Various affective disorders are linked with enhanced processing of unpleasant stimuli. However, this link is likely a result of the dominant negative mood derived from the disorder, rather than a result of the disorder itself. Additionally, little is currently known about the influence of mood on the susceptibility to emotional events in healthy populations.Event-Related Potentials (ERP) were recorded for pleasant, neutral and unpleasant pictures while subjects performed an emotional/neutral picture classification task during positive, neutral, or negative mood induced by instrumental Chinese music.Late Positive Potential (LPP) amplitudes were positively related to the affective arousal of pictures. The emotional responding to unpleasant pictures, indicated by the unpleasant-neutral differences in LPPs, was enhanced during negative compared to neutral and positive moods in the entire LPP time window (600–1000 ms). The magnitude of this enhancement was larger with increasing self-reported negative mood. In contrast, this responding was reduced during positive compared to neutral mood in the 800–1000 ms interval. Additionally, LPP reactions to pleasant stimuli were similar across positive, neutral and negative moods except those in the 800–900 ms interval.Negative mood intensifies the humans' susceptibility to unpleasant events in healthy individuals. In contrast, music-induced happy mood is effective in reducing the susceptibility to these events. Practical implications of these findings were discussed. Various affective disorders, such as depression, anxiety, and PTSD (Post-traumatic stress disorder), are known to be associated with enhanced brain processing of unpleasant stimuli Nevertheless, evidences from a few studies implied that the enhanced processing of unpleasant stimuli may not be a result of the disorder itself. Instead, this association may result from the predominant negative mood states derived from these disorders However, these studies used ambiguous facial expressions that were not emotionally salient. Without using emotionally salient materials, this approach is unable to clarify how mood state impacts the brain's sensitivity to emotional events. In addition, there was evidence showing that facial expressions involve more emotional perception or recognition rather than emotion responding Therefore, the present study investigated the impact of mood state on brain's susceptibility to unpleasant events using emotionally salient pictures. We used happy or sad music excerpts for mood induction, as music has been verified effective to induce a stable and intense mood state Prior studies showed that the negative bias of anxious individuals was accounted for by the enhanced state instead of trait anxiety Specifically, many studies have revealed that Late Positive Potential (LPP), a central- parietal positive slow ERP that reaches largest amplitudes 500–700 ms post-stimulus and lasts for several hundred milleseconds, was more pronounced for emotionally salient than for neutral stimuli Lastly, previous studies on affective priming consistently reported an emotional congruency effect when the task required assessing affective valence of the target stimuli As paid volunteers, 16 native Chinese students  with a mean age of 22.06 years (range: 20–24years) participated in the study. All subjects were healthy, right-handed, with normal or corrected to normal vision and audition, and reported no history of affective disorder. Each subject signed an informed consent form for the experiment, and filled in the Zung Self-rating Depression/Anxiety Scales The stimulus materials consisted of 480 prime–target pairs. Mood-inducing music excerpts were used as the primes. Pictures from the native Chinese Affective Picture System were used as the targets SD = 0.73) vs. neutral  excerpts whose valence scores, in turn, were significantly higher than those of sad excerpts  = 350.5, p<.001). In addition, happy  and sad  excerpts, which obtained similar arousal ratings , were both more arousing in comparison with neutral excerpts = 411.56, p<.001). Prime materials were controlled for the length of presentation. Happy music excerpts lasted for 11.52 s, sad excerpts lasted for 11.94 s and neutral excerpts lasted for 11.33 s  = 1.64; P = .20). Lastly, two judges  naive to our experimental purposes were invited to name the mood induced by each music excerpt by selecting an appropriate description from the following 7 choices: sad, disgust, fear (3 negative choices); happy, pride and serene (3 positive choices); and neutral. The results showed that both judges selected “sad” for each of 40 sad musical excerpts, and “happy” for each of 40 happy excerpts. Thus, the happy musical, sad musical and neutral broadcast excerpts used by the present study were valid to induce happy, sad and neutral mood states.Mood-inducing materials consisted of 40 sad musical excerpts, 40 happy musical excerpts and 40 neutral broadcast excerpts. Happy music materials were made of instrumentally played Chinese folk music like “Crave for the Red Army” which was characterized by cheerful rhythm and light-hearted, elated melodies. Also, sad music excerpts were made of the instrumental Chinese folk music like “Two Springs Reflect the Moon” that was played at a slow pace and was characterized by sad, depressed melodies. Because both happy and sad music excerpts were played by instruments, free of verbal words, we used Mongolian broadcast sentences whose meanings were unknown to the subjects as the baseline neutral excerpts, consequently to induce a neutral mood free of semantic contamination. All excerpts were matched for the sound intensity, and were digitized with a 44.1-kHz sampling rate and 16-bit resolution using the CoolEdit Pro software. Another sample of non-musician college students , who did not participate in the ERP experiment, were recruited to rate the valence (from 1 = ‘extremely unpleasant’ to 9 = ‘extremely pleasant’) and arousal  of these excerpts using a 9-point scale. The results showed significantly higher valence ratings for happy excerpts , neutral  and unpleasant picture sets  differed significantly from one another in valence  = 2809.63; P<.001). In addition, pleasant  and unpleasant  pictures, which were similar in arousal  = 1.92; P = .17), were both more arousing than neutral pictures  = 307.88; p<.001). Moreover, the valence and arousal of each picture category were kept highly similar across the three blocks, in order to attribute the results from dependent variables solely to mood induction. Unpleasant pictures were 1.91+0.32(or 6.18+0.92) for the sad mood block, 1.91+0.32(or 6.15+0.90) for the happy mood block, and 1.91+0.32(or 6.16+0.83) for the neutral mood block in valence  . Neutral pictures were 5.27+0.46 (3.82+0.73) for the sad mood block, 5.27+0.51(3.78+0.57) for the happy mood block, and 5.27+0.47(3.81+0.70) for the neutral mood block in valence  . Pleasant pictures were 7.30+0.27 (5.99+0.63) for the sad mood block, 7.32+0.28(6.01+0.63) for the happy mood block, and 7.32+0.32 (5.91+0.61) for the neutral mood block in valence  .The pictures used for this study were selected in such a way that the pleasant  picture was presented, and to press the “J” key if a non-emotional, neutral picture was presented. The response hands were reversed for the remaining subjects. The picture presentation was terminated by a key pressing, or was terminated when it elapsed for 1000 ms. Therefore, each subject was informed that their responses must be made under 1000 ms. Each response was followed by 1500 ms of a blank screen. The sequence of pleasant, unpleasant and neutral pictures was randomized for each subject. To avoid the fatigue effect that may contaminate the results, each block was divided into 8 sections, and the end of each section allowed subjects about 30 s to rest. The order of three blocks was balanced across subjects. Before the experiment and at the end of each block, subjects were asked to rate the valence (1 = ‘extremely unhappy’ to 9 = ‘extremely happy’) and arousal  of their mood using a nine-point scale , with references on the left and right mastoids and a ground electrode on the medial frontal aspect ; FC3, FC1, FCz, FC2, FC4 ; C3, C1, CZ, C2, C4 ; CP3, CP1, CPZ, CP2, CP4 ; P3, P1, PZ, P2, P4 . As shown by the averaged ERPs and their topographical distributions  method was used for post hoc pairwise comparisons if significant main or interaction effects were detected.In order to assess the emotional congruency effect, we averaged the P3 (or LPP) amplitudes across happy music-positive picture and sad music-negative picture conditions to compute the ERPs for the congruent condition. Similarly, we averaged the amplitudes across happy music-negative pictures and sad music-positive pictures to compute ERPs for the incongruent condition, an approach used by Kamiyama et al., M = 96.3%) obtained more accurate classification than pleasant pictures  which, in turn, was classified more accurately than neutral pictures , irrespective of mood state  = 11.25, p<.005).No other effects were detected in accuracy data. The analysis of reaction times (RT) with correct responses demonstrated faster RTs for pleasant pictures (M = 630 ms) than for unpleasant  = 10.03; p<.01) and neutral  pictures, irrespective of mood . The RT differences between unpleasant and neutral pictures were not statistically significant  = 1.60, p = .23).No other effects were detected in RT analysis.Unpleasant pictures  compared to the neutral mood  and the pre-experimental  phases. In addition, the valence rating scores were higher in the neutral mood  and the pre- experiment  phases compared to the negative mood phase (3.88±1.71). The mood valence ratings were not significantly different during neutral mood (5.56±1.05) and the pre-experiment (5.94±0.89) phases .Mood valence and arousal ratings were analyzed by a repeated measures ANOVA with four levels of mood induction phases: pre-experiment (no induction), neutral, negative and positive mood inductions. Valence ratings differed significantly depending on the induction phase . Positive (6.94±0.68) and negative (6.81±0.83) mood blocks elicited higher mood arousal than the neutral block. However, the arousal rating scores were similar for the positive and negative mood phases . Also, the mood arousal was not significantly different between pre-experimental (6.13±0.89) and neutral (5.81±1.05) mood phases . Thus, the results of mood valence and arousal ratings consistently showed that the mood induction procedure used in the present study was effective in inducing corresponding positive, negative and neutral mood states  = 10.43, p<.001, η2ates see .Pleasant pictures (9.38 µV) elicited enhanced amplitudes compared to unpleasant  = 46.46;p<.001) and neutral pictures  = 40.45; p<.001), and this effect was most pronounced at midline central-frontal sites . In addition, positive (7.05 µV) mood, but not negative mood (7.62 µV), was associated with smaller amplitudes in comparison with the neutral mood , irrespective of picture valence . No otheThe amplitudes were more pronounced during positive  and negative  compared to neutral (−3.63 µV) mood blocks. Frontal and central sites recorded larger amplitudes compared to parietal sites see , and thes<.05) in centraparietal and parietal sites  but not in central and frontal sites .Pleasant pictures (9.33 µV) elicited enhanced amplitudes than neutral pictures  which, in turn, elicited enhanced P3 amplitudes than unpleasant pictures . The P3 amplitudes were larger at parietal (8.76 µV) than at central  and frontal  sites. Whereas the P3 was more pronounced for pleasant than for neutral pictures across all frontality levels see , the P3 The