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Primary hyperoxaluria type I (PH1) is an autosomal-recessive inborn error of liver metabolism caused by alanine:glyoxylate aminotransferase (AGT) deficiency. In silico modeling of liver metabolism in PH1 recapitulated accumulation of known biomarkers as well as alteration of histidine and histamine levels, which we confirmed in vitro, in vivo, and in PH1 patients. AGT-deficient mice showed decreased vascular permeability, a readout of in vivo histamine activity. Histamine reduction is most likely caused by increased catabolism of the histamine precursor histidine, triggered by rerouting of alanine flux from AGT to the glutamic-pyruvate transaminase (GPT, also known as the alanine-transaminase ALT). Alanine administration reduces histamine levels in wild-type mice, while overexpression of GPT in PH1 mice increases plasma histidine, normalizes histamine levels, restores vascular permeability, and decreases urinary oxalate levels. Our work demonstrates that genome-scale metabolic models are clinically relevant and can link genotype to phenotype in metabolic disorders.

Patients with adrenal incidentalomas (AI) may experience detrimental consequences due to a minimal cortisol excess sustained by adrenal adenoma. SNPs of the glucocorticoid receptor gene (NR3C1) modulate individual sensitivity to glucocorticoids and may interfere with the clinical presentation.To compare the frequency of N363S, ER22/23EK and BclI SNPs in patients with AI with the general population and to evaluate whether these SNPs are linked to consequences of cortisol excess.Multicentric, retrospective analysis of patients referred from 2010 to 2014 to 4 centers (Orbassano, Milano, Messina [Italy] and Zagreb [Croatia]).411 patients with AI; 153 males and 258 females and 186 from blood donors.All patients and controls were genotyped for BclI, N363S and ER22/23EK and SNPs frequency was associated with clinical and hormonal features.SNP frequency was: SNP frequency was: N363S 5.4% (MAF 0.027), BclI 54.7% (MAF 0.328), ER22/23EK 4.4% (MAF 0.022), without any significant difference between patients and controls. N363S was more frequent in hypertensive patients (p = 0.03) and was associated with hypertension (p = 0.015) in patients with suppressed cortisol after the 1-mg DST.Our results demonstrate that SNPs of the glucocorticoid receptor gene do not play a pathogenetic role for AI. The impact of any single SNP on the phenotypic expression of minimal cortisol excess is limited and their analysis does not provide additional data that may be exploited for patient management.

Objective: To evaluate to what extent an inefficient statistical model affects the study of genetic factors in extra-intestinal manifestations of Crohn''s disease (CD) and how clinical predictions can be improved using more adequate techniques.Materials: Extra-intestinal manifestations were studied in 152 CD patients. Three sets of variables were considered: (1) disease characteristics – presentation, behavior, location; (2) generic risk factors – age, gender, smoke and familiarity; and (3) genetic polymorphisms of the NOD2, CD14, TNF, IL12B, and IL1RN genes, whose involvement in CD is known or suspected.Methods: Six statistical classifiers and data mining models were applied: (1) logistic regression as a benchmark; (2) generalized additive model; (3) projection pursuit regression; (4) linear discriminant analysis, (5) quadratic discriminant analysis; (6) artificial neural networks one-layer feed forward. Models were selected using the Akaike Information criterion and their accuracy was compared with several indexes.Results: Extra-intestinal manifestations occurred in 75 patients. The model with clinical variables only selected familiarity, gender, presentation, and behavior as significantly associated with extra-intestinal manifestations, whereas when the genetic factors were also included familiarity was no longer significant, being replaced by the NOD2, TNF, and IL12B single nucleotide polymorphisms. The projection pursuit regression performed best in predicting individual outcomes (Kappa statistics 0.078 [SE 0.09] without and 0.108 [SE 0.075] with genetic information). One-layer artificial neural networks did not show any particular improvement in terms of model accuracy over nonlinear techniques.Conclusions: The correct identification of factors associated with extra-intestinal symptoms in CD, in particular the genetic ones, is highly dependent on the model chosen for the analysis. By using the most sophisticated statistical models, the accuracy of prediction can be strengthened by 10–64%, compared with linear regression. [ABSTRACT FROM AUTHOR]