Horseshoe kidney malformation in Turner syndrome is not associated with HNF-1beta gene mutations

Low bone mineral density and renal malformation in Mexican patients with Turner syndrome are associated with single nucleotide variants in vitamin D-metabolism genes

Turner syndrome (TS) is a common genetic disorder. TS-phenotype includes short stature, gonadal dysgenesis, cardiac and kidney malformations, low bone mineral density (low-BMD) and thyroiditis. TS-phenotype varies from patient to patient and the cause is not clear, the genomic background may be an important contributor for this variability. Our aim was to identify the association of specific single nucleotide variants in the PTPN22, VDR, KL, and CYP27B1 genes and vitamin D-metabolism, heart malformation, renal malformation, thyroiditis, and low-BMD in 61 Mexican TS-patients. DNA samples were genotyped for SNVs: rs7975232 (VDR), rs9536282 (KL), rs4646536 (CYP27B1), and rs1599971 (PTPN22) using the KASP assay. Chi-square test under a recessive model and multifactorial dimensionality reduction method were used for analysis. We found a significant association between renal malformation and the rs9536282 (KL) variant and between rs4646536 (CYP27B1) and low-BMD, these variants may have modest effects on these characteristics but contribute to the variability of the TS phenotype. In addition, we identified gene-gene interactions between variants in genes KL, CYP27B1 and VDR related to vitamin D-metabolism and low-BMD in TS-patients. Our results support the idea that the genetic background of TS-patients contributes to the clinical variability seen in them.

Metabolomics reveals attenuation of the SLC6A20 kidney transporter in nonhuman primate and mouse models of type 2 diabetes mellitus

To enhance understanding of the metabolic indicators of type 2 diabetes mellitus (T2DM) disease pathogenesis and progression, the urinary metabolomes of well characterized rhesus macaques (normal or spontaneously and naturally diabetic) were examined. High-resolution ultra-performance liquid chromatography coupled with the accurate mass determination of time-of-flight mass spectrometry was used to analyze spot urine samples from normal (n = 10) and T2DM (n = 11) male monkeys. The machine-learning algorithm random forests classified urine samples as either from normal or T2DM monkeys. The metabolites important for developing the classifier were further examined for their biological significance. Random forests models had a misclassification error of less than 5%. Metabolites were identified based on accurate masses (<10 ppm) and confirmed by tandem mass spectrometry of authentic compounds. Urinary compounds significantly increased (p < 0.05) in the T2DM when compared with the normal group included glycine betaine (9-fold), citric acid (2.8-fold), kynurenic acid (1.8-fold), glucose (68-fold), and pipecolic acid (6.5-fold). When compared with the conventional definition of T2DM, the metabolites were also useful in defining the T2DM condition, and the urinary elevations in glycine betaine and pipecolic acid (as well as proline) indicated defective re-absorption in the kidney proximal tubules by SLC6A20, a Na(+)-dependent transporter. The mRNA levels of SLC6A20 were significantly reduced in the kidneys of monkeys with T2DM. These observations were validated in the db/db mouse model of T2DM. This study provides convincing evidence of the power of metabolomics for identifying functional changes at many levels in the omics pipeline.