analysis of the affective congruency effect showed no significant amplitude differences between congruent (6.77 µV) and incongruent (5.97 µV) conditions in 450–550 ms time window (t(15) = 2.11, p>0.05).p = 0.12). In order to disentangle the four-way interaction, we tested the significance of the frontality, mood and picture valence interaction in each of the four time windows.The LPP amplitudes were larger at central (10.91 µV) and centrofrontal (11.18 µV) sites compared to parietal (5.29 µV) sites . There wp = 0.42). On the other hand, positive pictures elicited larger amplitudes than neutral pictures during positive, neutral and negative mood states (t(15) = 5.92–9.07; all p<.001). The strength of the pleasant effect was not significantly different across the three mood conditions  = 1.59, p = .22).The three-way interaction was significant . This efp = 0.33). On the other hand, positive pictures elicited larger amplitudes than neutral pictures during positive, neutral and negative mood states (t(15) = 6.03–9.59; all p<.001). The strength of the pleasant effect was not significantly different across the three mood conditions  = 1.12, p = .33).The three-way interaction was significant . This efs<.001]. The strength of the pleasant effect, different from those in the above windows, was significantly compromised during positive (3.56 µV) compared to negative (6.07 µV) and neutral (6.58 µV) mood blocks .The three-way interaction was significant . This efThe three-way interaction was significant . This efs<.001]. The strength of the pleasant effect was not significantly different during positive (3.45 µV), negative (5.17 µV), and neutral (5.60 µV) moods .Decomposing the interaction effects at frontal and frontocentral regions showed more positive amplitudes for unpleasant versus neutral pictures during negative  and neutral  mood blocks, but not in the positive mood . The unpleasant effect, indicated by the amplitude differences between unpleasant and neutral pictures, was more pronounced during negative (4.85 µV) compared to neutral  mood blocks. The neutral block, in turn, elicited larger size of the unpleasant effect than the positive mood block  but not by Model 1. Thus, the LPP reactivity to unpleasant stimuli was enhanced similarly by negative mood, regardless of timing; while the reduction of LPP reaction to negative stimuli during positive mood was observed just in the 800–1000 ms time windows.The above analyses showed that 1), negative mood intensified LPP reactivity to unpleasant stimuli in every 100 ms interval of the 600–1000 ms time window; 2), positive mood decreased this reactivity in the 800–1000 ms interval but not in the 600–800 ms. To test whether positive and negative moods have different timing features in their influence on negative picture processing, we conducted 2 separate ANOVAs, with mood  * picture  * timing (4 windows) as factors in Model 1; mood  * picture valence * timing (4 windows) as factors in Model 2. The results showed a significant three-way interaction by Model 2 [F = 8.33, p = 0.001; η2Firstly, to test whether LPP amplitude was a valid index of subjective emotion arousal, we ran bivariate correlation analyses for the arousal value of each picture set and the LPP amplitudes in the four time intervals. The results showed that LPP amplitudes increased significantly with the arousal of pictures in the 700–800 ms , 800–900 ms , 900–1000 ms ; and marginally in the 600–700 ms . Because response times were distributed in the 600–700 ms interval, the LPP of this interval involves response execution that might obscure the interpretation of the correlation in subsequent time intervals. To exclude this confound, we conducted a partial correlation analysis for arousal values and the LPP amplitudes in the 700–800 ms, 800–900 ms, and 900–1000 ms intervals respectively, with the LPP amplitudes of the 600–700 ms as a control variable. The results continued to show larger LPP amplitudes with increasing arousal in the 700–800 ms ; 800–900 ms  and 900–1000 ms  intervals see . To testSecondly, negative mood state was linked with enhanced LPP responding to unpleasant pictures in the entire 600–1000 ms interval. To verify this observation, we computed the correlation between the enhancement of the LPP effect and the arousal of negative mood during negative mood induction. The enhancement of the LPP effect was computed by subtracting the unpleasant effect  in the neutral block from that in the negative mood block. The negative mood arousal was computed by subtracting pre-experiment mood arousal from that in the negative block. The results showed a significant positive correlation anxiety was associated with enhanced vigilance to threat stimulation On the other hand, positive mood, compared to neutral mood, was associated with decreased size of emotion effect for unpleasant pictures in the 800–1000 ms interval. This finding suggests that positive mood induction by happy music may be useful in dampening the intensity of emotion induced by negative stimuli. Also, this result was supported by prior findings of smaller attention bias for negative stimuli during positive vs. negative stimulus contexts In addition, we did not observe a similar LPP enhancement effect for positive pictures during happy mood, but instead observed smaller LPPs for positive pictures during positive mood state (800–900 ms). Prior studies indicated that the brain's sensitivity to positive stimuli is susceptible to habituation, as it is biologically significant for organisms to keep approach motivation to new arousing event that meets different needs The present study did not observe a significant affective congruency effect in both 450–550 ms (P3) and 600–1000 ms (LPP) time intervals. As noted before, many affective priming studies have reported an emotional congruency effect during affective valence evaluation or prime-target affective congruency judgment tasks We need to acknowledge that the number of subjects (sixteen) recruited for our ERP study did not constitute a big sample size. Consequently, one may question the reliability of our key findings. However, the present study observed significant mood by picture valence interaction  in four consecutive 100 ms time windows. Thus, this mood effect was reliably observed in different LPP phases. Also, we observed that the magnitude of this mood effect was larger with increasing negative mood arousal, reflected by a significant positive correlation between the two variables. Considering these converging evidences, the key findings of the present study should be statistically reliable, rather than being statistically vulnerable.The present study used non-emotional, Mongolian language broadcasts to induce a neutral mood state. Our manipulation checks confirmed that these materials effectively induced a neutral mood state, as the mood ratings were similar during pre-experiment and neutral mood phases. However, language and music are distinct categories of stimuli. Thus, one may question the validity of using language stimuli as the baseline condition. A number of studies have reported that language and music processing shared behavioral and physiological characteristics. For example, it was indicated that musical and linguistic syntactical capacities were positively correlated Appendix S1The number of CAPS pictures used for this study.(DOCX)Click here for additional data file.


Peroxisome proliferator-activated receptors (PPARs) are involved in the pathophysiology of the various types of diseases. Many types of PPAR-related medicines developed and utilized clinically all over the world exert multiple effects, including regulation of hypolipidemic, antidiabetic, anti-inflammatory, antifibrotic, and antiproliferative pathways, with emerging potential benefits in other diseases. On the other hand, these medicines may also exert various toxicities, and some PPAR drugs are no longer in use clinically because of serious complications arising in some patients. Thus, the authors here have focused on the benefits and risks of these medicines, and aim to clarify their therapeutic potential for appropriate clinical utilization. This special issue in PPAR research includes 6 review articles and 6 research articles, as follows.γ ligands: Having the right combinationThe key to unlocking the chemotherapeutic potential of PPAR” by G. Skelhorne-Gross and C. J. B. Nicol is a review of the vast in vitro, in vivo, and human clinical trial studies, using chemotherapeutic combinations that include PPARγ activating drugs. This review article reveals the novel chemotherapeutic potential of PPARγ activating drugs, and provides a guide for further basic and clinical research. This information is certainly useful for optimization of chemotherapeutic interventions that will reduce the number of cancer related deaths.The paper “PPAR medicines and human disease: The ABCs of it all” by A. J. Apostoli and C. J. B. Nicol is a review article that summarizes the advances of knowledge concerning effects of PPAR medicines on ATP-dependent binding cassette (ABC) transporters based on in vitro, in vivo, and human clinical trial studies. This review suggests the potential of PPAR-related medicines for controlling ABC transporter activity at the transcriptional level, and discusses their potential implications in human diseases with respect to cancer and atherosclerosis.The paper “The current knowledge of the role of PPAR in hepatic ischemia-reperfusion injury” by M. Elias-Miró et al. is a review article concerning the roles of PPARs signaling pathways in hepatic ischemia reperfusion injury that is inherent to human liver transplantation and resection surgery. A shortage of available healthy livers for organ transplantation calls for the potential use of any available organ, including, for example, steatotic livers; however, steatotic livers are more susceptible to ischemia-reperfusion injury. This paper reviews PPAR-signaling pathways, summarizes some of the lesser known functions of PPARs in liver regeneration, and discusses potential therapies based on PPAR regulation that may minimize the observed side effects in liver surgery. This review emphasizes the need for further research into the roles of PPARs in various liver conditions and surgical procedures before being translated into treatment of human disease.The paper “γ ligands in leukemiaEffects of PPAR” by Y. Tabe et al. is a review article that describes the antitumor advances of PPARγ ligands, alone and in combination with retinoic acid receptor ligands in control of cell proliferation, differentiation, and apoptosis, and discusses their potential therapeutic applications in hematological malignancies. Acute promyelocytic leukemia  is unique among myeloid leukemias in that it is sensitive to all-trans-retinoic acid (ATRA). However, a number of APL patients relapse and develop ATRA resistance. This review article provides evidence on the consequences of the treatment with PPARγ ligands, in particular the triterpenoid 2-cyano-3,12-dioxooleana-1,9-dien-28-oic acid (CDDO), on the epigenetic/transcriptional events induced by retinoic acid in APL cells, and supports the clinical utility of ATRA/PPARγ-ligand combinations for treating hematological malignancies.The paper “γ-based therapies: Lessons from bench and bedsideIdealized PPAR” by A. A. Amato and F. de A. R. Neves is a review about the knowledge acquired regarding efficacy and safety issues by PPARγ ligands. This body of work is attractive since the interest for PPARγ modulation as a strategy to treat metabolic diseases has increased recently, due to better understanding of PPARγ action.The paper “γNutraceuticals as ligands of PPAR” by M. Penumetcha and N. Santanam reviews