To examine the long-term changes in the astigmatism-correcting effect of a toric intraocular lens (IOL) after stabilization of surgically induced astigmatic changes due to cataract surgery.Unilateral eyes of 120 patients that received a toric IOL for against-the-rule (ATR) or with-the-rule (WTR) astigmatism were enrolled. Manifest refractive and anterior corneal astigmatism, and ocular residual astigmatism which is mainly derived from internal optics were examined preoperatively, at approximately 2 months postoperatively (baseline) and at 5 ~ 10 years postbaseline. The astigmatism was decomposed to vertical/horizontal (Rx) and oblique components (Ry), which was compared between baseline and 5 ~ 10 years postbaseline.p ≤ 0.0304), but those of ocular residual astigmatism did not change significantly between the 2 time points. In the eyes having WTR astigmatism, the Rx and Ry of refractive, corneal, and ocular residual astigmatism did not change significantly between the 2 time points. Double-angle plots revealed an ATR shift in refractive and corneal astigmatism and no marked change in the ocular residual astigmatism in the eyes with ATR astigmatism, and there is no change in this astigmatism in the eyes with WTR astigmatism.In the eyes having ATR astigmatism, the mean Rx and Ry of the manifest refractive and corneal astigmatism significantly changed toward ATR astigmatism between the baseline and 5 ~ 10 years postbaseline (The long-term changes with age in the effect of a toric IOL significantly deteriorated due to an ATR shift of corneal astigmatism in the eyes having ATR astigmatism, while it was maintained in eyes having WTR astigmatism, suggesting that ATR astigmatism should be overcorrected.The online version contains supplementary material available at 10.1007/s00417-021-05406-7. Toric intraocular lenses (IOLs) effectively correct preexisting corneal astigmatism when implanted during cataract surgery and substantially improve uncorrected visual acuity –5. Many The long-term changes in the visual effects of toric IOLs on astigmatic correction remain unclear. Some studies report that the visual effects and rotational stability of toric IOLs are well maintained over 1- or 2-year follow-up times –18. For The present study examined the changes with age in the astigmatism-correcting effects of toric IOLs for 5 ~ 10 years after stabilization of surgically induced astigmatic changes. Because astigmatism in the posterior cornea is an against-the-rule (ATR) astigmatism in most eyes and has a non-negligible effect in determining the IOL power , 20, theThis study was a retrospective observational study conducted at the Hayashi Eye Hospital, Fukuoka, Japan, between February 26 and October 14, 2020. The study protocol was approved by the Institutional Review Board of the Hayashi Eye Hospital on February 25, 2020. The study protocol adhered to the tenets of the Declaration of Helsinki.The medical records of all patients who had undergone cataract surgery with a toric IOL implantation performed by a single surgeon (KH) between 2010 and 2015 were reviewed beginning on February 26, 2020. The eyes that were continuously followed up at the Hayashi Eye Hospital for 5 ~ 10 years after surgery were included. Inclusion criteria were (1) eyes that underwent phacoemulsification using an approximately 2.4-mm clear corneal incision; (2) eyes that received a 1-piece hydrophobic acrylic IOL with a toric component ; (3) eyes that were examined at least twice using an autokerato/refractometer at approximately 2 months postoperatively, and a difference in corneal astigmatism between the 2 examinations was within 0.5 D in cylindrical power and within ± 15° in the cylindrical axis (the latter examination was defined as baseline); (4) eyes that underwent examinations using an autokerato/refractometer at 5 ~ 10 years after the baseline measurement; (5) first-operated eye when the patient underwent cataract surgery in both eyes; (6) eyes that underwent uneventful cataract surgery with no sutures; (7) eyes with no severe pathology of the cornea, optic nerve, or macula; and (8) eyes without previous history of other surgery or inflammation. Patient enrollment was continued until 120 eyes (60 eyes in each of the ATR and WTR astigmatism groups) were enrolled in the study, with the last eye enrolled on July 30, 2020.http://www.acrysoftoriccalculator.com; Alcon). Preoperative keratometry and biometry data, incision location and size, and the surgeon’s estimated surgically induced corneal astigmatism were entered into this calculator program.The spherical power of the IOL was calculated using the SRK/T formula with the optimized A-constant. For IOL power determination, the axial length was measured using the IOLMaster . In the eyes with a dense cataract, the axial length was measured using an ultrasonic applanation biometer . The corneal curvature at the steepest and flattest meridians of the anterior cornea was measured by anterior segment optical coherence tomography , and the mean value of both meridians was used. The appropriate model and meridian of the toric IOL to achieve emmetropia was determined using an online toric IOL calculator program  performed all cataract surgeries . Just beAll patients underwent examinations preoperatively and at approximately 2 months postoperatively (baseline) and at 5 ~ 10 years after baseline. Manifest refractive spherical and cylindrical powers were measured objectively using an autokerato/refractometer . To precisely measure the refractive states, automated capture of 3 measurements was repeated at least 4 times, and the mean value was used for analysis. The TONOREF I and II show a specific reliability index of each measurement ranging from 5 to 9, with 9 being the most reliable. When the optic medium is not clear due to corneal edema, anterior chamber inflammation, or cataract, the reliability index decreases. Only measurement values with a high-reliability index of 8 and 9 were included in the analysis. Manifest refractive spherical equivalent was determined as the spherical power plus half the cylindrical power. The magnitude and meridian of the anterior corneal astigmatism were also measured using TONOREF I and II. Furthermore, ocular residual astigmatism which was defined as the difference between manifest refractive astigmatism and anterior corneal astigmatism by Alpins was calculated , 25.\docManifest refractive astigmatism was converted to the value at the corneal plane for calculating ocular residual astigmatism and changed back to the value at the spectacle plane for data presentation. Ocular residual astigmatism was assumed to comprise overall astigmatism mainly due to the toric IOL, as well as IOL tilt/decentration, posterior corneal astigmatism, and other internal optic factors. Ocular residual astigmatism was calculated at the corneal plane. The type of astigmatism  was also recorded. Corneal astigmatism in which the steeper meridian was between 60° and 120° was defined as WTR astigmatism, that in which the steeper meridian was between 0° and 30° or between 150° and 180° was defined as ATR astigmatism and that in which the steeper meridian was between 30° and 60° or between 120° and 150° was defined as oblique astigmatism. Uncorrected or corrected distance visual acuity on decimal charts was recorded at all visits, and the decimal visual acuity was converted to the logarithm of minimal angle of resolution (logMAR) scale for statistical analysis.http://ascrs.org/) [Data on the astigmatism of the manifest refractive, anterior corneal, and ocular residual astigmatism at baseline and at 5 ~ 10 years after baseline were used to analyze the astigmatic change over a period of at least 5 years. The astigmatism was decomposed to vertical (90°)/horizontal (180°) and oblique (45° and 135°) astigmatism components using the X–Y coordinate analysis, originally described by Naeser and Hjortdal . This anrs.org/) . The cent test. The bivariate changes in manifest refractive, corneal, and ocular residual astigmatism between the 2 time points were compared using a multivariate analysis of variance (MANOVA) [t test. The ratio of males to females, the ratio of left and right eyes, and other categorical variables between the ATR and WTR groups were compared using the chi-square test or Fisher’s exact test where applicable. Any differences with a p value less than 0.05 were considered statistically significant.The normality of the data distribution was evaluated by inspecting histograms. The data for the manifest refractive, corneal, ocular residual astigmatism , manifest refractive spherical equivalent, uncorrected and corrected distance logMAR VA, and other continuous variables followed a normal distribution, and therefore, parametric analyses were used in this study. The univariate change in manifest refractive, anterior corneal, and ocular residual astigmatism  between the baseline and 5 ~ 10 years after baseline were compared using a paired (MANOVA) . Statistp < 0.0001 and p = 0.0032, respectively). Preoperatively, the mean magnitude of the anterior corneal astigmatism and manifest refractive spherical equivalent differed significantly between the ATR and WTR astigmatism groups (p ≤ 0.0030). The mean time interval between the surgery and the baseline evaluation and between the baseline evaluation and that at 5 ~ 10 years after baseline did not differ significantly between the ATR and WTR astigmatism groups. At baseline, the magnitude of corneal astigmatism and manifest refractive spherical equivalent did not differ significantly between the 2 groups.All data of the 120 enrolled eyes were collected. Mean patient age (± standard deviation) was 68.7 ± 8.3 years (range 43 to 84 years), and there were 55 men and 65 women. Preoperative patient characteristics and those at baseline are shown in Table t test indicated that, in the eyes having ATR astigmatism preoperatively, the mean Rx value of manifest refractive astigmatism and corneal astigmatism significantly decreased toward a negative value from the baseline to 5 ~ 10 years after baseline (p ≤ 0.0018), and the mean Ry value did not change significantly between the 2 time points (Table Univariate comparisons using the paired ts Table .Table 2Up ≤ 0.0304), and the mean Rx and Ry values of ocular residual astigmatism did not change significantly between the 2 time points Below is the link to the electronic supplementary material.


