Stanislav O. Zakharkin

Urine Metabolomics Analysis for Kidney Cancer Detection and Biomarker Discovery

Renal cell carcinoma (RCC) accounts for 11,000 deaths per year in the United States. When detected early, generally serendipitously by imaging conducted for other reasons, long term survival is generally excellent. When detected with symptoms, prognosis is poor. Under these circumstances, a screening biomarker has the potential for substantial public health benefit. The purpose of this study was to evaluate the utility of urine metabolomics analysis for metabolomic profiling, identification of biomarkers, and ultimately for devising a urine screening test for RCC. Fifty urine samples were obtained from RCC and control patients from two institutions, and in a separate study, urine samples were taken from 13 normal individuals. Hydrophilic interaction chromatography-mass spectrometry was performed to identify small molecule metabolites present in each sample. Cluster analysis, principal components analysis, linear discriminant analysis, differential analysis, and variance component analysis were used to analyze the data. Previous work is extended to confirm the effectiveness of urine metabolomics analysis using a larger and more diverse patient cohort. It is now shown that the utility of this technique is dependent on the site of urine collection and that there exist substantial sources of variation of the urinary metabolomic profile, although group variation is sufficient to yield viable biomarkers. Surprisingly there is a small degree of variation in the urinary metabolomic profile in normal patients due to time since the last meal, and there is little difference in the urinary metabolomic profile in a cohort of pre- and postnephrectomy (partial or radical) renal cell carcinoma patients, suggesting that metabolic changes associated with RCC persist after removal of the primary tumor. After further investigations relating to the discovery and identity of individual biomarkers and attenuation of residual sources of variation, our work shows that urine metabolomics analysis has potential to lead to a diagnostic assay for RCC.

Sources of variation in Affymetrix microarray experiments

BackgroundA typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates.ResultsWe performed a microarray experiment using a total of 24 Affymetrix GeneChip® arrays. The study included 4th mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates.ConclusionThe greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates.

The Evolution of Hexamerins and the Phylogeny of Insects

Abstract. The evolutionary relationships among arthropod hemocyanins and insect hexamerins were investigated. A multiple sequence alignment of 12 hemocyanin and 31 hexamerin subunits was constructed and used for studying sequence conservation and protein phylogeny. Although hexamerins and hemocyanins belong to a highly divergent protein superfamily and only 18 amino acid positions are identical in all the sequences, the core structures of the three protein domains are well conserved. Under the assumption of maximum parsimony, a phylogenetic tree was obtained that matches perfectly the assumed phylogeny of the insect orders. An interesting common clade of the hymenopteran and coleopteran hexamerins was observed. In most insect orders, several paralogous hexamerin subclasses were identified that diversified after the splitting of the major insect orders. The dipteran arylphorin/LSP-1-like hexamerins were subject to closer examination, demonstrating hexamerin gene amplification and gene loss in the brachyceran Diptera. The hexamerin receptors, which belong to the hexamerin/hemocyanin superfamily, diverged early in insect evolution, before the radiation of the winged insects. After the elimination of some rapidly or slowly evolving sequences, a linearized phylogenetic tree of the hexamerins was constructed under the assumption of a molecular clock. The inferred time scale of hexamerin evolution, which dates back to the Carboniferous, agrees with the available paleontological data and reveals some previously unknown divergence times among and within the insect orders.

Expectations, validity, and reality in gene expression profiling.

OBJECTIVE To provide a critical overview of gene expression profiling methodology and discuss areas of future development. RESULTS Gene expression profiling has been used extensively in biological research and has resulted in significant advances in the understanding of the molecular mechanisms of complex disorders, including cancer, heart disease, and metabolic disorders. However, translating this technology into genomic medicine for use in diagnosis and prognosis faces many challenges. In addition, gene expression profile analysis is frequently controversial, because its conclusions often lack reproducibility and claims of effective dissemination into translational medicine have, in some cases, been remarkably unjustified. In the last decade, a large number of methodological and technical solutions have been offered to overcome the challenges. STUDY DESIGN AND SETTING We consider the strengths, limitations, and appropriate applications of gene expression profiling techniques, with particular reference to the clinical relevance. CONCLUSION Some studies have demonstrated the ability and clinical utility of gene expression profiling for use as diagnostic, prognostic, and predictive molecular markers. The challenges of gene expression profiling lie with the standardization of analytic approaches and the evaluation of the clinical merit in broader heterogeneous populations by prospective clinical trials.

