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BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease consisting of emphysema, small airway obstruction, and/or chronic bronchitis that results in significant loss of lung function over time. METHODS: In order to gain insights into the molecular pathways underlying progression of emphysema and explore computational strategies for identifying COPD therapeutics, we profiled gene expression in lung tissue samples obtained from regions within the same lung with varying amounts of emphysematous destruction from smokers with COPD (8 regions × 8 lungs = 64 samples). Regional emphysema severity was quantified in each tissue sample using the mean linear intercept (Lm) between alveolar walls from micro-CT scans. RESULTS: We identified 127 genes whose expression levels were significantly associated with regional emphysema severity while controlling for gene expression differences between individuals. Genes increasing in expression with increasing emphysematous destruction included those involved in inflammation, such as the B-cell receptor signaling pathway, while genes decreasing in expression were enriched in tissue repair processes, including the transforming growth factor beta (TGFß) pathway, actin organization, and integrin signaling. We found concordant differential expression of these emphysema severity-associated genes in four cross-sectional studies of COPD. Using the Connectivity Map, we identified GHK as a compound that can reverse the gene-expression signature associated with emphysematous destruction and induce expression patterns consistent with TGFß pathway activation. Treatment of human fibroblasts with GHK recapitulated TGFß-induced gene-expression patterns, led to the organization of the actin cytoskeleton, and elevated the expression of integrin ß1. Furthermore, addition of GHK or TGFß restored collagen I contraction and remodeling by fibroblasts derived from COPD lungs compared to fibroblasts from former smokers without COPD. CONCLUSIONS: These results demonstrate that gene-expression changes associated with regional emphysema severity within an individual's lung can provide insights into emphysema pathogenesis and identify novel therapeutic opportunities for this deadly disease. They also suggest the need for additional studies to examine the mechanisms by which TGFß and GHK each reverse the gene-expression signature of emphysematous destruction and the effects of this reversal on disease progression.

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Body fluids such as urine potentially contain a wealth of information pertaining to age, sex, social and reproductive status, physiologic state, and genotype of the donor. To explore whether urine could encode information regarding environment, physiology, and development, we compared the volatile compositions of mouse urine using solid-phase microextraction and gas chromatography-mass spectrometry (SPME-GC/MS). Specifically, we identified volatile organic compounds (VOCs) in individual urine samples taken from inbred C57BL/6J-H-2(b) mice under several experimental conditions-maturation state, diet, stress, and diurnal rhythms, designed to mimic natural variations. Approximately 1000 peaks (i.e., variables) were identified per comparison and of these many were identified as potential differential biomarkers. Consistent with previous findings, we found groups of compounds that vary significantly and consistently rather than a single unique compound to provide a robust signature. We identified over 49 new predictive compounds, in addition to identifying several published compounds, for maturation state, diet, stress, and time-of-day. We found a considerable degree of overlap in the chemicals identified as (potential) biomarkers for each comparison. Chemometric methods indicate that the strong group-related patterns in VOCs provide sufficient information to identify several parameters of natural variations in this strain of mice including their maturation state, stress level, and diet.

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While the role cigarette smoke plays in chronic obstructive pulmonary disease (COPD) is undisputed, the molecular mechanisms by which inhaled smoke contributes to disease pathogenesis remains unclear. One of the major barriers to effective approaches to diagnose and manage COPD is the remarkable heterogeneity displayed by patients with the disease. Whole-genome gene-expression studies of airway and lung tissue from patients with COPD provide an opportunity to gain insights into disease pathogenesis, allowing for both a molecular understanding of the pathogenic processes that contribute to this heterogeneity, and the ability to target therapies to these processes. This review focuses on synthesizing and integrating the limited numbers of high-throughput gene expression studies that have been conducted on lung tissue and airway samples from smokers with COPD. Comparing several lung tissue studies using computational approaches, we find that the results suggest fundamental similarities and identify common biological processes underlying COPD, despite each study having identified largely nonoverlapping lists of differentially expressed genes. Given these similarities, we argue that additional lung tissue and airway gene-expression studies are warranted, and present a roadmap for how such studies could lead to clinically relevant tools that would impact COPD management.

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As bacteria grow and proliferate, they release a variety of volatile compounds that can be profiled and used for speciation, providing an approach amenable to disease diagnosis through quick analysis of clinical cultures as well as patient breath analysis. As a practical alternative to mass spectrometry detection and whole cell pyrolysis approaches, we have developed methodology that involves detection via a sensitive, micromachined differential mobility spectrometer (microDMx), for sampling headspace gases produced by bacteria growing in liquid culture. We have applied pattern discovery/recognition algorithms (ProteomeQuest) to analyze headspace gas spectra generated by microDMx to reliably discern multiple species of bacteria in vitro: Escherichia coli, Bacillus subtilis, Bacillus thuringiensis, and Mycobacterium smegmatis. The overall accuracy for identifying volatile profiles of a species within the 95% confidence interval for the two highest accuracy models evolved was between 70.4 and 89.3% based upon the coordinated expression of between 5 and 11 features. These encouraging in vitro results suggest that the microDMx technology, coupled with bioinformatics data analysis, has potential for diagnosis of bacterial infections.

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SUMMARY: The analysis of chromatographic data resulting from complex chemical mixtures is challenging. Components may co-elute, causing their signals to overlap. An algorithm that will increase the signal-to-noise ratio so compounds present in low abundance can be better distinguished from noise is useful in this type of analysis. The autoregressive (AR) filter offers the advantage of smoothing chromatograms to increase this ratio, while also offering data compression and increased resolution. Furthermore, this filter can be useful for classification, as the roots of the predictor coefficient vectors represent features present in the data and can therefore be used for pattern recognition. In this paper, we present a novel method for applying AR filtering to chromatogram data. We show that the AR filter outperforms the Savitzky-Golay filter for smoothing noise while retaining important information within chromatograms, and also that AR correlation coefficients have the potential to be used to classify chromatogram data into groups. CONTACT: cdavis@draper.com.

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