Wamelink, Mirjam M. C.

Mutations in the γ2-subunit of AMPK (encoded by PRKAG2 gene) cause glycogen storage cardiomyopathy. We generated a mouse model with cardiac specific deletion of γ2-AMPK (cKO) to define its function in the heart. The cKO mice were born normal and remained indistinguishable from control mice under baseline conditions but developed greater cardiac hypertrophy and dysfunction after transverse aortic constriction (TAC). The total AMPK activity was normal in cKO hearts and it increased after TAC to the same extent as that of the controls. However, the γ2-AMPK activity was abolished in the cKO and the overall AMPK activity was maintained by a compensatory increase in the γ1-AMPK activity. The lack of γ2-AMPK selectively reduced hexokinase II in the cytosol resulting in decreases in glycogen content and NADPH levels. Knock-down of γ2-AMPK subunit in neonatal rat ventricle cardiomyocytes resulted in increased oxidative stress and exacerbated response to phenylephrine and H 2 O 2 , both were rescued by HKII overexpression. Taken together, these data identify isoform-specific functions of γ2-AMPK in regulating glucose and glycogen metabolism that are critical for cardiac response to stress. The results also indicate that the specific activation of γ2-AMPK is responsible for the glycogen storage cardiomyopathy associated with PRKAG2 mutations.

Abstract BACKGROUND Urinary concentrations of creatine and guanidinoacetic acid divided by creatinine are informative markers for cerebral creatine deficiency syndromes (CDSs). The renal excretion of these substances varies substantially with age and sex, challenging the sensitivity and specificity of postanalytical interpretation. METHODS Results from 155 patients with CDS and 12 507 reference individuals were contributed by 5 diagnostic laboratories. They were binned into 104 adjacent age intervals and renormalized with Box–Cox transforms (Ξ). Estimates for central tendency (μ) and dispersion (σ) of Ξ were obtained for each bin. Polynomial regression analysis was used to establish the age dependence of both μ[log(age)] and σ[log(age)]. The regression residuals were then calculated as z-scores = {Ξ − μ[log(age)]}/σ[log(age)]. The process was iterated until all z-scores outside Tukey fences ±3.372 were identified and removed. Continuous percentile charts were then calculated and plotted by retransformation. RESULTS Statistically significant and biologically relevant subgroups of z-scores were identified. Significantly higher marker values were seen in females than males, necessitating separate reference intervals in both adolescents and adults. Comparison between our reconstructed reference percentiles and current standard age-matched reference intervals highlights an underlying risk of false-positive and false-negative events at certain ages. CONCLUSIONS Disease markers depending strongly on covariates such as age and sex require large numbers of reference individuals to establish peripheral percentiles with sufficient precision. This is feasible only through collaborative data sharing and the use of appropriate statistical methods. Broad application of this approach can be implemented through freely available Web-based software.