Though chicoric acid was first identified in 1958, it was largely ignored until recent popular media coverage cited potential health beneficial properties from consuming food and dietary supplements containing this compound. To date, plants from at least 63 genera and species have been found to contain chicoric acid, and while the compound is used as a processing quality indicator, it may also have useful health benefits. This review of chicoric acid summarizes research findings and highlights gaps in research knowledge for investigators, industry stakeholders, and consumers alike. Additionally, chicoric acid identification, and quantification methods, biosynthesis, processing improvements to increase chicoric acid retention, and potential areas for future research are discussed. Recent US consumer interest in boosting their dietary intake of chicoric acid followed popular-media coverage of claims that consumption of products containing chicoric acid had promising health benefits Drazen, . A recenCichorium intybus L.) plants, Scarpati and Oriente -(-)-tartaric acid] and has been reported in the majority of the plants listed in Table meso-chicoric acid -(-)-tartaric acid) has also been reported at a lesser levels in horsetail sprouts (Equisetum arvense L.)    were reported in chicory leaves . Hasegawa and Taneyama  the tartaric acid structure of purified meso-chicoric acid of horsetail barren sprouts , L- and D-chicoric acids (if D-chicoric acid exists) co-elute  Chromatography  can be made to aid peak identification   has also been evaluated in chicoric acid quantification in meso- form of chicoric acid) but also quantitative analyses. For example, Perry et al.  leaves was chicoric acid. Our suspicion is that identification of this basil compound was overlooked for so long was due to chicoric acid's prompt and rapid degradation during extraction procedures  Benth. Ex Kurz shoots and roots was greatest in aqueous acetonitrile > acidified (HCl) acetonitrile > aqueous methanol > aqueous ethanol > acidified (HCl) methanol > acidified (HCl) ethanol > 60°C water > ambient temperature water  involved in meso-chicoric acid biosynthesis  is still not well known; although it is generally understood to form via the shikimic acid/phenylpropanoid pathway as other phenolic acids, analogous to the conjugation of caffeic acid derivatives of rosmarinic acid or chlorogenic acid  yet chicoric acid has not been detected in other genera within these plant sub-families  of E. purpurea based on traditional uses and scientific data. Chicoric acid can vary widely with plant age and portion. For example, in roots and shoots of E. purpurea grown ranged from 203 to 3855 mg 100 g−1 dry weight, an over 18 fold difference in both young and mature plant fractions  maintains a list of the plant fractions approved for human herbal usage. For example, the European Medicine Agency lists the roots of E. purpurea, measuring over a 4 fold difference from sections highest to those lowest: leaves > roots > flowers > stems; 93–391 mg 100 g−1 fresh weight of fraction. Similar findings were reported by Molgaard et al. . In contrast, chicoric in different plant parts of E. purpurea were reported in the descending order: flowers > leaves > stems > roots  where no chicoric acid was detected in leaves , results in supply dependent upon the plant's need  of chicoric acid in Echinacea extracts, and a 3460 fold difference  of chicoric acid in Echinacea capsules. A range of 410–2140 mg 100 g−1 dry weight was reported for E. purpurea root and aerial samples (n = 62) purchased from herb traders  in fall dandelion flower heads  leaf terminals, but a recent study  leaves, though different species and fractions were examined. Only two studies have found chicoric acid found in fruit , mature leaves contained higher levels of chicoric acid compared to close-to-the-base and young leaves  and environmental triggers. Many of the seemingly conflicting reports concerning effects of tissue maturity and growing season on chicoric acid accumulation may be related to whether the plants being evaluated are annual or perennial in nature. Accumulation of phenolic acids in plants are the net end product of their anabolism and catabolism. The reason for anabolism and catabolism of chicoric acid during plant growth is unknown. For growers and producers, these fluctuations in chicoric acid levels with tissue maturation and throughout the growing season should be taken into consideration before scheduling harvest dates or planning herbal product manufacturing  of smooth hawksbeard  flower heads collected from altitudes of 180–1060 m  that had been collected at elevations from 10 to 2480 m  samples from a public marketplace contained chicoric acid only when fruit were identified as organically grown  increased production of chicoric acid in roots of E. purpurea  decreased chicoric acid levels in E. purpurea showed no change in chicoric acid levels due to AMF colonization, but the roots had more chicoric acid with colonization  can enhance plant growth but have been shown to have positive, negative, and no influence on accumulation on plant phenolic composition. For example, arbuscular mycorrhizal fungus (AMF) colonization did not alter chicoric acid levels in either “Genovese Italian” or “Purple Petra” basil leaves and stems  on chicoric acid accumulation have been investigated experimentally in very few crops. In most, but not all cases, increased levels of treatments that resulted in greater plant stress increased chicoric acid production.−2 s−1 for 1 day) exposure had no influence on chicoric acid 1 h after exposure, but increased concentrations 1 day and 3 days after exposure. On average the heat and cold stress treatments resulted in ~2 fold increase in chicoric acid while high light exposure increased concentrations by ~7 fold.Five-week old “Baronet” lettuce plants were subjected to brief simulated environmental stresses and sampled 1 day before and <1 day, 1 day, and 3 days after stress  even though lower light decrease plant dry weight and accumulation of reducing sugars in 4 week old plants  from 410 μmol to 225 μmol m−2 s−1 fluorescent light) in E. purpurea suspension cell cultures revealed that 20°C and 3 h light and 21 h dark for 5 weeks produced the most chicoric acid  and photoperiods  expressed chicoric acid concentrations differently in response to salinity   salinity; while in E. pallida chicoric acid concentrations were elevated in both of the higher (75 and 100 μm) salinity treatments  decreased plant dry weight and chicoric acid in leaves but increased chicoric acid in roots of 4-month-old E. angustifolia grown in the field  and storage  decreased chicoric acid accumulation in green leaf lettuce (“Baronet”)  can alter the phenolic composition of plant tissues. Of the few studies done in this area of research, mostly on lettuce, washing or rinsing plant material after harvest has little effect on chicoric acid retention. There was no treatment differences in chicoric acid levels in rinsed  shredded iceburg lettuce after air storage  of 13 days at 4°C  did not enhance chicoric acid retention from that of air in the red and green parts of the lettuce, though white lettuce tissues increased in chicoric acid with the MAP  in plant materials; however, the effects of drying will vary depending on the drying procedure, the type of plant material, and chemical in question  pasteurization to increase product microbial stability did not alter chicoric acid retention in E. purpurea flowers and roots over to unpasteurized samples  flower glycerin extract increased the retention of chicoric acid (and other bioactives) in E. purpurea extracts (in glycerin) for 4 months at 25°C  to control-fresh, though chicoric acid was not separately identified  after ingestion, but prior to absorption and metabolism. Due to the concerns surrounding all in vitro antioxidant measurements, chicoric acid antioxidant studies will not be summarized here, but the issues and research needed in this area have been well summarized elsewhere  associated with purported health benefits of E. purpurea supplements , or what level would be most effective for improving human health that minimizes possible risks of toxicity, herb/drug interactions, or synergistic and/or antagonistic effects. Despite excitement about chicoric acid's potential from antiviral researchers, or individuals and families affected by HIV, additional research is needed to understand the benefits of chicoric acid and its analogs.There is a strong need for a well-defined dietary supplement regulation in the US. Until dietary supplements receive the scrutiny as foods in the US, with minimum quantities of active ingredients, full ingredient disclosures, safety guidelines, sound science supporting claims, etc. the safest source of consuming chicoric acid is via food. Good review articles (Dufault et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The preclinical model of bleomycin-induced lung fibrosis, used to investigate mechanisms related to idiopathic pulmonary fibrosis (IPF), has incorrectly predicted efficacy for several candidate compounds suggesting that it may be of limited value. As an attempt to improve the predictive nature of this model, integrative bioinformatic approaches were used to compare molecular alterations in the lungs of bleomycin-treated mice and patients with IPF. Using gene set enrichment analysis we show for the first time that genes differentially expressed during the fibrotic phase of the single challenge bleomycin model were significantly enriched in the expression profiles of IPF patients. The genes that contributed most to the enrichment were largely involved in mitosis, growth factor, and matrix signaling. Interestingly, these same mitotic processes were increased in the expression profiles of fibroblasts isolated from rapidly progressing, but not slowly progressing, IPF patients relative to control subjects. The data also indicated that TGFβ was not the sole mediator responsible for the changes observed in this model since the ALK-5 inhibitor SB525334 effectively attenuated some but not all of the fibrosis associated with this model. Although some would suggest that repetitive bleomycin injuries may more effectively model IPF-like changes, our data do not support this conclusion. Together, these data highlight that a single bleomycin instillation effectively replicates several of the specific pathogenic molecular changes associated with IPF, and may be best used as a model for patients with active disease. Idiopathic pulmonary fibrosis (IPF) is a devastating disease characterized by excessive matrix deposition that disrupts the normal architecture of the lung parenchyma. The key pathological features of IPF include fibroblastic foci that are highly synthetic, areas of epithelial cysts associated with the honeycombing appearance of the lung, and mild lymphoplasmacytic interstitial inflammation that is associated with areas of type II cell hyperplasia in vivo system for investigating candidate therapies. Its failure as a prognostic tool may be ascribed to the fact that the model has not been well characterized in terms of identifying the clinically relevant molecular changes and when they occur. Bleomycin is well understood to induce lung injury that results in an acute inflammatory response that is unlikely to reflect the processes driving the disease in patients. The inflammatory phase is, however, followed by fibrotic changes that replicate certain pathological features consistent with those associated with IPF. Therefore, some have argued that it may be more appropriate to evaluate compounds after or during the onset of the fibrosis phase of the response (i.e. using therapeutic dosing regimens), which may be a more disease-relevant paradigm The lack of effective therapies is arguably due to an incomplete understanding of the molecular mechanisms driving the disease and the failure of preclinical experimental models to correctly predict the clinical efficacy of several molecules Using classic histopathology and physiology methods, we report that the repetitive model offered no significant improvement over the single challenge model. Integrative bioinformatic and pharmacological approaches revealed corresponding molecular changes in the lungs of bleomycin-treated mice and IPF patients, especially in genes associated with mitosis and extracellular matrix signaling. Interestingly, these same pathways appeared to be