the transcription factor PPARγ, which is the target for the thiazolidinediones, the first class of PPARγ agonist drugs used in the treatment of diabetes. Due to the increased adverse effects related to these drugs, newer safer drugs are being generated. This review paper describes some of the dietary components that have affinity for, and activate, PPARγ, as well as their pharmacology and potential toxicology.The paper “α activation protects against anti-Thy1 nephritis by suppressing glomerular NF-κB signalingPPAR” by K. Hashimoto et al. is the first to demonstrate the glomerular protective effects of treatment using a representative PPARα agonist, clofibrate, in rat mesangial proliferative glomerulonephritis model (MsPGN) anti-Thy1 nephritis. PPARα activation is known to exert anti-inflammatory effects in various cells and organs through suppression of NFκB signaling; however, its effect against glomerulonephritis has remained obscure. Because MsPGN is one of the significant factors leading to chronic kidney disease (CKD), the beneficial antinephritic effect of PPARα activation may provide a novel treatment strategy against CKD. Their findings may also be useful to create PPAR-based therapies to treat glomerular disease.The paper “α activation in miceHepatic cerebroside sulfotransferase is induced by PPAR” by T. Kimura et al. is the first to examine sulfatide levels and the expression of enzymes related to sulfatide metabolism using wild-type (+/+), Ppara-heterozygous (+/−), and Ppara-null (−/−) mice given a control diet or one containing 0.1% fenofibrate, a typical PPARα activator. Recent studies have revealed a protective role of serum sulfatides against arteriosclerosis and hypercoagulation. Their results suggest that PPARα activation enhances hepatic sulfatide synthesis mainly through cerebroside sulfotransferase (CST) induction. Accordingly, CST may be a novel PPARα target gene product candidate with implications in disease prevention and treatment.The paper “α activation in fibroblasts due to trifunctional protein deficiencyFatty acid accumulation and resulting PPAR” by M. Wakabayashi et al. demonstrates free fatty acid accumulation, enhanced three acyl-CoA dehydrogenases, and PPARα activation in the fibroblasts from six patients with mitochondrial trifunctional protein deficiency, who had abnormalities in the second through fourth reactions in fatty acid β-oxidation system. These novel findings suggest that the fatty acid accumulation and resulting PPARα activation are major causes of the increase in the β-oxidation ability in the patients' fibroblasts, and that enhanced cell proliferation and increased oxidative stress relate to the development of specific clinical features. Additionally, significant suppression of the PPARα activation by means of MK886 treatment may provide a new method of treating this deficiency.The paper “γ agonist-treated kidneys in an orthologous rat model of human autosomal recessive polycystic kidney diseaseGlobal gene expression profiling in PPAR” by D. Yoshihara et al., the authors explored the changes in gene expression by Pioglitazone (PIO), a PPARγ agonist, using polycystic kidney disease (PCK) rats. By analyzing globally, they successfully found that stearoyl-coenzyme A desaturase 1 (Scd1) was highly expressed in PCK kidneys, and PIO decreased its expression. Notably, they found that Scd1 plays a role in the early cystogenesis, and this is the point where PIO may intervene in the process of cystogenesis.In the paper “α less than that of a mouse but may activate constitutive androstane receptor (CAR) in liverPlasticizers may activate human hepatic peroxisome proliferator-activated receptor ” by Y. Ito et al. reported the species differences concerning activation of PPARα and CAR, which was induced by the oral exposure with industrial PPARα ligands, including dibutyl phthalate, di(2-ethylhexyl)phthalate, and di(2-ethylhexyl)adipate, between wild-type mice and humanized PPARα mice. These transcriptional species differences might cause different hepatic toxicities between murine model and human cases. This information would be valuable for the risk assessment of PPARα-related medicines.The paper “α agonists differentially regulate inhibitor of DNA binding expression in rodents and human cellsPeroxisome proliferator-activated receptor ” by M. del C. González et al. reported rodent versus human species differences in the regulatory manner of inhibitor of DNA binding (Id2) via PPARα agonists. Since Id2 protein is involved in cell differentiation and proliferation, this finding may help to understand the species differences in toxicity of PPARα agonists.The paper “Yuji KamijoYuji KamijoChristopher J. NicolChristopher J. NicolStefan E. H. AlexsonStefan E. H. Alexson


Formica yessensis has a nestmate recognition system similar to that of the multicolonial species, Camponotus japonicus with respect to the cuticular hydrocarbon-sensitive sensillum (CHC sensillum), which responds only to non-nestmate CHCs. We further investigated whether the sensory system reflects on the apparent reduced aggression between non-nestmates typical to unicolonial species.Territorial boundaries between conspecific social insect colonies are maintained through nestmate recognition systems. However, in supercolony-forming ants, which have developed an extraordinary social organization style known as unicoloniality, a single supercolony extends across large geographic distance. The underlying mechanism is considered to involve less frequent occurrence of intraspecific aggressive behaviors, while maintaining interspecific competition. Thus, we examined whether the supercolony-forming species, F. yessensis constructs supercolonies comprising numerous nests and constitutes the largest supercolonies in Japan. We compared the within-colony or between-colonies’ (1) similarity in CHC profiles, the nestmate recognition cues, (2) levels of the CHC sensillar response, (3) levels of aggression between workers, as correlated with geographic distances between nests, and (4) their genetic relatedness. Workers from nests within the supercolony revealed a greater similarity of CHC profiles compared to workers from colonies outside it. Total response of the active CHC sensilla stimulated with conspecific alien CHCs did not increase as much as in case of C. japonicus, suggesting that discrimination of conspecific workers at the peripheral system is limited. It was particularly limited among workers within a supercolony, but was fully expressed for allospecific workers.F. yessensis was not clear cut, probably because this species has only subtle intraspecific differences in the CHC pattern that typify within a supercolony. Such an incomplete chemical discrimination via the CHC sensilla is thus an important factor contributing to decreased occurrence of intraspecific aggressive behavior especially within a supercolony.We demonstrate that chemical discrimination between nestmates and non-nestmates in  Linepithema humileWasmannia auropunctataMost ant species and other social organisms rely on recognition systems for either accepting nestmates or aggressively rejecting non-nestmates Unicoloniality is not limited to invasive species, but under certain circumstances can also occur in native ant species populations Formica yessensis, a native Japanese unicolonial species, in the “Ishikari supercolony”, when reported in 1971, consisted of ∼45,000 nests with the total numbers of ∼306,000,000 workers and ∼1,080,000 queens versus non-nestmate discrimination was only recently discovered in Camponotus japonicusBehavioral experiments with various ant species revealed both indirectly and directly that aggressive behavior exhibited by workers toward alien ants is elicited by chemosensory detection of differences in chemical signals encoded in CHCs F. yessensis, migrate among neighboring nests in the “Ishikari supercolony”, they are predicted to be less aggressive toward non-nestmates within the supercolony. Unicoloniality allows a single supercolony to extend across extremely large geographic distances, exhibiting lower frequencies of severe aggressive behavior. If unicoloniality of F. yessensis is also based on similarity/disparity of CHC profiles, some sensory mechanism to detect such differences should be involved in the expression of individual aggressive behavior.When workers and queens of F. yessensis, we analyzed the CHC profiles and examined the responsiveness of the CHC sensillum, concomitant with recording workers’ aggressive behavior toward conspecific workers within and between supercolonies. We further studied the sensory response and accompanying behaviors also toward C. japonicus workers as an interspecific positive control. In the electrophysiological experiments, we investigated a particular type of antennal chemosensilla that responded to CHCs, as a putative sensory organ responsible for conspecific worker discrimination within or between supercolonies and intraspecific worker discrimination. Nonetheless, we cannot exclude additional or alternative contribution of unknown types of antennal chemosensilla. We further considered whether genetic relatedness or similarity/disparity of CHC profile affects the frequency of aggressive behavior occurrence in F. yessensis.Hence in the present study using the unicolonial F. yessensis workers from four nests  located within the 10-km-wide “Ishikari supercolony” along the Ishikari coast  within the “Ishikari supercolony” and the “independent nests” , respectively, or with the CHC blend of workers of C. japonicus  of “Hoshioki”, “Shinkawa”, “Hakkenzan”, or C. japonicus (C. japonicus CHCs recruited 10.60±6.11 (n = 5) and 18.80±4.54 impulse units (n = 6) being significantly higher than their respective controls, 1.80±1.64 impulse units (n = 5)  and 4.50±2.81 (n = 6) , respectively. Likewise, the responses of the CHC sensilla of “Shinkawa” workers generated 8.80±3.27 and 5.80±2.39 impulse units to stimuli with “Hoshioki” and “Shinkawa” CHCs, respectively (n = 5), but 10.60±3.36 and 18.00±4.00 impulse units to stimuli with “Hakkenzan” (n = 5) and C. japonicus CHCs (n = 5), respectively (C. japonicus were significantly different from each other (p<0.01) (F = 7.55 in Scanning electron microscopy (SEM) of worker  a scape , revealeaponicus . The samaponicus . Each reaponicus . The forectively . In the ectively  and “Shiectively , there wC. japonicus CHCs, we found 20.00±1.41 and 20.75±3.30 impulse units, which were significantly higher than the control, 3.00±1.15  and 3.00±0.82 impulse units , respectively. There was a significant difference between the number of impulse units recruited by stimulation with CHCs of nestmates and “Hakkenzan” (p<0.05) or C. japonicus (p<0.01)   in . Figure C. japonicus did not always respond to the CHCs of F. yessensis. It appears that not only responsiveness of each responding sensillum may contribute to the total chemosensory cue eliciting aggressive behavior, but also the ratio of the responding sensilla. Hence, C. japonicas workers, respectively. The ratio of sensilla that responded to CHC blends of “Oshoro” and C. japonicus workers was significantly higher than the ratio of sensilla that responded to “Hoshioki” CHCs, while the ratio of sensilla that responded to CHCs of “Hakkenzan” was not.During the electrophysiological experiments, we frequently found that a CHC sensillum that responded to CHCs of C. japonicus at p = 1.08×10−11<0.008 was significantly higher than the ratio of sensilla that responded to “Hoshioki” CHCs as a control .The results of the prolonged-antennation presented in F. yessensis workers from independent colonies of “Ishikari supercolony”, “Hakkenzan” and “Oshoro”, or C. japonicus workers was