Due to the importance of fat tissues in both energy balance and in the associated disorders arising when such balance is not maintained, adipocyte differentiation has been extensively investigated in order to control and inhibit the enlargement of white adipose tissue. The ability of a cell to undergo adipocyte differentiation is one particular feature of all mesenchymal cells. Up until now, the peroxysome proliferator-activated receptor (PPAR) subtypes appear to be the keys and essential players capable of inducing and controlling adipocyte differentiation. In addition, it is now accepted that adipose cells present a broad plasticity that allows them to differentiate towards various mesodermal phenotypes. The role of PPARs in such plasticity is reviewed here, although no definite conclusion can yet be drawn. Many questions thus remain open concerning the definition of preadipocytes and the relative importance of PPARs in comparison to other master factors involved in the other mesodermal phenotypes. Adipose tissues have long been associated with the invasive prevalence ofobesity and metabolic disorders.Recent advances have focused the attention onthe presence of intriguing cells able to differentiate towards variousphenotypes and recall the old use of fat in plastic and reconstructive surgery –3. Two rγ in particular in adipose-derived cellphenotype and plasticity.After a brief overview on adipose tissues, this review will focus on the importanceof PPARs and PPARThree functionally different types of adipose tissues are described in mammals: brown adipose tissue (BAT), white adipose tissue (WAT), and bone-marrow adiposetissue (BMAT) –6.BAT and WAT participate differently in energy balance and homeostasis. BAT is heavily involved in nonshivering thermogenesis (cold and diet-induced thermogenesis). In contrast, WAT is mainly involved in energy storage and is now considered an endocrine organ –7. BrownIn contrast to brown and white adipocytes, the role of medullar adipocytes has been poorly investigated and is not well-understood . The numBeside the physiological anatomy of adipose tissues, ectopic location of adipocytes is also observed in degenerative tissues. The origin of these adipocytes infiltrating thedegenerative tissues is still unclear.α and PPARγ, mediates the cell cycle arrest after clonalexpansion. Indeed, PPARγ induces cell cycle withdrawal by the inhibition of the transcription factor E2F/DP DNA-binding activity viadownregulation of the protein phosphatase 2A (PP2A) [γ expression [γ and its recovery into deficient cellsreduces adipocyte differentiation through the downregulation of PPARγ activity [γ and its interplay with cell cycle isillustrated by a report studying liposarcoma cells. Treating cells from theseaggressive tumours with ligands for both PPARγ and the retinoid X receptor (RXR) forces their terminal differentiation [γ is now investigated to limit and treat tumour cell proliferation although several PPARγ ligand-mediated antiproliferative effects act through a complexity of PPARγ-independent mechanisms [γ activation promotes the proliferation ofneural stem cells [As usual in any differentiation program, a subtle balance and interconnection exist betweencell cycle and commitment to adipocyte differentiation. In this context,overexpression of both C/EBPA (PP2A) . In the pression . In the activity . The potntiation . Indeed,chanisms . Neverthem cells , insteadγ as a dominantregulator of white-adipocyte differentiation is now well documented and largely reviewed [α can be overcome by the overexpression of PPARγ, the opposite does not hold true that PPARγ is absolutely required for the adipogenic program [γ expression via theinsulin/IGF-I receptor-AktSH2-B-Foxo1- pathway [The crucial role for PPARreviewed , 21. It  program . During  pathway .γ was described as a factor involved in white adipocyte differentiation, PPARα and δ were described for their role in fatty acid oxidation in several tissues includingadipose tissue [δ and PPARγ isoforms are required to facilitate maximallipid accumulation and differentiation during white adipogenesis [Whereas PPARe tissue –27. Howeogenesis .γ stimulates white adipogenesis, itsnegative control inhibits this process. In this way, hypoxia-mediatedinhibition of adipogenesis can be partly explained via the repression of PPARγ expression by the hypoxia-inducible factor-1(HIF-1)-regulated gene DEC1/Stra13, a member of the Drosophila hairy/enhancerof split transcription repressor family. Similarly, the inhibition ofadipogenesis by cytokines is mediated via the repression of PPARγ function through kinase cascade [γ expression and a consequent inhibition of adipocyte differentiation [As PPAR cascade , 30. It ntiation .γ ligand [γ ligand, although it appears to be at least partly a specific mechanism because this phenomenon is not totally reproducedby a high fat diet. Obviously, the underlined mechanisms and the relevance ofsuch observations need further investigation, but could participate in theectopic emergence of fat cells through the local recruitment of circulatingadipocyte progenitor cells.The first report in this field was published in 2005. Its authors described a circulating human cellpopulation able to differentiate towards adipocytes whencultured under adipogenic conditions. Moreover, these human progenitorsengrafted and formed adipose tissue following injection into SCID mice . This reγ ligand . No explγ activation take place whereas white phenotypecan be easily obtained [α and PPARγ are strictly subjected to opposite regulationby retinoids in brown fat, supporting the notion of specific physiologicalroles of each transcription factor in controlling brown fat differentiation andthermogenic activity [γ coactivator 1, wasidentified in brown fat. Initially claimed as a specific factor to brown fat,it was proposed to be a coactivator involved in brown versus white adipocytedifferentiation. This coactivator has been shown to control a subset of genesinvolved in mitochondrial activity and biogenesis [α that increases the expression of UCP1,respiratory chain proteins, and fatty acid oxidation enzymes in humanadipocytes differentiated in primary culture [The relationship between brown and white fat is complex because besides the typical interscapular brown adipose tissue, an extensive analysis of the different fat deposits revealed that scattered brown adipocytes are present in any white adipose depot also in primates , 35. Furobtained . In thisactivity . A furthogenesis , 41. Thi culture . Neverth culture . Other f culture  and the  culture .Although, it is well admitted that adipose cells arise from mesodermal origin, the exact origin of adipocyte precursors is not well defined as illustrated by the recent work on circulatingadipose precursors . RecentlMany reports have demonstrated that adipocytes and osteoblasts share a common precursor. Osteoblastic genes are expressed in cell lines able to differentiate towards adipocyte. Bipotent cells withfeatures specific to both osteoblasts and preadipose cells have been clonedfrom bone marrow  and moreγ and Runx2, have been proposed as playing a critical role in the commitment of bipotent stem cells towards the adipogenic or the osteogenic lineages [γ induces transdifferentiation of mouse osteoblastic MC3T3-E1 cells into mature adipocytes [γ expression. This genetic manipulation directs ES cells into an osteoblastic lineage suggesting that PPARγ can be considered a proadipogenic as well asan anti-osteoblastic factor [γ-dependent gene transcription could be also involved [Several transcription factors, such as PPARlineages . Howeveripocytes . A uniquc factor . The rolc factor . More rec factor . Therefoinvolved .γ or activation through its ligands in satellitecells transdifferentiates myoblasts towards the adipogenic phenotype. Thischaracteristic is shared by the other key adipogenic factor, C/EBPα [δ abolishes the development of multinucleated myotubes while inducing the expression of PPARγ gene. Loss and gain function experiments areconsistent with a role for PPARδ as an inducer of transdifferentiation into adipocyte-like cells, which precedes and triggers PPARγ expression [The deciphering and understanding of myogenesis is far beyond our current understanding of adipogenesis, but both programs are closely linked by their mesodermal origin and the well-described emergence of adipocytes in denervated muscle. From such observations, the question of aputative role of PPARs in directing cell fate towards one of the differentiation programs seems reasonable. As described for fibroblasts, overexpression of PPAR, C/EBPα . This tr, C/EBPα . In addipression .γ can be controlled by Wnt proteins and especially Wnt 1 and Wnt-10b that govern adipogenesis. Indeed, PPARγ as well as C/EBP proteins is inhibited by Wntsignalling, which in turn maintains preadipocytes in an undifferentiated state.When Wnt signalling in preadipocytes is prevented by the overexpression of Axinor dominant-negative T-Cell Factor-4 (TCF4), these cells differentiate intoadipocytes. These results are strengthened by the fact that the lack of Wntsignalling also induces transdifferentiation of myoblasts into adipocytes [γ seems to be complex because the impairment oractivation of Glycogen Synthase Kinase-3β (GSK3β)/β-catenin only affects a subset of PPARγ-dependent genes [On the other hand, PPARpocytes . Howevernt genes .β) family, blocks adipocyte differentiation viadown regulation of PPARγ in 3T3-L1 [Finally, it has been suggested that myostatin, a potent negative regulator of skeletal muscle growth member of the transforming growth factor beta (TGF-n 3T3-L1 . Howevern 3T3-L1 , 66.γ to direct cells towards adipocyte differentiation, it is reasonable to wonder if PPARs could be involved in such plasticity.Analysis of the literature lined out that adipocyte and monocyte/macrophage lineages have many features in common including aP2 and PPAR expression. More surprisingly, we demonstrated thatpreadipocytes efficiently phagocytoze yeasts and apoptotic bodies in a similarmanner, albeit to a lesser extent, than specialized phagocytic cells such asmacrophages , 68. Thiγ is expressed at low levels incirculating monocytes and its expression increases significantly upondifferentiation to macrophages, including in the foam cells of atheroscleroticlesions. Numerous studies have thus focused on its role in macrophagemetabolism and activation and its possible involvement in the process ofatherosclerosis. These studies have provided evidence supporting a role of PPARγ in both lipid uptake and lipid effluxpathways in macrophages [γ could influence both lipid uptake andefflux raised the question of whether the net effect of PPARγ ligands on macrophages within anatherosclerotic lesion would be to promote or impede foam cell formation. PPARγ is not essential for myeloid development orfor mature macrophage functions, such as phagocytosis and inflammatory cytokineproduction [γ [γ agonists inhibit foam-cell formation in vivo [γ seems fundamental in preadipocyte andmacrophage biology, adipocytes and foam cells are probably distinct cell typesand thus far no study has described a potential role of PPARγ in the differentiation of preadipocytes into macrophage-like cells.PPARrophages . The facoduction . It is action [γ . In addi in vivo . However in vivo . Althougγ can play its role only when all negativeregulators are missing. This could suggest that preadipocyte status resultsfrom a default pathway.The importance and the role of PPARs in adipogenic process is definitively demonstrated and brought numerous new insights in our understanding of adipogenesis but many questions remain openconcerning the definition of preadipocytes and the relative importance of PPARscompared to other master genes involved in other mesodermal phenotypes.Altogether, the negative regulation of several master proteins involved inother mesodermal differentiation programs suggests that at least PPARSimilarly, the tissue origin of preadipocytes is now challenged and needs to be clarified because it can help to build a more dynamic view of adipose tissue development and the role of PPAR proteins.