altered in fibroblasts isolated from IPF patients with rapidly progressing, but not slowly progressing disease. It did not appear that these changes in expression were directly associated with TGFβ signaling and furthermore, an inhibitor to the TGFβR1  could not completely attenuate bleomycin-induced fibrosis in mice. These data support the premise that the bleomycin model can recapitulate many of the complex profibrotic responses that are also elevated in the lungs of IPF patients, particularly in patients with active disease.To establish a dose of bleomycin that induced fibrosis but did not result in mortality, a preliminary bleomycin dose-response study was performed. Bleomycin induced a dose-dependent increase in lung fibrosis . SignifiIn response to repetitive bleomycin administration, we observed increased inflammatory cell infiltrate in the BALF of mice . InteresH (lung tissue elastance) and work of inflation (WoI) correlated significantly to collagen levels in the lung , although the changes were relatively modest and significant at 35 days post-administration , an active fibrosis phase (days 7–14) and a late fibrosis phase (days 21–35), with saline-treated samples clustering separately . Peak geGenes altered during the inflammation phase generally consisted of immune response and wound healing genes including various cytokines and chemokines. In addition to genes associated with immune and wound healing categories, genes altered during the active fibrosis phase were also associated with cell cycle-related processes. These changes in gene expression were validated by the increased expression of inflammatory cytokines early in the response to bleomycin , followeClustering analysis of genes involved in fibrosis-related pathways revealed preferential up-regulation of genes during the active fibrosis phase. Matrix metalloprotease (MMP) genes including MMP12, MMP19, as well as lysyl oxidase-like (LOXL) genes implicated in the pathogenesis of fibrotic diseases were induced from 7 days onwards following bleomycin treatment . PositivThe translational relevance of the bleomycin response signatures were assessed in clinical IPF datasets using gene set enrichment analysis (GSEA). Gene expression data from two publicly available clinical IPF datasets were compiled: GSE2052 in vivo datasets, GSEA also revealed that gene sets defined by changes in the clinical IPF datasets were enriched within the expression profiles of lung samples generated from the bleomycin model (data not shown). These results further highlight commonalities in genes altered in response to bleomycin treatment with those altered in clinical IPF samples.Closer examination of the leading edge genes  shows up-regulation of a common subset of genes across different clinical datasets . Genes iNext, we prospectively compared the gene expression profiles of fibroblasts obtained from patients with rapidly progressing and slowly progressing IPF to fibroblasts obtained from non-IPF control subjects, as these cells are centrally involved in the disease pathogenesis Given that several pathways associated with fibrotic processes were elevated in response to bleomycin-induced lung injury, we wanted to assess whether TGFβ was the dominant driver of fibrosis in this model. To this end, we assessed an ALK-5 inhibitor, SB525334, under both prophylactic and therapeutic conditions. SB525334 significantly inhibited bleomycin-induced fibrosis changes under both conditions . The maxThe data presented in this manuscript provide clear evidence that the bleomycin model of lung fibrosis induces molecular changes directly relevant to IPF and in particular may be more relevant for modeling “active” rather than slowly progressing disease. Additionally, these data indicate that there are multiple pathways involved in eliciting the pathophysiological changes in the model that are consistent with those observed in the lungs of patients with IPF. As such, it would appear that therapies aimed at affecting several of these key signaling networks either by targeting “nodes”, using combination therapy approaches, or broad spectrum inhibitors of key enzymes may be the only means of making a significant impact on disease progression in all patients with IPF.The response to bleomycin occurred in three distinct phases, which we have termed the (1) inflammation phase, (2) active fibrosis phase, and (3) late fibrosis phase. The inflammation phase was largely characterized by the presence of neutrophils in the airways and the increased expression of immune processes in the lung tissue. While some have proposed that neutrophils may be playing a role in IPF H) and our new measure of WoI Another commonly stated limitation of the bleomycin model is that the fibrosis is not progressive and in fact, resolves with time. Interestingly, we observed that there was still measureable fibrosis and changes in pulmonary mechanics in the late fibrosis phase (out to 5 weeks post-bleomycin). Furthermore, we took samples 56 days after bleomycin instillation in a separate study (data not shown) and measureable fibrosis was still present contrary to previous reports and dogma As an attempt to improve the single challenge system we performed repetitive bleomycin instillations, but found that repetitive exposures provided no additional value in terms of producing a more substantial or progressive fibrotic response in the lung. The one difference to the single challenge model was that epithelial remodeling was more evident at most of the time points assessed in the repetitive challenge system. Epithelial remodeling in IPF is associated with the deterioration of the lung parenchyma and the honeycombing pattern of the lung characteristic of IPF The most interesting observations from these studies occurred during the active fibrosis phase of the response to bleomycin. This phase was characterized by increased levels of (protein and lipid) pro-fibrotic mediators associated with increased matrix deposition in the lung that correlated with alterations in pulmonary mechanics. These changes were also temporally correlated with the increased expression of a variety of fibrosis-related gene sets including TGFβ signaling, matrix proteases, and structural proteins – observations that confirm previous findings reported by others Here, we have shown for the first time a direct correlation between the molecular changes observed in the lungs of bleomycin-treated mice and patients with IPF using novel translational and quantitative bioinformatics approaches. GSEA allows one to define gene signatures from one experimental system and subsequently quantify whether that signature is enriched in another biological system. The gene sets defined by changes during the active fibrosis phase (days 7–14) of the mouse bleomycin model generated the highest enrichment scores amongst the samples from IPF patients obtained from two separate publicly available data-sets. In fact, the changes occurring in the late fibrosis phase were also enriched in the clinical samples, an observation that is consistent with the lack of fibrosis resolution at these later times. To understand the biology behind the correlative gene changes, we overlapped a union of the leading edge gene lists with canonical signaling pathways. Interestingly, the leading edge genes were largely involved in mitotic processes likely due to the proliferation of mesenchymal cells. Additionally, there were changes in signaling pathways  associated with fibroblast proliferation and signaling in response to changes to the extracellular matrix. These data highlight the multifactorial molecular mechanisms involved in the pathogenesis of IPF Budd et al, under review). Additionally, the cells from rapidly progressing patients are able to induce substantial lung fibrosis when injected into immunocompromised mice (versus a mild response when using slowly progressing IPF patient fibroblasts) Although these are interesting observations, additional clinical information such as lung function data, high resolution computed tomography scores, and rate of disease progression associated with publicly available clinical datasets would improve the interpretation of these datasets. Such information would provide greater insights about whether these molecular changes that correlate with the model are generic amongst all IPF patients or whether the correlation is strongest within certain IPF patient sub-populations. Interestingly, many of the overlapping changes between the model and clinical samples were likely to be due to changes in fibroblast activation and proliferation. These same processes were enriched in fibroblasts isolated from the lungs of IPF patients with rapidly progressing disease. Surprisingly, these processes did not appear to be altered when comparing fibroblasts from patients with slowly progressing IPF to non-IPF controls. Together, these observations would suggest that the bleomycin model may be more effective at mimicking active disease processes that may be more associated with patients with a rapidly progressing disease course. That said, there are caveats to these data that need to be taken into account. First, the patients’ cells are not freshly isolated or laser capture microdissected, but rather they have been in culture for several passages, which may confound interpretation. Second, the controls are not healthy individuals, but cancer patients undergoing lung resection. Finally, these data were generated from 4–6 patients within each group and additional biological replicates would improve confidence in these observations. That said, it is still very interesting to note that the cells from the rapidly progressing IPF patients do have enhanced responses to PDGF  and TGFβ , whereas these responses are not as profoundly altered in fibroblasts from slowly progressing IPF patients relative to the non-IPF controls  was only enriched in the IPF patients who had experienced an acute exacerbation compared to non-IPF control subjects, possibly indicating its importance in active disease. Similarly, the down-regulated TGFβ gene set (comprised mainly of negative regulators of the TGFβ pathway) was only down-regulated in one clinical data-set. Although no one would discount TGFβ’s contribution to the lung remodeling associated with IPF, it does highlight the fact that it is unlikely to be the only pathway driving disease pathogenesis. These observations are also consistent with the data demonstrating that the ALK-5 inhibitor, SB525334, was not capable of fully blocking the fibrotic response elicited by bleomycin. This molecule consistently attenuates the response in our laboratory as well as in others The data described in this paper contradict current dogma and suggest that bleomycin is able to induce clinically meaningful molecular responses in the lungs of mice. Using innovative bioinformatic approaches we directly linked the molecular changes occurring in the bleomycin model to those occurring in the lungs of IPF patients in a quantitative manner. Genes involved in mitotic processes were expressed at higher levels in lung tissues from bleomycin-treated mice and IPF patients as well as fibroblasts isolated from IPF patients with rapidly progressing disease. These data along with pharmacological data indicate that bleomycin triggers complex, multifactorial (TGFβ dependent and independent) responses that result in pathophysiological changes in mice that are consistent with IPF. Furthermore, our analysis suggests that the changes in this model best represent changes occurring in patients with active (i.e. rapidly progressing) disease.ad libitum. All animal care and experimental procedures were approved by the Roche Animal Care and Use Committee, which is a facility accredited by the American Association for the Accreditation of Laboratory Animal Care (AAALAC).Male C57BL6/J mice (7–9 weeks old) were purchased from the Jackson Laboratory , housed under pathogen-free conditions with food and water Bleomycin sulphate  was dissolved in 0.9% saline and loaded into a IA-1C liquid MicroSprayer  as previously described 2O positive end-expiratory pressure).Invasive lung function measurements were taken at specified time points following saline or bleomycin administration as previously described Exercise capacity was measured based on a method previously published by Koch and colleagues Immediately following lung function measurements, mice were exsanguinated, bronchoalveolar lavage fluid (BALF) was collected, and