1.
Serum concentrations of insulin-like growth factor (IGF)-1 and IGF binding protein-3 (IGFBP-3), IGF-1/IGFBP-3 ratio, and markers of bone turnover: reference values for French children and adolescents and z-score comparability with other references.
Source
INSERM, CIC-EC CIE5, Paris, France.
Abstract
BACKGROUND:
A reference model for converting serum growth factor and bone metabolism markers into an SD score (SDS) is required for clinical practice. We aimed to establish reference values of serum insulin-like growth factor-1 (IGF-1) and IGF binding protein 3 (IGFBP-3) concentrations and bone metabolism markers in French children, to generate a model for converting values into SDS for age, sex, and pubertal stage.
METHODS:
We carried out a cross-sectional study of 1119 healthy white children ages 6-20 years. We assessed concentrations of serum IGF-1, IGFBP-3, carboxyterminal telopeptide α1 chain of type I collagen (CrossLaps), and bone alkaline phosphatase concentrations and height, weight, and pubertal stage, and used semiparametric regression to develop a model.
RESULTS:
A single regression model to calculate the SDSs with an online calculator was provided. A positive relationship was found between SDS for serum IGF-1 and IGFBP-3, IGF/IGFBP-3 mol/L ratio, and anthropometric parameters (P < 0.0001), with slightly greater effects observed for height than for body mass index (BMI). There was a negative relationship between serum CrossLaps concentration and BMI, and a positive relationship between serum CrossLaps concentration and height. A comparison of serum IGF-1 reference databases for children showed marked variation as a function of age and pubertal group; smooth changes with age and puberty were observed only in our model.
CONCLUSIONS:
This new model for the assessment of SDS reference values specific for age, sex, and pubertal stage may help to increase the diagnostic power of these parameters for the assessment of growth and bone metabolism disorders. This study also provides information about the physiological role of height and BMI for the interpretation of these parameters.
Using HPLC-mass spectrometry to teach proteomics concepts with problem-based techniques.
Source
Department of Chemistry, Hope College, Holland, Michigan 49423; Department of Biology, Hope College, Holland, Michigan 49423; Montana State University, Bozeman, Montana. short@hope.edu.
Abstract
Practical instruction of proteomics concepts was provided using high-performance liquid chromatography coupled with amass selective detection system (HPLC-MS) for the analysis of simulated protein digests. The samples were prepared from selected dipeptides in order to facilitate the mass spectral identification. As part of the prelaboratory preparation, students calculated the parent ion patterns of the dipeptides using peptide calculator websites. Following instruction on the use of the HPLC-MS instrument, students analyzed mixtures of the dipeptides and identified the individual dipeptides in the unknowns. In addition, purchased chicken egg white lysozyme alkylated with iodoacetamide and digested with trypsin was analyzed using the same approach. Key tryptic peptides were identified from the HPLC-MS chromatogram with information generated with the FindPept tool. This experiment demonstrates that complex concepts can be taught in the undergraduate biochemistry laboratory using a problem-based approach.
Copyright © 2010 The International Union of Biochemistry and Molecular Biology, Inc.
- PMID:
- 21567835
- [PubMed - in process]
Predicting peptide retention times for proteomics.
Source
Department of Internal Medicine, University of Manitoba, Winnipeg, Canada.
Abstract
The vast majority of modern bottom-up proteomic protocols include chromatographic reversed-phase (RP) fractionation ofpeptides prior to mass-spectrometric analysis. Retention time information can be easily extracted from LC-MS data and it can be used to improve protein identification/characterization procedures. The key to the success of this procedure is the correct retention time prediction based on compositional and structural properties of the separated species. Our Sequence Specific Retention Calculator (SSRCalc) is a Web-based peptide retention prediction that covers the separation selectivity of the most popular RP-HPLC conditions applied in proteomics. Procedures for the application of SSRCalc to proteomic analyses are described in this unit.
Memory-efficient calculation of the isotopic mass states of a molecule.
Source
Department of Chemistry and Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454, USA.
