Increased levels of plasma neudesin in adult growth hormone deficiency and their relationship with plasma liver-expressed antimicrobial peptide-2 levels: a cross-sectional study.

PURPOSE: Adult growth hormone deficiency (aGHD) is characterized by an altered metabolic profile and increased cardiovascular risk. Neudesin is a newly discovered protein mainly secreted from adipose tissue and brain, under evaluation for its possible activity as a negative regulator of energy expenditure. Liver-expressed antimicrobial peptide (LEAP)-2 is a competitive antagonist of ghrelin on its receptor. An observational cross-sectional study was performed to test the hypothesis that plasma neudesin levels may be modified in aGHD. Given the role played in the energy balance, any possible relationships between neudesin, LEAP-2 and metabolic and anthropometric parameters were evaluated. SUBJECTS AND METHODS: Thirty-eight patients were included: 18 aGHD patients (7 females and 11 males, aged 59.7 ± 2.6 years, BMI 30.2 ± 2.2 kg/m2); 20 healthy controls (12 females and 8 males, aged 47.1 ± 2.5 years, BMI 24.1 ± 0.9 kg/m2). All patients were evaluated for glucose, insulin, HOMA and QUICKI index, total/LDL/HDL cholesterol, triglycerides, uric acid, and IGF-1. Plasma neudesin, LEAP-2, and ghrelin were measured by ELISA. Fat mass was evaluated by DEXA. RESULTS: Neudesin levels were significantly higher in aGHD versus controls. We confirmed the finding of significantly lower ghrelin levels and significantly higher LEAP-2/ghrelin ratio in aGHD patients and found a significant direct correlation between neudesin and LEAP-2 levels. A significant direct correlation between neudesin and fat mass percentage was found in the whole population. CONCLUSION: These results suggest the onset of adaptive responses to an altered metabolic picture in aGHD. The changes in two distinct pathways that modulate food intake and the still limited knowledge about neudesin suggest future developments in this field.

Improvement of protein secondary structure prediction by combination of statistical algorithms and circular dichroism.

Three different approaches (propensity curve shifting, hydropathy index evaluation, and iterative attribution/cancellation of secondary structure) to the use of secondary structure percentages derived from circular dichroism measurements to improve the success rate of a protein secondary structure prediction method, without using decision constants, are described and compared. Propensity-curve shifting appears to be the best-performing approach, bearing an increase of 5.3% in the success rate of single-residue structural prediction when exact information on the secondary structure, obtained by X-ray crystallography, is employed; with information of an accuracy comparable to that obtainable by circular dichroism, the improvement stays between 3.5 and 4.9%, for a three-state prediction. Although developed with circular dichroism in mind, the method can use percentages of secondary structure obtained by any other experimental methodology from which they can be inferred, for instance Raman spectroscopy and infrared spectroscopy.