total and differential cell counts were performed, as previously described For the analysis of 4-hydroxyproline (HP), BALF samples were extracted with acetonitrile at volume of 1:6, respectively. After centrifugation, acetic acid (0.1% in water) was then added to the supernatants at volume of 1.5:1, respectively. Samples were mixed and centrifuged before injection for LC/MS/MS analysis. LC/MS/MS analysis was conducted by gradient HPLC with selective reaction monitoring (SRM). The calibration range was 40- 2,000 ng/ml. Since the control BALF matrix contained a basal level of HP (approximately 200 ng/mL), the calibration curve is corrected with the basal concentration determined by the standard addition. The LC/MS/MS system used for the analysis consisted of an AB-Sciex API4000 with an electrospray source connected to an Agilent 1200 pump, an Waters 2777 autosampler, and an Agilent 1100 series column oven. Chromatographic analysis was conducted by HILIC HPLC with an Ascentis Express HILIC  column. The SRM transition is m/z 132 to m/z 41.2O pressure with 10% neutral buffered formalin through the tracheal cannula and immersed in formalin for at least 24 h. After being processed into paraffin blocks, the lungs were sectioned (5 µm) and stained with either hematoxylin and eosin (H&E) or immunolabeled with an anti-collagen I antibody , to assess fibrotic changes in the lungs as previously described After performing lung function measurements, whole lungs  were inflated under 25 cm HAdditionally, staining for alpha-smooth muscle actin , and heat-shock protein-47 (HSP-47)  was done using a 3-step biotin-streptavidin-HRP detection method, and DAB  as the detection chromogen. Tissue sections were counterstained with hematoxylin, dehydrated, and mounted with Permount mounting media.Total RNA was isolated from the lung tissue of bleomycin and saline treated mice across 7 time points . The mice used for genomic profiling were not used for measuring lung function, performing bronchoalveolar lavage, and they were not used for histopathology analysis. The entire left and right lung tissues were homogenized in QIAzol reagent. Purified total RNA was amplified and labeled using NuGen Ovation kits  and samples were hybridized to Affymetrix Mouse 430 2.0 arrays. Array washing, staining and scanning was performed according to standard Affymetrix protocols . Probe level data was curated by first mapping individual probe sequences to their most current mouse genome sequences. Probes which were non-uniquely mapped to specific genes or contained outdated mappings were discarded, and the remaining probes were summarized into probesets and normalized using RMA. Potential outlier samples were assessed using principal component analysis (PCA) on all normalized probesets across all samples, resulting in 1 sample  being removed from subsequent analysis. Probes level data was subsequently summarized to unique genes based on a variance filter, yielding one expression value per unique gene across 111 samples. Mouse genes were then mapped to their human orthologs for subsequent pathway analysis. This resulted in 14643 unique mouse genes which mapped to human orthologs and were subsequently used for analysis. All data is MIAME compliant and all raw.CEL files from the arrays are deposited in NCBI’s GEO database (GSE40151).Differentially expressed genes were determined using an ANOVA, with pairwise comparisons between bleomycin and saline treatment at each time point. P-values for differentially expressed genes in pairwise comparisons were adjusted using a Benjamini-Hochberg correction to account for multiple hypothesis testing http://pid.nci.nih.gov/index.shtml). This repository includes pathway data imported from BioCarta and Reactome. Enrichment of functional ontologies and pathways for all DEGs was determined by a hypergeometric test, with p-values and FDR calculated to determine significance of enrichment.Gene ontology (GO) functional analysis and pathway enrichment analysis were performed on DEGs between saline and bleomycin treated mice at each time point using internally curated GO biological process annotations. Similarly, pathway enrichment for all DEGs was determined using internally curated data from the NCI Pathway Interaction Database , collagens (COLs), TGFβ signaling , alternative macrophage activation, Wnt signaling, and an Gene set enrichment analysis (GSEA) Clustering analysis was subsequently performed on leading edge genes from GSEA, in order to more closely examine genes within bleomycin signatures most contributing to enrichment of the signatures in clinical datasets. A union of leading edge genes from GSEA of IPF versus control comparisons from two datasets  was compiled and expression of these genes was assessed in the two clinical datasets with IPF-only subjects versus controls.In addition, GSEA was used to determine enrichment of genes differentially expressed in clinical IPF datasets in bleomycin versus saline treated mice. DEGs were determined in each IPF dataset between IPF and non-IPF groups . DEGs were determined based on being 1.5-fold altered between groups with an FDR <0.05. A union of DEGs from comparisons in each dataset was compiled resulting in two gene sets consisting of 30 genes . Enrichment of these gene sets was determined against genes from each time point in the mouse bleomycin data, ranked based on a composite of fold-change and FDR differences between bleomycin and saline treated samples per time point, with FDRs of gene set enrichment determined as described above.GSEA was also used to determine enrichment of internally curated canonical pathways from the NCI Pathway Interaction Database in both the mouse bleomycin and cultured human fibroblast datasets. Canonical pathways enriched in the bleomycin dataset were defined by ranking genes based on a composite score of fold-difference and FDR between bleomycin and saline-treatments for the 14 day time point (active fibrosis phase). Canonical pathways were then used as gene sets for GSEA, with FDR values being determined for gene set enrichment by permuting genes. Canonical pathway enrichment was similarly determined in the fibroblast dataset by generating ranked gene as described above in rapid progressing IPF vs. non-IPF samples. Significantly enriched canonical pathways  were then compared between the bleomycin-treated 14 day time point and fibroblasts from rapid progressing IPF patients.Primary cultures of LFs were isolated from the distal parenchyma of patients from unaffected donor controls (lung cancer patients) and patients with IPF as previously described Data are expressed as mean ± SEM unless specified otherwise. Unless stated otherwise elsewhere in the methods, statistical analyses of time course studies were determined using a Student’s t-test using GraphPad Prism (version 5.03).Figure S1Bleomycin induces dose-dependent increases in lung fibrosis. Fibrosis scores evaluated in H&E stained lung sections 21 days after a single instillation of bleomycin. Data expressed as mean ± SEM of n = 8 except for the 5 U/kg group where n = 5. Significance relative to the saline treated was determined using a one-way ANOVA and Dunnett’s post-hoc test and is denoted as, *p<0.05; or ***, p<0.001.(TIF)Click here for additional data file.Figure S2Bleomycin induces inflammatory cell infiltrate in the airways of mice. The numbers inflammatory cells increase in the BALF fluid after a single (A – C) or repetitive (D – F) bleomycin (black bars) or saline (white bars) administration. Differential cell counts revealed that neutrophils (A and D), lymphocytes (B and E), and macrophages (C and F) were all elevated in the BALF. Data are expressed as mean ± SEM of n = 7–8 mice. Significance (relative to the time-matched control at each time point) was determined using a Student’s t-test and is denoted as follows: *p<0.05; **p<0.01; and ***p<0.001.(TIF)Click here for additional data file.Figure S3Bleomycin induces collagen deposition in the lung. Measureable changes in matrix remodeling were observable after a single (A and B) or repetitive (C and D) bleomycin (black bars) or saline (white bars) administration. Lung collagen (A and C), and BALF hydroxyproline levels (B and D) were all elevated in response to bleomycin. Data are expressed as mean ± SEM of n = 7–8 mice. Significance (relative to the time-matched control at each time point) was determined using a Student’s t-test and is denoted as follows: *p<0.05; **p<0.01; and ***p<0.001.(TIF)Click here for additional data file.Figure S4Changes in lung mechanics correlate to changes in lung fibrosis. Changes in H (A) and volume normalized work of inflation (WoI) (B) after a single bleomycin instillation significantly correlated to the amount of lung tissue stained positive for collagen I as determined by Pearson correlation coefficients (R). The correlations for H (C) and WoI (D) with collagen I were similar after repetitive bleomycin instillations.(TIF)Click here for additional data file.Figure S5Bleomycin increased collagen deposition and myofibroblast activation. Serial lung tissue sections from one representative animal illustrate that areas positive for collagen I (D) were also positive for α-smooth muscle actin (E), as well as HSP47 (F). As a reference, sections from saline treated animals stained for collagen I (A), α-smooth muscle actin (B), and HSP47 (C) are also shown. Images were captured at 200X magnification.(TIF)Click here for additional data file.Figure S6Exercise capacity is reduced in bleomycin treated mice. The exercise capacity of mice treated with saline (filled circles) or 2 U/kg bleomycin (filled squares) was measured on a motorized treadmill. Data expressed as mean ± SEM of n = 19–20. Significance relative to the saline treated was determined using a one-way ANOVA and Dunnett’s post-hoc test and is denoted as, *p<0.05; or ***, p<0.001.(TIF)Click here for additional data file.Figure S7Bleomycin induces cytokine, chemokine, and pro-fibrotic mediator secretion in the BALF. BALF levels of KC (A), IL-16 (B), CCL17 (TARC) (C), total IL-12 (D), TGFβ (E), and LPA (18:1 isoform) (F)were measured. Saline treated controls represented by white bars and bleomycin treated animals by black bars. The following cytokines had no measurable change: IFN-g, TNF-α, IL-1β, IL-2, IL-4, IL-5, IL-10, GM-CSF, VEGF, IL-13, IL-17 (data not shown). Data are expressed as mean ± SEM of n = 8 mice. Significance (relative to the time-matched control at each time point) was determined using a Student’s t-test and is denoted as, *p<0.05.(TIF)Click here for additional data file.Figure S8Supervised hierarchical clustering of custom panels of genes across all mouse samples. Clustering of genes was performed for a panel of (A) genes involved in Wnt signaling, (B) genes altered downstream of PI3 kinase, and (C) genes involved in alternative macrophage activation. Samples are ordered based on treatment and time point.(TIF)Click here for additional data file.Figure S9Heatmap of gene set enrichment of custom signatures in clinical IPF datasets. GSEA was performed for gene sets of custom gene panels from fibrosis-related mechanisms including MMP, LOXL, collagen, and TGFβ signaling. Enrichment of each gene set (denoted in rows) was determined against ranked lists of genes from clinical datasets comparing IPF vs. non-IPF conditions from two datasets . Enrichment scores were plotted in a heatmap where gene sets enriched in IPF samples  were denoted in red while gene sets enriched in non-IPF samples were denoted in blue. Intensity of each cell was based on enrichment score .(TIF)Click here for additional data file.Figure S10Plasma exposure levels for SB525334 in bleomycin treated mice after initiating a diet using Purina Rodent Chow #5001 mixed with SB525334.(TIF)Click here for additional data file.Data File S1A union of leading edge genes from GSEA of bleomycin-induced gene sets in IPF vs. non-IPF subject comparisons from two clinical cohorts . This alphabetized list of genes corresponds to a union of the genes highlighted in (PDF)Click here for additional data file.Data File S2Canonical pathways enriched among the leading edge genes from GSEA of bleomycin-induced gene sets in IPF vs. non-IPF subject comparisons from two clinical cohorts . The canonical pathways, p value of the enrichment of leading edge genes in these pathways, and the leading edge genes contained within the pathways are highlighted in this table.(PDF)Click here for additional data file.