Abstract
Our previous work postulated a transition concept among different isotopic mass states (i.e., isotopic species) of a molecule, and developed a hierarchical algorithm for accurately calculating their masses and abundances. A theoreticalmass spectrum can be generated by convoluting a peak shape function to these discrete mass states. This approach suffers from limited memory if a level in the hierarchical structure has too many mass states. Here we present a memory efficient divide-and-recursively-combine algorithm to do the calculation, which also improves the truncation method used in the previous hierarchical algorithm. Instead of treating all of the elements in a molecule as a whole, the new algorithm first 'strips' each element one by one. For the mass states of each element, a hierarchical structure is established and kept in the memory. This process reduces the memory usage by orders of magnitude (e.g., for bovine insulin, memory can be reduced from gigabytes to kilobytes). Next, a recursive algorithm is applied to combine massstates of elements to mass states of the whole molecule. The algorithm described above has been implemented as a computer program called Isotope Calculator, which was written in C++. It is freely available under the GNU Lesser General Public License from http://www.cs.brandeis.edu/~hong/software.html or http://people.brandeis.edu/~agar.
2010 John Wiley & Sons, Ltd.
A versatile peptide pI calculator for phosphorylated and N-terminal acetylatedpeptides experimentally tested using peptide isoelectric focusing.
Source
Biomolecular Mass Spectrometry and Proteomics Group, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.
Abstract
We experimentally demonstrate the use of an in-house developed pI calculator which takes into account peptide PTM such as phosphorylation and N-terminal acetylation. The pI calculator was utilized for a large set of peptides derived from a complex zebrafish lysate fractionated using peptide IEF, whereby a good correlation between the calculated (theoretical) pI and the experimental pI could be established. This pI calculator permits the implementation of optimal pK values depending on the experimental conditions and a reliable calculation of peptide pI which can be utilized as a filtering technique in validating peptide identifications. Our data reveal that the shift due to a phosphorylation or N-terminal acetylation is highly dependent on the presence of acidic or basic residues in the peptide. Furthermore, using this pI calculator, we revealed previously unknown position-specific pKs of asparagine and carbamidomethylated cysteine depending on their location in the peptide. Collectively, this peptide pI calculator is a welcome addition to the versatility and robustness of IEF for the separation and confident identification of (post-translationally modified) peptides.
Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics.
Source
Manitoba Centre for Proteomics and Systems Biology, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg, MB, R3E 3P4, Canada.
Abstract
We describe the practical implementation of a new RP (pH 10 - pH 2) 2D HPLC-ESI/MS scheme for large-scale bottom-up analysis in proteomics. When compared to the common SCX-RP approach, it provides a higher separation efficiency in the first dimension and increases the number of identified peptides/proteins. We also employed the methodology of our sequence-specific retention calculator (SSRCalc) and developed peptide retention prediction algorithms for both LC dimensions. A diverse set of approximately 10,000 tryptic peptides from the soluble protein fraction of whole NK-type cells gave retention time versus hydrophobicity correlations, with R (2) values of 0.95 for pH 10 and 0.945 for pH 2 (formic acid) separation modes. The superior separation efficiency and the ability to use retention prediction to filter out false-positive MS/MS identifications gives promise that this approach will be a method of choice for large-scale proteomics analyses in the future. Finally, the "semi-orthogonal" separation selectivity permits the concatenation of fractions in the first dimension of separation before the final LC-ESI MS step, effectively cutting the analysis time in half, while resulting in a minimal reduction in protein identification.
Preanalytical, analytical, and computational factors affect homeostasis model assessment estimates.
Source
Clinical Biochemistry, University Hospital Birmingham NHS Foundation Trust, University of Birmingham, Birmingham, UK. susan.manley@uhb.nhs.uk
Abstract
OBJECTIVE:
We investigated how beta-cell function and insulin sensitivity or resistance are affected by the type of blood sample collected or choice of insulin assay and homeostatis model assessment (HOMA) calculator(http://www.dtu.ox.ac.uk).
RESEARCH DESIGN AND METHODS:
Insulin was measured using 11 different assays in serum and 1 assay in heparinized plasma. Fasting subjects with normoglycemia (n = 12), pre-diabetes, i.e., impaired fasting glucose or impaired glucose tolerance (n = 18), or type 2 diabetes (n = 67) were recruited. Patients treated with insulin or those who were insulin antibody-positive were excluded. HOMA estimates were calculated using specific insulin (SI) or radioimmunoassay (RIA) calculators (version 2.2).