significantly higher than the control with nestmate CHCs .As depicted in C. japonicus  and C. japonicus , “Hoshioki” workers were still estimated to have 60-fold lower mean threshold amount of CHCs for biting at C. japonicus CHCs than at nestmate CHCs.Fst were as large as 0.06–0.19 . This indicates that even within the supercolony, the nests were genetically differentiated from each other.In order to assess the genetic relatedness among the supercolony members, a total of 48 alleles were found at seven microsatellite loci by analyzing 48 ants from each of five nests at “Hoshioki” , “Shinkawa” , “Ishikari” , “Oshoro” , and “Hakkenzan”  see . The genMean within-nest relatedness±SD within the supercolony ranged from 0.11±0.27 (within Hoshioki nests) to 0.30±0.23 (within Ishikari nests), not different from the within-nest relatedness of nests outside the supercolony. Likewise, there were no apparent differences in mean between nest relatedness when comparing nests within or outside the supercolony, with relatedness varying from −0.10±0.25 (between “Ishikari” and “Oshoro” nests) to 0.07±0.27 (between “Shinkawa” and “Ishikari” nests), respectively . AlthougF. yessensis are organized in supercolonies. The slightly elevated aggression within the supercolony can be attributed to the observed limited migration distances of workers Our behavioral data  showed nF. yessensis workers are more similar within a supercolony compared to those among nests of independent supercolonies. In order to test the hypothesis we first ascertained that CHCs do indeed constitute the nestmate recognition cues also in F. yessensis, verifying this both through chemical comparisons of the CHC profiles and behavioral experiments using the purified CHCs  was greater than that between independent nests, each of which represents a different or incipient supercolony. However, even within the supercolony there was a statistical correlation between CHC profile disparity and geographic distance 1B, althoFormica exsecta, in which reduced aggression was linked to decrease rather than increase in diversity of recognition cue Although we do not have conclusive evidence of whether environmental or genetic factors are more prominent in shaping the recognition cues, the lack of a clear correlation between the genetic relatedness  and diffC. japonicusC. japonicus, was significantly higher than that of nestmates . This differential response was not due to the existence of two functionally different types of CHC sensilla, because the same sensillum occasionally responded with different appearances of impulse arrays, depending on the stimuli. Considering the “one receptor gene for one receptor neuron rule” in olfactory receptor neurons The neural response from the CHC sensilla further suggests that the hypothetical behavior-switching center is tuned not only to the number of activated sensilla but also to characteristic of their responsiveness  and S3. Pristomyrmex pungensCataglyphis fortisSuch a behavior-switching system can allow experience- or context-dependent modulation of the behavioral expression toward alien CHCs possibly by resetting the threshold via hormonal and/or neural regulations Camponotus floridanusA recent report in the carpenter ant On the other hand, F. yessensis, exhibits very low frequency of aggressive behavior occurrence among workers within the supercolony. We have further demonstrated, using purified CHCs, that the CHCs serve as recognition cues for eliciting aggressive behavior and congruent among workers of the same supercolony. Our data of the single sensillar recordings in the CHC sensilla indicated that subtle differences in CHC profile among nests within a supercolony were hardly discriminated by the CHC sensilla, whereas CHCs originated from workers outside the supercolony elicited distinct sensillar response and so did allospecific CHCs. In conclusion, we consider the best hypothesis to explain our results as follows; In respect of similarity/disparity of the CHC profile, discrimination of nestmates or supercolonymates from others is attained primarily at the antennal chemosensilla as peripheral filters and subsequently at a brain center responsible for triggering aggressive behavior, which can be activated only by the above-threshold inputs. This might underlie the maintenance of unicolonial society with little expression of conspecific aggression in a supercolony-forming ant, F. yessensis.In summary, we have demonstrated here that the unicolonial species, For our study, after behavioral tests in the field to assess whether the nests belonged to the supercolony or lay outside it n-hexane for 5 min for extraction, and the extract was further purified on a silica gel  column  to obtain a pure CHC fraction. The purified CHCs were recovered in 5 ml n-hexane, concentrated to several microliters, and stored at −20°C until gas chromatography (GC) analysis. Before analysis, the CHC samples were dissolved in 30 µl n-hexane, and 1 µl was injected to a Shimadzu GC-14B GC equipped with a flame ionization detector. Compound separation was achieved using a DB-1HT non-polar capillary column  that was temperature programmed from 60°C (1 min hold) to 210°C at 30°C/min, and from 210 to 350°C at 10°C/min with a final hold of 5 min. Helium was used as the carrier gas, at a column head pressure of 100 kPa. Injection was made directly onto the capillary column using an on-column injector that was temperature programmed from 60°C (0.01 min hold) to 350°C at 15°C/min, and then held at the final temperature for 24 min. The amounts of single CHCs were calculated by peak integration.For the CHC analysis, individual ants were immersed in 1 ml GC-MS analysis was carried out with a JMS-700 using EI mode at 70 eV, and equipped with a polar capillary column . The column oven was operated under the conditions described above, but the injection mode was splitless. Compound identifications were deduced from their mass fragmentation patterns.The discriminant analysis was conducted with 10 workers each from the “Hoshioki”, “Shinkawa”, “Tarukawa”, “Hakkenzan”, and “Oshoro” nests, respectively, and 9 workers from the “Ishikari” nest. We used the 25 CHC peaks indicated by A–Y in Mahalanobis distances from a group centroid to the position in the plot of each individual of the tested nests were calculated by software, JMP ver. 7, and statistically compared by Tukey HSD test, using SPSS ver. 15.F. yessensis were dehydrated in acetone for >2 days. After a brief sonication for cleaning, the whole body and the CHC sensilla on the antennae were observed under a scanning electron microscope . For counting the sensilla, newly-emerged workers were used. The isolated antennae were dehydrated with an ethanol series , cleaned by sonication and stored in acetone until SEM observation.Whole bodies of n-hexane and purified on a silica gel column as mentioned above for the CHC analysis. Before electrophysiological experiments on the same day, the CHC stimulus solution was prepared by dissolving the CHCs in 10 mM NaCl with 0.1% Triton X-100, to a final concentration of 0.8–1 ant equivalent CHCs/100 µl. The stimulus duration was 10 s. Each sensillum was used only once, and at least 20 sensilla of at least four “Hoshioki” workers, randomly chosen from our breeding box, were tested for each CHC stimulus. We recorded the response of the same sensillum, which received pairwise stimulation; the sensillum was first stimulated with 10 mM NaCl with 0.1% Triton X-100, and after >3 min interval, stimulated with one of the test CHCs dissolved in 10 mM NaCl with 0.1% Triton X-100. In the experiments using the CHC sensilla of “Shinkawa” and “Hoshioki”, stimulus presentation was done blindly. For recording from the CHC sensilla on the antenna detached from the head, the “Hoshioki” antenna was cut at the middle of the scape segment and connected with an indifferent glass capillary electrode filled with ant linger solution . Such isolated antennae, which are free from feedback control by the brain, can be alive and vigorously respond to CHC blend of C. japonicus as a positive control stimulus at least for 1 h.The isolated head of a “Hoshioki” worker was connected with a platinum indifferent electrode under a microscope. The electrophysiological response was recorded from the CHC sensillum by the tip-recording method, in which the target CHC sensillum was capped with the recording electrode of a glass capillary filled with stimulus solution, in accordance with Ozaki’s method http://www.chino-js.com/ja/service-engineer.html) The recorded impulses of various shapes were sorted into unit clusters through a 100–1800 Hz band-pass filter, using SpikeTaro , both sides of which was thinly covered with talc powder to prevent ants from escaping and intruding. A discriminator ant was put in the arena and left until it calmed down (at least for 5 min), and then an invader ant was gently transferred into the arena by using a tooth-pick. Upon the first interaction, we recorded both biting and prolonged-antennation exhibited by the discriminator toward the invader. We repeated that test for each invader with 5 different discriminators, which were subsequently encountered with an invader with 5 min intervals, thus obtaining the behavioral data of 5 discriminators toward the same invader. All the experiments were conducted double blindly; the observer-scorer who had no information about invader ants and the introducer who had no information about discriminator ants and randomly selected the invader ant for each assay.n-hexane, and then exposed for at least 10 min, to ensure full solvent evaporation, before being presented to the “Hoshioki” workers. For the blind tests, the prepared glass beads were marked and shuffled. On the following day, we conducted the blind experiments using the same 30 “Hoshioki” workers as discriminators, presenting one of the glass beads to each discriminator worker 5 times with 5 min intervals. When the discriminator workers bit the glass beads at least once, a positive score was given.The role of purified CHC in nestmate recognition was tested in the laboratory using CHC-coating glass beads (4 mm in diameter), according to Ozaki For the dose-dependent behavioral response test using CHC-coating glass beads, we employed the same behavioral assay with CHC amount of 8, 0.8, 0.08 or 0.008 ant equivalents.F. yessensis . The sample size (n = 48 workers) at each nest seemed sufficient for the analysis of genetic structure because the total number of alleles at the seven loci reached a plateau at n = 30–35 workers  and amplified at seven microsatellite loci, which have been developed for ta FE17, . The ampkers see . Based oFigure S1Discriminant analyses of the CHC profiles (A) and relationships between Mahalanobis distance and geographic distance (B). (A) Discriminant analyses using the 2 independent nests and either “Shinkawa”, “Tarukawa” or “Ishikari” from the “Ishikari supercolony”, employing the same parameters as in B) Correlation between geographic distance and the Mahalanobis distance (average±SE) between the 2 independent nests and either “Shinkawa”, “Tarukawa” or “Ishikari” from the “Ishikari supercolony”.