Cortex, medulla and spinal cord tissues were dissected, microglia were immunomagnetically isolated and mRNA levels of the inflammatory genes iNOS, COX-2, TNFα, IL-1β and IL-6 and the innate immune receptor TLR4 were compared to levels in normoxia. Inflammatory gene expression was also assessed in tissue homogenates . We found that microglia from different CNS regions responded to IH differently. Cortical microglia had longer lasting inflammatory gene expression whereas spinal microglial gene expression was rapid and transient. We also observed that inflammatory gene expression in microglia frequently differed from that in tissue homogenates from the same region, indicating that cells other than microglia also contribute to IH-induced neuroinflammation. Lastly, microglial TLR4 mRNA levels were strongly upregulated by IH in a region- and time-dependent manner, and the increase in TLR4 expression appeared to coincide with timing of peak inflammatory gene expression, suggesting that TLR4 may play a role in IH-induced neuroinflammation. Together, these data indicate that microglial-specific neuroinflammation may play distinct roles in the effects of intermittent hypoxia in different CNS regions.Intermittent hypoxia (IH) during sleep is a hallmark of sleep apnea, causing significant neuronal apoptosis, and cognitive and behavioral deficits in CNS regions underlying memory processing and executive functions. IH-induced neuroinflammation is thought to contribute to cognitive deficits after IH. In the present studies, we tested the hypothesis that IH would differentially induce inflammatory factor gene expression in microglia in a CNS region-dependent manner, and that the effects of IH would differ temporally. To test this hypothesis, adult rats were exposed to intermittent hypoxia (2 min intervals of 10.5% O Intermittent hypoxia (IH) during sleep is a hallmark feature of sleep apnea. IH induces significant cognitive and behavioral deficits that involve disruptions of connections among CNS regions underlying memory processing and executive function. For example, IH promotes apoptosis of hippocampal CA1 and frontal cortical neurons Many mechanisms are thought to contribute to neuronal death following chronic exposure to IH including oxidative stress Whether inflammatory molecules besides iNOS and COX-2 are produced in the CNS during IH is not known, nor is the cellular source of these molecules, although iNOS has been attributed to neurons Importantly, it is now well established that TLR4 also mediates “sterile inflammation” in the CNS, primarily by responding to a host of endogenous ligands including, proteins associated with cell damage (e.g. heat shock proteins 60 and 70), extracellular matrix turnover, and oxidatively modified lipids Because there is CNS regional susceptibility to neuronal damage during chronic IH, in the present studies, we tested the hypothesis that microglia differentially induce inflammatory factor gene expression in response to IH, and that the expression profiles of these genes will differ over time in IH-sensitive and -resistant CNS regions. Adult rats were exposed to a paradigm of intermittent hypoxia  for 8 hours/day during their respective sleep cycles for 1, 3 or 14 days. Cortex, medulla and spinal cord tissues were dissected, microglia were immunomagnetically isolated and mRNA levels of inflammatory genes were compared to those in tissue homogenates containing all CNS cells. We found that the inflammatory genes induced by IH differed in microglia from different CNS regions, and that the general temporal profiles of inflammatory gene expression varied in IH-sensitive and -resistant CNS regions. Further, we observed that the expression of inflammatory genes in microglia versus regional tissue homogenates frequently differed, especially at the 14 day timepoint, indicating that cells other than microglia also produce inflammatory factors in response to chronic IH exposure. Lastly, microglial TLR4 mRNA levels were strongly upregulated in a region- and time-dependent manner, and its expression often coincided with increased inflammatory gene expression, suggesting that TLR4 may play a role in IH-induced neuroinflammation.This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All surgical and experimental procedures were approved by the University of Wisconsin Madison Institutional Animal Care and Use Committee. All efforts were made to minimize the number of animals used and their suffering.ad libitum.Experiments were performed on Sprague-Dawley rats weighing 150 g±20 g . All animals were maintained in an AAALAC-accredited animal facility and housed under standard conditions, with a 12 hour light/dark cycle with food and water available Neural Tissue Dissociation Kit, anti-PE magnetic beads, and MS columns were purchased from Miltenyi Biotech (Germany). PE-mouse anti-rat CD11b was purchased from BD Biosciences . Hank's Buffered Salt Solution (HBSS) was purchased from Cellgro . TRI reagent was purchased from Sigma Aldrich . Glycoblue reagent was purchased from Ambion . MMLV Reverse transcriptase was purchased from Invitrogen . Oligo dT, Random Primers, and RNAse inhibitor were purchased from Promega . Primers were designed using Primer 3 software and were purchased from Integrated DNA Technologies . Power SYBR green was purchased from Applied Biosystems .2, N2, and CO2 to the desired concentration with a flow rate of 4 L/min to enable rapid dynamics and minimal CO2 accumulation. The treatment groups were exposed to alternating 2 minute episodes of hypoxia (10.5% inspired O2) and normoxia (21% inspired O2) for 8 h/day during their respective night cycle for 1, 3, or 14 days. The control groups  underwent identical handling, but their chambers were continually flushed with room air (21% inspired O2). Following daily exposure, rats were returned to their cages. Normoxic and corresponding IH groups were harvested at the same time. Because there was no significant difference between normoxic groups in the expression of any of the genes evaluated at any time point, we combined all normoxic groups for subsequent statistical analyses and graphical representations.Animals were placed in computer controlled, custom-manufactured chambers that mix O+ cells were isolated using previously described methods + cells were tagged with a PE-conjugated anti-CD11b+ antibody followed by an anti-PE antibody conjugated to a magnetic bead. Magnetically-tagged CD11b+ cells were isolated using MS columns according to the Miltenyi MACS protocol. Prior to CD11b+ cell isolation an aliquot of tissue cell suspension was taken for CNS homogenate tissue analysis. Reagents were kept chilled at 4°C and cells were kept on ice whenever possible. As we have previously shown, this method results in >97% pure population of CD11b+/CD45 low cells + cells will subsequently be referred to as “microglia.”Rats were harvested 16 hours after their last hypoxic exposure, and CD11bRNA was extracted from tissue homogenates and freshly-isolated microglia according to the TriReagent protocol, with the addition of Glycoblue during the isopropanol incubation. cDNA was synthesized from 1 µg of total RNA using MMLV Reverse Transcriptase as previously described T values from duplicate measurements were averaged and normalized to levels of the ribosomal RNA, 18s. Relative gene expression was determined using the relative standard curve method as previously described cDNA was used in real-time quantitative PCR with Power SYBR Green using either the ABI StepOne or ABI 7500 Fast system. Primers  were desT values from the standard curves for each gene, as previously described Statistical analyses were performed on the normalized, interpolated CIH treatment increased cortical microglial inflammatory gene expression (compared to normoxic controls) over the course of the 14-day IH exposure . There wMedullary microglia appeared to have a more rapid inflammatory response to IH relative to cortical microglia. Whereas in cortical microglia IL-1β and IL-6 mRNA levels did not appear to peak until 14 days of IH, in the medulla, microglial IL-1β and IL-6 mRNA levels were increased at 1 day of IH and remained elevated at 14 days . InteresIL-1β was the only inflammatory gene to exhibit a strong upregulation within 1 day of IH in spinal microglia (p = <0.001) . HoweverBecause TLR4 plays an important role in mediating inflammatory gene induction in microglia, and endogenous TLR4 ligands are increased in IH-susceptible CNS regions, we evaluated the expression of TLR4 over time following IH exposure . Very inIn this study we were interested in understanding whether microglial inflammatory gene expression was altered by IH, whether those responses differed based on CNS region from which the microglia were derived, if there was temporal regulation of IH-induced inflammatory gene expression, and whether assessments of neuroinflammation in tissue homogenates were accurately reflective of microglial activities in specific. We show here that IH induces differential inflammatory gene profiles in microglia from different CNS regions, and that patterns of IH-induced inflammatory gene expression differ in tissue homogenates  versus isolated microglia from the same region. Lastly, we find that the expression of the pattern recognition receptor TLR4 is time- and CNS region-dependently upregulated by IH in microglia in a manner that coincides with microglial inflammatory gene expression. This report is the first to directly evaluate microglial phenotype following intermittent hypoxia exposure, and to reveal differences in microglial responses based on CNS region and time of exposure. Our data also indicate that microglia are likely not the only CNS cell type contributing to neuroinflammation following chronic exposure to IH.Microglia make up approximately 5-10% of all CNS cells, with the cortex having more microglia than caudal CNS regions such as the brainstem and spinal cord . Because microglia comprise such a small percentage of total CNS cells, changes in microglial gene expression may not necessarily be evident in tissue homogenates unless the changes are very strong. For example, the approximately 50% reduction in TNFα or the 4-fold increase in COX-2 mRNA levels in cortical microglia are not apparent in cortical homogenates. Our data also indicate that cell types other than microglia can and do synthesize inflammatory molecules in response to IH exposure. For example, in spinal tissue homogenates, the mRNA levels of iNOS, COX-2 and IL-6 are significantly elevated above normoxic levels at 14 days of IH when expression of these genes in microglia is not different from controls. The identity of the cells contributing to the expression of these neuroinflammatory mediators is not yet known, but such results suggest that conclusions made about microglial activities based on the expression/presence of inflammatory molecules in tissue homogenates may not always be accurate.The observation that microglia respond to the same IH stimulus differently in different parts of the CNS may be related to differences in the local CNS environment as well as potential fundamental differences in the microglia themselves. In the cortex, microglial inflammatory gene expression  appears to increase over time after IH. However, these effects are gene-specific because iNOS expression had returned