RESULTS:
All glucose values were within model (HOMA) limits but not all insulin results, as 4.3% were <20 pmol/l and 1% were >300 pmol/l. beta-Cell function derived from different insulin assays ranged from 67 to 122% (median) for those with normoglycemia (P = 0.026), from 89 to 138% for those with pre-diabetes (P = 0.990), and from 50 to 81% for those with type 2 diabetes (P < 0.0001). Furthermore, insulin resistance ranged from 0.8 to 2.0 (P = 0.0007), from 1.9 to 3.2 (P = 0.842), and from 1.5 to 2.9 (P < 0.0001), respectively. This twofold variation in HOMA estimates from the various insulin assays studied in serum may be significant metabolically. Insulin was 15% lower in heparinized plasma (used in the original HOMA study) compared with serum, which is now more commonly used. beta-Cell function differed by 11% and insulin resistance by 15% when estimates derived from specific insulin were calculated using the RIA rather than the SI calculator.
CONCLUSIONS:
To enable comparison of HOMA estimates among individuals and different research studies, preanalytical factors and calculator selection should be standardized with insulin assays traceable to an insulin reference method procedure.
Association between smoking, pancreatic insulin secretion and insulin resistance in Chinese subjects with or without glucose intolerance.
Source
Hong Kong Institute of Diabetes and Obesity, Hong Kong, China. gtc_ko@yahoo.com.hk
Abstract
BACKGROUND:
There are studies suggesting smoking may increase the risk of type 2 diabetes. Effects of smoking on insulin secretion and insulin resistance (IR) are, however, controversial.
METHODS:
This is a cross-sectional study. Since there were very few smokers among Hong Kong Chinese women, only men (n = 1068) were analyzed in this report. Fasting and 2-hour plasma glucose and insulin were measured. Insulinogenic index as well as beta-cell function and IR based on homeostatic model assessment (HOMA) by computer model (HOMA Calculator v2.2) were calculated.
RESULTS:
Of the 1068 men, 147 had newly diagnosed diabetes, 131 newly diagnosed impaired glucose tolerance (IGT) and 790 were non-diabetic normal controls. Smokers had similar fasting and 2-hour insulin levels, insulinogenic index and HOMA derived beta-cell function as compared to non-smokers in the groups with diabetes, IGT or normal oral glucose tolerance test (OGTT). IR was also similar between smokers, ex-smokers and non-smokers in those with normal OGTT. In men with IGT or diabetes, after adjustment for age and body mass index, smokers were more insulin resistant as compared to non-smokers (IR, IGT: 1.59 +/- 1.07 vs 1.03 +/- 0.54, P < 0.05; diabetes: 1.96 +/- 1.36 vs 1.06 +/- 0.45, P < 0.01). With Logistic regression analysis, comparing smokers and non-smokers, IR was independently associated with smoking (odds ratio (95% CI), IGT: 2.23 (1.05, 4.71); diabetes: 3.92 (1.22, 12.58)). None of the other insulin parameters enter into the model among those with normal OGTT or comparing ex-smokers and non-smoker or smokers and ex-smokers.
CONCLUSIONS:
In Chinese men, smoking did not show any direct association with insulin levels and pancreatic insulin secretion. Smoking men with IGT or diabetes appeared more insulin resistant than their non-smoking counterparts.
Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: application to 300- and 100-A pore size C18 sorbents.
Source
Manitoba Centre for Proteomics and Systems Biology, University of Manitoba, 799 JBRC, 715 McDermot Avenue, Winnipeg, MB, R3E 3P4, Canada. krokhino@cc.umanitoba.ca
Abstract
Continued development of a new sequence-specific algorithm for peptide retention prediction in RP HPLC is reported. Our discovery of the large effect on the apparent hydrophobicity of N-terminal amino acids produced by the ion-pairing retention mechanism has led to the development of sequence-specific retention calculator (SSRCalc) algorithms. These were optimized for a set of approximately 2000 tryptic peptides confidently identified by off-line microHPLC-MALDI MS (MS/MS) (300-A pore size C18 sorbent, linear water/acetonitrile gradient, and trifluoroacetic acid as ion-pairing modifier). The latest version of the algorithm takes into account amino acid composition, position of the amino acid residues (N- and C-terminal), peptide length, overall hydrophobicity, pI, nearest-neighbor effect of charged side chains (K, R, H), and propensity to form helical structures. A correlation with R2 approximately 0.98 was obtained for the 2000-peptideoptimization set. A flexible structure for the SSRC programming code allows easy adaptation to different chromatographic conditions. This was demonstrated by adapting the algorithm (approximately 0.98 R2 value) for a set of approximately 2500 peptides separated on a 100-A pore size C18 column. The SSRCalc algorithm has also been extensively tested for a number of real samples, providing solid support for protein identification and characterization; correlations in the range of 0.95-0.97 R2 value have normally been observed.
