Prohormone convertase 1 in obesity, gestational diabetes mellitus, and NIDDM: no evidence for a major susceptibility role

Common variants in PCSK1 influence blood pressure and body mass index

Proprotein convertase subtilisin/kexin-type 1 (PCSK1) activates precursors pro-opiomelanocortin (POMC), proinsulin and prorenin. We investigated if common variants in the PCSK1 gene influence blood pressure and risk of hypertension. Additionally, we investigated the risk of obesity and type 2 diabetes (T2D). In the Rotterdam Study (RS1), a prospective, population-based cohort (n=5974), four single-nucleotide polymorphisms (rs10515237, rs6232, rs436321 and rs3792747) in PCSK1 were studied. Linear and Cox regression models served to analyze associations between variants and end points. Replication was performed in the Rotterdam Study Plus1 (RSPlus1, n=1895). Rs436321 was significantly associated with systolic and diastolic blood pressure and risk of hypertension (odds ratio (OR): 1.1-1.3; P<0.05 in both populations). Rs6232 was associated with body mass index (BMI) (P=0.007 and P=0.04 in RS1 and RSPlus1, respectively). In RSPlus1, heterozygotes for rs6232 had 1.5 times higher risk of obesity (OR: 1.46; 95% confidence interval: 1.04-2.03; P=0.03). We did not find significant associations of PCSK1 with fasting insulin levels and T2D. We found an association of genetic variation in the PCSK1 gene with blood pressure and hypertension. Furthermore, we replicated the association of PCSK1 with BMI and obesity. No relationship was found between PCSK1 variants and fasting insulin levels and T2D. Our findings suggest that genetic variation in PCSK1 may contribute to, at least, some of these interrelated disorders.

Missense polymorphism in the human carboxypeptidase E gene alters enzymatic activity

Carboxypeptidase E (CPE) is involved in the biosynthesis of peptide hormones and neurotransmitters, including insulin. One of the features of type 2 diabetes mellitus (T2DM) is an elevation in the proinsulin level and/or proinsulin/insulin molar ratio, suggesting that mutations in proinsulin processing enzymes may contribute to the development of T2DM. We scanned CPE for mutations in a collection of Ashkenazi T2DM families and identified five novel single nucleotide polymorphisms (SNPs). An SNP in the 283(rd) codon, c.847C>T, changes arginine to tryptophan (R283W). The residue Arg283 is conserved among CPE orthologs as well as most enzymatically active metallocarboxypeptidases. Of the 272 Ashkenazi T2DM pedigrees screened, we found four families segregating R283W. Within these four families, patients who inherited one copy of this variant had much earlier age of onset for T2DM. The R283W CPE protein cleaves peptide substrates with substantially lower efficiencies and is less stable at elevated temperature. In addition, the R283W CPE variant has a narrower pH optimum and is much less active at pH 6.0-6.5, indicating that the R283W CPE variant would be substantially less active than wild type CPE in the trans-Golgi network and immature secretory vesicles where the enzyme functions in vivo. To summarize, we uncovered a rare non-conservative missense mutation in CPE and demonstrated that the mutant protein has altered enzymatic properties. We predict that this mutant could cause hyperproinsulinism and diabetes in the homozygous state.

Mass isotopomer distribution analysis at eight years: theoretical, analytic, and experimental considerations

Mass isotopomer distribution analysis (MIDA) is a technique for measuring the synthesis of biological polymers. First developed approximately eight years ago, MIDA has been used for measuring the synthesis of lipids, carbohydrates, and proteins. The technique involves quantifying by mass spectrometry the relative abundances of molecular species of a polymer differing only in mass (mass isotopomers), after introduction of a stable isotope-labeled precursor. The mass isotopomer pattern, or distribution, is analyzed according to a combinatorial probability model by comparing measured abundances to theoretical distributions predicted from the binomial or multinomial expansion. For combinatorial probabilities to be applicable, a labeled precursor must therefore combine with itself in the form of two or more repeating subunits. MIDA allows dilution in the monomeric (precursor) and polymeric (product) pools to be determined. Kinetic parameters can then be calculated (e.g., replacement rate of the polymer, fractional contribution from the endogenous biosynthetic pathway, absolute rate of biosynthesis). Several issues remain unresolved, however. We consider here the impact of various deviations from the simple combinatorial probability model of biosynthesis and describe the analytic requirements for successful use of MIDA. A formal mathematical algorithm is presented for generating tables and equations (APPENDIX), on the basis of which effects of various confounding factors are simulated. These include variations in natural isotope abundances, isotopic disequilibrium in the precursor pool, more than one biosynthetic precursor pool, incorrect values for number of subunits present, and concurrent measurement of turnover from exogenously labeled polymers. We describe a strategy for testing whether isotopic inhomogeneity (e.g., an isotopic gradient or separate biosynthetic sites) is present in the precursor pool by comparing higher-mass (multiply labeled) to lower-mass (single- and double-labeled) isotopomer patterns. Also, an algebraic correction is presented for calculating fractional synthesis when an incomplete ion spectrum is monitored, and an approach for assessing the sensitivity of biosynthetic parameters to measurement error is described. The different calculation algorithms published for MIDA are compared; all share a common model, use overlapping solutions to computational problems, and generate identical results. Finally, we discuss the major practical issue for using MIDA at present: quantitative inaccuracy of instruments. The nature and causes of analytic inaccuracy, strategies for evaluating instrument performance, and guidelines for optimizing accuracy and reducing impact on biosynthetic parameters are suggested. Adherence to certain analytic guidelines, particularly attention to concentration effects on mass isotopomer ratios and maximizing enrichments in the isotopomers of interest, reduces error. Improving instrument accuracy for quantification of isotopomer ratios is perhaps the highest priority for this field. In conclusion, MIDA remains the "equation for biosynthesis," but attention to potentially confounding factors and analytic performance is required for optimal application.