Dataset Information

Data mining of plasma peptide chromatograms for biomarkers of air contaminant exposures.

ABSTRACT:

Background

Interrogation of chromatographic data for biomarker discovery becomes a tedious task due to stochastic variability in retention times arising from solvent and column performance. The difficulty is further compounded when the effects of exposure (e.g. to environmental contaminants) and biological variability result in varying numbers and intensities of peaks among chromatograms.

Results

We developed a software tool to correct the stochastic time shifts in chromatographic data through iterative selection of landmark peaks and isometric interpolation to improve alignment of all chromatographic peaks. To illustrate application of the tool, plasma peptides from Fischer rats exposed for 4 h to clean air or Ottawa urban particles (EHC-93) were separated by HPLC with autofluorescence detection, and the retention time shifts between chromatograms were corrected (dewarped). Both dewarped and non-dewarped datasets were then mined for models containing peptide peaks that best discriminate among the treatment groups using ClinproToolstrade mark. In general, models generated by dewarped datasets were able to better classify test sample chromatograms into either clean air or EHC-93 exposure groups, and 0 or 24 h post-recovery time groups. Peak areas of peptides in a model that produced the best discrimination of treatment groups were analyzed by two-way ANOVA with exposure (clean air, EHC-93) and recovery time (0 h, 24 h) as factors. Statistically significant (p < 0.05) time-dependent and exposure-dependent increases and decreases were noted establishing these as biomarker candidates for further validation.

Conclusion

Our software tool provides a simple and portable approach for alignment of chromatograms with complex, bi-directional retention time shifts prior to data mining. Reliable biomarker discovery can be achieved through chromatographic dewarping using our software followed by pattern recognition by commercial data mining applications.

SUBMITTER: Karthikeyan S

PROVIDER: S-EPMC2270821 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Quantitative analysis of discovery-based proteomic workflows now relies on high-throughput large-scale methods for identification and quantitation of proteins and post-translational modifications. Advancements in label-free quantitative techniques, using either data-dependent or data-independent mass spectrometric acquisitions, have coincided with improved instrumentation featuring greater precision, increased mass accuracy, and faster scan speeds. We recently reported on a new quantitative method called MS1 Filtering (Schilling et al. (2012) Mol. Cell. Proteomics 11, 202-214) for processing data-independent MS1 ion intensity chromatograms from peptide analytes using the Skyline software platform. In contrast, data-independent acquisitions from MS2 scans, or SWATH, can quantify all fragment ion intensities when reference spectra are available. As each SWATH acquisition cycle typically contains an MS1 scan, these two independent label-free quantitative approaches can be acquired in a single experiment. Here, we have expanded the capability of Skyline to extract both MS1 and MS2 ion intensity chromatograms from a single SWATH data-independent acquisition in an Integrated Dual Scan Analysis approach. The performance of both MS1 and MS2 data was examined in simple and complex samples using standard concentration curves. Cases of interferences in MS1 and MS2 ion intensity data were assessed, as were the differentiation and quantitation of phosphopeptide isomers in MS2 scan data. In addition, we demonstrated an approach for optimization of SWATH m/z window sizes to reduce interferences using MS1 scans as a guide. Finally, a correlation analysis was performed on both MS1 and MS2 ion intensity data obtained from SWATH acquisitions on a complex mixture using a linear model that automatically removes signals containing interferences. This work demonstrates the practical advantages of properly acquiring and processing MS1 precursor data in addition to MS2 fragment ion intensity data in a data-independent acquisition (SWATH), and provides an approach to simultaneously obtain independent measurements of relative peptide abundance from a single experiment.

Project description:BACKGROUND:Exposure to air pollution has been associated with cardiorespiratory morbidity and mortality. However, the chemical constituents and pollution sources underlying these associations remain unclear. METHOD:We conducted a cohort panel study involving 97 elderly subjects living in the Los Angeles metropolitan area. Airway and circulating biomarkers of oxidative stress and inflammation were measured weekly over 12 weeks and included, exhaled breath condensate malondialdehyde (EBC MDA), fractional exhaled nitric oxide (FeNO), plasma oxidized low-density lipoprotein (oxLDL), and plasma interleukin-6 (IL-6). Exposures included 7-day personal nitrogen oxides (NOx), daily criteria-pollutant data, five-day average particulate matter (PM) measured in three size-fractions and characterized by chemical components including transition metals, and in vitro PM oxidative potential (dithiothreitol and macrophage reactive oxygen species). Associations between biomarkers and pollutants were assessed using linear mixed effects regression models. RESULTS:We found significant positive associations of airway oxidative stress and inflammation with traffic-related air pollutants, ultrafine particles and transition metals. Positive but nonsignificant associations were observed with PM oxidative potential. The strongest associations were observed among PM variables in the ultrafine range (PM <0.18µm). It was estimated that an interquartile increase in 5-day average ultrafine polycyclic aromatic hydrocarbons was associated with a 6.3% (95% CI: 1.1%, 11.6%) increase in EBC MDA and 6.7% (95% CI: 3.4%, 10.2%) increase in FeNO. In addition, positive but nonsignificant associations were observed between oxLDL and traffic-related pollutants, ultrafine particles and transition metals while plasma IL-6 was positively associated with 1-day average traffic-related pollutants. CONCLUSION:Our results suggest that exposure to pollutants with high oxidative potential (traffic-related pollutants, ultrafine particles, and transition metals) may lead to increased airway oxidative stress and inflammation in elderly adults. This observation was less clear with circulating biomarkers.