Kenneth R. Williams

Effect of Rosiglitazone Treatment on Nontraditional Markers of Cardiovascular Disease in Patients With Type 2 Diabetes Mellitus

Background—Markers of systemic inflammation (eg, C-reactive protein [CRP] and interleukin-6 [IL-6]) have been proposed to be “nontraditional” risk factors for cardiovascular disease in patients with type 2 diabetes mellitus. Matrix metalloproteinase-9 (MMP-9) has been implicated in the pathogenesis of atherosclerotic plaque rupture, which raises the possibility of the use of MMP-9 levels as a marker for future myocardial infarction or unstable angina. In vitro and animal studies suggest that thiazolidinediones can reduce the expression of these markers. The purpose of this analysis was to determine whether rosiglitazone alters serum concentrations of CRP, IL-6, MMP-9, and white blood cell count (WBC) and to examine the relationship of these effects with demographic and disease variables. Methods and Results—CRP, IL-6, MMP-9, and WBC were analyzed from stored frozen serum samples obtained from patients with type 2 diabetes who completed a 26-week randomized, double-blind, placebo-controlled study. After 26 weeks of rosiglitazone treatment, the percentage reductions in mean CRP, MMP-9, and WBC levels were statistically significant compared with baseline and placebo (P <0.01). The percentage reduction in mean IL-6 was small and similar in the rosiglitazone and placebo groups. The change in each inflammatory marker from baseline to week 26 was significantly correlated (P <0.05) with each of the other markers, as well as with the homeostasis model assessment estimate of insulin resistance. Conclusions—Rosiglitazone reduces serum levels of MMP-9 and the proinflammatory marker CRP in patients with type 2 diabetes, which indicates potentially beneficial effects on overall cardiovascular risk.

Expression Profiling Reveals Novel Pathways in the Transformation of Melanocytes to Melanomas

Affymetrix and spotted oligonucleotide microarrays were used to assess global differential gene expression comparing normal human melanocytes with six independent melanoma cell strains from advanced lesions. The data, validated at the protein level for selected genes, confirmed the overexpression in melanoma cells relative to normal melanocytes of several genes in the growth factor/receptor family that confer growth advantage and metastasis. In addition, novel pathways and patterns of associated expression in melanoma cells not reported before emerged, including the following: (a) activation of the NOTCH pathway; (b) increased Twist expression and altered expression of additional transcriptional regulators implicated in embryonic development and epidermal/mesenchymal transition; (c) coordinated activation of cancer/testis antigens; (d) coordinated down-regulation of several immune modulation genes, in particular in the IFN pathways; (e) down-regulation of several genes implicated in membrane trafficking events; and (f) down-regulation of growth suppressors, such as the Prader-Willi gene NECDIN, whose function was confirmed by overexpression of ectopic Flag-necdin. Validation of differential expression using melanoma tissue microarrays showed that reduced ubiquitin COOH-terminal esterase L1 in primary melanoma is associated with worse outcome and that increased expression of the basic helix-loop-helix protein Twist is associated with worse outcome. Some differentially expressed genes reside on chromosomal regions displaying common loss or gain in melanomas or are known to be regulated by CpG promoter methylation. These results provide a comprehensive view of changes in advanced melanoma relative to normal melanocytes and reveal new targets that can be used in assessing prognosis, staging, and therapy of melanoma patients.

Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data.

MOTIVATION Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data. RESULTS We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.

Prediction of Type 2 Diabetes Mellitus With Alternative Definitions of the Metabolic Syndrome The Insulin Resistance Atherosclerosis Study

