Proteomic biomarkers in plasma that differentiate rapid and slow decline in lung function in adult cigarette smokers with chronic obstructive pulmonary disease (COPD).

Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of morbidity and mortality in the United States and cigarette smoking is a primary determinant of the disease. COPD is characterized by chronic airflow limitation as measured by the forced expiratory volume in one second (FEV(1)). In this study, the plasma proteomes of 38 middle-aged or older adult smokers with mild to moderate COPD, with FEV(1) decline characterized as either rapid (RPD, n = 20) or slow or absent (SLW, n = 18), were interrogated using a comprehensive high-throughput proteomic approach, the accurate mass and time (AMT) tag technology. This technology is based upon a putative mass and time tag database (PMT), high-resolution LC separations and high mass accuracy measurements using FT-ICR MS with a 9.4-T magnetic field. The peptide and protein data were analyzed using three statistical approaches to address ambiguities related to the high proportion of missing data inherent to proteomic analysis. The RPD and SLW groups were differentiated by 55 peptides which mapped to 33 unique proteins. Twelve of the proteins have known roles in the complement or coagulation cascade and, despite an inability to adjust for some factors known to affect lung function decline, suggest potential mechanistic biomarkers associated with the rate of lung function decline in COPD. Whether these proteins are the cause or result of accelerated decline will require further research.

A statistical method for excluding non-variable CpG sites in high-throughput DNA methylation profiling.

BACKGROUND: High-throughput DNA methylation arrays are likely to accelerate the pace of methylation biomarker discovery for a wide variety of diseases. A potential problem with a standard set of probes measuring the methylation status of CpG sites across the whole genome is that many sites may not show inter-individual methylation variation among the biosamples for the disease outcome being studied. Inclusion of these so-called "non-variable sites" will increase the risk of false discoveries and reduce statistical power to detect biologically relevant methylation markers. RESULTS: We propose a method to estimate the proportion of non-variable CpG sites and eliminate those sites from further analyses. Our method is illustrated using data obtained by hybridizing DNA extracted from the peripheral blood mononuclear cells of 311 samples to an array assaying 1505 CpG sites. Results showed that a large proportion of the CpG sites did not show inter-individual variation in methylation. CONCLUSIONS: Our method resulted in a substantial improvement in association signals between methylation sites and outcome variables while controlling the false discovery rate at the same level.

Cigarette smoke extract induced protein phosphorylation changes in human microvascular endothelial cells in vitro.

Phosphorylation is the most widely studied posttranslational modification (PTM) and is an important regulatory mechanism used during cellular responses to external stimuli. The kinases and phosphatases that regulate protein phosphorylation are known to be affected in many human diseases. Cigarette smoking causes cardiovascular disease (CVD). Endothelial cells play a pivotal role in CVD initiation and development; however, there have been limited investigations of the specific signaling cascades and protein phosphorylations activated by cigarette smoke in endothelial cells. The purpose of this research was to better understand the differential protein phosphorylation in endothelial cells stimulated with extracts of cigarette smoke total particulate matter (CS-TPM) in vitro. Human microvascular endothelial cells were exposed in vitro to CS-TPM at concentrations that were shown to cause endothelial cell dysfunction. The phosphorylated proteins were isolated using phosphoprotein-specific chromatography, followed by enzymatic digestion and nano-flow capillary liquid chromatography (ncap-LC) coupled to high resolution mass spectrometry. This study putatively identified 94 proteins in human microvascular endothelial cells that were differentially bound to a phosphoprotein-specific chromatography column following exposure to CS-TPM suggesting differential phosphorylation. Pathway analysis has also been conducted and confirmations of several observations have been made using immunoaffinity-based techniques (e.g., Western blotting).

Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles.

BACKGROUND: DNA methylation patterns have been shown to significantly correlate with different tissue types and disease states. High-throughput methylation arrays enable large-scale DNA methylation analysis to identify informative DNA methylation biomarkers. The identification of disease-specific methylation signatures is of fundamental and practical interest for risk assessment, diagnosis, and prognosis of diseases. RESULTS: Using published high-throughput DNA methylation data, a two-stage feature selection method was developed to select a small optimal subset of DNA methylation features to precisely classify two sample groups. With this approach, a small number of CpG sites were highly sensitive and specific in distinguishing lung cancer tissue samples from normal lung tissue samples. CONCLUSION: This study shows that it is feasible to identify DNA methylation biomarkers from high-throughput DNA methylation profiles and that a small number of signature CpG sites can suffice to classify two groups of samples. The computational method we developed in the study is efficient to identify signature CpG sites from disease samples with complex methylation patterns.

Identification of in vitro differential cell secretions due to cigarette smoke condensate exposure using nanoflow capillary liquid chromatography and high-resolution mass spectrometry.