to baseline by 14 days and TNFα expression was not increased at any time point evaluated. Indeed, TNFα expression was significantly decreased in cortical microglia at 1 day of IH. This was the only CNS region in which we observed a decrease in the expression of any inflammatory gene. We do not yet know the significance of this rapid and acute TNFα downregulation, as the biological functions of microglial produced TNFα versus TNFα produced by other cell types have not been distinguished. Additional studies are necessary to delineate mechanisms underlying this microglial-specific inhibition of TNFα gene expression in the cortex.When comparing the cortex where IH-induced neuronal apoptosis occurs To the best of our knowledge, there are no reports of neuronal loss occurring in the dorsocaudal brainstem of adult animals exposed to IH protocols in vitroin vivo, was not known prior to this study. We found that TLR4 gene expression was very strongly upregulated in cortical microglia after 14 days of IH and in medullary microglia at 3 and 14 days, consistent with the timing of peak inflammatory gene expression in the respective CNS regions. Since the function of upregulated TLR4 in CNS disease is thought to promote and/or maintain neuroinflammation, chronically upregulated microglial TLR4 in the cortex may contribute to IH-induced neuroinflammation and neuronal loss. In vitro, sustained hypoxia transcriptionally upregulates TLR4 expression via transactivation of the TLR4 promoter by hypoxia inducible factor (HIF)1α in macrophages TLR4 expression and activity is increased on monocytes from patients with sleep apnea The biological significance of microglia-mediated neuroinflammation in response to IH may be different in different CNS regions, especially given the differences in time domains. For example, in the cortex, microglial inflammatory gene expression appears to be maximal at 14 days (the longest time point measured here), suggesting that microglia may contribute to neuroinflammation and neuronal loss/damage in the chronic state. In contrast, spinal microglia also respond to IH by increasing inflammatory gene expression, but these effects appear to be rapid and acute as inflammatory mRNA levels had returned to baseline by 14 days. Although the function of transient microglial inflammation in the spinal cord is not yet known, our recent studies have suggested that acute spinal inflammation, contributed to in part by microglia, can abrogate phrenic nerve long-term facilitation (pLTF), a form of spinal motor neuron plasticity The IH model used here simulates a hallmark feature of sleep apnea. Recent estimates indicate that up to 34% of men and 13% of women between 30 and 70 yrs of age are affected by obstructive sleep apnea


Ventilation of septic patients often leads to the development of edema and impaired gas exchange. We hypothesized that septic alveolar epithelial monolayers would experience stretch-induced barrier dysfunction at a lower magnitude of stretch than healthy alveolar epithelial monolayers. Alveolar epithelial cells were isolated from rats 24 hours after cecal ligation and double puncture (2CLP) or sham surgery. Following a 5-day culture period, monolayers were cyclically stretched for 0, 10, or 60 minutes to a magnitude of 12% or 25% change in surface area (ΔSA). Barrier function, MAPk and myosin light chain (MLC) phosphorylation, tight junction (TJ) protein expression and actin cytoskeletal organization were examined after stretch. Significant increases in epithelial permeability were observed only in 2CLP monolayers at the 12% ΔSA stretch level, and in both 2CLP and sham monolayers at the 25% ΔSA stretch level. Increased permeability in 2CLP monolayers was not associated with MAPk signaling or alterations in expression of TJ proteins. 2CLP monolayers had fewer actin stress fibers before stretch, a more robust stretch-induced actin redistribution, and reduced phosphorylated MLCK than sham monolayers. Jasplakinolide stabilization of the actin cytoskeleton in 2CLP monolayers prevented significant increases in permeability following 60 minutes of stretch to 12% ΔSA. We concluded that septic alveolar epithelial monolayers are more susceptible to stretch-induced barrier dysfunction than healthy monolayers due to actin reorganization. Acute lung injury (ALI) is a significant clinical problem with a reported mortality rate between 25% and 40% et. al. and O’Mahony et. al. show increases in IL-6 expression following ventilation and either 2CLP in rats or administration of endotoxin (LPS) in mice, above levels observed with either ventilation or sepsis exposure independently. A clinical study on IL-6 conducted by Damas et. al. showed a strong correlation between IL-6 expression and patient mortality Previous studies have shown a synergistic relationship between ventilation, or stretch, and the expression of inflammatory mediators et. al. have shown that in combination, intra-tracheal administration of endotoxin (LPS) and ventilation increased pulmonary edema  compared to LPS or ventilation alone Data identifying a synergistic effect of inflammation and ventilation on the expression of inflammatory cytokines, the development of pulmonary edema, and alveolar epithelial cell mortality have been previously published In the lung, the alveoli are the principal sites of gas exchange. The alveolar wall is composed of opposing cell monolayers, the epithelium and endothelium, which are separated by a thin interstitial space. Of these two cell monolayers, the epithelium is thought to supply the majority of resistance to the motion of ions and proteins into the airways In the current study, we tested the hypothesis that the cyclic stretch magnitude required to induce barrier dysfunction would be lower for monolayers formed by cells from septic lungs compared to monolayers formed by healthy epithelial cells. We utilized a previously described culture model of septic epithelia to test the barrier function of septic epithelial monolayers following low (12% ΔSA or 70% TLC) and moderate (25% ΔSA or 90% TLC) magnitudes of cyclic stretch All animal use was done in accordance with, and with the approval of, the IACUC in the Office of Regulatory Affairs of the University of Pennsylvania.Male Sprague-Dawley rats  weighing 240–260 grams underwent cecal ligation and double puncture (2CLP) or control (sham) surgery as previously described Twenty-four hours following the 2CLP or sham procedures, animals were sacrificed and alveolar epithelial type II cells isolated and cultured for 5 days as previously described Monolayers (4–5 days in culture) were designated as stretched or unstretched groups in one of four conditions: DMEM, DMEM and a MAPk inhibitor, DMEM and vehicle control (DMSO), or DMEM and an inhibitor to actin depolymerization  +/K+-ATPase pumps which are inaccessible to the apically applied tracer when the monolayer is intact Paracellular permeability was assessed in 2CLP and sham monolayers by monitoring binding of the fluorescent tracer BODIPY-ouabain (approximate radius 20 Å) to basal Na2PO4, 0.35 mM KH2PO4, 0.8 mM MgCl2, 2.7 mM HEPES) with protease inhibitors at 4 degrees C, homogenized, and the nucleus and large debris removed by centrifugation . The supernatant was ultracentrifuged (1 hour 100 Kg) to separate the cytoskeletal fraction (pellet) from the cytosol. The pellet was re-suspended in incubation buffer containing 0.1% triton X-100 for 30 min at 4 degrees C. Following this incubation, the sample was re-centrifuged, the supernatant was collected (soluble fraction), and the pellet was re-suspended in incubation buffer and stored (insoluble fraction). Tight junction protein levels were analyzed by Western blot as described below and the ratio of soluble to insoluble protein was determined by densitometry.Unstretched 2CLP and sham monolayers were lysed in incubation buffer  and MAPk phosphorylation and expression of TJ proteins was analyzed via western blot as previously described +/K+-ATPase pump distribution on the basal surface was assessed using antibody to mouse Na+/K+-ATPase alpha-1 .Monolayers (N = 2 monolayers per group from each of 2 isolations per group) were fixed and actin fibers were stained with rhodamine-phalloidin and imaged as previously described +/K+-ATPase pumps was not different between sham and 2CLP at unstretched or stretched (25% ΔSA for 60 min) conditions indicating that an increase in BODIPY binding sites was not responsible for increased staining . Therefore we analyzed the phosphorylation of myosin light chain kinase (MLCK), a signaling molecule that directly regulates actin cytoskeletal contractility through activation of myosin motors, and a pathway we have demonstrated to be altered during stretch Because our data suggested that the actin cytoskeleton was altered in 2CLP monolayers, we tested the hypothesis that TJ proteins were not anchored to the cytoskeleton in 2CLP monolayers as well as they were in sham monolayers by analyzing the ratio of triton soluble to triton insoluble cytoskeletal associated protein. We found that in unstretched 2CLP monolayers, a greater fraction of the transmembrane proteins dissociated from the cytoskeleton . This reTo test the hypothesis that actin destabilization in 2CLP monolayers was responsible for the stretch-induced permeability increases at low stretch magnitudes, we used jasplakinolide to prevent actin depolymerization. In previous studies, jasplakinolide has been shown to prevent stretch-induced barrier dysfunction in healthy cell monolayers stretched to 37% ΔSA for 1 hour Ventilation of septic patients is necessary to maintain proper blood gas levels; however, the combination of sepsis and ventilation often leads to the development of acute lung injury (ALI) We showed that in a culture model of the alveolar epithelium, the threshold stretch magnitude above which permeability to a 20 Å tracer increases is lower in monolayers formed from cells isolated from septic animals compared to sham animals. These data confirm previous results from the ex vivo whole lung in which flux of a fluorescent tracer  across the endothelium and epithelium increases in lungs from 2CLP animals, not sham, ventilated with a tidal volume of 20 ml/kg body weight (∼25% ΔSA) for 30 minutes We previously observed both sepsis and mechanical deformation activate common MAPk signaling pathways. Studies in the intact animal have identified epithelial MAPk signaling as being activated following both large tidal volume ventilation  in rats and 2CLP  in mice The tight junction is an apically located protein complex that controls paracellular permeability of epithelial monolayers, and we hypothesized that the expression levels of TJ proteins, which determine how restrictive the tight junctions are to paracellular fluid and ion motion, would change with injurious levels of stretch. However, we observed no significant alterations in protein expression in 2CLP monolayers following stretch to 12% ΔSA, despite increases in monolayer permeability. Therefore, we conclude that stretch-induced (60 minutes) permeability changes observed in 2CLP monolayers are not due to loss of TJ proteins.2+ dependent signaling molecule that is activated by calmodulin 2+ due to reduced regulatory signaling. Studies by Turner and colleagues have demonstrated that constitutive MLCK activation increases