Background— In addition to predicting cardiovascular disease (CVD) morbidity and mortality, the metabolic syndrome is strongly associated with the development of type 2 diabetes mellitus (DM), itself an important risk factor for CVD. Our objective was to compare the ability of various metabolic syndrome criteria (including those recently proposed by the International Diabetes Federation), markers of insulin resistance (IR) and inflammation, and impaired glucose tolerance (IGT) in the prediction of DM and to determine whether various proposed modifications to the National Cholesterol Education program (NCEP) metabolic syndrome definition improved predictive ability. Methods and Results— We examined 822 subjects in the Insulin Resistance Atherosclerosis Study aged 40 to 69 years who were nondiabetic at baseline. After 5.2 years, 148 individuals had developed DM. IGT, metabolic syndrome definitions, and IR markers all significantly predicted DM, with odds ratios ranging from 3.4 to 5.4 (all P<0.001), although there were no significant differences in the areas under the receiver operator characteristic (AROC) curves between the definitions. Modifying or requiring obesity, glucose, or IR components in NCEP-defined metabolic syndrome did not significantly alter the predictive ability of the definition under AROC curve criteria (all P>0.05). Similarly, although IR and inflammation variables were significantly associated with incident DM when included in multivariate models with NCEP-defined metabolic syndrome (all P<0.01), expanding the definition by adding these variables as components did not significantly alter the predictive ability of the definition under AROC curve criteria (all P>0.05). Conclusions— The International Diabetes Federation and NCEP metabolic syndrome definitions predicted DM at least as well as the World Health Organization definition, despite not requiring the use of oral glucose tolerance testing or measures of IR or microalbuminuria. Modifications or additions to the NCEP metabolic syndrome definition had limited impact on the prediction of DM.

Structural specificity of substrate for S-adenosylmethionine protein arginine N-methyltransferases

The enzymatic methylation of polypeptides on the guanidino group of internal arginine residues by S-adenosylmethionine:protein arginine N-methyltransferase (protein methylase I) yields NG-monomethylarginine, NG,NG-dimethylarginine and NG,NG-dimethylarginine. It has commonly been observed that these arginine residues are present in glycine-and-arginine rich motifs. To understand structural features which are essential for serving as the methyl acceptor for protein methylase I, we have investigated substrate capacities of several synthetic oligopeptides whose sequences are homologous and/or analogous to the methyl acceptor region of the naturally occurring arginine-methylated proteins. These studies have led to the following conclusions. (i) The preferred amino-acid sequence of methyl-accepting peptides was shown to be an arginine-containing peptide with glycine in both the N- and C-flanking positions. While a tetrapeptide with such a sequence (residues 106-109 of bovine myelin basic protein) exhibited almost negligible substrate activity, an overlapping hexapeptide was a moderate substrate. (ii) Substitution of the C-flanking glycine in GKGRGL (residues 104-109 of myelin basic protein) with histidine, phenylalanine, lysine or aspartic acid completely abolished the ability of these hexapeptides to serve as substrates. (iii) A heptapeptide with a repeated glycine-arginine motif (GRGRGRG) was an excellent substrate for the enzyme. (iv) A cyclic octapeptide (CGKGRGLC), which was formed by cyclization of GKGRGL by introduction of disulfide bridge to cross-link N- and C-terminus of the hexapeptide, was an even better substrate than the hexapeptide. (v) Upon HPLC amino-acid analysis, all enzymatically methyl-14C-labeled oligopeptides were found to yield predominantly NG-monomethylarginine with a minor fraction of NG,NG-dimethylarginine in certain peptide samples. However, no NG,NG-dimethylarginine formation was detectable. (vi) The recombinant hnRNP protein A1 (residues 1-320) is known to be methylated at arginine-194 by nuclear-protein/histone protein methylase I (Rajpurohit et al. (1994) J. Biol. Chem. 269, 1079-1082). However, the hexapeptide (SSSQRG) which corresponds to residues 189-194 of protein A1 containing the methylatable arginine residue was relatively inert as a substrate. Furthermore, the N-terminal fragment of protein A1 (residues 1-196) generated by controlled trypsin digestion was also completely inactive as a substrate for the enzyme. These results indicate that the remainder of the A1 protein molecule plays an important though not yet understood role in enzymatic methylation of the arginine-194.

Why is non−high-density lipoprotein cholesterol a better marker of the risk of vascular disease than low-density lipoprotein cholesterol?

Low-density lipoprotein cholesterol (LDL-C) has been the focus of managing lipoprotein disorders for decades. It is now time to consider a change. Both apolipoprotein B (apoB) and non-high-density lipoprotein cholesterol (HDL-C) have been shown to be more accurate markers of cardiovascular risk than LDL-C. ApoB measures total atherogenic particle number, of which 90% are LDL particles. Therefore, LDL particle number determines plasma apoB in most patients. Non-HDL-C is widely assumed to be superior to LDL-C when triglyceride concentrations are elevated (even modestly) because it includes the cholesterol in very-low-density lipoprotein. However, evidence does not support this concept. Rather, non-HDL-C appears to be an indirect way of estimating apoB. We argue that we should integrate the information from non-HDL-C and apoB for better risk assessment and a better target of therapy.

