Targeting immunometabolic pathways with AZD1656 alleviates inflammation and metabolic dysfunction in type 2 diabetic cardiomyopathy

Figures

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  Figure 1.. AZD treatment attenuates diastolic dysfunction and reduces infarct size in dbCM a) Graphical summary of the AZD treatment protocol. Mice were fed nutritionally standardised and matched Teklad Standard Base Diet (Envigo) with the addition of AZD1656 to the diet used for db/db treatment group b) In vivo echocardiography assessment of mitral valve E/A, degrees of diastolic dysfunction, classification based on previously validated criteria in 20,21. Classification also summarized in Methods. Dotted line represents control E/A average. c) representative Langendorff perfused heart d) e)Langendorff perfused heart function data (Control n=9, db/db n=9, AZD n=6) f) Representative TTC stained cardiac cross sections post 20 min total global ischemia and 2 hr reperfusion used for quantification of the infarct size. g) Quantification of infarct size post I/R injury (db/db, AZD n=5/group, control n=3) h) Improved LVDP recovery post ischemia in Langendorff perfused hearts (Control n=5, db/db n=5, AZD n=6) f) *P<0.05, ***P<0.01 by ANOVA

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  Figure 2.. AZD treatment improves cardiac metabolism in dbCM a) PCA plot of 1H NMRs metabolomic profiling b) Fold change db/db vs control 1H NMR metabolomic spectroscopy profiling, n=6/group c) Fold change AZD treated db/db vs control 1H NMR metabolomic spectroscopy profiling, n=6/group d) Cardionet metabolic flux balance analysis based on the 1H NMR metabolomic profiling and plasma metabolomic analysis (n=6/group) e) Visualization of CardioNet metabolic flux predictions using Cytoscape. Metabolites and reactions are depicted as nodes and lines, respectively. The line thinckness corresponds with the calculate flux rate. f) Prediction of oxygen consumption rates based on CardioNet simulations. g) Representative annotated spectrum from In vivo 13C DNP MRS cardiac metabolic flux assessment h) In vivo 13C DNP MRS measured TCA cycle flux (Control n=12, db/db n=16, AZD n=10). Multiple group comparison by ANOVA. Two-tail comparison by student t-test. *P<0.05 ***P<0.01 ****P<0.001

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  Figure 3.. AZD treatment improved cardiac lipidomic profile a) Partial least square-discriminant analysis (PLS-DA) visualization of lipid abundances. Samples (n=5/group) are grouped according to biological replicates and dimensions (Dim) 1 and 2 together captured 81.35% of variability. b) Contribution of significantly altered phospholipid (PL) classes across biological samples. MG, Monoacylglycerols; PC, phosphatidylcholine c) Principal component analysis (PCA) of PC acyl chain profiles. Total carbon lengths were compared across biological samples. Dimensions 1 and 2 captured 84.4% of variability. PC(40) and PC(26) were the strongest contributors to PC alterations in treatments. d-e) Average total chain length and degree of saturation varied between experimental groups.

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  Figure 4.. The systemic effect of AZD treatment in db/db a) Representative images of control, db/db and AZD treated db/db mice from the study b) Body weight (Control n=23, db/db n=17, AZD n=20) c) Fasting plasma glucose (n=9/group) d) plasma insulin (Control n=10, db/db n=11, AZD n=6) e-g) circulating lipid metabolism constituents [free fatty acids (Control n=11, db/db n=9, AZD n=5), cholesterol (Control n=7, db/db n=10, AZD n=5), triacylglycerol (Control n=7, db/db n=11, AZD n=9). Liver phenotype profiling h) Liver weight (Control n=12, db/db n=11, AZD n=7) i) Fold change db/db vs control 1H NMR metabolomic spectroscopy profiling j) Fold change AZD vs control 1H NMR metabolomic spectroscopy profiling (Control n=6, db/db n=7, AZD n=7) Skeletal Muscle phenotype (Control n=6, db/db n=6, AZD n=5) k) lean muscle mass by body composition analysis (n=5/group) l) Fold change db/db vs control 1H NMR metabolomic spectroscopy profiling, m) Fold change AZD vs control 1H NMR metabolomic spectroscopy profiling, (n=5/group) *P<0.05, **P<0.01, ***P<0.001 multiple group comparison by ANOVA, two-tail comparison by student t-test. Note: Parameters measured over the course of 6-year study in multiple cohorts…

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  Figure 5.. AZD treatment reduces T-cell mediated cardiac inflammation and fibrosis in dbCM a) Circulating XXL cytokine plasma panel heatmap summary (Control n=10, db/db n=12, AZD n=10 Representative FACS contour plots c) Relative frequency of myocardial CD4+ T cells quantified by FACS d) Masson trichrome stained representative cardiac cross sections e) quantification of Masson trichrome fibrosis staining intensity (Control n=5, db/db n=4, AZD n=4) f) Relative frequency of B cells, dendritic cells, neutrophils and macrophages *P<0.05, **P<0.01, ***P<0.001.Multiple group comparison by ANOVA.

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  Figure 6.. Treatment with AZD improves the expression of genes regulating key intracellular pathways Visualisation of differentially expressed genes (DEGs) using hexbin plot summaries. X axis values are log2fold Change vs y axis − log 10 (P value) of a) db/db vs Control, AZD vs db/db, AZD vs Control. Y axis vertical cut off log2 (1.5) ± 0.585 and x axis horizontal cut off -np.log10(0.05) ~ 1.301 b) Top 30 most upregulated DEG in db/db vs Control, AZD vs db/db, AZD vs Control. Red bars pro-inflammatory regulators, blue bar- anti-inflammatory regulators. c) The Venn diagram of overlapping homologous DEGs among the three data sets (Control, db/db and AZD). Threshold was set to be FC > 1.5 (or <1/1.5) and P < 0.05. Sample genes ( increased and decreased) from each of the subsets are given: abs_diff_log2FC = abs(log2FoldChange_AZ_vs_dbdb − log2FoldChange_dbdb_vs_CTRL) d) Heatmap plots of − log p value of DEGs resulting from hypergeometric test and Benjamini-Hochberg p-value correction to identify all ontology and pathway terms that contain a greater number of genes in common with an input list than expected by chance using Qiagen IPA software41. This is expressed as a − log p value in the…

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  Figure 7.. Summary of the key study findings AZD treatment may represent a new therapeutic concept for the treatment of dbCM-the use of small molecules targeting immunometabolism to reduce cardiac inflammation in the diabetic heart. Figure generated with Biornder.com