(PPT)Click here for additional data file.Figure S2Comparison of the Mahalanobis distances, based on the discriminant analyses of the CHC profiles. (A) Among the 4 nests within the “Ishikari supercolony”; (B) Among the 3 independent nests: “Hoshioki” within the “Ishikari supercolony”, and “Oshoro” and “Hakkenzan” outside the “Ishikari supercolony”. Each column indicates the average Mahalanobis distance from the nest centroid (indicated by underlined letter) to individual data plots of nests within (white columns) and outside the “Ishikari supercolony” (hatched columns). Single and double asterisks indicate significant difference from the within-nest variation in the Mahalanobis distance of the underlined nests at P<0.05 and <0.001, respectively ; F = 434.39 for the graph underlining “Hoshioki”, F = 175.61 for the graph underlining “Hakkenzan”, F = 390.86 for the graph underlining “Oshoro” in the top panels of (B); F = 288.07 for the graph underlining “Shinkawa”, F = 402.73 for the graph underlining “Hakkenzan”, F = 203.29 for the graph underlining “Oshoro” in the second panels from the top of (B); F = 2070.93 for the graph underlining “Tarukawa”, F = 9805.86 for the graph underlining “Hakkenzan”, F = 6457.37 for the graph underlining “Oshoro” in the third panels from the top of (B); F = 3756.91 for the graph underlining “Ishikari”, F = 5213.13 for the graph underlining “Hakkenzan”, F = 3826.51 for the graph underlining “Oshoro” in the bottom panels of (B); Tukey HSD test). The degrees of freedom are 38 for (A), 28 for the 3 bottom panels of (B), and 29 for other panels in (B). Vertical bars represent the standard errors.(PPT)Click here for additional data file.Figure S3Raster plots for the experiments presented in. Impulses of the “Hoshioki” CHC sensilla on the antennae attached to the haeds (A) and “Shinkawa” CHC sensilla on the antennae attached to the haeds (B) and those of the “Hoshioki” CHC sensilla on the antennae detached from the haeds (C), which are sorted into different unit clusters, are plotted against time after beginning of stimulation. H-S-1 and 2, e.g., indicate clusters 1 and 2 corresponding to different shapes of impulse units that were sorted from the impulse train recorded in a “Hoshioki” sensillum stimulated with “Shinkawa” CHCs; H-s-1 to 4, e.g., indicate clusters 1 to 4 corresponding to different shapes of impulse units sorted from the impulse train recorded in the same “Hoshioki” sensillum stimulated with solvent without “Shinkawa” CHCs. Left, Raster plots for the representative electrophysiological responses to CHCs extracted from “Hoshioki”, “Shinkawa”, “Hakkenzan”, and C. japonicus. Right, Raster plots for control recordings of the same sensilla in response to the solvent, 10 mM NaCl plus 0.1% Triton X-100.(PPT)Click here for additional data file.Figure S4Blind experiments on aggressive behavior. (A) Percentage of workers from “Hoshioki” (H), “Shinkawa” (S), “Hakkenzan” (Ha) F. yessensis and C. japonicus (C)  that induced biting behavior of “Hoshioki” (upper plate) and “Shinkawa” workers (lower plate). (B) Percentage of workers from the same nests of F. yessensis and C. japonicus that induced prolonged-antennation behavior of “Hoshioki” (upper plate) and “Shinkawa” workers (lower plate). Resident nests are marked by under bars. Different letters indicate significant differences by Tukey WSD test.(PPT)Click here for additional data file.Figure S5Correlates of biting behaviors, Mahalanobis distances of CHC profiles, and genetic relatedness in resident or invader ants. (A) Correlation between % of invader ants and of glass beads inducing biting behavior of resident “Hoshioki” ants. (B) Correlation between aggressive behavior and average of Mahalanobis distances of CHC profiles between resident “Hoshioki” ants and invader ants. (C) Same as (B) but % of glass beads inducing biting instead of % of ants inducing biting. (D) Correlation between chemical similarity  and genetic relatedness between resident and invader ants. (E) Correlation between genetic relatedness and aggressive behavior in encounters involving resident “Hoshioki” and invader ants. (F) Same as (E) but % of glass beads inducing biting instead of % of ants inducing biting. Indicated r-values are correlation coefficients. The straight lines are regression lines drawn by the least-squares method.(PPT)Click here for additional data file.Figure S6Coleman rarefaction curve for “Hoshioki” workers. For the analysis of genetic structure, 48 ant individuals were used. The total number of alleles at seven microsatellite loci was counted by randomly choosing each number of individuals (n) from the 48 ants, and the mean number of alleles with standard deviation (bar) was calculated from 1,000 replications. The curve reaches plateau around n = 30, indicating that the sample size 48 is large enough for the analysis of genetic structure. In all five nests, the Coleman rarefaction curve reached plateau at n = 30–35 (data not shown).(PPT)Click here for additional data file.Table S1Average number of sensilla basiconica (CHC sensilla) in a worker’s antenna.(PPT)Click here for additional data file.


Here we introduce a novel mean-squared residue for such 3D dataset. Our proposed algorithm yields triclusters that have a mean-squared residue score below a threshold δ.Estrogen is a chemical messenger that has an influence on many breast cancers as it helps cells to grow and divide. These cancers are often known as estrogen responsive cancers in which estrogen receptor occupies the surface of the cells. The successful treatment of breast cancers requires understanding gene expression, identifying of tumor markers, acquiring knowledge of cellular pathways, etc. In this paper we introduce our proposed triclustering algorithm CCL2, CD47, NFIB, BRD4, HPGD, CSNK1E, NPC1L1, PTEN, PTPN2 and ADAM9 are identified as hub-genes which are already known to be associated with breast cancer. The other genes that have also been identified as hub-genes might be associated with breast cancer or estrogen responsive elements. The TFBS enrichment analysis also reveals that transcription factor POU2F1 binds to the promoter region of ESR1 that encodes estrogen receptor α. Transcription factor E2F1 binds to the promoter regions of coexpressed genes MCM7, ANAPC1 and WEE1.We have applied our algorithm on one simulated dataset and one real-life dataset. The real-life dataset is a time-series dataset in estrogen induced breast cancer cell line. To establish the biological significance of genes belonging to resultant triclusters we have performed gene ontology, KEGG pathway and transcription factor binding site enrichment analysis. Additionally, we represent each resultant tricluster by computing its eigengene and verify whether its eigengene is also differentially expressed at early, middle and late estrogen responsive stages. We also identified hub-genes for each resultant triclusters and verified whether the hub-genes are found to be associated with breast cancer. Through our analysis Thus our integrative approach provides insights into breast cancer prognosis. In the context of genomics research, the functional approach is based on the ability to analyze genome-wide patterns of gene expression and the mechanisms by which gene expression is coordinated. Microarray technology and other high-throughput methods are used to measure expression values of thousands of genes over different samples/experimental conditions. In recent years the microarray technology has been used to measure in a single experiment expression values of thousands of genes under a huge variety of experimental conditions across different time points. This kind of datasets can be referred to as time series microarray datasets. Because of the large data volume, computational methods are used to analyze such datasets. Clustering is one of the most common methods for identifying coexpressed genes . This kiδ (δ-bicluster) , where i ∈ I, j ∈ J and k ∈ K. The submatrix M represents a subset of genes (I) that are coexpressed over a subset of conditions (J) across a subset of time points (K).Definition 3 (Perfect Shifting Tricluster). A Tricluster M = m ijk, where i ∈ I, j ∈ J and k ∈ K, is called a perfect shifting tricluster if each element of the submatrix M is represented as: m ijk = Γ + αi + βj + ηk, where Γ is a constant value for the tricluster, αi, βj and ηk are shifting factors of ith gene, jth samples/experimental condition and kth time point, respectively. As the noise is present in microarray datasets, the deviation from actual value and expected value of each element in the dataset also exists. For this deviation, every tricluster is not a perfect one.δ in 2D microarray gene expression dataset. They also showed that MSR of a perfect δ-bicluster and perfect shifting bicluster is zero (S = δ = 0) [S) of a perfect shifting tricluster becomes also zero, where each element mijk = Γ + αi + βj + ηk. For delineating new MSR score (S), at first we need to define the residue score:Cheng and Church proposed an algorithm for retrieving large and maximal biclusters that have mean squared residue score (MSR) below a threshold = δ = 0) . Now extith gene (m ijk): jth sample/experimental condition (m ijk): kth time point (m ijk): ijk): Γ=mIJK. We can define the shifting factor for the ith gene (αi) as the difference between m ijk and m ijk i.e. αi=miJK-mIJK. Similarly, we can define shifting factor for the jth condition (βj) as βj=mIjK-mIJK and shifting factor for the kth time point (ηk) can be defined as ηk=mIJk-mIJK. Hence we can define each element of a perfect shifting tricluster as mijk=Γ+αi+βj+ηk=mIJK+(miJK-mIJK)+(mIjK-mIJK)+(mIJk-mIJK)=(miJK+mIjK+mIJk-2mIJK). But usually noise is evident in microarray gene expression dataset. Therefore to evaluate the difference between the actual value of an element (m ijk) and its expected value, obtained from above equation, the term “residue” can be used [ijk) can be defined as follows: rijk=mijk-(miJK+mIjK+mIJk-2mIJK)=(mijk-miJK-mIjK-mIJk+2mIJK).Let the mean of  be used . Thus thDefinition 4 (Mean Squared Residue). We define the term Mean Squared Residue MSR or S of a tricluster M to estimate the quality of a tricluster i.e. the level of coherence among the elements of a tricluster as follows:Lower residue score represents larger coherence and better quality of a tricluster.δ-TRIMAX aims to find largest and maximal triclusters in a 3D microarray gene expression dataset. It is an extension of Cheng and Church biclustering algorithm [S score i.e. S = 0 and this submatrix is each element of the dataset. But as mentioned above, our algorithm finds maximal triclusters having S score under a threshold δ, hence we have used a greedy heuristic approach to find triclusters. Our algorithm therefore starts with the entire dataset containing all genes, all samples/experimental conditions and all time points.lgorithm  that deaInput. D, a matrix that represents 3D microarray gene expression dataset, λ> 1, an input parameter for multiple node deletion algorithm, δ≥ 0, maximum allowable MSR score.Output. All possible δ-triclusters.Initialization. Missing elements in D ← random numbers, D’ ← DRepeat1 ← Results of Algorithm 2 on D’ using delta and λ. If the no. of genes (conditions/samples and/or no. of time points) is 50 , then do not apply Algorithm 2 on genes (conditions/samples and/or time points).a. D’2 ← Results of Algorithm 3 on D’1 usingδ.b. D’3 ← Results of Algorithm 4 on D’2.c. D’3 and replace the elements that exist in D’ and D’3 with random numbers.d. Return D’Until(No gene is found for δ-tricluster)S; this step is described in the following section in which a node corresponds to a gene or experimental condition or time point in the 3D microarray gene expression dataset.Initially, our algorithm removes genes or conditions or time points from the dataset to accomplish largest diminishing of score Input. D, a matrix of real numbers that represents 3D microarray gene expression dataset; δ≥ 0, maximum allowable MSR threshold, λ> 1, threshold for multiple node deletion. The value of λ has been set experimentally to optimize the speed and performance  of the algorithm.Output. M ijk, a δ-tricluster, consisting of a subset(I) of genes, a subset(J) of samples/experimental conditions and a subset of