Detecting and aligning peaks in mass spectrometry data with applications to MALDI

In this paper, we address the peak detection and alignment problem in the analysis of mass spectrometry data. To deal with the peak redundancy problem existing in the MALDI data acquired in the reflectron mode, we propose to use the amplitude modulation technique in peak detection. The alignment of two peak sets is formulated as a non-rigid registration problem and is solved using a robust point matching (RPM) approach. To align multiple peak sets, we first use a super set method to find a common peak set among all peak sets as a standard and then align all peak sets to the standard using the robust point matching approach in a sequential manner (i.e. We align only one peak set to the standard each time, thus reducing the multiple peak set alignment problem to a simpler two peak set alignment problem). Experimental results from a study of ovarian cancer data set show that the quantitative cross-correlation coefficients among technical replicates are increased after peak alignment. Additional comparisons also demonstrate that our method has a similar performance as the hierarchical clustering method, although the implementations of these methods are different.

Diabetes, Abdominal Adiposity, and Atherogenic Dyslipoproteinemia in Women Compared With Men

OBJECTIVE—To understand why atherogenic risk differs more between diabetic and nondiabetic women than between diabetic and nondiabetic men. RESEARCH DESIGN AND METHODS AND RESULTS—Measures of cardiovascular risk, body composition, and serum hormones from the baseline examinations of the Insulin Resistance Atherosclerosis Study on 524 nondiabetic women, 258 diabetic women, 421 nondiabetic men, and 220 diabetic men were compared to detect greater adverse differences in women than in men. Systolic blood pressure; apolipoprotein B (apoB); total cholesterol; apoB–to–apoA-I ratio; non-HDL cholesterol; LDL particle count, small LDL, and intermediate-density lipoprotein by nuclear magnetic resonance; and C-reactive protein exhibited significant diabetes-sex interaction (P < 0.05). ApoB exhibited the most significant interaction (P = 0.0005). Age- and ethnicity-adjusted apoB means were lower in nondiabetic women than nondiabetic men (102.4 vs. 106.8 mg/dl, P < 0.05) but higher in diabetes (115.7 vs. 110.2 mg/dl, P < 0.01). Plotted against BMI, waist circumference was 6% higher and hip circumference 10% lower in diabetic than nondiabetic women (both P < 0.05), whereas the circumference measures did not differ conspicuously between diabetic and nondiabetic men. CONCLUSIONS—In diabetic women, an elevated level of atherogenic particles, as manifested by apoB and LDL particle count, which may result from abdominal adiposity, represents a major treatable cardiovascular risk factor.

In gel digestion of SDS PAGE-Separated proteins: Observations from internal sequencing of 25 proteins

Publisher Summary Although, numerous approaches may be taken to obtain internal amino acid sequences from SDS PAGE-separated proteins, in situ gel digestion is particularly attractive in that it avoids preliminary procedures, such as elution or blotting, which may result in significant loss of protein. This chapter aims to compare different approaches to deriving internal sequences from SDS PAGE purified proteins, to identify and optimize critical parameters in this procedure, and to establish the realistic expectations with regards to data generated from varying amounts of unknown proteins. Based on the data presented in the chapter, it appears that a laboratory that can routinely sequence 25 pmol amounts of proteins that have been stained with Coomassie Blue and subjected to in gel digestion. Because, no significant correlation is found between the initial peptide sequencing yield and the amount of protein digested, the chapter suggests that the sensitivity of in gel digestion can be extended below the ∼25 pmol amounts by using thinner gels with narrower lanes and by going to lower flow rates and narrower HPLC columns.

Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach

In this paper, we address the multiple peak alignment problem in sequential data analysis with an approach based on the Gaussian scale-space theory. We assume that multiple sets of detected peaks are the observed samples of a set of common peaks. We also assume that the locations of the observed peaks follow unimodal distributions (e.g., normal distribution) with their means equal to the corresponding locations of the common peaks and variances reflecting the extension of their variations. Under these assumptions, we convert the problem of estimating locations of the unknown number of common peaks from multiple sets of detected peaks into a much simpler problem of searching for local maxima in the scale-space representation. The optimization of the scale parameter is achieved using an energy minimization approach. We compare our approach with a hierarchical clustering method using both simulated data and real mass spectrometry data. We also demonstrate the merit of extending the binary peak detection method (i.e., a candidate is considered either as a peak or as a nonpeak) with a quantitative scoring measure-based approach (i.e., we assign to each candidate a possibility of being a peak)