuclear phagocytes are due to monocytes acquiring a new phenotype once they arrive at inflamed tissue niches having migrated across the microvascular endothelium, as described above.\nIn tissue there will be a multitude of inflammatory cues that result in the differentiation of monocytes into different macrophage populations and monocyte derived DC populations. These signals will be absent from blood.\nAdditional comments\nComment 1. Figure 5 requires flow cytometry data to validate the CM-to-NCM differentiation model and demonstrate that differentiation of CM occurs in vitro.\nAuthor response: In Supplementary Figure 14 we have included gating strategies for monocytes from freshly isolated PBMCs and PBMCs that have been cultured for 24 hrs to show the transition from classical monocytes into intermediate monocytes 7\nComment 2. The claim that in vitro conversion to IM is blocked (Figure 5A) is overstated—there is only a slight decrease in IM numbers.\nAuthor response: The mean percent of intermediate monocytes of total monocytes decreases from 53% to 41% with addition of 12,13-EpOME, which we appreciate is not a total block and we have amended the language in the paper accordingly (line 238). We would point out that these lipids are biologically unstable and it is likely that their stability will be reduced in vitro compared to the continuous synthesis that occurs in vivo, especially given that this was a single dose of lipids over a 24h period. In vivo this is a more dynamic process where cells are continuously exposed to constitutively high level of lipids, result in the effect being more prolonged and potent\nComment 3. Figure 5B labelling should be consistent with the rest of the paper (e.g., CM instead of CD14+).\nAuthor response: To not tamper with the original western blot image we did not change the labelling of the monocyte subsets, however, if the editors wish us to do so then we can.\nComment 4. Representative histograms would strengthen Figure 5C, where gMFI changes are minimal.\nAuthor response: We have added in a histogram in figure 5C, showing the shift to the left of the curve with the addition of 12,13-EpOME. We appreciate the shift is subtle but is consistent across 10 experimental repeats.\nComment 5. The increase/stabilization of NCMs with losmapimod in Figure 5E is striking but unexplained. How does this fit with the decrease in IMs?\nAuthor response: This is likely a facet of the relative abundance of classical monocytes in resting blood of humans (80%) to that of intermediate monocytes (10%) as well as the rate of differentiation of classical monocytes to intermediate monocytes under steady data, which data in Figure 5E represents.\nDuring steady state, 1% of the abundant classical monocytes differentiate into less abundant intermediate monocytes within 24h. In contrast, intermediate monocytes persist in blood for 4.3 days while non-classical monocytes persist in blood for 7 days, (PMID: 28606987). As losmapimod was given to participants for four days without inflammation and, in keeping with the dynamic liner differentiation of classical to intermediate monocytes during steady state, it would be unlikely to see a significant reduction in non-classical monocytes during this time.\nComment 6. The conclusions from Figure 7 are speculative. For example the statement that “Neutrophil numbers were unaltered with the reduction in tissue intermediate monocyte-derived macrophages indicating that the removal of neutrophils was carried out by classical monocyte-derived macrophages earlier in the response” is not supported by the data. The authors have also not referred to monocyte-derived macrophages before this section. Other experiments are needed to demonstrate this interaction.\nAuthor response: We agree, and we have toned down this assertion, page 16 line 376.\nComment 7. More experiments are needed to directly demonstrate that IMs support CD4 T cells in the skin. This is derived from a trend towards a decrease in CD4 T cells in the context of fewer IMs which is not statistically significant, and the authors cannot infer a causal link between the 2 observations. The statement “This was confirmed by showing that intermediate monocytes directly activate CD4 T cells in the absence of any co-stimulation” does not support the conclusion.\nAuthor response: See response to Reviewer 1, point 7. Also in discussion, page 16 line 378.\nComment 8. The discussion over interprets the data and is very long, particularly in linking findings to opioid use—this section should be removed.\nAuthor response: Agreed! The discussion has been shortened.\nComment 9. Figures need better organization: e.g. graph sizes are inconsistent, axis labels are too small to read clearly, p-value notations vary (use of “ns” or the exact value is given), and legends lack clarity on n values and replicates.\nAuthor response: All have been corrected line with suggestions made."