time points, having MSR score (S) less than or equal to δ.Initialization. I ←{set of all genes }, J ←{set of all experimental conditions/ samples } and K ←{set of all time points } and to M ← DRepeatijk, ∀ i ∈ I; m ijk, ∀ j ∈ J; m ijk, ∀ k ∈ K; m ijk and S.Calculate m If S ≤δ return MElseDelete genes i ∈ I that satisfy the following inequalityijk, ∀ i ∈ I; m ijk, ∀ j ∈ J; m ijk, ∀ k ∈ K; m ijk and S Recalculate m Delete samples/experimental conditions j ∈ J that satisfy the following inequalityijk, ∀ i ∈ I; m ijk, ∀ j ∈ J; m ijk, ∀ k ∈ K; m ijk and S Recalculate m Delete time points k ∈ K that satisfy the following inequalityEnd ifUntilThe complexity of this algorithm is O) where m, n and p are the number of genes, samples and time points in the 3D microarray dataset.S of the resultant submatrix is less than or equal to δ. This step results in a δ-tricluster.In the second step, we delete one node at each iteration from the resultant submatrix, produced by Algorithm 2, until the score Input. D, a matrix of real numbers that represents 3D microarray gene expression dataset; δ≥ 0, maximum allowable MSR threshold.Output. M ijk, a δ-tricluster, consisting of a subset(I) of genes, a subset(J) of samples/experimental conditions and a subset of time points, having MSR score (S) less than or equal to δ.Initialization.I ←{set of all genes in D }, J ←{set of experimental conditions/samples in D } and K ←{set of time points in D } and to M ← Dijk, ∀ i ∈ I; m ijk, ∀ j ∈ J; m ijk, ∀ k ∈ K; m ijk and S.Calculate m While S >δDetect gene i ∈ I that has the highest score Detect sample/experimental condition j ∈ J that has the highest scoreDetect time point k ∈ K that has the highest scoreμ score and modify I or J or K. Recalculate m ijk, ∀ i ∈ I; m ijk, ∀ j ∈ J; m ijk, ∀ k ∈ K; m ijk and S. Delete gene or sample/experimental condition or time point that has highest End whileReturn MThe complexity of first and second steps is O(mnp) as those will iterate (m+n+p) times. The complexity of selection of best genes, samples and time points is O(log m + log n + log p). So it is suggested to use algorithm II before algorithm 3.S) below the threshold δ, the resultant tricluster M may not be the largest one. That means some genes and/or experimental conditions/samples and/or time points may be added to the resultant tricluster T produced by node deletion algorithm, so that the MSR score of new tricluster T’ produced after node addition does not exceed the MSR score of T. Now the third step of our algorithm is described below.As the goal of our algorithm is to find maximal triclusters, having MSR score , a subset of experimental conditions/samples (J) and a subset of time points (K).Output. Mδ-tricluster, consisting of a subset of genes (I’), a subset of samples/experimental conditions (J’) and a subset of time points (K’), such that I ⊂ I ′, J ⊂ J ′, K ⊂ K’ and MSR ≤ MSR of D.Initialization. M ← DRepeatijk, ∀ i; m ijk, ∀ j; m ijk, ∀ k; m ijk and S. Add genes i ∉ I that satisfy the following inequalityCalculate m ijk, ∀ j; m ijk, ∀; m ijk and SRecalculate m Add samples/experimental conditions j ∉ J that satisfy the following inequalityijk, ∀ i; m ijk, ∀ k; m ijk and SRecalculate m Add time points k ∉ K that satisfy the following inequalityUntilI’ ← I, J’ ← J, K’ ← K Return I’, J’, K’The complexity of this algorithm is O(mnp) as each step iterates (m+n+p) times.To find tricluster eigengene we applied singular value decomposition method (SVD) on the expression data of each tricluster . For insUi is a g ∗ (c ∗ t) matrix with orthonormal columns, Vi is a (c ∗ t) × (c ∗ t) orthogonal matrix and Di is (c ∗ t) × (c ∗ t) diagonal matrix of singular values.where U and V are the orthogonal matrices. Di are arranged in non-decreasing order, we can represent eigengene of ith tricluster by the first column of matrix Vi, i.e.Assuming that singular values in matrix affirmation score in the same way as Prelic et. al. defined for two sets of biclusters [1 with respect to T2 is as follows, where ) is the average gene affirmation score, ) is the average sample affirmation score and ) is the average time point affirmation score of T1 with respect to T2:We have produced one simulated dataset SMD of size 2000 × 30 × 30. At first we have implanted three perfect shifting triclusters of size 100 × 6 × 6, 80 × 6 × 6 and 60 × 5 × 5 into the dataset SMD and then implanted three noisy shifting triclusters of the same size mentioned before into it. To estimate the degree of similarity between the implanted and obtained triclusters, we define im and T res where T im represents the set of implanted triclusters and T res corresponds to the set of triclusters retrieved by any triclustering algorithm. Hence SM ∗ denotes how well the triclustering algorithm finds the true triclusters that have been implanted into the dataset. This score varies from 0 to 1 (if T im = T res).Suppose, we have two sets of triclusters T δ and λ, respectively. The value of δ varies from one dataset to another dataset. Then we have added noisy triclusters having different levels i.e. standard deviations . To have an idea about the δ value, we have first clustered the genes over all time points and then the time points over the subset of genes for each gene cluster in each sample plane using the K-means algorithm. Then we have computed the MSR value(S) of the submatrix, considering a randomly selected sample plane, gene and time-pont cluster for 100 times. Then we have taken the lowest value as the value of δ. For these noisy datasets, we have assigned 3.75 and 1.004 to the parameters δ and λ, respectively. In Figure TRICLUSTER algorithm [δ-TRIMAX algorithm performs better than TRICLUSTER algorithm for the noisy dataset. For perfect additive triclusters, performances of both these algorithms are comparable with each other.For the dataset containing perfect shifing triclusters, we have assigned 0.35 and 1.0005 to the parameters δ and λ respectively. In this case our algorithm results in 115 triclusters. From Figure This dataset contains 54675 affymetrix probe-set ids, 3 biological replicates and 4 time points. In this experiment MCF7 cells are stimulated with 100 nm estrogen for 0, 3, 6 and 12 hours and the experiments are performed in triplicate. This dataset is publicly available at Gene Expression Omnibus (GEO) (dataset id- GSE 11324). It was used for discovering of cis-regulatory sites in previously uninvestigated regions and cooperating transcription factors underlying estrogen signaling in breast cancer . We assiTo compare the performance of our proposed algorithm with TRICLUSER algorithm on real-life dataset, we have used three validation indexes.Coverage for any triclustering algorithm can be delineated asgalg, calg and talg denote total number of genes, experimental samples and time points retrieved by the triclustering algorithm. G, C and T represent number of all genes, experimental samples and time points in the dataset.where We can elucidate Triclusering Quality Index of a tricluster by equation 4.MSRi and Volumei represent mean-squared residue and volume of ith tricluster. Lower TQI score represents better quality of tricluster.where Here we have introduced another quality measurement, termed as Statistical Differences from Background (SDB)  asMSRi represents mean squared residue of ith tricluster retrieved by the algorithm and RMSRj represents mean squared residue of jth random tricluster having the same number of genes, experimental samples and time points as that of ith resultant tricluster. Here higher value of the denominator denotes better quality of the resultant tricluster. Hence, lower SDB score signifies better performance of the algorithm. Table δ-TRIMAX algorithm and TRICLUSTER algorithm in terms of coverage, SDB and TQI score.where n is the total number of triclusters extracted by the algorithm. We have established the biological significance of genes belonging to each resultant tricluster by performing (a) Gene Ontology (GO) and KEGG pathway enrichment analysis, (b) cogitating each tricluster with different estrogen-responsive stages (early (3 hour), middle (6 hour) and late (12 hour)), (c) identifying hub genes of each tricluster and (d) Transcription Factor Binding Site (TFBS) enrichment analysis.GOStats package [δ-TRIMAX algorithm with that of TRICLUSTER algorithm on real-life dataset. For comparison of the performances we have considered GO Biological Processes (GOBP) and KEGG pathway terms that have already been reported to play an important role in estrogen induced breast cancer cell. Table δ-TRIMAX and TRICLUSTER algorithm in terms corrected p-values of GOBP and KEGG pathway terms cell adhesion and Wnt signaling pathway that are observed to be associated with estrogen induced breast cancer [We have used  package  in R to  package  and const cancer ,14, respmTOR signaling pathway is observed to be meliorated in this tricluster and has been reported to be associated with estrogen induced breast cancer cell [PIK3CA, PRKAA1, RPS6, ULK2 participate in that pathway. The genes belonging to tricluster 50 are coexpressed over all samples across 0, 6 and 12 hours. The eigengene of tricluster 50 has been observed to be differentially expressed between 0 hour - 12 hours and 6 hours - 12 hours. 96% of the genes belonging to this tricluster are found to be differentially expressed. The genes in this tricluster are meliorated with the KEGG pathway term ubiquitin mediated proteolysis . It has been reported in a previous study that there is crosstalk between ER α and targets of ER α for ubiquitin mediated proteolysis [ubiquitin mediated proteolysis . Genes belonging to tricluster 48 are coexpressed across 0, 3 and 12 hours. Eigengene of tricluster 48 is significantly expressed between 0 and 12 hours, 3 and 12 hours. The KEGG pathway term TGF-beta signaling pathway is meliorated in this tricluster and the crosstalk between TGF-beta signaling pathway and ER α has been reported in a previous study [SKP1, BMPR2 are found to play a role in the enriched pathway. Eigengene of tricluster 95 is significantly upregulated between 0 and 12 hours. 