Schematic illustration of genetic criticality in DEAB of the expression.

<p>The putative genetic energy potential (15 min: blue; 20 min: red) with a fixed change in the expression (see the main text) describes the arrangement of mRNA expression in a transcriptional system, where the profile of the potential is anticipated from the scaling exponent of the frequency distribution of the expression (histograms: right panel; blue: 15 min; red: 20 min; gray: overlapped). The potential profile follows a change from single-well to double-well through a flattening profile as the <i>rmsf</i> is decreased (black arrow). The picture exhibits genetic criticality (details in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-t001" target="_blank">Table 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone.0097411.s001" target="_blank">File S1</a>) as interpreted by the Landau theory <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone.0097411-Landau1" target="_blank">[28]</a> on characteristic expression domains for the HRG genomic response: dynamic (dark red), transit (dark blue), and static (black) domains represent above-, near- and below-criticality, respectively. In the above-criticality, due to the unimodal to bimodal shift of the frequency distribution (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g006" target="_blank">Figure 6A</a>), the energy potential should also be shifted; in the near-criticality and below-criticality due to the overlapping frequency distributions between 15 min and 20 min, the energy potential should be (almost) temporally invariant. Note that, in the double-well potential (below-criticality), instead of generating two independent Boltzmann distributions (two equilibrium states), the frequency distribution shows broken distributions, which suggests non-linear interaction between coherent expression states in a non-equilibrium system.</p

Dynamic motion of the characteristic HRG domains in DEAB of the expression change.

<p>The coordinated expression dynamics around an equilibrated high-expression state exhibit the pendulum oscillation of CES (autonomous bistable switch) between different time periods (10–15 min, 15 min–20 min, and 20–30 min): A) in the static domain (9059 mRNAs) LES1 shows ON-OFF-EQ oscillation around HES1(EQ) through a unimodal shift, and B) in the dynamic domain (3269 mRNAs) the bifurcation of LES2 at 15 min shows a dynamic change from LES2(ON) to HES2(ON) through a unimodal to bimodal profile at 20 min, and the annihilation of HES2 through a bimodal to unimodal profile at 20–30 min around HES2(EQ). The annihilation of HES2 reveals a short-lived dynamic domain. First row: frequency distribution of the expression change. Second row: the genetic landscapes of A) the static domain and B) the dynamic domain from the top view with density color bars.</p

Existence of coherent expression states (CESs) as hill-like functions.

<p>Plots of single mRNA expression for <i>rmsf</i> >0.42 (blue dot: 15 min and red dot: 20 min) are superimposed in the left panel (first row: 3269 expressions for HRG; second row: 1482 for EGF). In the right panel, the probability density function (PDF) using a Gaussian kernel by Mathematica 9 (default setting) for each point (left panel) reveals hill-like functions in pseudo-3-dimensional space (genetic landscape; z-axis: probability density). Superimposition of the genetic landscapes between <i>t_i–</i>₁ = 15 min and <i>t_i</i> = 20 min - first row: the HRG response has three CESs; two independent CESs plus one CES that results from the overlap of CESs between 15 min (darker color) and 20 min (lighter color) around a zero change in expression at the y-axis; second row: the EGF response has a single CES as the overlap of two CESs around a zero change in expression. The legend shows a lighter (darker) color bar at 20 min (at 15 min) for PDF.</p

Bifurcation of CESs in DEAB of the expression.

<p>The bifurcations of CESs in DEAB of the expression for 15–20 min are examined with an incremental change in a segment, <i>v</i> < <i>rmsf</i> < <i>v</i> + <i>r,</i> as an extension of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g004" target="_blank">Figure 4</a>, where the range <i>r</i> is set to 0.4 and <i>v</i> is a variable of <i>rmsf</i>. The bifurcation diagrams of the expression (<i>v</i> against the expression; first row) at <i>t</i> = 20 min, and the expression change (<i>v</i> against the change in the expression for 15–20 min; second row) are plotted for HRG (left panel) and EGF (right). The bifurcation diagram of the expression defines the expression level at <i>ln</i>(<i>ε</i>)  = 2.075 (lower: low- and upper: high-expression) because of the existence of a valley, which separates the low and high CESs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g006" target="_blank">Figure 6</a>), whereas the bifurcation diagram of the expression change shows three activity levels of CES: ON (positive change in the expression), EQ (near zero) and OFF (negative change in the expression). The bifurcation diagrams clearly show distinct characteristic expression domains between HRG and EGF: static, transit and dynamic domains for <i>rmsf</i> <0.21, 0.21< <i>rmsf</i> <0.42, and 0.42< <i>rmsf</i> for HRG, and static and transit domains for <i>rmsf</i> <0.16 and 0.16< <i>rmsf</i> for EGF (see details in the main text).</p

Unimodal to bimodal frequency distribution for DEAB of the expression.