permeability and destabilizes the junction of non-pulmonary cell lines The actin cytoskeleton is integral to the formation and maintenance of the tight junction barrier, and depolymerization, or reorganization, of the actin cytoskeleton has been linked to disruption of junctional integrity in epithelial cells Furthermore, we observe that the cytoskeletal-tight junction complex is more detergent-soluble in unstretched 2CLP monolayers, as well as a significant decrease in occludin expression in 2CLP monolayers compared to sham without stretch, as reported previously Jasplakinolide prevents the depolymerization of actin fibers, and has been shown to reduce stretch-induced permeability increases in healthy epithelial monolayers cyclically stretched to 37% ΔSA for 1 hour In conclusion, we have shown that cultured epithelial monolayers composed of cells isolated from septic animals have a lower threshold for cyclic stretch-induced barrier dysfunction than one composed of cells from control animals The lower threshold is not due to enhanced activation of MAPk signaling pathways as hypothesized, but rather due to alteration in the actin cytoskeleton organization in septic monolayers. Furthermore, our data suggest that the proteins in the tight junction complex are more weakly associated with the cytoskeleton in monolayers composed of cells from septic animals. These data highlight a need for understanding how sepsis alters the tight junction, the actin cytoskeleton, and the molecules that regulate barrier properties.Figure S1Stretch to 25% ΔSA induced significant phosphorylation of JNK in sham cells at 60 minutes compared to unstretched (NS) , but did not induce JNK phosphorylation at either time point. Stretch to 25% ΔSA for 10 minutes induced significant ERK phosphorylation in both sham and 2CLP monolayers compared to unstretched sham only , or to both unstretched sham and unstretched 2CLP . At 60 minutes of stretch, ERK phosphorylation had subsided in 2CLP monolayers, and was no longer significant from any other time point, while ERK phosphorylation in sham monolayers was still significantly elevated above unstretched sham. .(TIFF)Click here for additional data file.Figure S2Inhibition of MAPk signaling  did not prevent stretch-induced permeability increases in 2CLP monolayers when stretched (ST) to 25% ΔSA. ERK inhibition prevented significant permeability increases in sham monolayers only, when stretched to 25% ΔSA. JNK inhibition did not prevent significant permeability increases in sham and 2CLP monolayers stretched to 25% ΔSA. All stretched for 60 minutes. *significantly greater than sham unstretched (NS), #significantly greater than both sham and 2CLP NS, p<0.05 .(TIFF)Click here for additional data file.Figure S3Inhibition of ERK activation in sham monolayers during stretch to 25% ΔSA for 60 minutes significantly reduces expression of claudin 18 compared to levels during stretch with DMSO treatment (θ). The stretch-induced reduction in sham treated with DMSO ZO-1 is reversed with ERK inhibition; however, this difference was not statistically significant. In 2CLP monolayers, inhibition of JNK activation during stretch significantly reduced occludin levels compared to unstretched (NS) 2CLP (#). *significantly different from DMSO NS, bar indicates significantly different from sham, p<0.05 .(TIFF)Click here for additional data file.


Sleep is generally categorized into discrete stages based on characteristic electroencephalogram (EEG) patterns. This traditional approach represents sleep architecture in a static way, but it cannot reflect variations in sleep across time and across the cortex. To investigate these dynamic aspects of sleep, we analyzed sleep recordings in 14 healthy volunteers with a novel, frequency-based EEG analysis. This approach enabled comparison of sleep patterns with low inter-individual variability. We then implemented a new probability dependent, automatic classification of sleep states that agreed closely with conventional manual scoring during consolidated sleep. Furthermore, this analysis revealed a previously unrecognized, interhemispheric oscillation during rapid eye movement (REM) sleep. This quantitative approach provides a new way of examining the dynamic aspects of sleep, shedding new light on the physiology of human sleep. Sleep is generally considered to be a global and highly symmetrical brain state. Several studies, however, have shown that sleep can exhibit local phenomena, such as electroencephalography (EEG) spectral changes in parts of the cortex in relation to learning By means of behavioral, neurophysiologic and electroencephalographic criteria, sleep may be roughly divided into rapid eye movement (REM) sleep and non-REM (NREM) sleep. NREM sleep may be further subdivided according to the amount of EEG slow wave activity (SWA). Developed in 1969 and adapted in 2007, this classification relies on manual scoring of sleep based on characteristic features of the EEG, electrooculogram, and electromyogram spatial domain, however, it is well established that during consolidated sleep, significant regional variability is observed Regarding the temporal variability of sleep, conventional scoring in 30 second epochs limits analysis of dynamic properties of sleep such as the assessment of changes of sleep behavioral states. For example, conventional scoring presents transitions between behavioral states as if they were instantaneous, though the visual appearance of the EEG suggests the transitions are more gradual with intermediate patterns of activity that defy clear-cut categorization. Similarly, scoring of EEG data in epochs makes it very difficult to examine fluctuations in EEG power over time within a given behavioral state. To overcome these difficulties, various quantitative approaches of EEG analysis have been proposed. Recently, through recurrence analysis of sleep EEG, dynamic markers over time scales of milliseconds have been used as a small scale measure of dynamic brain state activity in different sleep stages Considering the To gain new perspectives on the physiology of human sleep, we developed an adapted state space analysis technique to study the dynamics of state transitions, regional changes in sleep, and the velocity of transitions between states. We hypothesized that dynamic analysis of healthy sleep, applied in combination with simultaneous measurements in different brain regions, would improve upon the current methods of scoring behavioral states and provide new insights into sleep physiology.2 was accepted, if the polysomnographic findings were normal  and all subjects gave written informed consent prior to enrolment. Each subject had overnight polysomnography from 23∶00 to 07∶00, without a prior adaptation night. Six EEG channels were recorded  and referenced to linked bilateral auricular electrodes (A1+A2). All recordings were acquired using Embla N7000 Systems and Somnologica Software . EEG signals were recorded at a sampling rate of 100 Hz. Manual sleep scoring was performed in non-overlapping 30-s epochs by two experienced sleep researchers (E.W. and U.K.) according to standard criteria s epochs. In a second step, each epoch was sub-divided into epochs of 5s length and sleep scoring was adapted if necessary. The adaptation to 5s epoch length was performed in a systematic way as follows: After the conventional 30-s epoch scoring using the AASM rules to describe the clinical and polysomnographical characteristics of our healthy subjects , who had been screened as healthy controls for a clinical study. All participants reported a regular sleep pattern and had no history of sleep disturbances. Patients with regular alcohol consumption were excluded. BMI >25 kg/msubjects , sleep ss epoch length) was chosen for several reasons: First, the shorter time interval enabled to detect rapid changes in the sleep EEG, which allowed for the description of dynamic aspects of sleep. Second, when scoring in 30s intervals, we observe presumably mixed sleep states, that show characteristics of different sleep stages during short periods of time: These short lasting state transitions could thus be assigned to the corresponding sleep stage . Regarding spectral analysis, this sub-classification of 30s EEG epochs in 5s sub-epochs allowed therefore for a higher intra-state consistency of the EEG raw data. Finally, from a more methodological point of view, an interval of 5s length provides an optimal compromise between a high frequency resolution for spectral analysis (long epoch length) and an adequate temporal resolution (short epoch length) for the dynamic sleep analysis.This shorter epoch length of 5s .To produce a topographically well-defined state space for analysis of sleep dynamics, we adapted a dimensionality reduction approach. This method was recently introduced for analyzing rodent sleep In a second optimization step, we systematically varied the defining frequency parameters to maximize the predictive value of the derived classification . To address this high dimensional optimization problem, we first implemented a genetic algorithm Velocity in the state space is defined as the Euclidean distance between two subsequent states divided by the time interval between these states. Rodent studies revealed that high velocities in the state space are an important measure of behavioral state instability left and vright), we defined the relative laterality score as unsmoothed sleep trajectories were used to ensure accurate time resolution. The resulting time series was further subdivided into segments of 100 epochs (500 seconds). To detect potential periodic patterns in left/right inter-hemispheric differences, we determined the autocorrelation function  and checked for periodicity for each segment. Autocorrelation can be interpreted as the similarity of a signal with itself across time. High autocorrelation values indicate a repeating pattern in the original time series with the same time period. Frequency analysis of the autocorrelation function was done using FFT.To measure differences in state space velocities between the left and right hemispheres  allowed for a consistent, probability-dependent automatic classification of behavioral states in all 14 subjects, as illustrated by the projected 1-dimensional probability density estimations in within clusters  for all individuals , whereasl states . Automatl states .Because the frequency ratios were determined using the whole data set, there is a possible risk of over-fitting in the proposed model. To test for this possibility, we have confirmed the classification procedure by a post-hoc test/train approach for 2 randomly chosen groups (n = 7 each). The previously determined ratios were left unchanged, but the Linear Discriminant Analysis (LDA) classifier was fitted only on the training group (n = 7) and in a second step the derived discrete classifier function was tested on the ‘naive’ data set  without any information about human scoring. The positive predictive value was then evaluated by comparing the predicted and the manually derived sleep scoring as described above for the testing group only. As compared to the results for all subjects we have found similar values for the testing group , indicating that the proposed algorithm operates effectively also on a previously unknown data set .s epoch. This resulted