The clinical data of the enrolled subjects.

<p>Idiopathic PD: patients without any known mutations; <i>LRRK2</i> PD: patients carrying the <i>G2019S</i> mutation in <i>LRRK2</i>; Mut+: healthy family members with <i>G2019S</i> mutation in <i>LRRK2</i>; Mut−: healthy family members without mutation; Controls: healthy subjects without any sign of neurological diseases; AAO: age at disease onset.</p

Analytes discriminating between <i>LRRK2</i> mutation carriers and Controls.

<p>Analytes are defined by their retention time and dominant channel in LCECA profiles.</p><p>+ up-regulated in <i>LRRK2</i> mutation carriers; − down-regulated in <i>LRRK2</i> mutation carriers.</p

Purine metabolites in PD.

<p>Data are represented as mean±SEM of percentage of plasma pool value. Control group includes both normal control subjects and healthy family members from <i>LRRK2</i> PD patients who did not have G2019S mutation. HX–hypoxanthine, X–xanthosine, XAN–xanthine, UA–uric acid, G- guanosine.</p

Analytes discriminating between idiopathic and <i>LRRK2</i> PD.

<p>Analytes are defined by their retention time and dominant channel in LCECA profiles. + up-regulated in <i>LRRK2</i> PD; − down-regulated in <i>LRRK2</i> PD.</p

Structural Characterization of Plasma Metabolites Detected via LC-Electrochemical Coulometric Array Using LC-UV Fractionation, MS, and NMR

Liquid chromatography (LC) separation combined with electrochemical coulometric array detection (EC) is a sensitive, reproducible, and robust technique that can detect hundreds of redox-active metabolites down to the level of femtograms on column, making it ideal for metabolomics profiling. EC detection cannot, however, structurally characterize unknown metabolites that comprise these profiles. Several aspects of LC-EC methods prevent a direct transfer to other structurally informative analytical methods, such as LC-MS and NMR. These include system limits of detection, buffer requirements, and detection mechanisms. To address these limitations, we developed a workflow based on the concentration of plasma, metabolite extraction, and offline LC-UV fractionation. Pooled human plasma was used to provide sufficient material necessary for multiple sample concentrations and platform analyses. Offline parallel LC-EC and LC-MS methods were established that correlated standard metabolites between the LC-EC profiling method and the mass spectrometer. Peak retention times (RT) from the LC-MS and LC-EC system were linearly related (<i>r</i>² = 0.99); thus, LC-MS RTs could be directly predicted from the LC-EC signals. Subsequent offline microcoil-NMR analysis of these collected fractions was used to confirm LC-MS characterizations by providing complementary, structural data. This work provides a validated workflow that is transferrable across multiple platforms and provides the unambiguous structural identifications necessary to move primary mathematically driven LC-EC biomarker discovery into biological and clinical utility

PLS-DA score plots of controls and subjects with <i>G2019S LRRK2</i> gene mutation.

<p>PLS-DA scores plot showing a significant separation between controls (n = 10) and asymptomatic subjects with <i>G2019S LRRK2</i> gene mutation (n = 14) using preprocessed datasets. Five controls subjects and seven mutation carriers were randomly selected as the test set and were not used in PLS-DA model construction. Class membership of the subjects in the test set was predicted using this PLS-DA model shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007551#pone-0007551-g007" target="_blank">Figure 7</a>.</p

PLS-DA scores plots of <i>LRRK2</i> PD patients and their family members.

<p>PLS-DA scores plot showing a separation between <i>LRRK2</i> PD patients (n = 12) and their healthy family member with (n = 21) or without (n = 10) the gene mutation. All peaks (no pre-processing) were used for this analysis. Ages of the individual subjects are shown next to their symbols.</p

PLS-DA prediction plots of controls and subjects with <i>G2019S LRRK2</i> gene mutation.

<p>Nine analytes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007551#pone-0007551-t004" target="_blank">Table 4</a>) were used to build PLS-DA separation model, based on randomly selected 10 controls and 14 <i>LRRK2</i> gene carriers (a representative plot is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007551#pone-0007551-g006" target="_blank">Figure 6</a>). The resulting models were used to predict class membership of the remaining 5 controls and 7 <i>LRRK2</i> gene carriers. This procedure was carried out 4 times with different controls and gene carriers included in the test and training sets each time; the results are presented in panels A–D for all four individual models. Predictions were made with a cutoff of 0.5 for class membership. Numbers next to the symbols refer to the sample codes of the subjects.</p

PLS-DA prediction plots of IPD patients and <i>LRRK2</i> PD patients.

<p>Twelve analytes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007551#pone-0007551-t003" target="_blank">Table 3</a>) were used to build PLS-DA separation model, based on randomly selected 30 IPD and 8 <i>LRRK2</i> PD patients (a representative plot is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007551#pone-0007551-g003" target="_blank">Figure 3</a>). The resulting models were used to predict class membership of the remaining 11 IPD and 4 <i>LRRK2</i> PD patients. This procedure was carried out 4 times with different IPD and <i>LRRK2</i> PD patients included in the test and training sets each time; the results are presented in panels A–D for all four individual models. Predictions were made with a cutoff of 0.5 for class membership. Numbers next to the symbols refer to the sample codes of the subjects.</p

PLS-DA scores plots of control subjects and PD patients.

<p>PLS-DA scores plots showing a separation between control subjects (n = 15) and idiopathic Parkinson's Disease (IPD) patients (n = 41), and between control subjects (n = 15) and <i>LRRK2</i> PD patients (n = 12). All peaks (no pre-processing) were used for these analyses. The data from control subjects and from the healthy family members of <i>LRRK2</i> PD patients without the mutation (n = 10) were used for the analysis either separately (panels A and B), or were combined (panels C and D).</p