Interactions between multiple biological phenotypes are difficult to model. Simultaneous equation modelling (SEM), as used in econometric modelling, may prove an effective tool for this problem. Generalized linear models were used to derive the structural equations defining the interactions between cholesterol, glucose, triglycerides and high-density lipoprotein cholesterol (HDL-C). These structural equations were then applied, using SEM, to Cohort 2 data (replicates 1–100) to estimate the phenotypic structure underlying the simulation. The goal was to determine if this empiric method of deriving structural equations for use in SEM was able to recover the simulation model better than generalized linear models.First, the underlying structural equations were estimated using generalized linear model techniques, which found strong a relationship between glucose, triglycerides and HDL-C. Using these structural equations, I used SEM to evaluate these relationships jointly. I found that a combination of the empiric structural equations and the SEM method was better at recovering the underlying simulated relationship between biologic measures than generalized linear modelling.The empiric SEM procedure presented here estimated different relationships between dependent variables than generalized linear modelling. The SEM procedure using empirically developed structural equations was able to recover the underlying simulation relationship partially and thus holds promise as a technique for complex phenotype analysis. Robust methods for determining the structural equations must be developed for application of SEM to population data. To investigate complex relationships of interrelated phenotypes, I investigated whether simultaneous equation modelling (SEM) techniques can be used to detect this relationship in the absence of knowledge about the system. Simultaneous equation models describe two or more structural equations in which the dependent variable in one equation is a predictive variable in another. A classic example from econometrics is the description of supply and demand within a population .SEMs are attractive in longitudinal genetic studies because they have the ability to include 1) fixed data , 2) variable data , and 3) stochastic data  [A summary of the GLM results is presented in Table Cholesterol = c1 + α1 (age) + α (spb) + α3 (sex) + U1     (1)Glucose = c2 + α4 (HDL-C) +α5 (sex) + α6 (trig) + α7 (wgt) + U2     (2)HDL-C = c3 + α8 (age) + α9 (cpd) + α11 (gluc) + α12 (sex) + α13 (trig) + α14 (wgt) + U3     (3)Trig = c4 + α15 (age) + α16 (cpd) + α17 (drink) + α18 (gluc) + α19 (HDL-C) + α20 (hgt) + α21 (sex) + α22 (wgt) + U4     (4)These results indicate the cholesterol is not a component of this system of structural models and thus was not included in the SEM analysis.Table Fitting the GLMs consistently included more covariates than were used in the actual simulation equations. However, when the linear models were used to screen variable for structural equations and then SEM were used to determine the system, I was more successful in defining the underlying system.The research presented here does not adequately address a number of key features that must be evaluated before endorsing this method. These include detection of nonlinear and higher order relationships, the appropriate detection and adjustment of the correlation structure within the covariates, and estimation procedures in nonreplicate data. However, despite these elements being excluded from this research, I was encouraged by the ability of this method to provide a closer approximation to the simulation system than did GLMs.These results suggest that SEM can provide an alternative way to recover unknown relationships in complex phenotype data. The method presented here may result in a reduction in the model parameters that is overly conservative. Factors that must be evaluated in this relationship include the impact of the degree of correlation between the dependent and independent variables and the ability to detect a relationship with SEM.This data structure seemed ideal to explore the usefulness of simultaneous equations for detailed deconstruction of complex phenotypes. This methodology, however, will need to overcome the challenges of defining robust structural models in the absence of knowledge of the underlying system. In this simulation study, I had the advantage of a large number of replicates, which, in real data, does not exist. I am currently investigating additional methods for determining the structural equations in undefined systems.Cohort 2 from replicates 1–100 was used in the complete data set without knowledge of the simulation conditions. The structural models were developed around four phenotypes at the first measurement time: cholesterol, glucose, HDL-C, and triglycerides, primarily because of literature focusing on the interrelationship of these agents ,4. CovarThe first step was to determine the structural equations that would be used in this analysis. To establish the structural equations, GLMs were fit in each of the 100 replicates to determine which of the covariates was significantly associated with each of the phenotypes. Using Proc GLM, within each replicate, the four phenotypes were analyzed with the following model structure.phenotypea = intercept + phenotypeb + phenotypec + phenotyped + age + cpd + chol + drink + htg + sbp + sex + wgt     (5)p < 0.05), the average of regression coefficient, and whether the distribution of the regression coefficient was normally distributed. To establish the structural equations, covariates were selected that had regression coefficients that were significant in more than 25% of the replicates. It is important to note that I was not interested in the value of the regression coefficient per se, but rather if that regression coefficient was significant in a percentage of GLM models.Over the 100 replicates, the following information was collected on the regression coefficients: number of replicates in which the regression coefficient was significant (4 - α22) were estimated using Proc Syslin within SAS [Using equations (1–4) above, the associated parameters (αthin SAS  for each


Ovarian cancer is characterized by a wide-spread intra-abdominal metastases which represents a major clinical hurdle in the prognosis and management of the disease. A significant proportion of ovarian cancer cells in peritoneal ascites exist as multicellular aggregates or spheroids. We hypothesize that these cellular aggregates or spheroids are invasive with the capacity to survive and implant on the peritoneal surface. This study was designed to elucidate early inherent mechanism(s) of spheroid survival, growth and disaggregation required for peritoneal metastasesIn this study, we determined the growth pattern and adhesive capacity of ovarian cancer cell lines (HEY and OVHS1) grown as spheroids, using the well established liquid overlay technique, and compared them to a normal ovarian cell line (IOSE29) and cancer cells grown as a monolayer. The proteolytic capacity of these spheroids was compared with cells grown as a monolayer using a gelatin zymography assay to analyze secreted MMP-2/9 in conditioned serum-free medium. The disaggregation of cancer cell line spheroids was determined on extracellular matrices (ECM) such as laminin (LM), fibronectin (FN) and collagen (CI) and the expression of α2, α3, αv, α6 and β1 interin was determined by flow cytometric analysis. Neutralizing antibodies against α2, β1 subunits and α2β1 integrin was used to inhibit disaggregation as well as activation of MMPs in spheroids.We demonstrate that ovarian cancer cell lines grown as spheroids can sustain growth for 10 days while the normal ovarian cell line failed to grow beyond 2 days. Compared to cells grown as a monolayer, cancer cells grown as spheroids demonstrated no change in adhesion for up to 4 days, while IOSE29 cells had a 2–4-fold loss of adhesion within 2 days. Cancer cell spheroids disaggregated on extracellular matrices (ECM) and demonstrated enhanced expression of secreted pro-MMP2 as well as activated MMP2/MMP9 with no such activation of MMP's observed in monolayer cells. Flow cytometric analysis demonstrated enhanced expression of α2 and diminution of α6 integrin subunits in spheroids versus monolayer cells. No change in the expression of α3, αv and β1 subunits was evident. Conversely, except for αv integrin, a 1.5–7.5-fold decrease in α2, α3, α6 and β1 integrin subunit expression was observed in IOSE29 cells within 2 days. Neutralizing antibodies against α2, β1 subunits and α2β1 integrin inhibited disaggregation as well as activation of MMPs in spheroids.Our results suggest that enhanced expression of α2β1 integrin may influence spheroid disaggregation and proteolysis responsible for the peritoneal dissemination of ovarian carcinoma. This may indicate a new therapeutic target for the suppression of the peritoneal metastasis associated with advanced ovarian carcinomas. Cancer cells are very responsive to their microenvironment and have been shown to acquire resistance in response to physical and chemical stress associated with the changed microenvironment . The vasin vivo three-dimensional growth conditions  followed by 3 washes in incubation buffer  then incubated for 48 h at 37°C in incubation buffer before being stained with coomassie blue (G-250) for visualization of activation. After destain , areas void of blue stain indicated areas of enzyme activity. Molecular markers were used to identify pro-MMP2/9 and MMP2/9.Gelatin zymography was performed as described previously . Briefly©  and sealed with nail polish. Fluorescence was imaged using a Leica TCS SP2 AOBS laser confocal microcope  and associated software.Cryostat sections were fixed in 4% paraformaldehyde, permeablized in 0.1% Triton X-100, and blocked in 1% BSA. Sections were probed with primary antibody (dilution 1/100 to 1/500) for 2 h followed by 1 h with Alexa Fluor 488 labeled secondary. Sections were counter stained with ethidium bromide  and coverslips were mounted using Fluorgaurd6 cells were incubated with primary antibody for 1 h at 4°C and excess unbound antibody was removed by washing twice with PBS. Cells were stained with secondary antibody conjugated with phycoerythrin for 20 min at 4°C, washed twice with PBS and then re-suspended in 0.5 ml phosphate buffered saline (PBS) prior to FACScan analysis. In each assay background staining was detected using an antibody-specific IgG isotype. All data were analysed using Cell Quest software . Results are expressed as mean intensity of fluorescence (MIF).Flow cytometric method was used as described previously . BrieflyStudent's t-test was used for statistical analyses of proliferation, adhesion, migration and invasion assays. Statistical significance was indicated by p < 0.05. Data are presented as mean ± SEM. Each experiment was repeated three times with a minimum of three replicates.Ovarian cancer cell lines HEY and OVHS1 and immortalized ovarian surface epithelial (IOSE) cell line IOSE29 were analysed for formation of spheroids and subsequent proliferation when maintained in a suspension culture. Figure Cellular growth of OVHS1, HEY and IOSE29 cell lines was analysed using an MTT assay. In both cancer and normal ovarian cell lines the growth of spheroids was significantly reduced when compared to growth in traditional monolayer culture Figure . On compThe