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:BackgroundBottled water (BW) consumption in the United States and globally has increased amidst heightened concern about environmental contaminant exposures and health risks in drinking water supplies, despite a paucity of directly comparable, environmentally-relevant contaminant exposure data for BW. This study provides insight into exposures and cumulative risks to human health from inorganic/organic/microbial contaminants in BW.MethodsBW from 30 total domestic US (23) and imported (7) sources, including purified tapwater (7) and spring water (23), were analyzed for 3 field parameters, 53 inorganics, 465 organics, 14 microbial metrics, and in vitro estrogen receptor (ER) bioactivity. Health-benchmark-weighted cumulative hazard indices and ratios of organic-contaminant in vitro exposure-activity cutoffs were assessed for detected regulated and unregulated inorganic and organic contaminants.Results48 inorganics and 45 organics were detected in sampled BW. No enforceable chemical quality standards were exceeded, but several inorganic and organic contaminants with maximum contaminant level goal(s) (MCLG) of zero (no known safe level of exposure to vulnerable sub-populations) were detected. Among these, arsenic, lead, and uranium were detected in 67 %, 17 %, and 57 % of BW, respectively, almost exclusively in spring-sourced samples not treated by advanced filtration. Organic MCLG exceedances included frequent detections of disinfection byproducts (DBP) in tapwater-sourced BW and sporadic detections of DBP and volatile organic chemicals in BW sourced from tapwater and springs. Precautionary health-based screening levels were exceeded frequently and attributed primarily to DBP in tapwater-sourced BW and co-occurring inorganic and organic contaminants in spring-sourced BW.ConclusionThe results indicate that simultaneous exposures to multiple drinking-water contaminants of potential human-health concern are common in BW. Improved understandings of human exposures based on more environmentally realistic and directly comparable point-of-use exposure characterizations, like this BW study, are essential to public health because drinking water is a biological necessity and, consequently, a high-vulnerability vector for human contaminant exposures.

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.

Project description:Few epidemiologic studies have evaluated the effects of air pollution on the risk of Parkinson disease (PD).We investigated the associations of long-term residential concentrations of ambient particulate matter (PM) < 10 ?m in diameter (PM10) and < 2.5 ?m in diameter (PM2.5) and nitrogen dioxide (NO2) in relation to PD risk.Our nested case-control analysis included 1,556 self-reported physician-diagnosed PD cases identified between 1995 and 2006 and 3,313 controls frequency-matched on age, sex, and race. We geocoded home addresses reported in 1995-1996 and estimated the average ambient concentrations of PM10, PM2.5, and NO2 using a national fine-scale geostatistical model incorporating roadway information and other geographic covariates. Air pollutant exposures were analyzed as both quintiles and continuous variables, adjusting for matching variables and potential confounders.We observed no statistically significant overall association between PM or NO2 exposures and PD risk. However, in preplanned subgroup analyses, a higher risk of PD was associated with higher exposure to PM10 (ORQ5 vs. Q1 = 1.65; 95% CI: 1.11, 2.45; p-trend = 0.02) among women, and with higher exposure to PM2.5 (ORQ5 vs. Q1 = 1.29; 95% CI: 0.94, 1.76; p-trend = 0.04) among never smokers. In post hoc analyses among female never smokers, both PM2.5 (ORQ5 vs. Q1 = 1.79; 95% CI: 1.01, 3.17; p-trend = 0.05) and PM10 (ORQ5 vs. Q1 = 2.34; 95% CI: 1.29, 4.26; p-trend = 0.01) showed positive associations with PD risk. Analyses based on continuous exposure variables generally showed similar but nonsignificant associations.Overall, we found limited evidence for an association between exposures to ambient PM10, PM2.5, or NO2 and PD risk. The suggestive evidence that exposures to PM2.5 and PM10 may increase PD risk among female never smokers warrants further investigation. Citation: Liu R, Young MT, Chen JC, Kaufman JD, Chen H. 2016. Ambient air pollution exposures and risk of Parkinson disease. Environ Health Perspect 124:1759-1765;?http://dx.doi.org/10.1289/EHP135.