60% of all genes belonging to this tricluster are differentially up regulated at late responsive stage. The genes in this tricluster has been found to be coexpressed across 0 hour, 12 hours over all samples. The KEGG pathway term apoptosis  is observed to be meliorated in this tricluster and it has been found in a recent study that apoptosis can be induced by estrogen in estrogen deprivation-resistant breast cancer cell [ErbB signaling pathway that is found to be associated with estrogen induced breast cancer cell [NCK1, SOS2 in this tricluster participate in that pathway.To cogitate each tricluster with different estrogen responsive stages of the experiment, we represent each tricluster by eigengene. Then we have examined whether the eigengene of each tricluster is differentially expressed at early, middle and late estrogen responsive stages using Limma package in R   is known to play an important role in breast cancer [CD47 has been found to intervene killing of breast cancer cells [HPGD plays important role in epithelial-mesenchymal transition and migration in breast cancer cells [IGKV1-13, FAM69C, SGCD, CSNK1E, TRMU, CRYBA2, IGKV1D-13, IGSF11, PACS1, IQCK are identified as hub-genes. CSNK1E has been observed to play an important role proliferation of breast cancer cells and act as a regulator of activated -catenin driven transcription [ESYT3, SERINC2, LRRC14, ALDH4A1, RPL10, BRD4, DEC1, ZFP30, TCP11L2, ALDOA as hub-genes. The gene for Aldolase A (ALDOA) plays an instrumental role in hypoxia which is a feature of solid tumors in breast cancer [BRD4 known as Bromodomain 4 is found to be associated with breast cancer progression [PFKFB1, TAF1, PIKFYVE, MEMO1P1, KIF1B, PHF20L1, ARHGAP24, TSC22D1, AK7, DPY30, MEMO1, PTEN, ADAM9, PTPN2, MTSS1L are found as hubgenes of tricluster 95. PTEN is known to be a tumor suppressor gene in breast cancer [PTPN2, ADAM9 have been reported to be associated with breast cancer in previous studies [PIKFYVE has been found to intervene epidermal growth factor receptor that is associated with human breast cancers [ANTXR2, RHBDL2, GSTCD, DENND1B, KLC3, PREP, NOS1, STOML3, CDK5R1, CLEC7A, HGD, FOXC1, MSRB3, TEX34, SLC36A1 are appeared as hub-genes that are coexpressed over all samples and across 0, 12 hours. In a recent work, the activity of RHBDL2 has been identified in many tumour cells including breast cancer [FOXC1 as a regulator of human breast cancer cells by activating NF κB signaling has been discovered in a recent work [To identify hub genes of each tricluster, we have computed tricluster membership of each gene by calculating Pearson correlation coefficient between each gene and the eigengene of that tricluster. We have considered the top fifteen genes as hub genes having highest correlation coefficient with the eigengene of that tricluster. For tricluster 1, we have identified nsporter . CCL2 iser cells . DNAJC3- cancers . Transiet cancer . CD47 haer cells . HPGD pler cells . In triccription . For trigression . Genes Pt cancer . PTPN2,  studies ,33. PIKF cancers . In caset cancer . The rolTo analyse the potential coregulation of coexpressed genes, we have done transcription factor binding site (TFBS) enrichment analysis using the TRANSFAC library (version 2009.4)  that conδ-TRIMAX triclustering algorithm that aims to retrieve large and coherent groups of genes, having an MSR score below a threshold δ. Genes belonging to each tricluster are coexpressed over a subset of samples/ experimental conditions and across subset of time points. The results of GO and KEGG pathway enrichment analysis show that our proposed algorithm is able to extract group of coexpressed genes that are biologically significant. We have performed TFBS enrichment analysis to establish the fact that the promoter regions of the genes having similar expression profile are bound by the same transcription factors. We have compared the performance of our algorithm with that of existing algorithm using one artificial dataset in terms of affirmation score and one real-life dataset in terms of coverage, statistical difference from background and triclustering quality index score. In case of these two datasets our proposed algorithm outperformed the existing one. Additionally, here we have represented the expression profiles of genes belonging to each tricluster by eigengene and then identified hub genes using the profile of eigengene. We have observed that most of the identified hub-genes are previously reported to be associated with breast cancer and estrogen responsive elements. The other identified hub genes might be associated with breast cancer and need to be verified experimentally. Hence our integrative approach and findings might provide new insights into breast cancer prognosis.In this work we have proposed The authors declare that they have no competing interests.AB, MH, AM, UM and SB carried out the literature study and preplanning of this work. AB, MH collected the datasets. MH participated in the prediction of transcription factor binding sites. AB developed the code, did the experiments, analysed the results and wrote the draft of the manuscript. MH, AM, UM, SB and EW corrected the draft. EW supervised the entire work. All authors read and approved the final manuscript.


We recently identified two PPARG transcripts encoding dominant negative PPARγ isoforms. The existence of different PPARG variants suggests that alternative splicing is crucial to modulate PPARγ function, underlying some underestimated aspects of its regulation. Here we investigate PPARG expression in different tissues and cells affected in metabolic syndrome and, in particular, during adipocyte differentiation of human mesenchymal stem cells. We defined the transcript-specific expression pattern of PPARG variants encoding both canonical and dominant negative isoforms and identified a novel PPARG transcript, γ1ORF4. Our analysis indicated that, during adipogenesis, the transcription of alternative PPARG variants is regulated in a time-specific manner through differential usage of distinct promoters. In addition, our analysis describes—for the first time—the differential contribution of three ORF4 variants to this process, suggesting a still unexplored role for these dominant negative isoforms during adipogenesis. Therefore, our results highlight crucial aspects of PPARG regulation, suggesting the need of further investigation to rule out the differential impact of all PPARG transcripts in both physiologic and pathologic conditions, such as metabolism-related disorders.The nuclear receptor PPAR Three members of the PPAR family—known as PPARentified .γ is the most extensively studied and characterized member of PPARs, given its involvement in several physiological states, as well as pathological conditions. Indeed, it modulates the expression of several genes that play a central role in glucose, lipid and cholesterol metabolism, inflammation, angiogenesis, proliferation, and differentiation [γ is the master regulator of adipogenesis, since it regulates the transcription of a wide number of genes involved in cellular differentiation and lipid accumulation [γ, signaling its altered expression and/or activation, as well as polymorphisms/mutations, are implicated in different pathological conditions occurring in metabolic syndrome, such as insulin resistance, obesity [Undoubtedly, PPARntiation –7. In pamulation , 9. Defe obesity , dyslipi obesity –13, as w obesity , 14–17.γ, belonging to the class of thiazolidinediones (TZDs), such as troglitazone, pioglitazone, and rosiglitazone, function as insulin sensitizers and are used for treating hyperglycemia in patients with type 2 diabetes [γ signaling is crucial to develop more effective and targeted therapeutic strategies to treat metabolic syndrome and its complications.The prevalence of metabolic syndrome is increasing to epidemic proportions and, to date, an adequate therapy has not been yet established. Of great clinical interest, synthetic ligands of PPARdiabetes , 18–20. γ activity, some relevant aspects need to be taken into account. One of the most relevant features is the ability of PPARG gene to give rise to different transcripts. Indeed, the human PPARG gene consists of nine exons and—by differential promoter's usage and alternative splicing—generates at least four main splice variants . These transcripts display different 5′ untranslated regions (UTRs), followed by six coding exons. However, despite the presence of such a variable number of PPARG transcripts, this gene encodes only two protein isoforms. Indeed, PPARG1, PPARG3, and PPARG4 encode the same protein PPARγ1—localized in the adipose tissue, liver, heart, and skeletal muscle—whereas PPARG2 yields a protein with 28 additional amino acids at the N-terminus, known as PPARγ2, exclusively localized in the adipose tissue [However, to fully define the landscape of PPARe tissue –23.γ1 and PPARγ2, indicating a more relevant adipogenic activity for PPARγ2. Although both isoforms are thought to be essential during adipocyte differentiation, their relative contribution is not yet well clarified [Different ability to induce adipogenesis has been shown for PPARlarified –27. PPARG transcripts harboring a read-through in intron 4, named γ2ORF4 and γ3ORF4, displaying the same 5′UTRs of PPARG2 and PPARG3, respectively [γ. Although it has been shown that γORF4 plays a role in pathogenesis of colorectal cancer, its presence and expression levels have not yet been investigated in other cells and/or tissues.More recently, our group identified in sporadic colorectal cancers two novelectively . The pro PPARG transcript are still missing. For instance, to the best of our knowledge, this consideration holds true particularly for the adipogenesis, in which PPARγ is the main driver [ PPARG transcripts—and its dominant negative isoforms—in adipogenesis, as well as in tissues and cells related to processes altered in metabolic syndrome, will provide a solid basis to rule out if, and how, they may account for metabolism-related diseases.To date, accurate analyses of the expression pattern of eachn driver , 7, 29. n driver , 31. Des PPARG transcripts—PPARG1, PPARG2, PPARG3, and PPARG4—as well as its dominant negative isoform γORF4 in human tissues and cells affected in metabolic syndrome. In particular, we focus on their differential expression during human adipogenesis, using human mesenchymal stem cells (hMSCs) isolated from the stromal vascular fraction of adipose tissue [ in vitro differentiation of hMSCs in adipose cells, by using transcript-specific RT-PCR and Quantitative Real-Time PCR assays, we measured the expression of PPARG transcripts at various time points from the induction of adipocyte differentiation, demonstrating the differential contribution of each alternative splice variant. A similar pattern of expression was also observed for total PPARγ and γORF proteins. In addition, here we describe, for the first time, a novel transcript of PPARG, named γ1ORF4, similar to the dominant negative γ2ORF4 and γ3ORF4, previously identified [Here we describe a complete expression analysis of all annotatede tissue . After ientified . FinallyMedia, sera, and antibiotics for cell culture were from Lonza . Human Embryonic Kidney 293 cells (HEK239) were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 2 mmol/L glutamine, 100 units/mL penicillin, and 100 units/mL streptomycin.2 at 37°C.Human Mesenchymal Stem Cells (hMSCs) were obtained by abdominal biopsy and cultures established as described previously . The cel2) and cultured in six-well plates until confluence. Adipocyte differentiation was induced with a differentiation cocktail consisting of 850 nmol/L insulin, 10 μmol/L dexamethasone, 0.5 mmol/L IBMX (isobutylmethylxanthine), 10 μmol/L pioglitazone, 33 μmol/L biotin, and 17 μmol/L pantothenate in DMEM-F12 (1 : 1) supplemented with 3% FBS, 2 mmol/L glutamine, and antibiotics. After 3 days, the medium was changed to a medium containing only insulin and pioglitazone in DMEM-F12 (1 : 1) supplemented with 10% FBS, glutamine, and antibiotics. Culture medium was then changed every 2 days for another 8 days up to obtain a complete adipocyte differentiation of hMSCs. Lipid accumulation was determined by Oil Red O staining as described by Isakson and colleagues [Adipocyte differentiation was achieved as previously described . Brieflylleagues . AdipocyμL of the reverse transcription reaction as template in PCR reactions set up with AmpliTaq Gold (Perkin Elmer). PCR assays have been performed using these amplification conditions: 95°C for 10 minutes, followed by 35 cycles at 95°C for 40 sec, 60°C for 40 sec, 72°C for 30 sec, and 70°C for 7 min. RT-PCR products were of expected length  according to the manufacturer's instructions. RNA extracted from the other human tissues, heart, liver, and thyroid, and cells, human colon carcinoma, endothelial progenitor (EPCs), macrophages, and breast cancer (MCF7), employed in our analysis, was obtained in previous studies , 35, 36.ngth see . In eachThe multiple PCR products , obtained in RT-PCR assays of PPARG1/PPARG4, have been cloned into Topo Vector II (Invitrogen) according to the manufacturer's instructions. Clones and other RT-PCR products were directly sequenced by Sanger method, confirming the specificity of reactions.