Catechol-O-Methyl-Transferase Functional Polymorphism and Nicotine Dependence: an Evaluation of Nonreplicated Results

Review articles have focused attention on and cited possible reasons for the nonreplication of genetic association studies. Herein, we illustrate how one might work through these possible reasons to make a judgment about the most plausible reason(s) when faced with two or more studies which yield seemingly inconsistent results. In the first study, 342 treatment-seeking smokers were genotyped for the Val108Met polymorphism in the functional catechol-O-methyl-transferase (COMT) locus. Alleles coding Val at codon 108 are denoted as H and those coding Met are denoted as L. An association between presence of the “H” (high activity) allele and pretreatment level of nicotine dependence level using the Fagerstrom Test for Nicotine Dependence was detected (P = 0.0072), after controlling for baseline body mass index (BMI, kg/m2), depression symptoms, and age. To validate this initial finding, 443 treatment-seeking smokers from an independent smoking cessation clinical trial were genotyped for the COMT polymorphism. Within the second study, no association between presence of the “H” allele and nicotine dependence was detected (P = 0.6418) after controlling for baseline BMI, depression symptoms, and age. We critically reviewed both studies with regard to often cited reasons for nonreplication, including type I error, population stratification, low statistical power, and imprecise measures of phenotype. Although in our opinion the failure to replicate the initial association in the second study is likely either the result of low statistical power to detect a small effect or effect heterogeneity, thorough analyses failed to definitively identify the reason for nonreplication.

Differential Accumulation and Tissue Distribution of Mosquito Hexamerins During Metamorphosis

The pupal hexamerins were characterized for two mosquitoes representative of the culicine and anopheline families, Aedes aegypti and Anopheles gambiae. Like higher Diptera, both mosquito species express two types of hexamerins, Hex-1 and Hex-2, whose subunits are distinguished by different levels of methionine and aromatic amino acids. In A. aegypti there are two heterohexamers, AaHex-1 and AaHex-2. In A. gambiae there are two homohexamers, AgHex-1.1 and AgHex-1.2, and one heterohexamer, AgHex-2. These hexamerins are rich in aromatic residues, with 18-23% Phe + Tyr for Hex-1 subunits and 13-17% Phe + Tyr for Hex-2 subunits. In addition, both mosquito species synthesize methionine-rich Hex-1 subunits: Aedes AaHex-1 gamma (8% met) and Anopheles AgHex-1.1 (3.9% met). Aedes Hex-1 and Hex-2 proteins exhibit different, stage-specific tissue distributions: AaHex-2 is the primary hexamerin of late larval hemolymph whereas AaHex-1 is the most important non-hemolymph protein of early pupae. Although both proteins are stored in the pupal fat body, peak AaHex-1 levels are 2-fold higher. Both pupal protein levels decline rapidly between 25 and 36 h after pupation. Furthermore, AaHex-1 not only reaches peak values in female Aedes pupae later than in males, but the methionine-rich AaHex-1 gamma subunit level is specifically higher in females. These observations suggest different roles for Hex-1 and Hex-2 during mosquito development.

Applications of Bayesian Statistical Methods in Microarray Data Analysis

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data.Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

Sex-, stage- and tissue-specific regulation by a mosquito hexamerin promoter.

A portion of the 5′‐flanking region of the female‐specific hexamerin gene, Hex‐1.2, from the mosquito Ochlerotatus atropalpus was used to drive expression of the luciferase reporter gene in Drosophila melanogaster. The proximal 0.7 kb of 5′‐flanking DNA were sufficient to partially repress reporter gene activity in males and to drive tissue‐ and stage‐specific expression comparable with that of the endogenous O. atropalpus Hex‐1.2 gene. The Drosophila doublesex transcription factor (DSX), expressed in Escherichia coli, bound putative DSX sites of the Hex‐1.2 gene differentially in vitro. Blocking expression of the female isoform of the Doublesex transcription factor in transgenic female flies resulted in reduction of luciferase expression to levels comparable with those in males, suggesting that Doublesex could contribute to regulation of female‐specific expression of the O. atropalpus Hex‐1.2 gene.

Lack of association between polymorphism of the human cyclic GMP-dependent protein kinase gene and obesity

OBJECTIVE:To investigate whether genetic variation in the cyclic GMP-dependent protein kinase gene (PRKG1) is associated with obesity.METHODS:The study included 143 individuals from New York City area, NY, USA. The subjects were sampled on the basis of body mass index (BMI): obese (BMI ranging from 33.8 to 89.5 kg/m2), and nonobese (BMI ranging from 16.0 to 29.4 kg/m2). The association between C2276T polymorphism in PRKG1 gene and obesity was tested using linear regression analysis.RESULTS:BMI levels were predicted by linear regression models adjusted for demographic factors. An analysis was performed twice: in individuals of all ethnical backgrounds and in European-Americans only. In both cases, genotype did not have a significant effect.CONCLUSION:We found no evidence that the C2276T polymorphism in the PKRG1 gene is associated with obesity.

Optimal allocation of replicates for measurement evaluation studies.

Optimal experimental design is important for the efficient use of modern high-throughput technologies such as microarrays and proteomics. Multiple factors including the reliability of measurement system, which itself must be estimated from prior experimental work, could influence design decisions. In this study, we describe how the optimal number of replicate measures (technical replicates) for each biological sample (biological replicate) can be determined. Different allocations of biological and technical replicates were evaluated by minimizing the variance of the ratio of technical variance (measurement error) to the total variance (sum of sampling error and measurement error). We demonstrate that if the number of biological replicates and the number of technical replicates per biological sample are variable, while the total number of available measures is fixed, then the optimal allocation of replicates for measurement evaluation experiments requires two technical replicates for each biological replicate. Therefore, it is recommended to use two technical replicates for each biological replicate if the goal is to evaluate the reproducibility of measurements.