specific ability of cells within the spheroids to contribute to growth was subsequently confirmed using immunohistochemical staining of Ki67 on OVHS1 and HEY spheroid sections. Nuclear staining of Ki67, a standard histological marker for proliferation, identified a population of proliferating cells in spheroids of both OVHS1 and HEY cell lines Figure . These dThe ability of spheroid cultures to metabolize and proliferate in culture was correlated by western blot analysis of key mediators for cell cycle progression and pro/anti-apoptotic proteins in spheroids collected at 0 (monolayer control) and 6 h and 1, 2 and 4 days. The D-cyclins are integral in early G1 to S phase transition in the cell cycle . The actIn order for spheroids to contribute to cancer metastasis, they must be able to adhere to the extracellular matrix (ECM) of the peritoneal cavity. Compared to monolayer cells, the adhesion of day 2 IOSE29 spheroids decreased by approximately 50% or more (p < 0.05), while adhesion of OVHS1 and HEY remained unchanged with no significant differences on any of the ECM components Figure . Similarin vitro disaggregation assay on various ECM components. Similarly, a comparison of proteolytic activity of spheroids versus monolayer cells was examined by testing the expression and activation of MMP2/9 using gelatin zymography.Peritoneal dissemination of ovarian cancer spheroids occurs when cells within the free-floating spheroids attach to the mesothelial lining of the peritoneum and disaggregate, spreading to the secondary site. This process also involves invasion, which requires proteolysis of ECM proteins underlying the mesothelial layer. To understand if cells within the spheroids exhibit such metastatic properties, spheroids were analyzed by OVHS1 and HEY spheroids began to transform from a three-dimensional structure into flattened cell clusters within 8 h. Figure Serum-free medium (SFM) was collected from monolayer cells and cells grown as spheroids from day 1 to 7. In monolayer cultures, only the expression of pro-MMP2/9 was observed whereas in spheroids the expression of active MMP2 and MMP9 was induced and pro-MMP9 expression was replaced with active MMP9 Figure . The ideCell migration and invasion is facilitated by the cell-surface expression of specific integrins. Expression of α2, α3, α6, αv, and β1 integrin subunits was determined in spheroids over 4 days of culture and compared to that of monolayers. The expression of α3, αv and β1 integrin subunits remained unchanged, however, a significant increase was observed in the expression of α2 integrin within 24 h of spheroid culture and there was a decrease in the expression of α6 integrin subunit expression over the 4 days of culture  was significantly reduced by α2, β1 and α2β1 blocking antibodies (p < 0.05) with the greatest inhibition seen in the presence of anti-α2β1 integrin Figure . Under tIn the normal ovarian tissues (n = 10) the expression of α2 and α6 integrin subunits was confined to the basal layer of epithelial cells and displayed continuous labeling Figures  and 9B. Immunohistochemical staining of ascites smears (n = 4) from high-grade ovarian cancer patients demonstrated strong staining for α2 integrin subunits in clusters of malignant cells. Some α6 subunit staining was present in malignant cellular aggregates but it was weaker than α2 subunit staining. Weak staining of α2 and α6 subunits in some single cells  was also present. These results suggest that α2 and α6 integrin is expressed by aggregates of malignant cells present in ascites.in vitro spheroid model which mimics in vivo multicellular spheroids in the peritoneal effusions of women with ovarian cancer. The ability of spheroids to contribute to the spread of cancer has been assessed by growth, adherence and disaggregation capabilities and by investigating the profile of integrins and MMP2/MMP9 that may mediate the dissemination process. In some instances comparisons have been made to a normal ovarian cell line grown under similar conditions.Some recent studies have demonstrated that a significant proportion of ovarian cancer cells in ascites exist as multicellular aggregates and have the capacity to adhere and invade the mesothelial cells lining the peritoneum ,10. FreeCancer cell line spheroids resembled those present in the ascites of cancer patients. Spheroids formed from ovarian cancer cell lines increased in size with time, and formed compact regular spheroid structures. As a product of anchorage-independent culture, multicellular spheroids had decreased proliferative abilities compared to the cells cultured as monolayers. Hence, the monolayer cultures approached confluence within seven days and decreased proliferation, while the slower growing spheroids continued to grow for at least 10 days. This response was supported by staining for the proliferation marker Ki67, which demonstrated the presence of proliferating cells throughout both HEY and OVHS1 spheroids. In addition, cancer cell line spheroids sustained activation of Akt, expression of cyclin D2 and did not display any activation of caspase-3. On the other hand, the normal ovarian cell line (IOSE29) failed to proliferate under similar conditions and its growth response was consistent with reduced size and disintegration of spheroids with time. This disparity in the response of normal versus cancer cell lines in response to anchorage-independent surroundings reflects major differences in the cohesive response required for cell-cell contact in order to maintain survival. Expression of Akt is amplified in many cancers, including ovarian cancer . Akt kinWe and others have shown that ovarian cancer cell lines have the ability to adhere to ECM proteins such as FN, LM, CI, etc . Some stThe peritoneum, omentum and the bowel surfaces are the frequent sites for implantation of metastatic ovarian cancer cells. The outer lining of these metastatic sites is comprised of a single layer of mesothelial cells, which express a variety of ECM proteins, including LM, FN, CI and hylauronan to which tumor cells can adhere before spreading ,41. Signin vitro.In order for the spheroids to disseminate to a secondary site they not only need to adhere, disaggregate and migrate but they also need to invade the mesothelial cell layer to form a stable secondary growth . MMPs plThe biological mechanism(s) by which spheroids are formed and sustained is not known. Both HEY and OVHS1 cell lines express a variety of integrin receptors. We report an enhanced expression of α2 and a decrease in α6 integrin when comparing spheroidal cells to cells grown as a monolayer, changes which were observable within 24 hours. While the increased expression of α2 was sustained in spheroids for 4 days, α6 expression gradually decreased over the same period of time (data not shown). On the other hand, no change in α3, αv and β1 subunits were observed. In IOSE29 spheroids however, except αv, there was a decrease in the expression of integrin subunits ranging from 1.5–7.5-fold. These results were supported by immunofluorescence studies performed on cancer spheroids that displayed distinct high α2 subunit expression at the periphery of the spheroid and at the outer membraneous layer forming the cell-cell interface of aggregated cells. Very little or no α6 subunit staining was evident in the cell-cell contact regions within the spheroids suggesting very low expression. Based on these observations one can conclude that cellular aggregation and the environmental factors within the spheroids can regulate the expression and localization of a specific sub-set of integrins. Differences in the α2 and α6 integrin expression between monolayer cells and spheroids however, had no effect on their adhesion capabilities on LM or CI suggesting that α2 subunit up regulation may compromise diminution of expression of α6 integrin subtype and hence adhesion on LM. It is reasonable to speculate that in a spheroid scenario it is more cell-cell rather than cell-ECM interaction that will influence integrin expression profile.To assess if increased α2 expression had any effect on spheroid function, spheroids were treated with blocking antibodies for α2 and β1 subunits and α2β1 integrin and then tested for disaggregation on CI-coated plates. Disaggregation was reduced for all three antibodies, with an apparent accumulative inhibition occurring when α2β1 integrin function was blocked in comparison to individual α2 and β1 subunit function. Parallel to that, blocking α2 and β1 subunits and α2β1 integrin also inhibited activation of MMP2, with no observable change in the expression of pro-MMP's. Although the specific participation of individual integrins in spheroid phenotype is not understood, our results suggest that α2β1 integrin may have a role in the disaggregation and invasion of ovarian carcinoma spheroids.Selective regulation of integrin receptors in spheroids of squamous cell carcinoma has been reported previously . In thatStrong expression of α2 and α6 integrin was observed at the basal layer of surface epithelial cells of normal ovaries. In ovarian tumors there was a loss of regular basement membrane structure resulting in irregular staining of α2 and α6 subunits. Intense staining of α2 integrin was observed in ascites spheroids while staining of α6 subunit occurred to a lesser degree. As α2β1 and α6β1 are the major collagen and laminin receptors on basement membranes, one can speculate that a tumor-induced irregular pattern of matrix-modelling can result in the irregular distribution of these subunits in cancer. In ovarian carcinoma, the expression of α6 subunit has been shown to correlate with the expression of basement membrane protein laminin . The samin vitro model for the dissemination of ovarian carcinoma. Using this model we were able to show that α2β1 integrin is up regulated in the spheroids and that functional blockade using monoclonal antibodies reduced the extent of disaggregation and proteolysis of spheroids. These data suggest that molecules that regulate α2β1 integrin functions may have a potential role in inhibiting the invasiveness of peritoneal tumor aggregates or spheroids and may aid in suppressing the dissemination of ovarian carcinoma.The spread of ovarian carcinoma is unique as it involves localized invasion and is rarely dependent on dissemination through lymphatics. In this context, the role of shed tumor cells forming spheroids, implantation onto the mesothelial lining of the peritoneum with consequent disaggregation and dissemination is not well understood. As little is known about the ascites tumor cell aggregates or spheroids and the fact that these cells are often dismissed as non-metastatic and undergoing apoptosis is somewhat disturbing. Better outcomes for ovarian cancer patients can only be projected if a targeted approach can be accomplished to disrupt the invasive processes of spheroids requisite for peritoneal spread. Hence, a more comprehensive understanding of ascites spheroid biology is needed to combat the dissemination of ovarian carcinoma. In this study, we aimed to address this issue by characterizing an


