Clinical Characteristics of Study Subjects
Our study population (including both the main transcriptomics and cell culture perturbation analyses) consisted of 13 T2D cases and 8 healthy controls with clinical and demographic information on: age, gender, self-reported ethnicity, self-reported smoking status, body-mass index (BMI), blood total cholesterol, blood glucose, percent hemoglobin A1C (HbA1c), periodontal condition, neutrophil cell counts, and monocyte cell counts (Table 1). The T2D individuals in the cohort had higher blood glucose and more advanced periodontal disease compared to healthy individuals. Diabetes diagnosis was confirmed by elevated blood glucose and HbA1c levels, with blood glucose > 126 mg/dl and HbA1c > 6.5 % indicating diabetes, consistent with clinical criteria and the literature (16); HbA1c was not measured for healthy control individuals in this cohort. T2D individuals tended to have higher cholesterol, BMI, neutrophil counts, and monocyte counts, but these factors did not reach statistical significance, while T2D individuals were significantly older than healthy individuals (p=0.007).
Global Gene Expression Levels of Neutrophils in Health and Disease
We observed statistically significant differential expression of 50 genes (FDR-corrected p<0.05, Figure 1A, Table 2, Supplementary Fig. S1) between T2D and healthy neutrophils. Differentially expressed genes were primarily inflammatory- or lipid-associated and were almost universally downregulated in T2D individuals relative to healthy individuals, with the exception of GTSCR1 (Gilles de la Tourette syndrome chromosome region, candidate 1), a non-coding RNA gene that was upregulated in T2D neutrophils.
The top 3 differentially expressed genes were downregulated in T2D neutrophils (Figure 1B): inflammatory signaling gene SLC9A4 (solute carrier family 9 member A4; log(FC)=-5.42, FDR-corrected p=0.001), immune regulating gene NECTIN2 (nectin cell adhesion molecule 2; log(FC)=-3.77, FDR-corrected p=0.002), and anti-inflammatory gene PLPP3 (phospholipid phosphatase 3; log(FC)=-5.37, FDR-corrected p=0.002); while only GTSCR1 was upregulated in T2D neutrophils (log(FC)=3.71, FDR-corrected p=0.02, Figure 1B).
Of the significant differentially expressed neutrophil genes, 34% (17/50) had known immune or inflammation related roles, with 24% (12/50) of genes linked to lipid or glucose metabolism (Table 2). In addition to NECTIN2 and PLPP3, 15 other annotated immune or inflammation associated genes had significantly lower gene expression in T2D neutrophils (Table 2), while KCNH4, GNPDA1, TLCD2, VEGFA, MFSD2A, PNPLA1, GPR4, and LPCAT1 showed lower expression in T2D neutrophils and had previously been linked to lipid or glucose metabolism. Indeed, several significant differentially expressed genes, including PLPP3, SPHK1, ABCG1, and PTGES, have previously been indicated to have both lipid- and inflammatory-related roles.
This interaction between lipid and inflammatory pathways was further evident when we investigated neutrophil gene expression by biological pathways and observed that most of the significant biological pathways were lipid-related, despite the abundance of immune- and inflammatory-related genes among our significant results (Table 3, Supplementary Figs. S2-S3).
Overall, 13 pathways and 19 diseases were over-represented (uncorrected p<0.05) in our cohort by gene expression differences. The top 5 over-represented KEGG pathways were heavily lipid-related: sphingolipid metabolism, ether lipid metabolism, phospholipase D signaling pathway, Fc gamma R-mediated phagocytosis, and glycerophospholipid metabolism; though only sphingolipid metabolism was statistically significant after Bonferroni or FDR correction (corrected p=0.02, Supplementary Table S1). Of the 19 over-represented diseases, 15 remained significant by FDR correction, but none remained significant after Bonferroni correction, and all 19 had only a single differentially expressed gene represented in that particular disease pathway. The top 5 disease pathways were: citrullinemia, Paget's disease, endometriosis, junctional epidermolysis bullosa, and avascular necrosis/osteonecrosis of femoral head (Supplementary Table S2). Additionally, top GO biological process, molecular function, and cellular component pathways were interrogated (17,18). Though none reached significance after Bonferroni or FDR correction, the top GO pathways also included lipid-related and plasma membrane pathways (Table 3).
When we investigated the top KEGG and GO biological pathways (Table 3) by individual gene expression across the top 50 genes, we observed hierarchical clustering of pathway and gene expression by groups of individuals (Figure 2). These clusters primarily separated out diabetic and healthy individuals and included disease heterogeneity in gene levels.
Neutrophil Gene Expression by Lipid Ligand Resolvin E1 Dose-Response
Because endogenous inflammatory pathways are deficient in chronic inflammatory diseases like T2D, we aimed to understand whether treating isolated neutrophils with an exogenous lipid ligand resolution mediator, RvE1, would impact transcription phenotypes.