<p>The profiles of the frequency distribution of the expression (<i>ln</i>(<i>ε</i>(<i>t</i>))) from 15 min to 20 min change from unimodal to bimodal for A) high-variance expression (the root mean square fluctuation, <i>rmsf</i> >0.42) and B) low-variance expression (<i>rmsf</i> <0.42). First row: the HRG response for <i>rmsf</i> >0.42 shows a peak-shift of unimodal profiles from <i>t = </i>15 min (blue histogram) to <i>t</i> = 20 min (red) with a change in the lower to higher value of the expression, while binomial frequency distributions between 15 min (blue polygonal line) and 20 min (red histogram) almost perfectly overlap each other for <i>rmsf</i> <0.42. Second row: the EGF response shows almost the perfect overlap of profiles for both unimodal (<i>rmsf</i> >0.42) and bimodal (<i>rmsf</i> <0.42) distributions, which suggests that there is no temporal averaging response, consistent with DEAB of the expression for the EGF response (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g001" target="_blank">Figure 1A</a>). For all histograms in this report, the bin size is set to 0.05.</p

Schematic illustration of autonomous bistable switch (ABS) with genetic ‘energy profile’ in DEAB of the expression change.

<p>First row: the schematic illustration depicts the temporal development of ABS showing the opposite changes of pendulum oscillation of CES between the static and dynamic domains (refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g007" target="_blank">Figure 7</a>). In the HRG static domain (left panel), the temporal change of CES (LES1) occurs without the bifurcation of CES; in the dynamic domain (right), the pendulum oscillation occurs through the dynamic bifurcation of CES: bifurcation of a low-expression state with a change in a putative potential profile from single- to double-well at 15 min, a change from the low- to the high-expression state at 20 min (refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g006" target="_blank">Figure 6A</a>) with a single-well potential shift (small dashed box), and annihilation of the high-expression state with a change from double- to single-well at 20–30 min. Second row: schematic illustration describes the dynamics of the genetic energy potential as a function of the expression change (with a fixed expression; see details in the main text): 1. purple line: 10–15 min (at 15 min); 2. blue: 15–20 min (at 15 min); 3. red: 15–20 min (at 20 min); 4. black: 20–30 min (at 30 min). The picture shows the energy flow between the pendulum motions, which reflects the non-equilibrium dynamics of CES.</p

Genetic landscape of the characteristic expression domains.

<p>Each row (A: HRG and B: EGF) corresponds to frequency distributions of mRNA expression (first) and genetic landscapes (second: side view). In the genetic landscape, a static domain with a valley is characterized by two CESs: A) HES1(EQ) and LES1(OFF) for <i>rmsf</i> <0.21; and B) HES(EQ) and LES(EQ) for <i>rmsf</i> <0.16, a transit domain is characterized by A) HES1(EQ) for 0.21< <i>rmsf</i> <0.42; and B) HES(EQ) for <i>rmsf</i> >0.16, and a dynamic domain is characterized by three CESs: A) LES2(ON), HES2(ON) and HES1(EQ) for <i>rmsf</i> >0.42, which is the result of superimposition of the genetic landscapes between 15 min and 20 min (right panel in the second row); a state shift occurs from LES2(ON) at 15 min to HES2(ON) at 20 min, consistent with the unimodal shift of the frequency distribution (A: first row). The red arrow points to the valley to separate the low and high CESs. The activity level of a coherent expression state (ON, EQ and OFF) refers to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097411#pone-0097411-g005" target="_blank">Figure 5</a>.</p

Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery

<div><p>Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens’ pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.</p></div

Changes in the metabolomic profiles caused by the storage of EDTA blood are visualized by PCA (score plot) based on the chemical features in the plasma samples as detected by the HILICpos assay.

<p>Sample storage conditions are represented by symbols colour-coded blue and red for 4°C and 25°C, respectively; dots, squares, triangles, crosses, and circles represent 3, 6, 12, 24, and 48 h, respectively. Control and SQC samples are represented by black dots and diagonal crosses, respectively.</p

Quality markers identified in this study for the evaluation of pre-analytical conditions.

<p>Quality markers identified in this study for the evaluation of pre-analytical conditions.</p