The amino acid neurotransmitter GABA is abundant in the central nervous system (CNS) of both invertebrates and vertebrates. Receptors of this neurotransmitter play a key role in important processes such as learning and memory. Yet, little is known about the mode and tempo of evolution of the receptors of this neurotransmitter. Here, we investigate the phylogenetic relationships of GABA receptor subunits across the chordates and detail their mode of evolution among mammals.A receptor α, γ, and ε subunits, and another one containing GABAA receptor ρ, β, δ, θ, and π subunits. The presence of GABA receptor subunits from each of the major clades in the Ciona intestinalis genome suggests that these ancestral duplication events occurred before the divergence of urochordates. However, while gene divergence proceeded at similar rates on most receptor subunits, we show that the mammalian-specific subunits θ and ε experienced an episode of positive selection and of relaxed constraints, respectively, after the duplication event. Sites putatively under positive selection are placed on a three-dimensional model obtained by homology-modeling.Our analyses support two major monophyletic clades: one clade containing GABAOur results suggest an early divergence of the GABA receptor subunits, before the split from urochordates. We show that functional changes occurred in the lineages leading to the mammalian-specific subunit θ, and we identify the amino acid sites putatively responsible for the functional divergence. We discuss potential consequences for the evolution of mammals and of their CNS. Gene duplication followed by gene divergence is one of the major mechanisms responsible for the evolution of new functions A, GABAB and GABAC. These are distinguished according to their composition, pharmacology and localization. Ionotropic GABAA receptors are ligand-gated chloride (Cl−) channels consisting of both high abundance subunits  and low abundance subunits  A receptor sensitivity and response A receptor is a pentameric receptor with binding sites for the GABA ligand and for receptor modulation by benzodiazepines, neurosteroids, ethanol, and barbiturates B receptors are members of the seven transmembrane domain family and are coupled to downstream calcium and potassium channels via G-proteins C receptors are also pentameric ionotropic Cl− channels that show similar membrane topology as the GABAA receptors. However, GABAC receptors have unique functional and electrophysiological characteristics, including a slower Cl− conductance and insensitivity to bicuculline and other GABAA receptor modulators C receptors are composed of ρ subunits that are highly expressed in the vertebrate retina and preferentially localized to bipolar cells The γ-aminobutyric acid (GABA) is the major inhibitory neurotransmitter found in the vertebrate brain and is involved in CNS development and organization A receptor genes are clustered on four chromosomes  but are not part of the four GABAA receptor gene clusters C receptor genes share only about 35% amino acid sequence identity with GABAA receptor genes Fourteen of the human GABACiona intestinalis and the pufferfish Takifugu rubripes. Second, we test whether positive selection can explain the evolution of the mammalian-specific GABA receptor subunits θ and ε.Our goal here is twofold. First, we extend our knowledge of the phylogenetic relationships between ionotropic GABA receptor subunits, in particular by including recently available genomes such as those of the sea squirt A receptor phylogeny shows two major monophyletic clades  is uncertain and depends on whether invertebrate GABA/glycine-like receptor sequences are included or not in the analysis. Indeed, when the invertebrate GABA/glycine-like receptor sequences are included (81-sequence data set), subunits δ and π are found to be the sister group to the other GABA receptor sequences in this clade . In contd groups  and 3.ω, with ω = 1, <1 and >1 indicating neutral evolution, purifying and positive selection, respectively 0) against a branch-specific or “free-ratio” model , the model does not significantly explain the data better (at the 1% level) than the constant ω rate ratio model  under model B . Similarity searches based on BLASTp using the protein sequence of the human θ paralog (accession number: AAI09211) returned no structure from PDB. Homology modeling with SWISS-MODEL 3, GABAC, and glycine receptors ω to differ among branches leading to the β paralogs (models H2 and H3) are consistent with an episode of positive selection leading to the θ paralogs, followed by a regime of negative selection. To summarize, evidence for positive selection acting in this branch is strong, seems to have affected all three putative domains of the receptor subunit, but the identification of the actual sites should be taken with caution due to the relatively small number of sequences analyzed here.As of July 2007, no three-dimensional (3D) structures for GABA receptor subunits were deposited in the Protein DataBank (PDB: 1 is not significantly better than that of the one ratio model (H0), but neither test with model A or with model B are significant  are responsive to benzodiazepine modulation and this response is similar to the response to GABA C. intestinalis does not necessarily indicate benzodiazepine sensitivity.The present analyses and previous work A receptor δ subunit is the most primitive subunit within the GABAA clade not involved in benzodiazepine binding A receptor β subunit clade, it is interesting to note that our genome searches did not identify any β1 sequences in pufferfish or chicken genomes. This suggests that the pufferfish and chicken lineages may have independently lost their β1 paralog.It was previously suggested that the GABAA subunits ε are derived from the γ4 subunit . However in pufferfish, five of these gene families contain two paralogous gene copies instead of a single copy. Three of these pairs  are composed of very similar genes that have undergone recent duplication events. It would be interesting to test experimentally whether the subunits encoded by these gene copies have similar functions. Examples of recent gene duplication events show that it is not necessarily the case: receptors for proglucagon-derived peptides for instance exhibit differences in ligand-binding capabilities between recent paralogs A receptor subunit families found in the pufferfish genome is consistent with a complete genome duplication event that occurred in the pufferfish lineage Although we could only identify a small number of GABAA receptor subunits θ and ε experienced positive selection and relaxed constraints, respectively  and (3) the function of the GABA receptor subunits might have changed adaptively in the mammalian-specific GABAA subunit θ, while relaxed constraints acted on subunit ε. These changes of selective regime might have played a role in the evolution of neuroendocrine functions controlling feeding and reproduction in mammals. We caution however that further research should be performed to experimentally test these functional divergence hypotheses.To conclude, our results show that (1) the two major clades of ionotropic GABA receptors arose before the split from urochordates, (2) the GABAhttp://www.ncbi.nlm.nih.gov) server. BLASTn and BLASTp searches Ciona intestinalis sequences were obtained from the Joint Genome Institute (http://genome.jgipsf.org/ciona4/ciona4.home.html). Sequence alignments are available as supplementary Homologous gene queries were performed on the National Center for Biotechnology Information  and 55 chordates including sea squirt and vertebrate BZ– protein sequences . Again, discrepancies between actual protein lengths and length of the alignment reflect the presence of indels.We obtained and aligned a data set of 81 homologous protein sequences of the GABA receptor subunits queried above. This data set contains almost exclusively complete protein sequences with 997 aligned amino acid positions; this alignment is much longer than the actual length of GABA receptors (ca. 470 amino acids) because of the presence of large indels in some specific subunits. All aligned positions were conserved to help tease apart highly conserved proteins. To test for robustness of these analyses, we increased the species sampling of receptors involved in benzodiazepine binding  and those that are not  AIC), that the JTT+I+Γ model of amino acid substitution Ipˆ = 0.01; αˆ = 0.819); the second to most appropriate model was JTT+Γ (ΔAIC = 48.81), while the second to most appropriate rate matrix was WAG . Among-site rate variation modeled by a discrete Γ distribution with eight rate categories. This maximum likelihood model was fitted independently to each data set.Phylogenetic trees were inferred using both the maximum likelihood approach as implemented in PHYML 2.4.4 Because model choice as performed by ProtTest can lead to underestimating uncertainty, the maximum likelihood analyses were complemented by a Bayesian analysis that integrates over different models of evolution: rather than selecting an a priori empirical model of substitution, a reversible-jump Markov chain Monte Carlo (RJ-MCMC) was constructed to integrate over model uncertainty Homo sapiens), rat (Rattus norvegicus) and mouse (Mus musculus) copies of θ and ε paralogs, as well as the corresponding members of the group in which this clade was located: β for θ; γ for ε. Each tree was rooted by the closest human paralog . Sites with ambiguous data were removed. A statistical approach was then used to detect functional divergence at individual codon sites within the pre-specified branches based on a procedure similar to that by Bielawski and Yang 0 was that of no variable selective pressure among branches and among sites  is said to have a foreground rate, while all the other branches have the same background rate. In a second model, H2, the θ or the ε clade was allowed to evolve at a rate that differs from both the foreground and the background rates. Finally in H3, each paralog was allowed to evolve at its own rate after duplication.Codon data were obtained and split into two smaller data sets, one for each of the paralogous clade of interest: θ and ε. Each data set contained 13 sequences human (ng sites . This hyω to vary among sites within the branch of interest using the “test of positive selection” or “test 2” described in ω ratio to vary both among branches and among the sites in the foreground branch. Model A has four classes of sites: class 0 includes conserved codons with 0<ω5(0)<1 is estimated from the data; class 1 includes codons that evolve neutrally (ω5(1) = 1); classes 2a and 2b include codons that are conserved or neutral on the background branches, but are under positive selection on the foreground branch, with ω5(2)>1 estimated from the data. The “test of positive selection” compares this model against a simpler (null) model, that does not allow for positive selection (ω5(2) = 1), so that this model estimates one fewer parameters than its alternative. To be conservative, we used χ12 as an approximation to the distribution of the test statistic under the null ω5(1) is free to vary. Model B is compared to model M3 In some cases, only a few sites are affected by an episode of positive selection within a given branch. A more powerful approach to detecting sites undergoing positive selection in such cases is to allow Three-dimensional (3D) structure predictions were carried out with 3D-JIGSAW Text S1Amino acid alignment of the 81 full-length GABA receptor subunits.(0.08 MB TXT)Click here for additional data file.Text S2Codon alignment of the GABA receptor subunits β, θ, γ and ɛ for human.(0.09 MB TXT)Click here for additional data file.