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This manuscript is the first report of investigating clinical candidate of sEH inhibitor GSK2256294 effect on acute inflammation in human patient. In addition, this study demonstrated that sEH inhibitor modulates intermediate monocyte population, having a huge implication of chronic inflammatory diseases and the mechanism of the effect of sEH inhibition in different diseases. Given that sEH has beneficial effects in multiple inflammatory disease models, this studies demonstrated the potential translatability of sEH inhibitors in human diseases. The studies are clearly described and easy to follow. The authors did an excellent job to address previous concern. There are still some minor concerns as below.\n1) on Page 7, 12,13-EpOME is not sEH metabolites. Also, 17,18-DiHETE is sEH metabolites but not CYP450 metabolites.\n2) The figure 5Bi needs a proper standard size markers. It is also recommended to submit all western blot pictures.\n3) The caption of Figure 5C is not very clear.\n4) The author should upload the metadata of the oxylipin analysis.", "reviewer_2": "The authors have worked hard to address my comments and have included a significant amount of new information which strengths the manuscript. Whilst the data on GSK2256294 are interesting and very relevant to the management of human pain and inflammation, my concern remains that the link to a mechanistic role for intermediate monocytes cannot be made from these data without more mechanistic experiments. Because of this I find the statements in the manuscript linked to this, such as in the abstract \"revealing a role for intermediate monocytes in maintaining CD4 T cell viability during inflammation-resolution\" are still over-stated.\nWith respect to the naming of the cell clusters, I still believe that there is inconstancy between the figures that should be resolved. If, as the authors say in their response, they have selected genes in figure 4 to capture the full spectrum of monocytes/mo-macrophages that could be seen in blood and tissue, why have they selected to used a different panel including CD163 macrophages in the skin? Likewise the \"CM\" in Figure 6 in the skin do not express CD14 so why are they labelled as such. This is important because subjective labelling of the clusters makes it harder for the reader to make their own conclusions about the cells present in the blood versus tissue.\nFor Figure 7 flow cytometry data showing shared expression of the defining CD4 T cell markers would help the reader understand which populations are present. For these data I am missing a crucial control of CM + T cells. How can the authors be confident that the T cell phenotype is IM-specific rather then due to T cell interaction with any APC? This would be a vital step towards the claim that IM mechanistically alter T cell responses in the skin in the presence of GSK2256294."}, "author_response": "Comment 1. on Page 7, 12,13-EpOME is not sEH metabolites. Also, 17,18-DiHETE is sEH metabolites but not CYP450 metabolites.\nAuthor response: We thank the reviewers for pointing this out. We have amended the language on p7 line 145 to clarify that 12,13-EpOME is a CYP450 metabolite. Furthermore, we have amended the language for 17,18-DiHETE on page 7 line 150.\nComment 2. The figure 5Bi needs a proper standard size markers. It is also recommended to submit all western blot pictures.\nAuthor response: We have uploaded all the western blot images for the four donors. Exposure of the western blot was carried out using x ray film, as such, the ladder does not show up. The ladder was marked in situ using pen and is clearly labelled on the left hand side. We have included pictures of all western blots for P-p38 and the GAPDH control in supplementary figure 19.\nComment 3. The caption of Figure 5C is not very clear.\nAuthor response: We have amended the caption of figure 5C to be clearer:\n‘PBMCs (2×106/well) were treated with 1 μM 12,13-EpOME or vehicle (MA) for 24 h, then re-stimulated with 12,13-EpOME or MA plus 100 ng/ml LPS for 30 min. Cells were fixed and stained for HLA-DR and phosphorylated p38 (P-p38). (i) Phosphorylation of p38 in PBMCs treated with 12,13-EpOME or MA following LPS stimulation (representative of 10 experiments). (ii) Representative histogram of P-p38 MFI in PBMCs treated with LPS + MA, LPS + 12,13-EpOME, LPS + losmapimod (Los), or unstimulated.’\nWe hope that this is clearer for the reader.\nComment 4. 4) The author should upload the metadata of the oxylipin analysis.\nAuthor response: The raw LCMS files, protocols and metadata were uploaded to MassIVE (https://massive.ucsd.edu/). The accession numbers are MSV000099360 and MSV000099361.\nComment 1. The authors have worked hard to address my comments and have included a significant amount of new information which strengths the manuscript. Whilst the data on GSK2256294 are interesting and very relevant to the management of human pain and inflammation, my concern remains that the link to a mechanistic role for intermediate monocytes cannot be made from these data without more mechanistic experiments. Because of this I find the statements in the manuscript linked to this, such as in the abstract \"revealing a role for intermediate monocytes in maintaining CD4 T cell viability during inflammation-resolution\" are still over-stated.