Fig 2: PD-1 expression on Treg, CD4 and CD8 T cells in HPV-positive and -negative patients. (a) The percentages of PD-1-positive T cells were quantified as shown in Figure 3 for Tregs, conventional CD4s and CD8s. Means for the HPV-positive and -negative groups are depicted as red and blue lines, respectively, in the plots; P-values are from non-parametric repeated-measures (blood, TIL) models of each cell type. ((+)=HPV positive, N=14), ((o)=HPV−, N=11) P-values for HPV+ vs − were not significant. (b) MFI: Tregs, convention CD4s and CD8 T cells were gated on as individual populations and evaluated for PD-1 MFI in the blood and TIL of head and neck cancer patients. Because of physical changes in our flow cytometer over the course of the study, the fold-change in MFI of TIL relative to blood was used to normalize the MFI data set and is shown for each subset of T cells. As in Figure 4, in a subset of samples with consistent cytometer settings there was significant differences in MFI in the Treg population (P=0.0313) and conventional CD4 population (P=0.0313), as well as the CD8 population (P=0.002) when the TIL were compared with blood (data not shown).

Fig 3: Cpt1a genetic knockdown modulates the metabolism of 12-HETE and its metabolites by RAW cells, etomoxir reduces the metabolism of tetranor triene in vivo, and prevents LPS stimulation of RANTES/TNFa generation, oxylipins regulate leukocyte responses, and transcriptomics of human neonatal sepsis proposes multiple genes that support a mitochondrial oxylipin ß-oxidation pathway.a–c Knockdown of Cpt1a alters 12-HETE metabolism. RAW cells were incubated ± LPS (100 ng/ml, for 4 h) with 12-HETE (2.34 µg/ml) added after the first hour (thus added for 3 h). Supernatants were harvested, then lipids were extracted and analyzed for 12-HETE and metabolites using LC/MS/MS as described in Methods (n = 4 (basal nonsil), 5 (basal Cpt1aKD), or 6 (other groups), separate wells of cells, mean ± SEM). d Tetranor triene 12-HETE is increased in vivo during inflammation with CPT1 inhibition. Wild-type mice (female, 7–9 weeks) were injected i.p. with vehicle (PBS), etomoxir (100 µg), and/or LPS (1 µg). After 6 h, lavage was harvested and lipids extracted and analyzed using LC/MS/MS as outlined in Methods (n = 10, individual mice). Outliers are shown as red, or with values. e, f Etomoxir dampens the generation of RANTES and TNF by peritoneal macrophages. Supernatants from macrophages cultured in vitro for 24 h with LPS (100 ng/ml)/etomoxir (25 µM) were tested for RANTES/CCL5 or TNF using ELISAs as described in Methods (n = 3 per group, separate wells of cells, mean ± SEM). a–f *p < 0.05, **p < 0.01, ***p < 0.005 with/without etomoxir, one-way ANOVA with Tukey post hoc test was used. g, h Oxylipin levels detected in vivo promote neutrophil ROS generation. Whole blood was incubated with oxylipin mixtures (Supplementary Table 1) , and then ROS generation in response to stimulation using opsonized S. epidermidis was determined by APF fluorescence as indicated in Methods. Three individual donor samples were separately tested. Monocytes and neutrophils were analyzed using the gating strategy as outlined in Methods. Response to high or low oxylipin doses was expressed either as %APF + cells or % of the vehicle (ethanol) response. Each donor is shown separately. Data were analyzed by repeated-measures two-way ANOVA (g—pairing p < 0.0001, cell type p = 0.018, lipid p = 0.0002, and interaction p = 0.0005; h—pairing p = 0.0008, cell type p = 0.0094, lipid p = 0.0082, and interaction p = 0.0024), Sidak’s post hoc tests are indicated—g Neutrophils ethanol vs low lipid p = 0.0001, neutrophils ethanol vs high lipid p < 0.0001, neutrophils low lipid vs high lipid p = 0.0350, monocytes ethanol vs low lipid p = 0.7020, monocytes ethanol vs high lipid p = 0.8075, monocytes low lipid vs high lipid p = 0.8075—h neutrophils low lipid vs high lipid p = 0.0023, monocytes low lipid vs high lipid p = 0.2363). i Oxylipin levels detected in vivo modulate CD4+ and CD8+ T-cell activation. Whole blood was incubated with oxylipin mixtures (Supplementary Table 1), and then generation of TNF was determined in response to activation using CD3/CD28 beads as indicated in Methods. CD8+ and CD4+ T cells were analyzed using the gating strategy outlined in Methods. Data from individual donors is shown. Two-way ANOVA (repeated measures) was used, pairing p = 0.0039, interaction p = 0.1690, cell type p = 0.0007, effect of lipid p = 0.0253, Sidak’s post hoc tests are indicated—CD4: no lipid vs low lipid p = 0.0461, CD4: no lipid vs high lipid p = 0.7115, CD4: low lipid vs high lipid p = 0.9989, CD8: no lipid vs low lipid p = 0.3742, CD8: no lipid vs high lipid p = 0.0449, CD8: low lipid vs high lipid p = 0.9952). j Human transcriptomics data show significant upregulation of several genes including ACSL and CPT isoforms. Gene expression (log2 expression data) from 35 controls and 26 bacterial confirmed sepsis cases published in57 were compared for expression of 32 genes putatively involved in mitochondrial import and ß-oxidation of oxylipins, based on known metabolic pathways for long-chain native FA. *p < 0.05, ***p < 0.005, Student’s t-test, two-tailed, then adjusted using Benjamini–Hochberg test.

Fig 4: ZFP36L2 and ZFP36 knockout increases the intracellular concentration of 3′ ARE-containing cytokinesFrequency of CD4+ (top rows) and CD8+ (bottom rows) T cell staining for the cytokines indicated on the y axis.(A) Frequency of cytokine-positive cells from ZFP36L2 knockout T cells shown in green for each of two gRNAs (L2g1 and L2g2, biological triplicate experiments).(B) Frequency of cytokine-positive cells from ZFP36 knockout T cells shown in orange for two gRNAs (36g1 and 36g2, biological triplicate experiments).AAVS1 and NT are AAVS1 safe-harbor-targeting and non-targeting negative control gRNAs. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, Student’s two-sample, two-tailed t tests. Error bars: SEM. Sanger gene-inactivation percentages displayed at bottom. Selected cytokine assessments, including the L2g1 ZFP36L2 guide for IL-17A, were performed using an additional donor (Figure S5).

Fig 5: (a) Flow cytometry gating strategy: lymphocytes were obtained from the tumor and PBL of head and neck cancer patients and were analyzed by flow cytometry. A typical lymphocyte forward and side scatter gate was used to enrich for lymphocytes and exclude larger cells, such as tumor cells. A singlet gate was used to remove doublets from the analysis. Live cells are then selected by gating on the population that excludes the viability dye. Cells were then gated on CD3+ CD14− cells to establish a pure T-cell population. This is then followed by the CD4 and CD8 gate. Tregs were identified by CD25-positive FoxP3-positive staining. This example was stained using TIL. (b) Tumor vs PBL: CD3 T cells were subfractionated into CD4 and CD8 T cells and the CD8, Tregs and conventional CD4s were assessed for OX40, CTLA-4 and PD-1 in the blood and TIL of cancer patients. Tregs were differentiated from the conventional CD4s using the CD25 and FoxP3 markers as shown above. Expression of OX40, CTLA-4 and PD-1 were assessed in combination with the activation marker HLA-DR in the three T-cell subsets. It should be noted that, in the subsequent histogram figures and throughout the rest of the manuscript, we use this T-cell gating strategy.