We conducted a perturbation experiment across a range of clinically relevant RvE1 doses (0-100nM) and observed that, as expected, dosing of the RvE1 ligand impacted T2D neutrophils differently than healthy neutrophils. We investigated this dose-response phenotype between three comparisons: 1) T2D neutrophils only, 2) healthy neutrophils only, and 3) T2D vs. healthy neutrophils.
RvE1 treatment induced differential gene expression (uncorrected p<0.05) across doses (Figure 3), including 59 genes in healthy and 216 genes in T2D neutrophils. Comparing T2D to healthy neutrophils, 1097 genes were differentially expressed across treatment doses, including two statistically significant (FDR-corrected p<0.05) inflammatory genes, LILRB5 (leukocyte immunoglobulin like receptor B5) and AKR1C1 (aldo-keto reductase family 1 member C1). Neutrophils from T2D subjects had a stronger response to RvE1 treatment dose-dependently (more differentially expressed genes), particularly at the clinically relevant dose, 100nM (11) (117 differentially expressed genes between100nM and 10nM RvE1), showing modified gene expression by RvE1 treatment. In contrast, healthy neutrophils were not as perturbed by RvE1 dosing (few differentially expressed genes between doses), though some effect was observed at the 10nM dose (25 differentially expressed genes between 10nM and 0nM RvE1). Treatment of T2D neutrophils with 100nM RvE1 resulted in a reduction in the number of differentially expressed genes in T2D neutrophils compared to healthy neutrophils (98 genes when T2D neutrophils were treated with 100nM RvE1 versus 169 genes without RvE1 treatment, 0nM).
An interesting observation was that, in general, the differentially expressed neutrophil genes across RvE1 doses tended to be unique to each comparison: 1) T2D, 2) healthy, or 3) T2D vs. healthy groups. Within each comparison the genes differentially expressed remained mostly consistent across RvE1 doses. However, the genes differentially expressed between T2D and healthy neutrophils in the cell culture perturbation model without RvE1 treatment were generally not the same as the significant differentially expressed serum neutrophil genes observed in the main analysis, with the exception of NECTIN2, HTRA3, and ABCG1, which were differentially expressed in both serum and cell culture. Interestingly, when we perturbed T2D neutrophils with 100nM RvE1 and did not perturb healthy neutrophils, only HTRA3 remained strongly differentially expressed (p<0.05), while NECTIN2 and ABCG1 showed less differential expression. Overall, there were distinct trends in neutrophil responses to RvE1 perturbation between diabetic and healthy neutrophils in cell culture.
Diabetic and Healthy Neutrophil Cytokines Respond Differently to Resolvin E1 Perturbation
To confirm that gene expression changes influenced neutrophil protein expression, we analyzed cytokine production of neutrophil cell culture supernatants following the same RvE1 dose-response studies. Results showed trends of differential inflammatory cytokine concentrations between T2D and healthy neutrophils and across RvE1 doses. Out of a panel of 20 human cytokines, the concentration of 8 cytokines (MIP-1α, IL-4, IL-8, MIP-1β, P-Selectin, sICAM-1, TNF-α, and IL-1α) were within the range of quantification and were included in the analysis (Figure 4, Supplementary Tables S3-S4). Without any RvE1 perturbation there were differences in levels of these cytokines between T2D and healthy cell cultured neutrophils, with T2D neutrophils having higher TNF-α and P-Selectin levels but lower MIP-1β, IL-8, and sICAM-1 levels. Across all RvE1 doses, P-Selectin and sICAM-1 levels remained consistently higher, while IL-8 levels remained lower in T2D compared to healthy neutrophils. However, at 1nM RvE1 treatment, MIP-1β levels in T2D neutrophils rose to the baseline levels of healthy neutrophils, while healthy neutrophil levels stayed consistent. At higher RvE1 doses, including 10nM and 100nM, MIP-1β levels rose in both T2D and healthy neutrophils; there was a stronger, dose-dependent increase among healthy neutrophils. After treatment with 10nM or 100nM RvE1, TNF-α levels were elevated in healthy neutrophils, reaching levels seen in T2D neutrophils; a similar effect was observed at 100nM RvE1 for P-Selectin, though healthy levels (1511pg/mL) did not rise to fully match T2D levels (1751pg/mL). We also observed some distinct neutrophil cytokine profiles in individual subjects, consistent with known inter-individual cytokine variation. The cytokine level differences did not reach statistical significance at this sample size. Overall, we observed trends of several potentially interesting differences in cytokines between T2D and healthy neutrophils, indicating that both diabetic and healthy neutrophils may have a distinct functional signaling response to RvE1 treatment in a dose-dependent manner.