\nAuthor response: We thank the reviewer for this comment and understand their concerns. We have further toned down the language in the abstract on page 2, line 39. We have also put the new sentence below:\n‘Fewer intermediate-like monocytes were observed at the site of inflammation, accompanied by reduced tissue CD4 T cells, suggesting that intermediate monocytes may contribute to supporting CD4 T cell viability during inflammation resolution.’\nWe ask the review to look at our response to comment 3 for more information.\nComment 2: With respect to the naming of the cell clusters, I still believe that there is inconstancy between the figures that should be resolved. If, as the authors say in their response, they have selected genes in figure 4 to capture the full spectrum of monocytes/mo-macrophages that could be seen in blood and tissue, why have they selected to use a different panel including CD163 macrophages in the skin? Likewise the \"CM\" in Figure 6 in the skin do not express CD14 so why are they labelled as such. This is important because subjective labelling of the clusters makes it harder for the reader to make their own conclusions about the cells present in the blood versus tissue.\nAuthor response: We appreciate these comments. The panel that has been used to identify the clusters in both figure 4 and figure 6 is identical, as can be seen in the heatmap in both figures. The markers that have been used for both are HLA-DR, CD16, CD14, CD206, CD205, CCR2, CD64, Tim-4, CD11c, CD86 and CD163. The markers appear in a different order on each heatmap due to the differential hierarchical clustering. The list of antibodies used in clustering has also been added to the figure legends in Figure 4 and Figure 6.\nTo the reviewer’s second concern regarding CM in the skin not expressing CD14, we have chosen to represent the marker expression in the clusters as a heatmap, which means that the expression of each marker is a scaled expression. Although there is CD14 expression on the CM, it is significantly lower than that on the IM and CM-IM (hence a darker colour on the scaled expression). For the avoidance of any doubt, we have included in supplementary figure 18 box plots of the marker expression from the clusters in Figure 6 from the supervised clustering and we have included the marker expression of HLA-DR, CCR2, CD64, CD86, CD14, CD16, CD11c and CD163 in each of the monocyte subsets from manual gating analysis. Both of these demonstrate that, in the blister, CD14 expression is significantly higher in the intermediate monocytes compared to CM. This differs from peripheral blood, where CD14 expression is equal between CM and IM. We hope that the inclusion of these data will also allow the reader to make their own conclusions on the clusters and clarify the significant changes to each monocyte marker upon entering the tissue.\nComment 3. For Figure 7 flow cytometry data showing shared expression of the defining CD4 T cell markers would help the reader understand which populations are present.\nAuthor response: The gating strategy for each population has been shown in Supplementary Figure 21 and this has been added to the manuscript on p13, line 303.\nFor these data I am missing a crucial control of CM + T cells. How can the authors be confident that the T cell phenotype is IM-specific rather than due to T cell interaction with any APC? This would be a vital step towards the claim that IM mechanistically alter T cell responses in the skin in the presence of GSK2256294.\nAuthor response:\nWe did these experiments and found that CMs exert similar effects on CD4 T cells as IMs and we have included this data in Supplementary Figure 20. However, given that IMs are significantly higher in number compared to CM (Figure 7c) – 350 CM versus 1800 IMs – and that only IMs are reduced in the skin following GSK2256294 coincident with reduced T cells (Figure 7A) we logically concluded that during resolving inflammation when IM predominate, they maintain CD4 T cell viability. We have, therefore, altered the abstract and discussion to reflect a more cautious interpretation of these data. We have added a sentence for this additional supplementary information on page 13 line 304."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have addressed my comments and I appreciate the addition of the new supplementary figure 18 which is useful in understanding the spread of gene expression between blood and skin monocytes.\nSince the co-culture data do not show a clear difference between classical and intermediate monocytes (and, in several cases, non-classical monocytes) in their effect on T cell phenotype, I maintain that the claim that intermediate monocytes specifically support CD4⁺ T cell viability can be discussed in the manuscript but omitted from the abstract.\nI suggest that the paper and the abstract contain enough interesting information on the impact on monocytes without the need to infer a T cell mechanism that hasn't been proved."}, "author_response": "Author Response: We have altered the abstract and removed the claim that IMs specifically support CD4 T cell viability, line 39.\nAuthor response: We have altered the discussion to include the reviewer’s suggestions, line 379."}]

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