Therapeutic regulation of PPARδ activity using selective agonists has been proposed for various disorders. However, the consequences of altered peroxisome proliferator-activated receptor delta (PPARδ) activity in the context of intestinal tumourigenesis remain somewhat unclear. Contradictory evidence suggesting PPARδ either attenuates or potentiates intestinal neoplasia. To further investigate the PPARδ dependency of intestinal tumourigenesis, we have analysed the consequences of PPARδ deficiency upon intestinal neoplasia occurring in mice with impaired mismatch DNA repair.Mice deficient for both PPARδ and the mismatch repair gene Mlh1 were produced and the incidence and severity of intestinal neoplasia recorded.No significant differences between the control genotypes and the double mutant genotypes were recorded indicating that deficiency of PPARδ does not modify impaired mismatch repair induced neoplasia.ApcMin/+ mouse, PPARδ deficiency does not alter the phenotype of mismatch repair deficiency. This data supports the notion that PPARδ is not required for adenoma formation and indicate that any pro-tumourigenic effect of PPARδ inactivation may be highly context dependent.In contrast with the previously observed acceleration of intestinal neoplasia in the context of the  Peroxisome proliferator-activated receptors (PPARs) are lipid-activated transcription factors exerting several functions in development and metabolism. There are 3 major PPAR isoforms; α,β/δ and γ and each has distinct agonist binding properties and different regulation of expression resulting in distinct distributions . The rolMin mouse), found either no change, or reduced PPARδ expression in colonic adenomas compared to normal tissues in this model [ApcMin/+ mice, but indeed promotes some aspects of intestinal neoplasia [Although a great deal of evidence exists to show that PPARδ is potentially important in intestinal tumourigenesis, it is currently unclear whether PPARδ attenuates or potentiates this condition. Several studies have shown that activation of PPARδ or increased PPARδ levels are associated with increased intestinal neoplasia in a variety of tissues -13. Alsois model -20, whileoplasia ,20,21. TMlh1 null mice [Thus, given the obvious disparity within the published literature, we have utilized another mouse neoplasia model to further investigate the role of PPARδ in intestinal tumourigenesis. Mice possessing null mutations in the mismatch repair (MMR) gene Mlh1 are prone to develop different types of neoplasia and present with lymphomas and intestinal tumours but show increased mutation in all tissues examined . Likewisull mice  and prodPPARδ-/-) to the mismatch repair defective Mlh1 null mice (Mlh1-/-) produced cohorts containing a minimum of 16 animals for each genotype combination of interest . Littermates were genotyped by PCR on DNA from tail biopsy and allowed to age and monitored for signs of intestinal tumours. Animals were harvested when they displayed overt symptoms of disease, and tumour burden was ascertained upon dissection.Mice were generated from sixth generation C57BL 6 backcrossed mice. All experiments were performed according to UK Home Office regulations. Inter-crossing the PPARδ null mice (PPARδ-/-) to the mismatch repair defective Mlh1 null mice (Mlh1-/-) we produced cohorts containing a minimum of 16 animals for each genotype combination of interest and examined survival, intestinal adenoma multiplicity and tumour size at death for each of the cohorts . Up-regulation of β-catenin in large intestinal adenomas was observed in both genotypes. Furthermore in the small intestine, in addition to lesions displaying increased β-catenin levels, it was also possible to identify a small subset of lesions that maintain normal levels of β-catenin, a phenomenon previously described within defective MMR intestine [To assess the nature of the ntestine . Thus, tMlh1-/- background. Given that MMR deficiency is considered to lead to a mutator phenotype which thereby increases the rate of mutation of elements of the Wnt pathway, our findings argue that PPARδ deficiency does not enhance or diminish such a mutator phenotype. By implication, the increased adenoma formation seen in the ApcMin/+PPARδ-/- mutants may arise through PPARδ dependent modification of the frequency of gene conversion events that are known to underlie the majority of polyp formation in the ApcMin/+ mouse [ApcMin/+), and does not generally enhance adenoma formation per se. This genetic dependency may reflect differences in the mutational events occurring in the Mlh1 and Apc mutant backgrounds. By analogy, any potential danger in the therapeutic use of PPARδ agonists to activate PPARδ may critically depend upon subtle changes in the underlying genetic predisposition.We therefore find that loss of PPARδ does not alter tumour incidence or morphology of tumourigenesis on the /+ mouse . Alterna/+ mouse , as thisIn summary, we show that PPARδ deficiency does not alter either lymphomagenesis or adenoma formation in mice with defective MMR. This data again support the notion that PPARδ is not required for adenoma formation and indicate that any pro-tumourigenic effect of inactivation may be highly context dependent. Thus, in the context of a defective MMR environment, PPARδ agonism is unlikely to be pro tumourigenic.The author(s) declare that they have no competing interests.KRR, OJS and AJH participated in the animal studies; KRR and OJS carried out the statistical analysis and data presentation; ARC, JMP and AJG conceived the study, and participated in its design and coordination. KRR drafted the manuscript and all authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


Theoretical work suggests that data from multiple nuclear loci provide better estimates of population genetic parameters than do single loci, but just how many loci are needed and how much sequence is required from each has been little explored.θ (4eμN) accurately under ideal circumstances, we simulated datasets of DNA sequences under three values of θ per site , varying in both the total number of base pairs sequenced per individual and the number of equal-length loci. From these datasets we estimated θ using the maximum likelihood coalescent framework implemented in the computer program Migrate. Our results corroborated the theoretical expectation that increasing the number of loci impacted the accuracy of the estimate more than increasing the sequence length at single loci. However, when the value of θ was low (0.001), the per-locus sequence length was also important for estimating θ accurately, something that has not been emphasized in previous work.To investigate how much data is required to estimate the population genetic parameter θ required data from at least 25 independently evolving loci. Beyond this, there was little added benefit in terms of decreasing the squared coefficient of variation of the coalescent estimates relative to the extra effort required to sample more loci.Accurate estimation of  Coalescent theory, placed in a maximum-likelihood framework, allows researchers to explicitly test hypotheses concerning the processes that shape patterns of genetic variation in natural populations It is well understood that coalescent estimates of population genetic parameters  are often estimated with large errors when calculated from single-locus datasets eNμ) With the development of analytical methods for multiple-locus parameter estimation and the relative ease of collecting large amounts of sequence data, researchers with limited resources are concerned increasingly with how much data is required to estimate the parameters of interest accurately Our primary motivation was to investigate the accuracy and precision of estimates of θ, using a coalescent-based analytical method, across simulated datasets which vary in both the number of loci and the total number of base pairs sequenced per individual . Whereas previous work in this area has focused primarily on making theoretical predictions of optimal sampling strategies −6 for θ = 0.1; 1×10−7 for θ = 0.01, 0.001); number of loci sampled per individual ; population size ; subdivision ; and recombination (0.0). Sequences were simulated under the Felsenstein '84 For three different per-site values of θ , we simulated DNA sequence data using Treevolve version 1.3 −6, eN = 25000), we first simulated 100 replicate datasets, each containing 10 single-locus DNA sequences 0.5 kb in length. Keeping θ = 0.1, we then simulated 100 more datasets that differed from the first datasets only in the length of each DNA sequence, which was increased to 1 kb. Next, 100 datasets were generated that contained DNA sequences from two unlinked loci, each 0.5 kb in length, sampled from 10 individuals. In these datasets the amount of DNA sequence per individual was equal to the previous datasets (1 kb), but was partitioned into two independent  loci instead of being sampled from a single locus. In this way, we generated 100 replicates of datasets which varied in total DNA sequence length per individual from 0.5 kb to 100 kb, and the number of equal-length loci from which the sequences were sampled: 1 to 100  using a Perl script we wrote. We chose the values of θ to span the extremes of variation observed in natural populations By manipulating the user-defined mutation rate and population size, we were able to simulate sequences under known values of θ. With θ = 0.1 , and the three long chains . We set the transition/transversion ratio at 2.0; other parameters were left at their default settings.A number of software packages and analytical methods are available for estimating θ, such as such as SITES To test for bias in the parameter estimates we investigated whether any mean value of θ differed from the mean values for other scenarios within a given simulated value of θ. Statistical analyses were performed using SAS version 9.0 . We used the Tukey multiple comparison adjustment to test all pairwise combinations igrate produced biased estimates of θ (see below)As predicted by theory In terms of further decreasing the squared coefficient of variation, little additional improvement was gained by sampling more than 25 loci . For exaAlthough increasing the number of sampled loci improved accuracy and decreased the squared coefficient of variation when θ = 0.001, the means of the estimates decreased (see below), precluding calculations of the improvement gained by sampling additional loci.In contrast to increasing the number of loci sampled, increasing the length of sequence at a particular locus had relatively little impact on improving the accuracy associated with estimating θ, except when the known value of θ was small . Increasigrate performed well at recovering the known value of θ . The mean values of θ under some scenarios were significantly less than the true value of 0.001 , which are commonly observed in empirical data Accurate estimation of θ required data from at least 25 independently evolving loci. Beyond this, there was little added benefit in terms of decreasing the squared coefficient of variation of the coalescent estimates relative to the extra effort required to sample more loci. Interestingly, a recent paper concluded that to accurately recover phylogenetic relationships with maximum support, researchers should use at least 20 genes Our analyses were based on a large, panmictic population at equilibrium, and without recombination or selection. Application of our results to empirical data must be considered in light of the restrictive assumptions under which the data were simulated. Multi-locus empirical datasets have many more sources of variation that would elevate the number of loci needed to estimate θ accurately. Another important source of variation not considered here were the theoretical limits of current coalescent methods to correctly infer population processes The calculations based on the simulated datasets were focused on generating average, multi-locus estimates of θ. Evolutionary forces  whose impacts are heterogeneous across the genome further complicate the interpretation of empirical estimates of θ measured from loci distributed across the genome