Secreted proteins, the secretome, can be isolated from biological fluids (e.g., blood) and are often responsible for the regulation of biological processes such as cell signaling, growth, and apoptosis. The identification of secreted proteins can lead to an understanding of disease mechanisms and they can serve as early candidate biomarkers of disease and exposure. However, it is time-consuming and costly to conduct in vivo interrogations of the human secretome. The purpose of this article is to provide a detailed description of a rapid in vitro technique for the analysis of differential protein secretion due to exposure to smoking-machine-generated cigarette smoke (CS) condensate (total particulate matter, TPM). Endothelial cells were exposed to CS-TPM, the supernatant was collected, and the secretome was elucidated by nano liquid chromatography coupled with high-resolution mass spectrometry. A total of 1,677 unique peptides were identified in the cell culture supernatants. Several proteins were differentially expressed following CS-TPM exposure that relate to several biological processes, such as metabolism, development, communication, response to stimulus, and response to stress.

Cis-regulatory variations: a study of SNPs around genes showing cis-linkage in segregating mouse populations.

BACKGROUND: Changes in gene expression are known to be responsible for phenotypic variation and susceptibility to diseases. Identification and annotation of the genomic sequence variants that cause gene expression changes is therefore likely to lead to a better understanding of the cause of disease at the molecular level. In this study we investigate the pattern of single nucleotide polymorphisms (SNPs) in genes for which the mRNA levels show cis-genetic linkage (gene expression quantitative trait loci mapping in cis, or cis-eQTLs) in segregating mouse populations. Such genes are expected to have polymorphisms near their physical location (cis-variations) that affect their mRNA levels by altering one or more of the cis-regulatory elements. This led us to characterize the SNPs in promoter (5 Kb upstream) and non-coding gene regions (introns and 5 Kb downstream) (cis-SNPs) and the effects they may have on putative transcription factor binding sites. RESULTS: We demonstrate that the cis-eQTL genes (CEGs) have a significantly higher frequency of cis-SNPs compared to non-CEGs (when both sets are taken from the non-IBD regions, i.e. regions not identical by descent). Most CEGs having cis-SNPs do not contain these SNPs in the phylogenetically conserved regions. In those CEGs that contain cis-SNPs in the phylogenetically conserved regions, enrichment of cis-SNPs occurs both within and outside of the conserved sequences. A higher fraction of CEGs are also seen to harbor cis-SNP that affect predicted transcription factor binding sites, a likely consequence of the higher cis-SNPs density in these genes. CONCLUSION: This present study provides the first genome-wide investigation of the putative cis-regulatory variations in a large set of genes whose levels of expression give rise to cis-linkage in segregating mammalian populations. Our results provide insights into the challenges that exist in identifying polymorphisms regulating gene expression using bioinformatic sequence analysis approaches. The data provided herein should benefit future investigations in this area.

Integrating QTL and high-density SNP analyses in mice to identify Insig2 as a susceptibility gene for plasma cholesterol levels.

The use of inbred strains of mice to dissect the genetic complexity of common diseases offers a viable alternative to human studies, given the control over experimental parameters that can be exercised. Central to efforts to map susceptibility loci for common diseases in mice is a comprehensive map of DNA variation among the common inbred strains of mice. Here we present one of the most comprehensive high-density, single nucleotide polymorphism (SNP) maps of mice constructed to date. This map consists of 10,350 SNPs genotyped in 62 strains of inbred mice. We demonstrate the utility of these data via a novel integrative genomics approach to mapping susceptibility loci for complex traits. By integrating in silico quantitative trait locus (QTL) mapping with progressive QTL mapping strategies in segregating mouse populations that leverage large-scale mapping of the genetic determinants of gene expression traits, we not only facilitate identification of candidate quantitative trait genes, but also protect against spurious associations that can arise in genetic association studies due to allelic association among unlinked markers. Application of this approach to our high-density SNP map and two previously described F2 crosses between strains C57BL/6J (B6) and DBA/2J and between B6 ApoE(-/-) and C3H/HeJ ApoE(-/-) results in the identification of Insig2 as a strong candidate susceptibility gene for total plasma cholesterol levels.

A comprehensive transcript index of the human genome generated using microarrays and computational approaches.

BACKGROUND: Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet microarrays. Further, expression activity over at least six conditions was more generally assessed using genomic tiling arrays consisting of probes tiled through a repeat-masked version of the genomic sequence making up chromosomes 20 and 22. RESULTS: The combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts. This set of high-confidence transcripts represents the first experimentally driven annotation of the human genome. In addition, the results from genomic tiling suggest that a large amount of transcription exists outside of annotated regions of the genome and serves as an example of how this activity could be measured on a genome-wide scale. CONCLUSIONS: These data represent one of the most comprehensive assessments of transcriptional activity in the human genome and provide an atlas of human gene expression over a unique set of gene predictions. Before the annotation of the human genome is considered complete, however, the previously unannotated transcriptional activity throughout the genome must be fully characterized.