Modulation of Insulin Resistance, Dyslipidemia and Serum Metabolome in iNOS Knockout Mice following Treatment with Nitrite, Metformin, Pioglitazone, and a Combination of Ampicillin and Neomycin

Abstract

Oxidative and nitrosative stress plays a pivotal role in the incidence of metabolic disorders. Studies from this lab and others in iNOS^-/- mice have demonstrated occurrence of insulin resistance (IR), hyperglycemia and dyslipidemia highlighting the importance of optimal redox balance. The present study evaluates role of nitrite, L-arginine, antidiabetics (metformin, pioglitazone) and antibiotics (ampicillin-neomycin combination, metronidazole) on metabolic perturbations observed in iNOS^-/- mice. The animals were monitored for glucose tolerance (IPGTT), IR (insulin, HOMA-IR, QUICKI), circulating lipids and serum metabolomics (LC-MS). Hyperglycemia, hyperinsulinemia and IR were rescued by nitrite, antidiabetics, and antibiotics treatments in iNOS^-/- mice. Glucose intolerance was improved with nitrite, metformin and pioglitazone treatment, while ampicillin-neomycin combination normalised the glucose utilization in iNOS^-/- mice. Increased serum phosphatidylethanolamine lipids in iNOS^-/- mice were reversed by metformin, pioglitazone and ampicillin-neomycin; dyslipidemia was however marginally improved by nitrite treatment. The metabolic improvements were associated with changes in selected serum metabolites-purines, ceramide, 10-hydroxydecanoate, glucosaminate, diosmetin, sebacic acid, 3-nitrotyrosine and cysteamine. Bacterial metabolites-hippurate, indole-3-ethanol; IR marker-aminoadipate and oxidative stress marker-ophthalmate were reduced by pioglitazone and ampicillin-neomycin, but not by nitrite and metformin treatment. Results obtained in the present study suggest a crucial role of gut microbiota in the metabolic perturbations observed in iNOS^-/- mice.

Keywords: dyslipidemia; iNOS-/-; insulin resistance; metabolomic analysis.

Figure 1

Effect of NO precursors on systemic glucose homeostasis and dyslipidemia in iNOS^-/- mice. Systemic glucose and lipid homeostasis in iNOS^-/- mice following treatment with nitrite or L-arginine. (A) Intraperitoneal glucose tolerance test (GTT) with AUC calculated from IPGTT data, (B) Fasting blood glucose levels, (C) Relative liver-body weight ratio and (D) Serum lipids and total nitrite levels. Data are represented as mean ± SD (n = 7–12). * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001 between indicated groups. ## p < 0.01 and ### p < 0.001 and vs. iNOS^-/- in GTT curve.

Figure 2

Effect of anti-diabetics on systemic glucose homeostasis and dyslipidemia in iNOS^-/- mice. Systemic glucose and lipid homeostasis in iNOS^-/- mice after treatment with metformin or pioglitazone. (A) Intraperitoneal glucose tolerance test (GTT) with AUC calculated from IPGTT data, (B) Fasting blood glucose levels, (C) Relative liver-body weight ratio and (D) Serum lipids and total nitrite levels. Data are represented as mean ± SD (n = 7–12). * p < 0.05, ** p < 0.01 and **** p < 0.0001 between indicated groups. # p < 0.05, ## p < 0.01, ### p < 0.001 and #### p < 0.0001 vs. iNOS^-/- in GTT curve.

Figure 3

Effect of antibiotics on systemic glucose homeostasis and dyslipidemia in iNOS^-/- mice. Systemic glucose and lipid homeostasis in iNOS^-/- mice upon treatment with ampicillin-neomycin combination and metronidazole. (A) Intraperitoneal glucose tolerance test (GTT) with AUC calculated from IPGTT data, (B) Fasting blood glucose levels, (C) Relative liver-body weight ratio and (D) Serum lipids and total nitrite levels. Data are represented as mean ± SD (n = 7–12). * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001 between indicated groups. # p < 0.05, ## p < 0.01, ### p < 0.001 and #### p < 0.0001 vs. iNOS^-/- in GTT curve.

Figure 4

Modulation of insulin homeostasis in iNOS^-/- mice by nitrite, metformin, pioglitazone, and a combination of ampicillin-neomycin. (A) Fasting serum insulin levels; Indices of insulin resistance (B) HOMA-IR and insulin sensitivity (C) QUCIKI (n = 6–12). (D) Relative tissue weights (n = 4–12). Data are represented as mean ± SD. *** p < 0.001 and **** p < 0.0001 vs. WT; # p < 0.05, ## p < 0.01, ### p < 0.001 and #### p < 0.0001 vs. iNOS^-/-.

Figure 5

Alterations in the serum metabolome of iNOS^-/- mice following treatment with various interventions. Serum metabolomic analysis in chow fed WT, iNOS^-/- and iNOS^-/- mice treated with various interventions in ESI (+) mode (A) PLS-DA score plot. Heat map of differential metabolites identified by metabolomics analysis related to (B) Nucleic acids metabolism, (C) Carbohydrate metabolism and (D) Miscellaneous/microbiota derived metabolites. Data are represented as mean (n ≥ 4). * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 vs. WT; # p < 0.05, ## p < 0.01, ### p < 0.001 and #### p < 0.0001 vs. iNOS^-/-.

Figure 6

Alterations in the amino acid and lipid metabolites in iNOS^-/- mice following treatment with various interventions. Heat map of differential metabolites found by metabolomics analysis related to (A) Amino acids metabolism and (B) Lipid metabolism. Data are represented as mean (n ≥ 4). * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 vs. WT; # p < 0.05, ## p < 0.01, ### p < 0.001 and #### p < 0.0001 vs. iNOS^-/-. MG: Monoacylglycerols, DG: Diacylglycerols, MGDG: monogalactosyldiacylglycerol, DGDG: digalactosyldiacylglycerol, PE: Phosphatidylethanolamine, PC: Phosphatidylcholine, PA: Phosphatidic acid, PS: Phosphatidylserine and CerP: Ceramide phosphate.

Figure 7

Association of serum metabolites with metabolic profile of iNOS^-/- mice following treatment with various interventions. Heat map and analysis based on Pearson’s correlation coefficients suggest direct correlation between metabolic biomarkers and serum metabolites from WT, iNOS^-/- mice following treatment with various interventions. (A) Lipid metabolism, (B) Nucleic acid and amino acid metabolism and (C) Carbohydrate metabolism, vitamins and hormones metabolism, and miscellaneous/microbiota derived metabolites. * p < 0.05 represent significant correlations between metabolic biomarker and serum metabolites. Green color represents negative and red positive correlations.