Local neocortical circuits are characterized by stereotypical physiological and structural features that subserve generic computational operations. These basic computations of the cortical microcircuit emerge through the interplay of neuronal connectivity, cellular intrinsic properties, and synaptic plasticity dynamics. How these interacting mechanisms generate specific computational operations in the cortical circuit remains largely unknown. Here, we identify the neurophysiological basis of both the rate of change and anticipation computations on synaptic inputs in a cortical circuit. Through biophysically realistic computer simulations and neuronal recordings, we show that the rate-of-change computation is operated robustly in cortical networks through the combination of two ubiquitous brain mechanisms: short-term synaptic depression and spike-frequency adaptation. We then show how this rate-of-change circuit can be embedded in a convergently connected network to anticipate temporally incoming synaptic inputs, in quantitative agreement with experimental findings on anticipatory responses to moving stimuli in the primary visual cortex. Given the robustness of the mechanism and the widespread nature of the physiological machinery involved, we suggest that rate-of-change computation and temporal anticipation are principal, hard-wired functions of neural information processing in the cortical microcircuit. The cerebral cortex is the region of the brain whose intricate connectivity and physiology is thought to subserve most computations required for effective action in mammals. Through biophysically realistic computer simulation and experimental recordings in brain tissue, the authors show how a specific combination of physiological mechanisms often found in neurons of the cortex transforms an input signal into another signal that represents the rate of change of the slower components of the input. This is the first report of a neurobiological implementation of an approximate mathematical derivative in the cortex. Further, such a signal integrates naturally into a neurobiologically simple network that is able to generate a linear prediction of its inputs. Anticipation of information is a primary concern of spatially extended organisms which are subject to neural delays, and it has been demonstrated at various different levels: from the retina to sensori-motor integration. We present here a simple and general mechanism for anticipation that can operate incrementally within local circuits of the cortex, to compensate for time-consuming computations and conduction delays and thus contribute to effective real-time action. Complex brain functions emerge from the orchestrated activity of neural circuits in the cerebral cortex. This emergent brain activity is dependent on many properties of the cortex, from areal specializations to microcircuit connectivity or cellular and synaptic mechanisms. At the microcicuit level, the cortex is characterized by a fairly stereotyped architecture and physiology –5. TheseWe show here, by means of computational modelling and experiments in cortical slices, how the combination of STD and SFA approximates the computation of the rate of change (or derivative) of the input, and how this can be used as the critical building block of a general predictive scheme for neural information processing. Indeed, according to the Taylor approximation for a smooth function, by combining the current stimulus value and its instantaneous rate of change it is possible to estimate the value of the stimulus slightly ahead in time ,20. We iinI and projected to a postsynaptic neuron through conductance-based synapses  / dt = ω · sin(ωt + π/2)]: 90° phase advancement irrespective of sinusoidal input frequency, and modulation amplitude gain proportional to sinusoidal input frequency  into our network model and analyzing the resulting postI. A spectral analysis of the transfer function inI and postI (H(ω) of the derivative operator is very different both in phase and magnitude, for instance, from that of a time-shift operator  can be obtained exactly under the assumption of asynchronous presynaptic firing [s and τD (see e = τD /[1 + (1 − Γ) r0 τD]. This time constant is the effective time constant for the relaxation of the synaptic efficacy (D) to its steady-state value when stimulated at rate r0. Because the decay time constant of synaptic conductances τs is very small, on the order of a few milliseconds, it does not play a significant role in the dynamics at the low frequencies (<100 Hz) that we are now interested in, and all the dynamics in this frequency range are determined by the time constants τD and τe that define synaptic depression dynamics. We analyzed the modulus and phase of the transfer function when τs → 0 and we found that precisely in the range of frequencies between 1/τD and 1/τe the modulus of the transfer function has an inflection point at infω and the phase of the transfer function has a maximum at maxω  does not vary by more than a factor of 2 and the phase of the transfer function only drops below half the maximum phase if τe < 0.07τD  τD r 0 > 13). Thus, for frequencies within the interval , the modulus of the transfer function is approximately linear and the phase advance sizable. This range of frequencies is large when τe is small, i.e., when either r0 is large or Γ is small. This is in agreement with our numerical exploration of the parametric dependence of approximate differentiation as illustrated in e and presynaptic firing is modulated around a high firing rate .Specifically, the transfer function c firing  and is gd τD see , but alsωmax see . This meω) input rate, one obtains similar results that concur with the transfer function analysis described above (see D) modulations upon sinusoidally modulated input rate are already phase-shifted (by an amount given by π − arctan ωτe), but the amplitude of these temporal modulations decreases with input modulation frequency ω. It is only after considering summation of responses to a great number of asynchronously firing afferents D(t), see [and a fluctuation amplitude that increases linearly with ω. Thus, the critical mechanisms required for the emergence of a regime of approximate derivative operation at low frequencies are not only STD, but also synaptic summation of asynchronous afferents. However, this is insufficient for differentiation, as high frequency components will be amplified , see ,23) thatfied see A and wilfied see D to forminI has sinusoidal components with frequencies between 1 and 7.5 Hz. In an additional series of simulations, satisfactory rate-of-change computation (defined as cross-correlation between network output and derivative above 80%) occurred for complex inputs with power content spanning from 1 to 20 Hz (see H(ω) magnitude in -frequency signals, since it leads to the filtering out of high-frequency components, which would otherwise dominate the derivative calculation , as it has been theoretically suggested [postI that approached the rate of change of the input . We wondered whether the backbone mechanisms of neural function, synaptic integration and spike generation, posed a problem for the eventual representation of the temporal derivative at the membrane voltage and firing rate levels. We thus repeated the simulations in inI could be well-represented not only at the level of synaptic inputs  ≈ I(t) + τ dI(t) / dt. We tested whether the addition in this formula was performed naturally by a postsynaptic neuron receiving inputs both from the network that we have been discussing (contributing in / dtk dI) and from a new set of presynaptic neurons that neither adapted nor had depressing synapses , and predicted it quite accurately , often encountered side-by-side in cortical circuits ,10,11, lB activation. Still, we worked with the assumption that if there was some inhibitory shunting, this was constant through each stimulation train. Thus, nonstationary shunting could have been a source of error in our analysis. On the other hand, slow depolarizing NMDA-independent effects were sometimes seen upon repeated electrical stimulation  and postsynaptic (postI) neurons. Thus, any processing that occurs when postsynaptic neurons are activated would correspond to some further computation on the postsynaptic neuron inputs. In the case of our networks, these inputs postI would be slightly advanced with respect to inputs to presynaptic cells and thus all ensuing processing will have this anticipation component. Therefore, the networks we identified are interposed conduction elements between two layers of processing, each of which has its own mechanisms to further elaborate information processing. Note, however, that this does not concern feedforward inhibition, which must also be considered a driving element of postsynaptic neurons. The effect of feedforward inhibitory dynamics on these networks thus remains to be elucidated.We propose that our anticipatory schemes are integrated into the cortical local circuit, even though we do not consider mechanisms operating upon postsynaptic neuron function such as postsynaptic SFA or feedback inhibition. This is justified by the fact that we are claiming that the operation occurs between the driving currents to presynaptic  (where η(t) is a Gaussian white noise with unit variance) independent from neuron to neuron and a common input term inI. In inI = 2.8 + 0.8cos(2π3.5t + 0.8), while in in = Isignal + InoiseI, with signalI = 2.8 + 0.9cos(2πt) + 0.25cos(2π2.5t + 0.2) + 0.3cos(2π3.5t + 1.5) + 0.25 · cos(2π7.5t + 1.8) and noiseI = 1.5 η(t). In in = IsignalI, the same signalI as in The model consisted of a population of 300–500 presynaptic neurons connected with fast excitatory (AMPA-type) synapses to one postsynaptic neuron. The subthreshold membrane voltage of neurons was modeled according to the equation:thV = −60 mV, at which point the membrane potential was reset to resetV = −70 mV (integrate-and-fire model). The potential was then held fixed at resetV for a refractory period of 2 ms. Other currents were incorporated at the location of other:I presynaptic adapting cells could have a calcium-dependent potassium current other = IKCaI for the generation of SFA, and postsynaptic neurons could have a synaptic input current other = IpostI due to the activity of their presynaptic partners.Presynaptic neurons fired an action potential whenever their membrane potential reached the threshold KCaI as:DK = 30 mM, KV = −80 mV, and KCag = 5 mS/cm2  due to the influx of calcium, and then decreased exponentially with τCa = 80 ms (holdV − synV) where holdV = −65 mV, synV = 0, and synG(t) denotes the total synaptic conductance open in the postsynaptic membrane. synG(t) decayed exponentially to zero with a time constant sτ = 2 ms, and when a presynaptic spike occurred, synG rose instantaneously to syn + gsyn D,G where syng represents the maximal synaptic conductance in a unitary synaptic connection , and D is the depression variable of the corresponding presynaptic neuron . The recovery of the synaptic resources toward the maximal value (D = 1) occurred between presynaptic action potentials: DdDτ / dt = 1 − D, with τD = 0.4 s (except for otherI = synG(t) (V − synV) and corresponding descriptions above).Voltage-clamped excitatory synaptic currents impinging on the postsynaptic neuron were modeled as http://www.finescience.com) and bathed in ACSF containing (in mM): NaCl, 124; KCl, 2.5; MgSO4, 2; NaHPO4, 1.25; CaCl2, 2; NaHCO3, 26; and dextrose, 10, and was aerated with 95% O2, 5% CO2 to a final pH of 7.4. Bath temperature was maintained at 34–35 °C. Intracellular recordings were initiated after 2 h of recovery.Slices of the visual cortex of adult (3–6 mo) ferrets were prepared as described previously . Experimhttp://www.sutter.com) P-97 micropipette puller from medium-walled glass and bevelled to final resistances of 50–100 MΩ. Sharp electrodes were considered adequate given the long duration (>2 h) of the protocols run consecutively in each recorded neuron. Micropipettes were filled with 2 M KAc. Recordings were digitized and acquired using a data acquisition interface and software from Cambridge Electronic Design (http://www.ced.co.uk). For the study of synaptic depression, electrical stimulation  was delivered by means of a WPI A-360 stimulus isolation unit (http://www.wpiinc.com) that prevents electrode polarization. A concentric bipolar stimulating electrode  was placed in layer 4 and the postsynaptic neurons recorded in layer 2/3. Only monosynaptic connections were included, the criteria being: reliably evoked synaptic potentials (no failures) of constant amplitude and shape and with a constant latency (jitter <1 ms) of 1.5–3 ms [n = 300–400) on to a postsynaptic neuron, we applied the stimulation trains generated by each model presynaptic neuron (duration 1–1.5 s) sequentially (5-s recovery periods). The bridge-balance was monitored and adjusted in between trains. The putative postsynaptic response was reconstructed off-line as if those trains of stimuli had been simultaneous (see below). All synaptic and injection currents in Sharp intracellular recording electrodes were formed on a Sutter Instruments . A reconstructed membrane potential was then generated by multiplying the measured size of each excitatory postsynaptic potential by the difference of exponentials:t > spkt and maxt = τ1τ2 log(τ1 / τ2) / (τ1 − τ2), where τ1 = 10 ms and τ2 = 2 ms. By doing this, we reconstructed a smoother voltage time series that only incorporated events associated with our stimulation protocol. We used this approach to remove all spontaneous synaptic activity not associated with stimulation events, which could otherwise have interfered with our analysis. The resultant reconstructed potential was then cut in individual responses i,V and translated into putative synaptic conductance changes from the equation iG(t) = −iI(t) / iV(t) with iI = iC dV / dt + Lg(iV – LV) – I. The values of C and Lg were derived from voltage responses to hyperpolarizing pulses of current injected intracellularly (for the cell in C = 0.31 nF and Lg = 0.02 μS). The resulting synaptic conductance traces were added together, replicating the postsynaptically integrated conductance evoked by ∼300 simultaneously active presynaptic neurons: synG(t) = ∑iG(t). Total synaptic conductance was translated to voltage-clamped postsynaptic current by means of postI = −syn VholdG. Thus, the experimental information incorporated into our analysis was that concerning the synaptic efficacy change operated by in vitro synaptic plasticity.We analyzed off-line the intracellularly recorded membrane potential during each series of extracellular stimulation trains, measuring the size of excitatory postsynaptic potentials at the time of each stimulus trigger inI and the postsynaptic current postI . To maintain information about the magnitude of current modulations in postI, we did not normalize by its s.d. when computing the cross-correlation functions in postI relative to inI. These measures provide a way to determine whether postI approximates the derivative of a sinusoidal in:I the maximum of the cross-correlation should be close to 100% and the phase of the central peak close to 90° Click here for additional data file.Figure S2(260 KB TIF)Click here for additional data file.Figure S3(353 KB TIF)Click here for additional data file.Figure S4(189 KB TIF)Click here for additional data file.Protocol S1(91 KB PDF)Click here for additional data file.