in a rapidly fluctuating time series representing a constantly changing hemispheric predominance of velocity in state space . Left/right asymmetry was determined by calculating a relative laterality score for each 5te space . During te space . This osp phases .All 14 subjects had stereotypic oscillating patterns in REM sleep with a mean period of 48 seconds (range: 32–80). This interhemispheric oscillation occurred predominantly in central cortical regions but less in frontal and occipital regions . During Finally, to determine whether this interhemispheric oscillation is an artefact of our method, we tested our data against uncorrelated random velocities and a modelled sinusoid-alternating pattern. As expected, for simulated random velocities with the same distribution as the original data, we found no significant autocorrelations. The modelled sinusoid function, however, showed an alternating pattern very similar to the one observed for the left-right hemispheric oscillations . FurtherState space EEG analysis of human sleep generated well-defined patterns of sleep behavioral states with low inter-individual variability. Compared to manually scored sleep stages, the overall positive predictive value for correct automated identification of traditional behavioral states was 74% for all states and 80% for consolidated sleep states, i.e. clusters with low velocities (n = 14 subjects). Contrariwise, we propose that high velocities in state space reflect transitions and represent less stable behavioral states. In addition, this technique revealed an interhemispheric oscillation of behavioral state stability during consolidated REM sleep in humans that to the best of our knowledge has not been described previously.s time scale. Although our approach is fully dependent on the underlying spectral information, our classification algorithm works particularly well for REM sleep , which consists of long-lasting periodic activity of two alternating electroencephalogram patters ratio of different spectral power bands for each sleep epoch, the noise level of the EEG spectra is actually diminished for all epochs equally. Furthermore, noise levels could not explain the exclusively higher velocities for state transitions . For this reason, we have worked with a logged ratio approach: Taking the log of a ratio converts the division into a subtraction  = log(a)-log(b)). Small changes of either the denominator or the numerator are therefore represented indeed by small changes in the state space .In a similar way, we have to consider that frequency Furthermore, we have to consider the possibility that the slow oscillating pattern of velocity that we have observed might be an artefact of the rather complex analysis method. This seems rather unlikely for several reasons. Regarding the predominant occurrence in REM sleep and the strictly rhythmic inter-hemispheric alteration, muscle artefacts or signals generated by the electrodes are unlikely to cause a rhythmically recurring oscillating pattern with a constant frequency. Furthermore, as seen in As an additional limitation, all volunteers were recorded during their first night in the sleep lab without any prior adaptation. Although the first night effect is known to influence the occurrence of different sleep stages, it seems rather unlikely to affect the findings of the inter-hemispheric oscillation that occurred during consolidated REM sleep. However, an influence of the first night effect on our findings cannot be ruled out.s would also change the observed oscillations. At this point, it cannot be ruled out completely, that changing the underlying analysis method would also result in a different inter-hemispheric pattern (possibly in other sleep stages). However, we are confident that our method provides a sufficient resolution for detecting the presented slow oscillation: Analyzing the data in 5s intervals corresponds to a sampling rate of 0.2 Hz, which allows for detecting oscillations with frequencies up to ∼0.1 Hz. The observed oscillations are at approximately 0.02 Hz (50s Time-period) and lie therefore well within the spectral range of this technique. However, the occurrence of faster inter-hemispheric oscillations or more local EEG variations cannot be ruled out because of limited temporal  and spatial (8 electrode montage) resolution in the current setting. Finally, we have to address the question of consistency between the scoring with shortened epoch length as compared to the traditional 30s-scoring. To validate the described adapted scoring rules , the independently derived sleep scores (5s vs. 30s) have been compared for all volunteers and showed a high level of overall agreement  Mean velocities showed a characteristic distribution with highest velocities in NREM1 for all 14 subjects. Average over all individuals demonstrated significantly higher velocities in NREM1 as compared to NREM2, NREM3 and REM sleep . Error bars indicate SEM (n = 14). (b) Velocity in state space showed a fronto-occipital gradient for all subjects with highest velocities in frontal and central electrodes and lower velocities in occipital derivations for NREM (filled blue) and REM sleep (white). Average over all individuals are shown .(TIF)Click here for additional data file.Figure S2Comparison of laterality time series with random models. (a) Time series raw data: Histogram plot of relative laterality showed a symmetrical distribution over time (left panel), while time series raw data fluctuated rapidly between left and right hemispheres . Sample autocorrelation of the same period with respect to autocorrelation time lag showed an oscillating pattern (right panel). (b)Random model: For comparison, the same analysis was performed on Rayleigh-distributed random numbers with a distribution of laterality (left panel) and a corresponding rapidly fluctuating time series (middle panel) similar to real data (a). However, autocorrelation of the random data shows no oscillating pattern (right panel) (c) Sinusoid model: Histogram (left panel), time series (middle panel) and autocorrelation (right panel) for a sinus function underlying uniformly distributed random noise simulated the rhythmically oscillating pattern as observed in REM sleep (compare to right panel in (a)). Dotted blue lines represent approximate 95% confidence measures, as described in (TIF)Click here for additional data file.Figure S3Simultaneous measurements of interhemispheric oscillation in different brain regions. The laterality score of velocity was calculated in corresponding electrodes for frontal (F3 vs F4), central (C3 vs C4), parietal (P3 vs P4) and occipital (O1 vs O2) derivations. The analysis was performed for the same 100 successive REM sleep epochs simultaneously in each electrode pair. The typical oscillating pattern was only seen in central and parietal brain regions, whereas in frontal and occipital derivation no oscillating pattern could be observed (upper panels). Frequency analysis for the oscillating pattern (FFT of REM sleep autocorrelations) confirmed the predominant slow oscillation in central and parietal electrodes, but no definable frequency peak in frontal and occipital regions was observed (lower panels).(TIF)Click here for additional data file.Figure S4Test/Train Approach for 2 randomly selected subgroups (2x n = 7). Left panel: Summary scatter plot of all sleep states for 7 subjects that were used for training the LDA algorithm. Colors represent manual scoring. Right panel: State space scatter plot of all sleep states for the other 7 subjects that were used for testing of the classifier. Epochs were scored by the automatic classifier without prior knowledge of the manual scoring. Colors represent automatic scoring. Agreement with manual scoring was 70% for all states and 80% for slow sleep states. Color-coding is the same as in (TIF)Click here for additional data file.Figure S5Comparison of 30 s- and 5 s-scoring. (a/b) Sleep hypnograms 5 s/30 s for 1 representative volunteer. Scoring of behavioural states was performed for 30s epochs (a) and 5s epochs (b) showing a similar sleep structure with a higher variability in the 5s scoring. (c) Number of different 5 s behavioural states per 30 s epoch for all n = 14 subjects. For each 30s epoch the number of different behavioural states (1–4 bs) in the 6 corresponding 5s epochs was determined and shown for each traditional behavioral state separately. (d) Validation of 5 s scoring as compared to the 30 s scoring epoch for all n = 14 subjects. Comparison of the 5s-scoring with the traditional 30s-scoring showed a high level of agreement in all behavioural states  and overall agreement of 85% (OVERALL).(TIF)Click here for additional data file.Table S1Relative abundance of sleep stage and positive predictive values for slow and fast states in all individuals (n = 14). (a) Considering all epochs, we find an overall agreement with manual scoring in 74% (ALL). Differentiation of high velocity and low velocity states resulted in an increased positive predictive value for automatic classification of slow sleep stages (SLOW), whereas fast states are less reliably predicted (FAST). This effect was predominantly observed in consolidated deep sleep , whereas for the transitional sleep stage NREM1 a poor performance of the automatic classification was observed. (b) Comparison of manual and automated classification of behavioral states by a confusion matrix. Numbers indicate fractions of correctly assigned sleep stages, when comparing manual (y-axis) with automated scoring (x-axis) for both fast (left panel) and slow (right panel) sleep states for all volunteers (n = 14). Color-coding refers to the fractions as shown on the matrix .(TIF)Click here for additional data file.Table S2Clinical characteristics and polysomnographical findings of all included volunteers (n = 14). Abbreviations: gender (g), Epworth Sleepiness Scale (ESS), Body Mass Index (BMI), Sleep Efficiency (Seff), Sleep Latency to S2 (SL[S2]), Rem Sleep Latency (RL), Periodic Limb Movements (PLM), Apnea-Hypopnea-Index (AHI), Total Time in Bed (TIB), Total Sleep Time (TST), Total sleep Time from sleep onset (SPT), Relative occurrence of stage NREM1 (S1), NREM2 (S2), Deep Sleep (S3+S4), REM Sleep (REM) and Wake (Wake).(TIF)Click here for additional data file.MatLab m-file S1MatLab function for automatic scoring of sleep EEG-spectra by linear discriminant analysis. Requires MatLab mat-files 1–3: mu_sigma.mat, P_si.mat, xmat.mat (See file header for instructions).(score_p10): (M)Click here for additional data file.MatLab m-file S2MatLab function for plotting the sleep spectrum spec on a 2d state space (See file header for instructions). MatLab mat-files containing all parameters for automatic sleep scoring.(oplot2d_50): (M)Click here for additional data file.MatLab mat-file S1MatLab mat-files containing all parameters for automatic sleep scoring.(M)Click here for additional data file.MatLab mat-file S2MatLab mat-files containing all parameters for automatic sleep scoring.(M)Click here for additional data file.MatLab mat-file S3MatLab mat-files containing all parameters for automatic sleep scoring.(M)Click here for additional data file.Mathematical description S1Provides a detailed mathematical description of the optimization algorithm for the derived state space model.(DOCX)Click here for additional data file.


