A simple stochastic step model explains Reactive Oxygen Species production in cell populations undergoing senescence and in immortal cells.

<p>A) Hypothesis: Schematic representation of a step increase in phenotype for 100 cells. Each coloured trace represents one cell as it undergoes transition from proliferation-competence to senescence. Phenotype for each cell in the proliferating state is drawn from a relatively narrow distribution; whereas the phenotype of senescent cells is drawn from a broader distribution, with a higher expected value. All phenotype transitions are strictly non-decreasing. With increasing PD a higher proportion of cells in the population become senescent. B) Normalised, mean MitoSOX fluorescence measured by flow cytometry varies with PD in MRC5 fibroblasts cultivated until senescence (blue points). Normalised mean mitochondrial mass in a distinct population of the same cell type varies with PD (red points). Linear interpolation approximations to these curves were constructed (red and blue lines) and MitoSOX/mitochondrial mass estimated (green line); C) Kinetics of interpolated (solid lines) normalised MitoSOX fluorescence (blue), normalised mitochondrial mass fluorescence (red) and MitoSOX/mitochondrial mass ratio (green) are compared with mean population estimates from a stochastic step model (dashed lines). Stochastic step model parameters are estimated from the distributions for a wholly proliferative population (PD₂₇) and for a wholly senescent population (PD₄₇).</p

Ki67 negative cells containing more than 5 γH2A.X foci have significantly higher MitoSOX levels than Ki67 positive cells.

<p>A) Representative fluorescent images of YOUNG (PD₃₀) and senescent (SEN, PD₄₇) cells stained with MitoSOX followed by immunostaining against Ki67 and γH2A.X. MitoSOX staining (red); Ki67 (yellow); γH2A.X (green); DAPI (blue). B) Boxplots showing MitoSOX intensity in Ki67+ fibroblasts at 3 different PDs (∼50 cells per condition were quantified) C) MitoSOX intensity in Ki67+, Ki67−<5 γH2A.X foci and Ki67−>5 γH2A.X foci MRC5 fibroblasts (∼50 cells per condition were quantified). Asterisk indicates significance when comparing Ki67−>5 foci with the two other groups by 2-Way ANOVA; D) Comparison between MitoSOX fluorescence kinetics obtained by flow cytometry and microscopy in human fibroblasts; E) MitoSOX data (grey area) and growth curve (red circles) of mouse ear fibroblasts grown under 20% oxygen (data are from 3 independent mice).</p

Mitochondrial ROS does not increase significantly with population doublings following <i>hTERT</i> overexpression.

<p>Mean MitoSOX fluorescence measured by flow cytometry at increasing PDs in a purely proliferative population of MRC5 fibroblasts (blue points), in the same population of cells undergoing transition to senescence (light blue points), and in two independent experiments using MRC5 fibroblasts immortalised by transfection with hTERT (green points & purple points). Fluorescence in MRC5 fibroblasts is normalised according to the value at PD₂₇, as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032117#pone-0032117-g001" target="_blank">Fig. 1B</a>. All other datasets are normalised according to the fluorescence levels at the earliest observed PD.</p

A workflow of the gene expression analysis.

<p>The gene expression data were analysed to produce a list of fatigue-related features which were used as inputs for a support vector machine classifier of fatigue. 1. Differentially expressed genes were identified between fatigue groups. 2. Linear regression was used to analyse fatigue as a continuous variable. 3. The interferon type I signature was calculated for all the patients and compared to fatigue levels. 4. Gene set enrichment analysis was carried out using the high and low fatigue groups. 5. A support vector machine classifier was created using fatigue-related features as inputs and its performance assessed using receiver-operator characteristic (ROC) curves.</p

The characteristics of the patients.

<p>A heatmap of the clinical scores for the 133 patients included in this study. The values have been scaled between zero (absent) and one (worst). ESSDAI = EULAR Sjögren’s Syndrome Disease Activity Index, SSDDI = Sjögren’s Syndrome Disease Damage Index, ESSPRI = EULAR Sjögren’s Syndrome Patient Reported Index, HAD = Hospital Anxiety and Depression, PROFAD = Profile of Fatigue and Discomfort, VAS = Visual Analogue Scale.</p

Interferon type I signature and fatigue.

<p>(A) The IFN score ranges for the 133 patients. (B) The Fatigue VAS scores for the IFN-active and IFN-inactive groups. (C) The ESSDAI scores for the IFN-active and IFN-inactive groups.</p

Support vector machine (SVM) classification of fatigue groups.

<p>The receiver operator characteristic curves for the SVM output. Ten curves are shown on each plot. The area under the curve (AUC) is calculated as the mean over the ten curves. (A) All 181 enriched pathway genes as input. (B) The 55 leading edge genes as input.</p

Correction for other clinical factors.

<p>Volcano plots for the Fatigue VAS fatigue groups corrected for clinical factors: (A) Age at UKPSSR cohort recruitment. (B) Disease activity measured using the EULAR Sjögren’s Syndrome Disease Activity Index. (C) Disease damage measured using the Sjögren’s Syndrome Disease Damage Index. (D) The EULAR Sjögren’s Syndrome Patient Reported Index dryness sub-domain. (E) The EULAR Sjögren’s Syndrome Patient Reported Index pain sub-domain. (F) Anxiety measured using the Hospital Anxiety and Depression scale. (G) Depression measured using the Hospital Anxiety and Depression scale. (H) Pain and depression (E & G). (I) Pain, depression, dryness and anxiety (D-G). (J) All seven factors (A-G). No significantly differentially expressed genes were identified following any correction.</p