PPARα knockout mice. Moreover, the increase in data over the last 5 years from the broader literature on PPARα agonists presents a more complex array of extraperoxisomal effects and actions, suggesting the possibility that PPARα may be involved in modes of action (MOAs) not only for liver tumors but also for other effects of TCE and its metabolites. In summary, recent studies support the conclusion that determinations of the human relevance and susceptibility to PPARα-related MOA(s) of TCE-induced effects cannot rely on inferences regarding peroxisome proliferation per se and require a better understanding of the interplay of extraperoxisomal events after PPARα agonism.Peroxisome proliferator–activated receptor α (PPARα) is thought to be involved in several different diseases, toxic responses, and receptor pathways. The U.S. Environmental Protection Agency 2001 draft trichloroethylene (TCE) risk assessment concluded that although PPAR may play a role in liver tumor induction, the role of its activation and the sequence of subsequent events important to tumorigenesis are not well defined, particularly because of uncertainties concerning the extraperoxisomal effects. In this article, which is part of a mini-monograph on key issues in the health risk assessment of TCE, we summarize some of the scientific literature published since that time on the effects and actions of PPARα that help inform and illustrate the key scientific questions relevant to TCE risk assessment. Recent analyses of the role of PPARα in gene expression changes caused by TCE and its metabolites provide only limited data for comparison with other PPARα agonists, particularly given the difficulties in interpreting results involving  HoweverThere are a number of both long-standing and emerging issues with respect to evaluating the role of PPARα in MOAs for TCE toxicity. The U.S. EPA draft TCE risk assessment  concludeIn the present article we highlight some of the recently published literature on PPARα for TCE, its metabolites, and other PPARα agonists to help inform and illustrate the key scientific issues relevant to TCE risk assessment. Although some scientific conclusions can be drawn from this updated body of data, speculation as to its impact on the final TCE risk assessment would be premature at this point, given the ongoing National Academy of Sciences consultation discussed in the overview article  and the PPAR α knockout mice. Recent data on DNA methylation changes and other MOAs for a number of agonists, including TCE and its metabolites DCA and TCA, are reported elsewhere in this mini-monograph ] at the highest dose tested  and repression of cyp2a at a much lower single dose (10 mg/kg) of TCE after a single intraperitoneal injection in corn oil. Using a similar paradigm with 260 genes, cyp4a10, and insulin-like growth factor (IGF), with clofibrate also inducing greater expression of these genes and additional induction of cyp2b9, a fatty acid–binding protein, and metallothionein II. The pattern of induction differed between kidney and liver for DEHP and clofibrate. It is difficult to discern a clear pattern of common gene expression changes among TCE and its metabolites or for peroxisome proliferators in general for use in making inferences regarding common MOAs. For example, in a screening analysis of 148 genes for xenobiotic-metabolizing enzymes, DNA repair enzymes, heat-shock proteins (hsp), cytokines, and housekeeping genes in mouse liver, PPARα knockout mice to investigate the importance of PPARα to TCE toxicity. However, interpretation of PPARα knockout mice data in general poses some unique difficulties due to differences in baseline responses, some of which were observed in the TCE-specific studies as well. In one study, PPARα knockout mice after 3 days of exposure to up to 1,500 mg/kg/day TCE. The authors reported that of the 43 genes with altered expression in wild-type mice after TCE exposure, 40 genes were dependent on PPAR α. These genes included cyp4a12, epidermal growth factor receptor, and additional genes involved in cell growth. However, the interpretation of this information is difficult because a comparison of gene expression profiles between controls (wild-type and PPARα knockout) was not reported. Moreover, after 3 weeks of TCE treatment , Two studies have used PPARα knockout mice after 2 weeks of TCE exposure by gavage (0.75 g/kg). However, TCE induced increased liver weight in both male and female wild-type and knockout mice, suggesting hepatic effects independent of PPARα activation. Interestingly, In an earlier study, In another study One study showed that extraperoxisomal effects of PPARα agonists that may be related to tumor induction are effects on mitochondria, which have a role in several aspects of tumor biology and whose DNA may have increased susceptibility. 2O2 and DNA damage seems unlikely, free radicals may be important in signaling Kupffer cells to produce mitogenic cytokines .Another area of active investigation has been whether PPARα agonists activate nonparenchymal liver cells such as Kupffer cells independently of PPARα activation and whether such activation may be necessary for tumor induction, particularly due to their role in parenchymal cell proliferation and apoptosis suppression , 2001. AAlthough studies of TCA, DCA, and other PPARα agonists in human hepatocyte cultures seem to indicate that the human liver is refractory to markers of PP e.g., , 2000b, 2μ-globulin, a mouse homologue of α2μ, glutathione S-transferase, glutathione reductase, and the CYP genes cyp2b, cyp2c, cyp3a, cyp1a1, and cyp4a). Effects on the vulnerability of the liver to other insults such as acetaminophen toxicity have also been reported. Moreover, because some of these extraperoxisomal effects of PPARα agonists may not depend on interaction with PPARα, In the liver, PPARα responses involve not only the parenchymal cells of the liver (hepatocytes), but also macrophages (Kupffer cells). Activities affected include lipid and glucose metabolism; bile acid synthesis; macrophage cholesterol homeostasis, inflammatory cytokine production, and recruitment to inflammatory sites; actions and control of hormones ; and protein expression  and to whether the intrinsic characteristics of these knockout mice mean that they exhibit responses that differ from those of wild-type mice independent of effects related to PPARα agonism. The recent study by Several studies have used PPARα knockout mice after fasting or pharmacologic inhibition of fatty acid oxidation. Such mice have reduced cardiac expression of genes involved in the cellular uptake, mitochondrial transport, and mitochondrial  oxidation of fatty acids. After exposure to stress, PPARα knockout mice have decreased ATP concentration with abnormal cristae of the mitochondria, abnormal caveolae, and fibrosis in the myocardium in an age-dependent manner (PPARα knockout mice have a 12- to 24-hr delay in liver regeneration and hepatic gene expression with a delayed onset and lower peak magnitude of hepatocellular DNA synthesis (1/S checkpoint regulator genes cyclin D1 (Ccnd1) and c-myc and increased expression of the interleukin-1β cytokine gene . Epidermal regeneration has also been reported to be affected in PPARα knockout mice .PPARα knockout mice to have significant steatosis without treatment. PPARα knockout mice to have a sexually dimorphic phenotype, with PPARα influencing the IGF/IGF-binding protein response to feeding, particularly in males, and suggested that gender differences in the IGF system contribute to the PPARα knockout phenotype. It has been suggested that elevated serum levels of IGF1 and leptin are associated with increased risk of developing cancer ]. PPARα gene for mutations to test the genetic contribution of the PPARα in diabetes and its vascular complications and concluded that it is unlikely that the PPARα gene had a major role in diabetes and CAD in their populations.PPARα gene regulatory pathway. Mice genetically modified to mimic the metabolic derangements of the diabetic heart  (f PPARα)  had ventRegarding life stages, there is also evidence that peroxisome proliferators are much more potent in producing tumors in older rats than in younger ones, even though effects on PP and cell proliferation were the same . A promoThe studies reviewed here suggest that, given its pleiotropic responses, PPARα agonism may play a complex role in cell signaling and gene expression changes that contribute to a variety of different diseases and effects. Unfortunately, common patterns of gene expression changes among TCE, its metabolites, and other PPARα agonists, particularly those related to tumorigenic responses, have yet to be identified, precluding their use in delineating common MOAs. Recent data also suggest that even for liver tumor induction, extraperoxisomal effects such as changes in mitochondria and activation of Kupffer cells may play an important role, so inferences based on PP or purified hepatocyte cultures alone may be misleading. Recent studies also suggest that knockout and wild-type mice have baseline differences in liver parameters before treatment and exhibit differences in response to agonists, including TCE and its metabolites, independent of the peroxisomal effects, making interpretation of such studies challenging. On the whole, recent studies suggest that inferences regarding the MOA(s)—and hence the human relevance and susceptibility—of TCE-induced effects require a better understanding of the interplay of extraperoxisomal events after PPARα agonism.