Tissue-infiltrating multinucleated giant cells (MNGs) within geographic necrosis are pathologic hallmarks of granulomatosis with polyangiitis (GPA). However, the origin, phenotype, and function of these cells in GPA remain undefined.MNG phenotype in GPA lung tissue was examined by immunohistochemistry using antibody directed against cathepsin K and calcitonin-receptor. Tartrate-resistant-acid-phosphatase (TRAP) expression was assessed using enzymatic color reaction. Peripheral blood mononuclear cells (PBMCs) from 13 GPA patients  and 11 healthy controls were cultured in the presence of RANKL and M-CSF for 9 days, and TRAP+ MNGs containing 3 or more nuclei were identified. GPA lung granulomata contained numerous MNGs that expressed osteoclastic TRAP and cathepsin K but not calcitonin receptors. In the presence of RANKL and M-CSF, PBMCs of GPA patients formed significantly more MNGs than healthy controls . In a subgroup analysis, patients with systemic disease generated significantly more MNGs than patients with localized disease  or healthy controls (P<0.01). MNG production did not differ between localized GPA and control subjects (P = 0.96).MNGs in granulomata in the GPA lung express osteoclastic enzymes TRAP and cathepsin K. GPA patients have a higher propensity to form TRAP+ MNGs from peripheral blood than healthy controls. These data suggest that (i) the tendency to form MNGs is a component of the GPA phenotype itself, and (ii) that lesional MNGs might participate in the destructive process through their proteolytic enzymes. Granulomatosis with polyangiitis (GPA), previously known as Wegener’s granulomatosis, is a systemic inflammatory disease whose phenotypic hallmarks include necrotizing vasculitis and granulomatous inflammation Multinucleated giant cells (MNGs) can be found in both ostotic and non-ostotic sites In this study, we demonstrate for the first time the presence of TRAP-expressing MNGs in lung granulomata and an increased propensity to form TRAP+ MNGs from circulating precursors in the peripheral blood in patients with GPA. This propensity appears to be associated with disease subtype rather than activity or immunosuppressive therapy.To evaluate the lineage and phenotype of infiltrating MNGs in the GPA granulomata, lung biopsies from GPA patients (N = 11) containing areas of geographic necrosis and granulomatous inflammation were examined for expression of TRAP, which is a key degradative enzyme of bone-resident osteoclastic MNGs or osteoclasts. Alveolar macrophages in healthy and GPA lung expressed TRAP (bright purple cytoplasmic staining). MNGs were seen in 10/11 (91%) biopsies studied. They were not seen in healthy alveolar spaces . MNGs weWe next investigated whether MNGs shared other critical markers of osteoclasts including cathepsin K and calcitonin receptor. A subset of MNGs expressed cathepsin K  and theyGiven the presence of intra-lesional MNGs with TRAP expression in GPA lung, we explored whether patients with GPA have an increased propensity to form cells of this phenotype from circulating cells in the peripheral blood. In these experiments, PBMCs were isolated from 13 consecutive patients with GPA and 11 control subjects, and cultured in the presence of the OC-differentiating factors RANKL and M-CSF. After 9 days, PBMCs from GPA patients formed large TRAP+ MNGs with multiple nuclei. Further the generated cells were able to degrade bone matrix. In contrast, PBMCs from healthy controls formed fewer MNGs with smaller cytoplasm and had only minimal osteolytic activity . In addiAfter 9 days, PBMCs from healthy controls formed 22.4±8.8 TRAP+ MNGs/well, while those from GPA patients formed 114.1±28.7/well (P = 0.022) . Of notes = −0.1749, P = 0.57), or immunosuppressive medication regimen.Given the heterogeneity of the patients evaluated in terms of disease activity and immunosuppressive medications , we examTo address the possibility that the increased number of generated TRAP+ MNGs in GPA patients is a result of an increase in the total peripheral blood monocyte pool (where the MNG precursors might reside), the percentages of CD14+ monocytes were enumerated in each group by FACS. No significant difference was observed in the percentage of circulating monocytes between healthy controls and GPA patients . Further, no significant differences in monocyte percentages were noted between patients with limited and systemic phenotypes . PatientA pathologic hallmark of granulomatosis with polyangiitis (GPA) is granulomatous inflammation with necrobiosis and tissue destruction. Despite the diagnostic specificity of this pathology, little is known about the lineage of the MNGs seen in this condition. In this study, we show for the first time that MNGs within the GPA lung granulomata express TRAP and cathepsin K, markers shared with osteoclasts. These specialized cells sharing biochemical features of bone-resorbing cells may contribute to the destructive lesions characteristic of this disease through the elaboration and release of proteolytic enzymes in the disease microenvironment We also demonstrate that GPA patients have a higher propensity to form TRAP+ MNGs from the peripheral blood, and that this appears to be most pronounced in patients with more widespread disease manifestations. These data are consistent with those reported by De Costa and colleagues, who reported the presence of cathepsin K and TRAP-expressing MNGs in the destructive extra-ostotic lesions of Langerhans cell histocytosis The observation that GPA patients have an increased propensity to form MNGs over healthy controls and that this difference is most prominent in patients with lung and systemic involvement is of great interest . This diper se, have a higher propensity to form TRAP+ MNGS. Recent work on osteoclastogenesis and foreign body giant cell formation indicates that the interactions between precursor cells, cytokines and growth factors, and tissues in the local microenvironment all contribute to a precise MNG differentiation program While we have broadly defined that the circulating TRAP+ MNG precursor resides in the monocyte pool, the precise phenotype of these precursors is not yet defined. However, our findings cannot be explained by an increased number of circulating monocytes in GPA patients . When isUsing finer phenotyping of the peripheral monocyte pool, Chiu and colleagues reported that the OC precursor may reside in the CD14+CD16+ monocyte compartment In summary, we demonstrate here for the first time the presence of TRAP and cathepsin K expressing MNGs in GPA lung granulomata and show that an increased propensity to form TRAP+ MNGs in circulating blood is a feature of generalized and not localized disease. These data suggest that the capacity to form MNGs might prove to be an important pathogenic biomarker in systemic disease and a possible novel therapeutic target in GPA.Thirteen consecutive GPA patients receiving clinical care in the Johns Hopkins Vasculitis Center and 11 control subjects in good health were enrolled in the study. Venous blood was collected from healthy volunteers under aseptic conditions. All subjects gave written informed consent, and all samples were obtained under the auspices of a Johns Hopkins University human subject institutional review board-approved protocol. All patients met both the 1990 American College of Rheumatology and 1994 Chapel Hill classification criteria for GPA Peripheral blood mononuclear cells (PBMCs) were isolated from heparinized peripheral venous blood by density gradient centrifugation using Ficoll-Paque . Cell viability was assessed with trypan blue dye exclusion.5 per well) were cultured in OPTI-MEM® I , supplemented with 10% heat-inactivated fetal bovine serum, 1% penicillin and 1% streptomycin in the presence or absence of 100 ng/ml RANK-L  and 25 ng/ml M-CSF  in a 96 well plate in 5% carbon dioxide at 37°C. On day 9, cells were fixed with 3% formaldehyde and stained for expression of TRAP per the manufacturer’s protocol . TRAP+ cells with three or more nuclei were counted as TRAP+ MNGs under light microscopy Olympus CKX41 using magnification X 20. Numbers were expressed as mean ± SEM.PBMCs  which are coated with inorganic crystalline calcium phosphate. After 9 days formed pits were counted using light microscopy.Formalin-fixed and paraffin- embedded tissues were de-parraffinized with xylenes and rehydrated. Antigen retrieval was performed at 95°C in citrate buffer (DAKO) and endogenous peroxidase was blocked with hydrogen peroxide (DAKO) for 10 minutes. After blocking, tissues were incubated with antibodies directed against cathepsin K  or calcitonin receptor  overnight at 4°C. After washing in PBS, HRP conjugated secondary antibodies were applied for 1 hour at 37°C and the staining visualized with 3,3-diaminobenzidine (DAKO). Nuclei were counterstained with Meyer’s hematoxylin.After tissues were de-paraffinized and dehydrated as above, TRAP assay was performed using Leukocyte Acid Phosphatase Kit for TRAP per manufacture’s instruction . Nuclei were counterstained with Meyer’s hematoxylin. TRAP converts yellow color of substrate into bright pink.PBMCs were incubated with anti-human CD14 PerCP  and anti-human CD3  antibodies for 30 minutes at 4°C in the dark. Cells were washed with PBS and analyzed on a FACSCalibur flow cytometer. Flow data were analyzed by FlowJo software version 8.8 .Mann-Whitney tests for continuous variables were performed. Correlations between data were performed using Spearman correlation. P-values of <0.05 were considered statistically significant. Statistical analyses were computed in GraphPad Prism .