A new computer program (GlycoX) to determine simultaneously the glycosylation sites and oligosaccharide heterogeneity of glycoproteins.
Source
Department of Chemistry, Division of Biostatistics, Graduate School of Management, Biochemistry and Molecular Medicine, University of California, Davis, California 95616, USA.
Abstract
A new computer program, GlycoX, was developed to aid in the determination of the glycosylation sites and oligosaccharide heterogeneity in glycoproteins. After digestion with the nonspecific protease, each glycan at a specific glycosylation site contains a small peptide tag that identifies the location of the glycan. GlycoX was developed in MATLAB requiring the entry of the exact masses of the glycopeptide and the glycan spectra in the form of a mass-intensity table and taking advantage of the accurate mass capability of the mass analyzer, in this case a Fourier transform ion cyclotron resonance (FT ICR) mass spectrometer. This program computes not only the glycosylation site but also the composition of the glycans at each site. Several glycoproteins were used to determine the efficacy of GlycoX. These glycoproteins range from the simple, with one site of glycosylation, to the more complex, with multiple (three) sites of glycosylation. The results obtained using the computer program were the same as those determined manually. Model glycoproteins yielded the correct results, and new glycoproteins with unknown glycosylation were examined with the site of glycosylation and the corresponding glycans determined. Furthermore, other functions in GlycoX, including an auto-isotope filter to identify monoisotopic peaks and an oligosaccharide calculator to obtain the oligosaccharide composition, are demonstrated.
Use of peptide retention time prediction for protein identification by off-line reversed-phase HPLC-MALDI MS/MS.
Source
Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada. krokhino@cc.umanitoba.ca
Abstract
A new algorithm, sequence-specific retention calculator, was developed to predict retention time of tryptic peptidesduring RP HPLC fractionation on C18, 300-A pore size columns. Correlations of up to approximately 0.98 R2 value were obtained for a test library of approximately 2000 peptides and approximately 0.95-0.97 for a variety of real samples. The algorithm was applied in conjunction with an exclusion protocol based on mass (15 ppm tolerance) and retention time (2-min tolerance for 0.66% acetonitrile/min gradient), MART criteria to significantly reduce the instrument time required for complete MS/MS analysis of a digest separated by RP HPLC. This was confirmed by reanalyzing the set of HPLC-MALDI MS/MS data with no loss in protein identifications, despite the number of virtually executed MS/MS analyses being decreased by 57%.
Some quantitative aspects of the labelling of proteins with 125 I by the iodine monochloride method.
Abstract
The labelling of proteins by the iodine monochloride method was studied by using a mathematical model. The equations used were primarily derived from the mass law equation of the isotopic exchange reaction between [(125)I]iodide and iodine monochloride. For convenient application, all equations were programmed into a computing desk-top calculator. To support the validity of the theoretical model, a series of iodinations of insulin were performed under various labelling conditions. The results of these experiments compare well with the theoretically derived values. Deviations from the theoretical values occurring at molar ratios of [(125)I]iodide to iodine monochloride < 0.1 and > 4.0 are explained and suggestions made about how to prevent them. The mathematical model was used to simulate the isotopic exchange, and the iodination reaction under various conditions, to study (a) the influence of the amount of [(125)I]iodide on the amount of [(125)I]iodine monochloride formed, (b) the influence of the specific radioactivity of [(125)I]iodide on the amount of [(125)I]iodine monochloride formed, and (c) the influence of the specific radioactivity of [(125)I]iodide on the number of millicuries needed for labelling to a desired extent.
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