−ΔΔCt method. For each assay, expression levels were normalized for the reference values (time point at 0 hours or 6 hours) using glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as housekeeping gene. qRT-PCRs data were reported as mean values and standard deviation of three biological replicates and results analyzed by paired Student t test. P value <0.05 was considered statistically significant.Quantitative Real-Time PCRs were performed on cDNA samples of hMSCs and undifferentiated at different stages of adipocyte differentiation . Amplification reaction mix contained 1x SYBR Green PCR master mix (Applied Biosystems), 160 nM of each primer, and 50 ng of cDNA  as template. Quantitative Real-Time PCR assays were performed in according to the manufacturer's instructions for the 7900HT Real-Time PCR system (Applied Biosystems) in the same conditions described in . Each as4P2O7, 2 mM sodium orthovanadate, 50 mM NaF, 1 mM phenyl-methyl-sulfonyl fluoride, 10 μg/mL aprotinin, 10 μg/mL leupeptin, pH 7.4, and 1% (v/v) Triton X-100 . The lysates were clarified by centrifugation at 12,000 rpm for 20 min at 4°C. Proteins were separated by SDS-PAGE  and blotted on Immobilon-P membranes . Membranes were incubated with a polyclonal antibody directed against the N-terminal domain of PPARγ  and with antiactin antibodies . Detection of blotted proteins was performed by enhanced chemiluminescence  according to the manufacturer's instructions. Densitometric analysis was performed using Image Lab Software . For each protein isoform (PPARγ and γORF4), data are shown as pixel density ratio versus control protein (actin).Total cell lysates were obtained and separated by sodium dodecyl sulfate—polyacrylamide gel electrophoresis (SDS-PAGE) as previously described . Briefly PPARG transcripts are currently known, as described by Costa et al. [γ [Four maina et al. . Additiot al. [γ , transcrUsing specific primers pairs , we perf PPARG alternative variants, including also transcripts encoding the same protein , is shown in γ1 protein, whereas PPARG3 is expressed at low levels only in EPCs and heart. Noteworthy, also PPARG2 transcript is expressed in EPCs, as well as in the heart, whereas its expression is undetectable in other examined tissues and cell lines. This finding—possibly correlated to the anti-inflammatory role of this nuclear receptor in the cardiovascular system [γ2 is predominantly expressed in these adult tissues. Surprisingly, the dominant negative isoform γORF4, till now associated with tumor pathogenesis, is expressed in all analyzed tissues and cell lines, suggesting a not negligible contribute to PPARG activity also in other physiologic and pathological cell processes. Of note, the results shown in The tissue-specific expression pattern ofr system –43—sugge in vitro induction of hMSCs toward adipogenic differentiation (see Methods), we selected seven different time points  is very high throughout the process. In detail, using variant-specific primers we observed that all PPARG transcripts are expressed—albeit at variable levels—in the examined differentiation stages . Subsequent independent analysis of the three ORF4 transcripts revealed that γ3ORF4 variant is expressed throughout the process, whereas γ2ORF4 mRNA undergoes a dramatic increase at 2 days from differentiation's induction. Noteworthy, the novel variant γ1ORF4—identified in undifferentiated hMSCs—is expressed at variable levels during adipogenesis, although it is undetectable at some stages . For PPARG2 and PPARG3 the expression values at 6 hours were used as baseline, since they are not expressed in undifferentiated hMSCs , day 2 , and day 10 . As no commercially available antibodies exist for ORF4 protein, we used a polyclonal antibody directed against the N-terminal domain, able to recognize both the canonical and the shortest PPARG isoforms. We detected canonical PPARγ at 67 kDa and immunoreactive bands at 40 kDa, the predicted weight of ORF4 protein isoform. As expected, consistently with the changes in mRNA levels, after 2 days by differentiation induction, the expressions of PPARγ—and of the shortest isoforms—were higher compared to both undifferentiated and completely differentiated cells . Notably, the most striking increase has been observed for PPARG2 and PPARG3 variants , suggesting the inducible nature of their promoters during this process. On the opposite, highly significant decreases were observed—for these two splice variants—immediately after day 2 from the induction of the process. A common behavior was observed for PPARG1 and ORF4 transcripts. In particular, these variants undergo mild expression changes in the transitions among the stages, showing a quite constant basal expression throughout the adipogenic process  and S1. ed cells .Epidemiological studies demonstrate that the prevalence of the metabolic syndrome is increasing in the Western world and developing countries, and to date an adequate therapy has not been yet established , 44. PPARG is one of the most studied genes accounting for metabolic disorders. Indeed, it modulates the expression of several genes with a crucial role in glucose, lipid and cholesterol metabolism, insulin signaling, and adipokines' production, whose imbalance leads to insulin resistance, obesity, type 2 diabetes, and cardiovascular diseases [γ is also a drug target and, currently, its synthetic ligands are used to treat hyperlipidemia and as insulin-sensitizing antidiabetic agents [ PPARG activity in tissues and cells related to energy metabolism may provide useful insights to develop new and effective therapeutic strategies to treat the metabolic syndrome and its complications.Undoubtedly,diseases , 7, 14. c agents . Thus, d PPARG gene generates multiple variants encoding two proteins, PPARγ1 and PPARγ2. Since different PPARG splice variants encode the same protein isoform, their differential expression, both spatial and temporal, may reflect a different regulation, translation, mRNA stability, and/or localization. To complicate the picture, the recent identification of γORF4 isoform—able to act as dominant-negative and with a tumorigenic effect [γ activity is modulated through transcript-specific regulation.It is currently known that—by different promoter usage and alternative splicing—the humanc effect —suggests PPARG expression in different tissues and cells—affected in metabolic syndrome—and during hMSCs' adipocyte differentiation. Other than focusing on canonical PPARG transcripts, a particular emphasis was posed toward defining the expression pattern of its variants encoding dominant negative isoforms. In our study we identified γ1ORF4, a novel PPARG transcript that, similarly to the previously described γ2ORF4 and γ3ORF4 [γ.Therefore, our effort has been to investigated γ3ORF4 , may act PPARG have a peculiar transcriptional activity. Such finding is particularly relevant in the adipocyte differentiation, in which PPARγ is a key player [γ protein synthesis. Furthermore, the mild expression changes of PPARG1 along adipocyte differentiation strengthen the hypothesis that its promoter provides constitutive levels of PPARG messengers. On the opposite, the tissue- and stage-specific PPARG2 expression, as well as its dramatic variations throughout the adipogenesis, clearly demonstrate its inducible nature.Our expression analysis has clearly demonstrated that the different promoters ofy player , 9, 29.  PPARG regulates itself through dominant negative isoforms. Furthermore, our results suggest that similarly to PPARG canonical transcripts, the three ORF4 variants give a different contribution to PPARγ activity. Indeed, whereas γ1ORF4 and γ2ORF4 exhibit stage-specific expression, γ3ORF4 is constantly expressed along adipocyte differentiation but not in mature adipose cells. These findings, strictly correlated with those regarding the canonical isoforms, suggest that (1) the promoter upstream exon B is inducible for both the canonical and ORF4 variants, (2) constitutive levels of PPARG variants, encoding dominant negative isoforms, are provided throughout differentiation by the promoter upstream noncoding exon A1.2, and (3) it almost exclusively transcribes γ3ORF4 rather than the canonical PPARG3. Therefore, such evidences suggest a relevant—if not exclusive—role of promoter of γ3ORF4 and PPARG3 variants in negative PPARγ regulation. In addition, protein analysis confirmed that after 2 days by differentiation induction PPARγ protein has a higher expression compared to undifferentiated and completely differentiated cells. Moreover, we observed the same trend of expression also for a shorter protein of 40 kDa, corresponding to the predicted weight of ORF4 isoform.Interestingly, the almost ubiquitous expressions of ORF4 variants in tissues and cells, as well as during adipogenesis, support the hypothesis that PPARG transcripts along human adipogenesis, they support the notion that this generegulates such crucial process through balancing the levels of its different splicing variants. Further studies— particularly taking into account PPARG protein products—are strictly required to definitely establish the role of all splicing variants in adipocyte differentiation. Notably, our results shed light on previously underestimated aspects of PPARG regulation and propose a yet unexplored role of its dominant negative isoforms during adipogenesis. Indeed, the finding that—during a crucial process in which PPARG is a “master gene”—both the transcripts and the proteins encoding dominant negative isoforms are constitutively expressed and/or can be modulated similarly to the canonical PPARG variants, enforces the need to investigate toward this direction. Understanding more about PPARG activity in the adipogenic process is directly linked to its possible contribution to the onset and progression of metabolism-related pathologies, including the metabolic syndrome and its complications.Although the results described herein represent only a starting point to understand the impact of PPARG dominant negative proteins in other human tissues may underlie their interesting roles in physiological processes as well as in other pathological conditions.Finally, we cannot exclude that the presence of transcripts encodingPairwise comparison of fold change variations for PPARG transcripts between two subsequent time points in hMSCs' differentiation into adipocytes.Click here for additional data file.


Candidatus Liberibacter asiaticus” strain A4, isolated from a mandarin citrus in Guangdong, People’s Republic of China, is reported. The A4 strain has a genome size of 1,208,625 bp, G+C content of 36.4%, 1,107 predicted open reading frames, and 53 RNA genes.The draft genome sequence of “ Candidatus Liberibacter asiaticus” is an unculturable alphaproteobacterium associated with citrus huanglongbing (HLB) (yellow shoot disease), a devastating disease in citrus production worldwide  G. Don.] in an insect-proof screen house on the campus of the South China Agricultural University in Guangzhou, China. The strain was originally collected from an HLB mandarin citrus (Citrus reticulata cultivar Shatangju) in Sihui City  of Guangdong Province and was transmitted to a healthy Shatangju plant through grafting in 2005. In 2007, “Candidatus Liberibacter asiaticus” was transmitted from citrus to periwinkle via dodder (Cuscuta campestris Yunck). For sequencing, DNA was extracted from infected periwinkle tissue using the cetyltrimethylammonium bromide (CTAB) method as previously described  with a mean of 249 bp per read were generated from the “Candidatus Liberibacter asiaticus”-enriched DNA sample. Using the whole-genome sequence of “Candidatus Liberibacter asiaticus” strain Psy62   (,204 bp (JFGQ00000000.This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession number