Coronary artery disease (CAD) is a leading cause of mortality in many countries. Considerable studies have been carried out to investigate the relationship between the C242T and A640G polymorphisms of CYBA gene and CAD, but the results were still inconsistent. Hence we conducted a meta-analysis to clarify the association.A total of 21 eligible literatures were included in the meta-analysis. We observed a significant decreased risk of CAD for C242T polymorphism in Asian population under an allelic model  and a dominant model , however, in overall population and other population no significant association was revealed. We also found A640G polymorphism may contribute to reducing CAD risk under an allelic model , dominant model  and recessive model . No publication bias was found.Our meta-analysis confirmed a protective effect of C242Tpolymorphism on CAD in Asian population and indicated that A640G polymorphism was significantly associated with decreased risk of CAD. Coronary artery disease (CAD), also namely ischemic heart disease (IHD) or coronary heart disease (CHD), mainly including angina and myocardial infarction (MI), is still a leading cause of mortality in many countries with three-fourths of global deaths due to CAD in the low- and middle-income countries Evidence over recent years has indicated that the predominant cellular source of superoxide anion in the context of cardiovascular diseases is the NADPH oxidase family in-vivo study P22-phox is encoded by the CYBA gene, which is located on chromosome 16q24 and consists of six exons and five introns Since Inoue first found T allele of the C242T polymorphism might have a protective effect against CAD An extensive literature searching of PubMed, EMBASE, ISI Web of Science and Chinese Wan Fang Data was performed for relevant articles without restricting language from June 1996 to May 2012, using the combinations of the keywords “Coronary artery disease”, “Coronary heart disease”, “ischemic heart disease”, “angina pectoris”, “myocardial infarction”, “Polymorphism”, “CYBA”, “P22phox”, “C242T”, “A640G”, “NADPH oxidase”. References of reviews and retrieved studies were also scanned and request to the author to access to the paper has been also tried. We conducted the meta-analysis and reported its results according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement .The following inclusion criteria had to be fulfilled: (1) case-control study or large-scale replication study assessing the association between C242T polymorphism, A640G polymorphism and CAD risk as an original study. (2) Numbers of case and control groups or available data for calculating genotypic OR with 95% CI. (3)The CAD cases defined as coronary heart disease (CHD), coronary artery disease (CAD), myocardial infarction (MI), acute myocardial infarction (AMI), or unstable angina (UA).All the data were extracted independently by two reviewers who reached a consensus on all of the items. Following information was extracted from the eligible literature: first author’s last name, year of publication, country, ethnic origin of the studied population, definition of cases, number in case and control groups, genotype distributions, male percentage and mean ages in case and control groups.Pooled effect was calculated for the allelic model, dominant model and recessive model in both C242T polymorphism and A640G polymorphism respectively.Q test I2P<0.05 for Q statistic. Then heterogeneity was qualified by 2I: 2I = 0%–30%, no or marginal between-study heterogeneity; 2I = 30%–75%, mild heterogeneity; 2I = 75%–100%, notable heterogeneity P<0.05), otherwise a random effect model (DerSimonian and Laird method) was adopted R software (version 15.3) and a probability value of P<0.05 was considered statistically significant.Heterogeneity among studies was assessed using The study selection process is shown in P values for heterogeneity. For both C242T polymorphism and A640G polymorphism, no significant association was observed under all the three genetic models. The results are presented in P = 0.0154, 2tau reducing from 0.0506 to 0.0335) and dominant model .The meta-regression results were presented at To explore the source of heterogeneity, a meta-regression analysis of C242T under the allelic model and the dominant model respectively was performed. We conducted a series univariate model by adding single covariates including ethnicity, publication year, disease, sex and age. In the univariate analysis, only the ethnicity can explain the between-study heterogeneity in allelic model (2I = 62.53%)  .This may 62.53%) . We willP for Egger’s test>0.05  The C242T polymorphism of CYBA gene provided significantly protective effect on CAD in Asian populations under an allelic model  and a dominant model , while no protective effect was found in Caucasian population and other population, just consistent with the results in Di Castelnuovo A, et al’s meta-analysis In a Japanese population Inoue For the first time this meta-analysis was conducted to explore the association between p22phox gene A640G polymorphism and CAD. And interestingly, we found a significant association between A640G and CAD under three genetic models after removing Reyes’s study In conclusion, this meta-analysis was a renewed and confirmed study to assess the association between C242T polymorphism and CAD. It’s the first time that a meta-analysis was conducted to summarize the relationship of A640G polymorphism and CAD. Our combined results implicated the p22phox gene C242T and A640G SNPs played a vital role in coronary artery disease, collectively confirming a genetic involvement of the two polymorphisms in CAD. Further large and well-designed studies will be needed to clarify the association of the polymorphisms and CAD risk. Additional meta-analysis based on GWAS data will also be essential in the future.Table S1The meta-regression analysis for heterogeneity under the allelic model and dominant model of p22phox gene C242T polymorphism.(DOC)Click here for additional data file.Table S2Sensitive analysis of pooled OR.(DOCX)Click here for additional data file.Checklist S1PRISMA 2009 Checklist.(DOCX)Click here for additional data file.


Discovering patterns from gene expression levels is regarded as a classification problem when tissue classes of the samples are given and solved as a discrete-data problem by discretizing the expression levels of each gene into intervals maximizing the interdependence between that gene and the class labels. However, when class information is unavailable, discovering gene expression patterns becomes difficult.For a gene pool with large number of genes, we first cluster the genes into smaller groups. In each group, we use the representative gene, one with highest interdependence with others in the group, to drive the discretization of the gene expression levels of other genes. Treating intervals as discrete events, association patterns of events can be discovered. If the gene groups obtained are crisp gene clusters, significant patterns overlapping different gene clusters cannot be found. This paper presents a new method of “fuzzifying” the crisp gene clusters to overcome such problem.To evaluate the effectiveness of our approach, we first apply the above described procedure on a synthetic data set and then a gene expression data set with known class labels. The class labels are not being used in both analyses but used later as the ground truth in a classificatory problem for assessing the algorithm’s effectiveness in fuzzy gene clustering and discretization. The results show the efficacy of the proposed method. The existence of correlation among continuous valued gene expression levels suggests that certain genes in the gene groups have high interdependence with other genes in the group. Fuzzification of a crisp gene cluster allows the cluster to take in genes from other clusters so that overlapping relationship among gene clusters could be uncovered. Hence, previously unknown hidden patterns resided in overlapping gene clusters are discovered. From the experimental results, the high order patterns discovered reveal multiple gene interaction patterns in cancerous tissues not found in normal tissues. It was also found that for the colon cancer experiment, 70% of the top patterns and most of the discriminative patterns between cancerous and normal tissues are among those spanning across different crisp gene clusters.We show that the proposed method for analyzing the error-prone microarray is effective even without the presence of tissue class information. A unified framework is presented, allowing fast and accurate pattern discovery for gene expression data. For a large gene set, to discover a comprehensive set of patterns, gene clustering, gene expression discretization and gene cluster fuzzification are absolutely necessary. In the recent years, with the progress of microarray technology, the parallel execution of microarray experiments on a very large set of genes accelerates investigations in many ways. Microarray chips are used to calibrate changes in gene expression levels and for classifying gene groups. Their data represented by expression tables consist of rows of genes and columns of samples. Many potential applications of gene expression data analysis such as medical diagnosis, gene function prediction, cancer classification, etc as technology progresses  are becoIn microarray gene expression data analysis, many approaches have been proposed. They can be grouped into supervised approach -5 and unMixed-Mode Attribute Clustering Algorithm was proposed for discovering patterns on large mixed-mode data sets without relying on prior classificatory knowledge. A mixed-mode data set is referred to one that contains numeric, symbolic or categorical data. MACA can also be applied to continuous valued data by converting the continuous data into interval events. Hence, it brings out the association patterns among genes explicitly. To apply this to gene expression data analysis, each gene is considered as an attribute and each sample a tuple. Thus, for a very large data set, we will apply MACA which maximizes the interdependence among attributes (genes) within attribute clusters (groups) , yG = , zG = } with an adjusted residual of a certain value. It is interpreted as a 3rd-order pattern containing statistically significant associations of 3 primary events - xG = , yG = , zG =  from 3 attributes - xG, yG and zG. If the association pattern is conditioned by the class attribute, it can be treated as a classification rule  and yG = } then {“Normal”} with a weight of evidence of a certain value.In this phase, pattern discovery  method fsumption . The adjpendence . An examion rule , i.e. ifn theory  is used 1A, …, 20. AA1 and A2 are discrete attributes which can take on a value from alphabets {“T”, “F”}. A3, A4, and A5 are discrete attributes taking on a value from alphabets {“X”, “Y”, “Z”}. A6, …, A20 are continuous attributes taking on values in {0 ≤ ℜ ≤ 1} where ℜ is a real number.The synthetic data set is designed to show the necessity and the capability of our proposed method. It is composed of 20 attributes: 5 discrete and 15 continuous  >μAC2(A6). A1 and A13 is the mode of attribute cluster 1, AC1, and attribute cluster 2, AC2, respectively. The attribute values are generated in the following manner.Each tuple is pre-classified into 1 of the 5 classes: s Figure  for defi2A: “T” if A13 < 0.2; “F”, otherwise.A3: “X” if A13 < 0.2; “Y” if 0.2 ≤ A13 < 0.4; “Z”, otherwise.A4: “X” if A6 < 0.3; “Y” if 0.3 ≤ A6 < 0.6; “Z”, otherwise.A5: “Y” if A6 < 0.2; “Z” if 0.2 ≤ A6 < 0.4; “X”, otherwise.6A: uniformly distributed within  if A1 = “T” and A13 < 0.5; uniformly distributed within  if A13 < 0.3; uniformly distributed within , otherwise.A18-20: uniformly distributed within  if 0.3 ≤ A13 < 0.6; uniformly distributed within [0, 0.3), otherwise.C1, …, C5 contain 500, 300, 300, 400 and 300 tuples respectively. For practicality, 25% noise is added to the data by replacing 450 tuples with random values. We first use Mixed Mode ACA (MACA) to obtain attribute clusters, cluster modes and optimal cluster configuration k in order to reveal the correlated relationship. Then, in order to reveal the overlapping relationship, we use Fuzzy Mixed Mode ACA (FMACA), with fuzzification parameter f = 1.5 to obtain the degree of membership of each attribute iA to each attribute cluster jAC,.In our experiment, 1800 tuples of mixed mode attributes are generated. A4, A5 and A6) are indeed overlapping with AC1, AC2 and AC3 with different degrees of membership (μ1(A4) = 8.9602%, μ1(A5) = 2.4429%, μ1(A6) = 0.3961%, μ2(A4) = 0.0005%, μ2(A5) = 0.0031%, μ2(A6) = 0.0001%, μ3(A4) = 91.0393%, μ3(A5) = 97.5540%, μ3(A5) = 99.6037%).As shown in Table From this experiment and result, we realize that MACA is able to handle mixed mode data for effectively grouping of correlated attributes while FMACA, in addition, uncovers the overlapping relationship of each attribute to different attribute groups.We next apply FMACA to a colon-cancer gene expression data set. The colon-cancer data set  . As our FMACA supports mixed mode data, it is unnecessary to discretize the continuous data initially. As expected, FMACA found 7 optimal gene groups, the same result as reported by . The resIn the second experimental phase, we first discretize each mode into 3 intervals by entropy maximization and then discretize the other genes by OCDD. After all genes are discretized, we put back the tissue class labels as an attribute to the data set. This preprocessed data, which is processed in an unsupervised manner, is trained by popular classification methods for building classifiers. We compare our results with those reported in .The classification rate using 1) C5.0 and 2) our pattern discovery with data preprocessed by ours is 85.48% and 91.94% respectively while those as reported by , which pTo show the transparency of our system, we here provide some patterns and rules for reference and further discussion. Top 10 patterns and rules are shown in Figure Among the top 10 patterns and rules, we observe that some are composed of genes spanning across different gene groups. It reflects the usefulness of gene cluster fuzzification. Without it, some significant patterns will not be uncovered. For instance, 7 patterns out of the top 10 contain genes from different gene groups. From the rules discovered, we also believe that genes spanning across gene groups are important. In Figure We have shown that the proposed method for analyzing the error-prone microarray is effective even without the presence of tissue class information. Here we would like to highlight several key points stressing the significance of our proposed methodology. First, even without class information, our method is effective for analyzing the error-prone microarray data. Second, the existence of correlation among continuous valued gene expression levels suggests members in the gene groups have high interdependence. Third, overlapping relationship among attribute clusters could be uncovered through cluster fuzzification. Forth, previously unknown hidden patterns residing in overlapping attribute clusters can be discovered in the fuzzy attribute clusters. Furthermore, the discovered high order patterns reveal multiple gene interaction patterns in cancerous tissues not found in normal tissues.From the experimental results, we observe that to discovery a comprehensive set of patterns for a large gene set, gene clustering, gene expression discretization and gene cluster fuzzification are absolutely necessary. Attribute clustering enables us to partition a large gene set (2000 for colon cancers) into correlated subsets, making selection of representative genes from each subset more meaningful and effective. Discovering patterns from fuzzy attribute clusters allows us to find those patterns spanning across different crisp attribute groups. As revealed in our colon cancer data experiment, without fuzzification, we may miss 70% of the significant patterns spanning across gene groups and also the high order patterns associated with different tissue classes. In conclusion, this paper renders a unified framework which allows fast and accurate pattern discovery for gene expression data - an important computational step closer to meeting the challenge of discovering new biological knowledge from biological data.R: Interdependence redundancy measure; MR: Multiple interdependence redundancy measure; SR: Sum of multiple significant interdependence redundancy measure; ACA: Attribute clustering algorithm; MACA: Mixed-mode attribute clustering algorithm; FMACA: Fuzzy mixed-mode attribute clustering algorithm.The authors declare that they have no competing interests.GPKW carried out the pattern discovery studies, participated in the experiments and drafted the manuscript. KCCC conceived of the study and participated in its design. AKCW participated in the design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Although adverse childhood experiences (ACEs) have previously been demonstrated to be adversely associated with a variety of health outcomes in adulthood, their specific association with sleep among adults has not been examined. To better address this issue, this study examines the relationship between eight self-reported ACEs and frequent insufficient sleep among community-dwelling adults residing in 5 U.S. states in 2009.To assess whether ACEs were associated with frequent insufficient sleep (respondent did not get sufficient rest or sleep ≥14 days in past 30 days) in adulthood, we analyzed ACE data collected in the 2009 Behavioral Risk Factor Surveillance System, a random-digit-dialed telephone survey in Arkansas, Louisiana, New Mexico, Tennessee, and Washington. ACEs included physical abuse, sexual abuse, verbal abuse, household mental illness, incarcerated household members, household substance abuse, parental separation/divorce, and witnessing domestic violence before age 18. Smoking status and frequent mental distress (FMD)  were assessed as potential mediators in multivariate logistic regression analyses of frequent insufficient sleep by ACEs adjusted for race/ethnicity, gender, education, and body mass index.Overall, 28.8% of 25,810 respondents reported frequent insufficient sleep, 18.8% were current smokers, 10.8% reported frequent mental distress, 59.5% percent reported ≥1 ACE, and 8.7% reported ≥ 5 ACEs. Each ACE was associated with frequent insufficient sleep in multivariate analyses. Odds of frequent insufficient sleep were 2.5  times higher in persons with ≥5 ACEs compared to those with no ACEs. Most relationships were modestly attenuated by smoking and FMD, but remained significant.Childhood exposures to eight indicators of child maltreatment and household dysfunction were significantly associated with frequent insufficient sleep during adulthood in this population. ACEs could be potential indicators promoting further investigation of sleep insufficiency, along with consideration of FMD and smoking. Adverse childhood experiences (ACEs) have been associated with a variety of negative behavioral and health outcomes in adulthood. SpecifiFurthermore, ACEs have been demonstrated to be highly interrelated to each other. Using data obtained from 8,629 members of a managed healthcare plan who completed a survey about exposure to ACEs, Dong et al. found thThe effects of ACEs continue long after their initial occurrence. Anda et al. examinedLikewise, sleep is another event posing serious consequences to health and well-being. It has been estimated that 28% of adults suffer from frequent sleep insufficiency – defined as 14 or more days out of the past 30 that the respondent reported that they did not get enough rest or sleep, insuffiNotably, ACEs have been linked to sleep disturbances among adults. In a retrospective cohort study of health maintenance organization members in California which examined respondents’ reports of ever having trouble falling or staying asleep and feeling tired even after a good night’s sleep, Chapman et al. found thBoth smoking,15 and fData were obtained from the 2009 Behavioral Risk Factor Surveillance Survey (BRFSS), a state-based random-digit dialed telephone survey of non-institutionalized U.S. adults aged 18 years and older. Trained interviewers administered standardized questionnaires to a sample of households with landline telephones. Five states  administered a supplemental ACE survey question module as part of the BRFSS survey. In the five states investigated, cooperation rates – the proportion of all respondents interviewed -- ranged from 70.1% (Washington) to 78.8% (Arkansas). The BRFThe outcome variable of interest, frequent insufficient sleep, was defined as a response of ≥14 days to the following question, “During the past 30 days, for about how many days have you felt you did not get enough rest or sleep?”The eleven questions included about ACEs were adopted from a longer set of ACE-related questions featured in a study, conducted jointly by the Kaiser Permanente Health Appraisal Clinic in San Diego, CA and the Centers for Disease Control and Prevention, to assess associations between childhood maltreatment and well-being later in life. QuestioVerbal abuse was defined as a response of “more than once” to the question “How often did a parent or adult in your home ever swear at you, insult you, or put you down?”. Physical abuse was defined as a response of either “once” or “more than once” to the question “How often did your parents or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.” A response of either “once” or “more than once” to any of the following three questions defined sexual abuse: “How often did anyone at least 5 years older than you or an adult, ever touch you sexually?”, “How often did anyone at least 5 years older than you or an adult try to make you touch them sexually?” or “How often did anyone at least 5 years older than you or an adult force you to have sex?”Affirmative responses to questions about living with a household member who “was depressed, mentally ill, or suicidal” or who “served time or was sentenced to serve time in prison, jail, or other correctional facility” defined two respective household dysfunction variables. Living with a household substance abuser was defined as a “yes” response to at least one of two questions about living with anyone who “was a problem drinker or alcoholic” or “used illegal street drugs or abused prescription medications.” Having “parents who were separated or divorced” was defined by a “yes” response to a question asking about that as opposed to answering “no,” or “parents not married.” A response of “once” or “more than once” to the question “How often did your parents or adults in your home ever slap, hit, kick, punch, or beat each other up” defined witnessing domestic violence.The ACE score was created by summing the total number of ACE categories which elicited positive responses (range: 0-8). ACE scores of 5 or more were combined into one category (≥5) due to small sample sizes. Six categories of ACE score  were featured in analyses, with zero experiences serving as the referent. Specifically, ≥5 ACEs were combined into a single category to obtain adequate statistical power and because of the consistently graded relationship observed between frequent insufficient sleep and high (≥5) ACE score (data not shown).Smoking status was defined as current smokers (smoked ≥100 cigarettes in the past and were smoking every day or some days now), former smokers , or non-smokers (those who never smoked). Respondents were asked to report their perceived mental health in a question gauging frequent mental distress (FMD). Respondents were considered as having FMD if they answered ≥14 days to the question, “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”2  were examined as covariates in the association between ACEs and frequent insufficient sleep.Gender, age in years , race/ethnicity , education , and body mass index in kg/mno mediator - OR with mediator )/(OR no mediator -1)] * 100 * 100,20. To b)] * 100 only if )] * 100.All analyses were conducted using SAS-callable SUDAAN to account for the complex study design, with a statistical significance level of p<0.05.The study population was comprised of 16,474 women (52%) and 9,336 men (48%) from five U.S. states (Table The majority of respondents reported ≥1 ACE (59.5%) and 8.7% reported ≥5 ACEs. Specifically, the most frequently reported ACEs were having lived with a substance abusing household member (29.1%), parental separation or divorce (26.6%), and exposure to verbal abuse (26.1%) during childhood . Frequent insufficient sleep was also higher among those reporting FMD than among those who did not  (Table Current smoking and FMD were significantly higher (p <0.05) among persons with each individual ACE than among those without that ACE Table . SmokingRespondents reporting each individual ACE had a significantly greater prevalence of frequent sleep insufficiency relative to respondents who indicated they had not been exposed (p <0.05) Table . SimilarThe effects of variables potentially mediating the relationship between ACEs and frequent sleep insufficiency are presented in Table Similarly, Model 3 includes all covariates featured in Model 1, while additionally adjusting for FMD. Further attenuation of many of the ACEs is observed, which is consistent with partial mediation of the relationship between ACEs and frequent insufficient sleep. However, frequent insufficient sleep was no longer significant for the associations with an incarcerated household member (AOR=1.23[0.95-1.60]), or parental divorce/separation (AOR=1.14[0.99-1.31]) with the addition of FMD to the model. This suggests that FMD exerted a greater effect on frequent sleep insufficiency than these two ACEs. A graded -- albeit further attenuated -- relationship was observed between the ACE score and frequent sleep insufficiency: AOR for 1 ACE=1.21 (1.04-1.41), while AOR for ≥5 ACEs=1.80 (1.46-2.21) compared to persons with no ACEs  between the Association for Prevention Teaching and Research and the Centers for Disease Control and Prevention. This project was supported, in part, by cooperative agreement U58/CCU324336-05 between the National Association of Chronic Disease Directors and the Centers for Disease Control and Prevention.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/13/3/prepub


Diet-induced obesity (DIO) is a significant health concern which has been linked to structural and functional changes in the gut microbiota. Exercise (Ex) is effective in preventing obesity, but whether Ex alters the gut microbiota during development with high fat (HF) feeding is unknown.Determine the effects of voluntary Ex on the gastrointestinal microbiota in LF-fed mice and in HF-DIO.Male C57BL/6 littermates (5 weeks) were distributed equally into 4 groups: low fat (LF) sedentary (Sed) LF/Sed, LF/Ex, HF/Sed and HF/Ex. Mice were individually housed and LF/Ex and HF/Ex cages were equipped with a wheel and odometer to record Ex. Fecal samples were collected at baseline, 6 weeks and 12 weeks and used for bacterial DNA isolation. DNA was subjected both to quantitative PCR using primers specific to the 16S rRNA encoding genes for Bacteroidetes and Firmicutes and to sequencing for lower taxonomic identification using the Illumina MiSeq platform. Data were analyzed using a one or two-way ANOVA or Pearson correlation.2 = 0.35, p = 0.043).HF diet resulted in significantly greater body weight and adiposity as well as decreased glucose tolerance that were prevented by voluntary Ex (p<0.05). Visualization of Unifrac distance data with principal coordinates analysis indicated clustering by both diet and Ex at week 12. Sequencing demonstrated Ex-induced changes in the percentage of major bacterial phyla at 12 weeks. A correlation between total Ex distance and the ΔCt Bacteroidetes: ΔCt Firmicutes ratio from qPCR demonstrated a significant inverse correlation (rEx induces a unique shift in the gut microbiota that is different from dietary effects. Microbiota changes may play a role in Ex prevention of HF-DIO. Approximately 35% of adults Recent research indicates that the gut microbiota induces the development of obesity in both humans While the relationship between diet-induced changes in gut microbiota may be model dependent, “microbiota transplant” models using germ-free mice demonstrated that gut microbes from obese mice have a direct effect in increasing fat mass and body weight compared to introduction of microbes from lean mice Exercise (Ex) is known to confer numerous health-related benefits. Recent studies indicate that Ex can normalize body weight, body fat, hyperglycemia and markers of inflammation in mouse models of diet-induced obesity (DIO) n-butyrate concentration as a consequence of a corresponding change in the n-butyrate producing bacteria compared to sedentary rats. More recently, Choi et al. examined the effect of Ex on polychlorinated biphenyl metabolism and the gut microbiota. They found that Ex altered the gut microbiota in normal chow-fed mice, predominantly affecting the families Erysipelotrichaceae and EnterococcaceaeFew studies have specifically examined the effect of Ex on the gut microbiota. An early study by Matsumoto et al. examined the effect of chronic, load-bearing Ex on cecal microbiota in a non-obese rat model In this study, we tested the hypothesis that voluntary Ex would reshape the gut microbiota in both LF and HF-fed mice and that Ex would normalize the gut microbiota in HF mice at the phylum level, in addition to mitigating the development of HF-DIO. Our results indicate that HF diets do indeed promote increased fatness, hyperglycemia and an altered gut microbiota profile compared to LF fed mice. Voluntary Ex prevented these changes in HF mice and resulted in restoration of the phylotypic profile observed with LF feeding. There was a significant correlation between the distance run and alterations in the major gut bacterial phyla for the Ex mice. In addition, differences were also observed at the sub-phylum level between the LF and HF groups with Ex. These findings may offer new insights into the beneficial effect of regular Ex in preventing DIO by modifying the bacterial balance of the gut microbiota.ad libitum.All experimental protocols were approved by the Midwestern University Institutional Animal Care and Use Committee (IACUC) and all procedures were performed in accordance with Midwestern University's guidelines for the care and use of laboratory animals. Five week old, male C57BL/6 mice (n = 48)  were received as littermates (15–19 g) and allowed to acclimate in Midwestern University's animal facility for 1 week prior to their random assignment to one of 4 diet and activity treatment groups. During the acclimation period, mice were kept in their respective litters and provided standard chow  and reverse osmosis water Following the acclimation phase, mice were randomly assigned to diet and activity groups based on equal distribution of littermates . Dietary intake included either high fat diet  or low fat diet . Mice were individually housed in rat cages in the absence  or presence  of a non-load bearing 14.6 cm hamster wheel  suspended from a metal rod based on a design reported by Konhilas et al. Littermates were separated into groups as follows: LF/Sed, n = 12, LF/Ex, n = 10, HF/Sed, n = 14, and HF/Ex, n = 12. Two separate batches of mice were used: an initial batch of mice were studied for preliminary indications of a global microbiota shift ) and measurement of gut transit  and a second batch for quantitative PCR (qPCR) and sequencing studies . All mice were followed on the protocol for 12 weeks for collection of fecal samples and then an additional two weeks for collection of blood samples and harvesting of tissues.Initial, weekly and final body weights were determined for all mice. Twenty-four hour food intake was measured at week 12 of the experimental period. For these measurements, mice were placed in cages without bedding and each mouse was given one pre-weighed pellet of their respective diet, either LF or HF food, in a ceramic dish designed to minimize food loss or contamination by feces. After 24 hours, the pellet and its remnants were weighed to determine food consumption. Food intake was expressed as kcal/24 hours.Fasting blood glucose (FBG) and oral glucose tolerance (OGT) were measured at 13 weeks on the diet and activity program. Mice were fasted overnight in cages without bedding prior to measuring tail vein blood glucose levels using a OneTouch UltraMini® monitor . Oral glucose tolerance was measured immediately following the FBG test. Mice were gavaged with 25% glucose in saline (1 g/kg body weight) followed by blood glucose measurements at 15, 30, 60 and 120 minutes post glucose challenge 2 and then subjected to pneumothorax. Heart and soleus muscles were removed, rinsed several times, and blotted dry to determine their respective wet weights. Epididymal fat pads were excised, rinsed, blotted dry, weighed, and used as indicators of adipose tissue accumulation.At the end of week 13 of the protocol, mice were anesthetized with COad libitum. At 30 minutes prior to euthanasia, 0.1 ml of 0.5 mmol/L 70 kDa fluorescein isothiocyanate (FITC)–dextran  was administered by oral gavage Measurement of intestinal transit was performed as a terminal experiment. Mice were fasted for 16 h with water available Extraction of DNA from fecal bacteria followed the protocol of Wang et al. 5′-AGAGTT TGATCCTGGCTCAG-3′) with a 6′ carboxyfluorescein (6-FAM) label and 1492R (5′-GGTTACCTTGTTACGACTT-3′). Each 50 μl PCR reaction contained 50 ng of DNA, 5.0 μl 10× Taq Buffer, 4.0 μl dNTP mixture (2.5 mM each), 0.25 μl TaKaRa Ex Taq , 1 μl forward primer and 1 μl reverse primer (10 pmol/μl each). PCR was run for 30 cycles using the thermal profiles of: 1 min at 94°C; 30 cycles of ; and a final extension period of 6 min at 72°C. PCR products were verified by electrophoresis and visualization of a band at ∼1400 bp. Upon confirmation, the remaining PCR products were purified and stored at −80°C.The T-RFLP analysis was performed as previously described MspI enzyme (New England Biolabs Inc.) in a total volume of 15 μl  at 37°C for 3 hours. To enhance the fluorescent signal, each digested T-RFLP sample was desalted by single drop analysis. Digested samples were desalted, mixed with deionized formamide and 0.5 μl GeneScan-500 size standard (Applied Biosystems) followed by capillary electrophoresis . Fragment length and abundance were determined using GeneMapper software . Raw electropherograms were collected and analyzed for artifacts. To visualize relationships between the samples, sample distance values were analyzed by T-RFLP Analysis EXpedited (T-REX) and plotted using principal coordinates analysis (PCA) Purified 16S rDNA was digested using 20 U The two gut bacterial phyla of interest, Bacteroidetes and Firmicutes, were targeted by modifying a quantitative PCR assay developed by Armougom et al. using primer and probe sets that recognized a conserved region of the 16S rRNA encoding gene unique to these taxonomic groups The assay was performed on the ABI-PRISM 7700 Sequence Detection System . The methods on which this assay was based have been published previously Microbial sequencing was performed on the MiSeq Illumina platform  as previously described by Caporaso et al. Bioinformatic analysis of sequencing data was conducted using the QIIME 1.5.0 software suite http://metagenomics.anl.gov/linkin.cgi?project=7038) based at Argonne National Laboratory, including instant availability of the sequence data, bioinformatic analyses and tools.The data are publicly available via NCBI GenBank , a Pearson's r for correlation, a one-way ANOVA for qPCR data  and a two-way ANOVA for all other data comparisons on Prism 5  and SPSS 18  software. Two-way ANOVA results were reported as the F-statistics and p values and a Sidak post-hoc test was used for within group comparisons. To determine the significance of microbial community partitioning due to experimental treatments, permutational multivariate analysis of variance (PERMANOVA) was employed to generate p-values based on the observed Unifrac distance matrix relative to 10,000 randomly rearranged distance matrices. For display purposes only, the effect of exercise on the qPCR fold change in Bacteroidetes and Firmicutes in LF/Ex and HF/Ex mice is shown by the 2Mice in the Ex groups (LF/Ex and HF/Ex) spontaneously ran on the cage-mounted wheel and averaged 82±3.9 km/week and 89±7.9 km/week for the LF/Ex and HF/Ex groups, respectively p>0.05; . By the Metabolically, the HF/Sed mice demonstrated elevated body fat and altered glucose metabolism. Body fat, as measured by epididymal fat pad weight , was eleTwenty-four hour food intake, measured during week 13 of the protocol, was elevated by Ex  = 5.31, p = 0.032), but not diet  = 0.008, p = 0.93), with Ex influencing food intake of LF and HF mice similarly  = 2.17, p = 0.15; Diet and Ex did not significantly influence gastrointestinal transit under fasting conditions. Based on the geometric center measurements reported in Characterization of changes in the gut bacterial community as a consequence of diet and Ex was examined in three ways using bacterial 16S rRNA encoding genes present in the fecal pellets: terminal restriction fragment length polymorphism (T-RFLP), sequencing, and quantitative PCR (qPCR).T-RFLP was used to determine whether there was a global shift in the gut microbial community after the 12 week diet and activity protocol using the 16S rRNA encoding genes present in the fecal pellets. Subjective analysis of raw electropherograms from T-RFLP analysis demonstrated visible changes in the abundance and distribution of community groups as a result of diet and activity . PrincipCloser analysis of bacterial differences induced by diet and Ex were determined by sequencing the 16S rRNA encoding genes present in the fecal pellets. Principal coordinates analysis of Illumina MiSeq amplicon data demonstrated significant clustering at week 0 based on litter , which wSix different bacterial phyla were identified in the MiSeq 16S rRNA based amplicon libraries at the start and completion of the diet and Ex protocol . While there were no differences at baseline (data not shown), there were measureable differences in the percentage of these bacterial phyla among the 4 diet and activity groups by week 12 . For the−ΔΔCt values . As expected, there was no relationship between the data points determined for week 0 .As a test of the criterion validity of qPCR, a comparison was made between the qPCR and sequencing data. The ΔCt Bacteroidetes:ΔCt Firmicutes ratio determined by q-PCR and %Bacteroidetes:%Firmicutes ratio from sequencing was compared using a Pearson test on all data points from weeks 6 and 12. As shown in 2 = 0.35) based on a Pearson test .To validate the activity effect on bacterial balance, total Ex distance was correlated to the resultant ΔCt ratio of Bacteroidetes:Firmicutes determined using qPCR . The ratFamily Level Analysis. Family-level changes within the major phyla induced by the diet and Ex protocol were revealed by the sequencing data. Within the Firmicutes phylum, there were significant changes in the classes Clostridia, Bacilli, and Erysipelotrichi  = 13.76, p = 0.001), Lachnospiraceae  = 56.7, p<0.001) and Ruminococcaceae  = 37.38, p<0.001) in Sed and Ex mice. Ex significantly elevated the content of Lachnospiraceae  = 7.91, p = 0.011) and Ruminococcaceae  = 14.91, p = 0.001), with a tendency for an effect on Clostridiacea  = 3.17, p = 0.090). An interaction between diet and Ex was determined for Clostridiaceae  = 6.54, p = 0.019; LF/Ex compared to HF/Ex, p = 0.0015) and Ruminococcaceae  = 5.05, p = 0.036; activity had a greater effect in the HF fed mice compared to the LF mice). Lachnospiraceae had no interaction effect  = 1.72, p = 0.20).lotrichi . Three foccaceae . High faLactobacillaceae and Turicibacteraceae  = 11.27, p = 0.003), but there was no effect of diet  = 0.16, p = 0.69), although a trend for an interaction was detected  = 4.14, p = 0.055). HF/Sed mice demonstrated a bloom in Lactobacillaceae relative to the other treatment groups. Turicibacteraceae were significantly reduced by Ex in both LF and HF mice. High fat consumption decreased the percentage of Turicibacteraceae in the Sed and Ex groups  = 17.07, p<0.001; activity F = 5.20, p = 0.034), with a trend for an interaction effect  = 3.23, p = 0.087).The percentages of two different families within the class Bacilli were altered by the diet and activity protocol: teraceae . Ex decrErysipelotrichaceae was reduced by both HF diet and Ex  = 17.15, p<0.001; activity F = 6.17, p = 0.022), but Ex only exerted an effect in the LF fed mice  = 5.64, p = 0.03; p = 0.016 LF/Sed greater than LF/Ex).A third class of Firmicutes, Erysipelotrichi, was also altered by diet and activity . Within S24-7, S24-7  = 5.89, p<0.025), but Ex increased it  = 5.63, p = 0.028) equally in LF and HF mice  = 0.204, p = 0.66). Differences in the percentage of the phylum Actinobacteria based on the diet and activity protocol could be explained by differences in only one class (Actinobacteria), one order  and one family  = 6.45, p = 0.02; activity F = 4.86, p = 0.039), with Ex affecting only the LF condition  = 4.86, p = 0.039). Within the phylum Proteobacteria, a family in the Deltaproteobacteria class, Desulfovibrionaceae, demonstrated a trend toward a diet effect (Differences in the percentage of the phylum Bacteroidetes based on the diet and activity protocol could be explained primarily by differences in one class (Bacteroidia) and one family S24-7, . High fat effect   elevated levels of Firmicutes, Tenericutes, Proteobacteria, and Actinobacteria relative to the LF controls S24-7. High-fat sedentary mice had the lowest level of S24-7 family compared to the other treatment groups and the level was elevated by Ex. Exercise also led to an increase in butyrate-producing Firmicutes in fecal samples: Clostridiaceae, Lachnospiraceae and Ruminococcaceae. These bacteria are all members of Clostridiales order and are also linked to butyrate production n-butyrate in non-obese rats In the current study, Ex significantly increased the relative proportion of butyrate-producing bacteria such as Bacteroidales Lactobacillaceae in the HF mice, dropping it from greater than 20% in the HF/Sed mice to 0.35% in the HF/Ex mice. This dramatic reduction with Ex suggests an important role for Lactobacillaceae in sedentary lifestyle-associated weight gain. Lactobacilli are often used as probiotic agents. Depending on the Lactobacillus species and experimental model, the reported relationship between detectable Lactobacillus spp and the effects of Lactobacillus on weight gain and obesity can be quite different. Lactobacillus spp are reported to be elevated in obese children Lactobacillus reuteri is elevated, but L. paracasei and L. plantarum are reported to be reduced Lactobacillus spp given as probiotic therapy have dose-dependent effects with regards to weight changes L. plantarum has been reported to improve glucose sensitivity in response to a HF diet Exercise also reduced the percentage of the family The central questions raised by this study are, how does Ex alter the gut microbiota and does this contribute to the prevention of HF-DIO? Whether Ex operates to prevent HF-DIO through direct changes in the gut microbiota is an intriguing question because the answer could lead to new probiotics or prebiotics that provide for some of the benefits of Ex. The presence of a gut microbiota is known to help break down ingested food and likely allows for additional nutrient extraction not available to a germ free host Three factors determine the bacterial production of SCFA in the colon: the gut microbiota, availability of substrates, and intestinal transit time The amount of nutrients ingested could also impact bacterial content. In this study, Ex increased 24-hour caloric intake compared to Sed mice. Thus, microbial changes detected in the HF/Ex group beyond that of the HF/Sed could be related to even greater fat intake compared to HF/Sed or to an independent effect of Ex. A recent study by Simoes et al. demonstrated a change in the proportion of Bacteroidetes in monozygotic twins who were discordant with respect to diet An Ex-induced gut microbiota may increase fatty acid oxidation and utilization or decrease fat storage in the intestine. Exercise up-regulates AMP-activated protein kinase (AMPK) Another way in which Ex could exert a potentially beneficial effect through the gut microbiota is by suppressing inflammation. Exercise might prevent HF-DIO by blocking HF feeding-associated inflammation. Obesity and HF diets are linked to increased markers of endotoxemia and inflammation 2 = 0.33). This may be due to differences in the two techniques, including the methods of quantification (cycle number versus database-matching), universal primer sets used, and the specificity and inclusiveness of sequencing compared to qPCR. However, it can be seen in the One of the limitations of this study is that bacterial changes were only measured in fecal samples. Differences in the composition of the gut microbiota have been shown as one moves from the epithelial surface out into the lumen of the intestine Lactobacillaceae, Lachnospiraceae, and Ruminococcaceae and a decrease in the major constituent group of Bacteroidetes, S24-7. Exercise in the HF condition prevented some of the dietary changes, such as with Lactobacillaceae and S24-7, but intensified the blooms seen with HF feeding in others such as with Lachnospiraceae, and Ruminococcaceae. Further exploration of the gut microbiota changes induced by Ex may allow for exploitation of this effect and the development of treatments for obesity and dysbiosis associated with high fat intake.Exercise alters the gut microbiota in mice on both a LF and HF diet and normalizes major phylum-level changes for mice on the HF diet; furthermore, the volume of exercise  inversely correlates with the Bacteroidetes:Firmicutes ratio. The Ex-induced gut microbiota in both LF and HF conditions are different than their sedentary counterparts and yet, also different from each other. At the taxonomic level of bacterial family, HF diet induced blooms in some of the major constituent groups within Firmicutes such as Figure S1Identification of Amplicon Size for Bacteroidetes, Firmicutes and the Universal Primer Sets.(PDF)Click here for additional data file.Figure S2Diet and Activity Changed Bacterial Profiles Determined by T-RFLP and PCA.(PDF)Click here for additional data file.Figure S3Dendrogram Analysis of Diet and Activity Effects.(PDF)Click here for additional data file.Figure S4Diet and Activity Altered Major and Minor Bacterial Phyla.(PDF)Click here for additional data file.Figure S5Changes in the Bacteroidetes:Firmicutes Ratio Determined by qPCR and Sequencing.(PDF)Click here for additional data file.Table S1Primer and probe sets for Bacteroidetes and Firmicutes qPCR.(PDF)Click here for additional data file.Table S2Effect of diet and exercise on bacterial families with total contributions between 0% and 1.25%.(PDF)Click here for additional data file.File S1Commands used in the analysis of the sequencing dataset.(PDF)Click here for additional data file.


Mb). Here we investigate seasonal differences in the jump take-off performance of high Arctic Svalbard rock ptarmigan (Lagopus muta hyperborea) resulting from around a 50% increase in Mb during winter as a result of fat deposition. Using force-plate data and videography, we reveal that, in the absence of alterations to take-off angle, winter Svalbard rock ptarmigan are unable to increase hind-limb power output during jumping to compensate for their increased Mb. As a result, peak take-off velocity is reduced by 42% and jump duration is also extended during winter. The consequences of reduced jumping performance upon Svalbard ptarmigan during winter may be relatively small given their low risk of predation during this season. It may be, however, that the observed reduction in jumping performance when fat may contribute to the sub-maximal pattern of fat acquisition observed in other bird species.Fat storage is essential to the survival of many bird species, providing energy reserves, but can have an effect on locomotor performance with an associated potential increase in predation risk. In particular, the ability to initiate flight through jumping is critical to predator avoidance and may be influenced by changes in body mass ( Mb)  . The relMb) . Energy Mb) . An incrMb) . UnderstTringa totanus) suffer 83% higher mortality when they are unable to get airborne before attack from sparrowhawks (Accipiter nisus)  adding loads of between 7 and 14% of Mb resulted in a reduced take-off angle, allowing maintenance of velocity  (Taeniopygia guttata) maintained take-off angle, but kept energy expenditure at pre-loaded levels by reducing velocity, when carrying loads of 27% of their Mb (Sylvia atricapilla) decreased take-off angle and velocity in response to 60% increases in Mb (Poecile montanus), great tits (Parus major), yellowhammers (Emberiza citrinella) and greenfinches (Carduelis chloris) with 7–8% increases in Mb were unaffected (Erithacus rubecula) with intermediate Mb increases (27%) were able to reduce take-off angle in order to maintain velocity , coupled with differences in the calculation of performance indicators.Jumping is an important part of the locomotor repertoire for birds, as they rely on it to become airborne. Furthermore, this behaviour forms the primary escape response of most avian species from aerial and terrestrial predators, particularly when the flight is initiated from the ground. The maintenance of effective jumping performance is therefore critical to survival . For exar nisus) . Howeverr nisus) . It is tr nisus) . Additioelevated . Empiriccreased) . Converstheir Mb . Some stes in Mb , whereasaffected . Europeavelocity , similarvelocity  but not velocity  starlingNumida meleagris) are capable of exceptional take-off performance, with the legs producing peak vertical forces of 5.3 times body weight during jumps (Coturnix coturnix) and European starlings produce forces of 7.8 and 4 times body weight, respectively, yielding 80–90% of the velocity required for ground take-off  is a ground-dwelling phasianid bird resident on the Arctic archipelago of Svalbard. It is non-migratory and individuals undergo marked seasonal variations in Mb. During summer, birds weigh around 500 g. Prior to the onset of winter, however, Mb rapidly increases, and may double by mid November , serving as an energy source during times of limited food availability . After the initial crouch the sternum ascended (marking the start of the jump), moving forwards and upwards as the legs extended  during summer (110±1 ms) and during winter (110±4 ms)  during summer (102±2 ms) than winter (125±3 ms). Therefore, the downstroke occurred after toe-off during summer and prior to toe-off during the winter. The horizontal and vertical paths of the sternum were similar throughout the duration of the jump during both seasons, validating the method for maintaining a similar take-off angle .During both seasons the mechanics of the jumps were the same, with the birds briefly adopting a crouched position immediately before the jump itself. Although the duration of this crouch appeared to be higher and more variable during summer (160±50 ms) than winter (100±9 ms), a clear difference was not detectable (extended . The win10±4 ms) . Toe-offff angle . Althougt = 4.92, df = 7, P<0.01). The timing of peak vertical force generation did not differ between seasons  occurring at 48±3 ms and 49.1±10 ms after the start of the jump during summer and winter, respectively  between summer (3.99±0.12 N or 0.74±0.029 times body weight) and winter (2.75±0.45 N or 0.35±0.058 times body weight), but occurred at the same time (30 ms) into the jump   and peak horizontal velocities  in winter compared to in summer  in winter birds and was 34.49±2.41 W and 20.59±4.71 W in summer and winter, respectively  between summer (1.01±0.15 W) and winter (0.35±0.13 W)  in summer birds, as was average body mass-specific total power   ; Table 1n summer . Peak veMb compromises jump performance in Svalbard rock ptarmigan and potentially other species as well. Increasing power requirements associated with the seasonal acquisition of body fat clearly places constraints upon the ability to jump and therefore, to become airborne from the ground. In summer, jumping performance was consistent with that found in other bird species. The 200 ms duration of the pre jump crouch lies between previously reported values in guinea fowl, which utilize a squat jump (400 ms crouch (Desmodus rotundus) there is no characteristic ‘shoulder’ present in the trace that is thought to be associated with sequential activation of the hind-limb muscles early in the jump (Coturnix coturnix japonica (3.9 times body weight)  and rufous hummingbirds (Selasphorus rufus), which produce around 1.6 times body weight but likely compensate for this by high levels of wing-driven lift generation (Mb) showed a 32% lower angle of ascent and 17% lower take-off velocity than lean individuals (Mb (7–8%), other studies have found even smaller effects of elevated Mb upon take-off performance in birds faced with a simulated predator (The results of this study support the hypothesis that an increased s crouch ), and sts crouch ). The obs crouch . Counters crouch  and batss crouch . Followis crouch . Unlike the jump . The pea weight)  and guin weight) , but low weight) . The resth perch  and grouth perch  take-offneration . The redividuals . Althougividuals . Perhapspredator . It is rpredator , despitepredator . In the predator , with mupredator . The hinMb. The relative importance of velocity and angle, however, may vary among species, depending upon the nature of the predation risk and the physical environment , is an opportunistic scavenger, that itself relies heavily on body fat and food caches  to survive the winter  held by Dr Codd and under ethical approval of the National Animal Research Authority of Norway (permit number 1333/2008) and the University of Manchester.n = 5, Mb = 551.5±9.4 g) and winter . All means are displayed as ± the standard error. Birds were maintained indoors with ad libitum access to high quality food and water in line with previous studies  positioned adjacent to a wooden board of the same height and length  and vertical (aV) acceleration were calculated from the measured horizontal (FH) and vertical (FV) forces as aH = FH/Mb and aV = (FV−gMb)/Mb. Total forces were assumed to be double those of the measured (single leg) forces. aH,fp and aV,fp were integrated to calculate horizontal (VH) and vertical (VV) velocity, respectively. The starting point  of the jump used for integration was determined from the video data as the instant during which the sternal marker no longer moved downwards but reversed direction and started its upwards motion  and means are displayed as ± standard error.The mean data for each individual were used for all statistical analyses. Differences in the magnitude and occurrence of peak vertical and horizontal forces, velocities and powers were determined using independent samples θ  =  take off angleFH  =  horizontal forceFV  =  vertical forceMb  =  body massPH  =  horizontal powerPsum  =  total powerP*sum  =  average body mass-specific total powerPV  =  vertical powerVH  =  horizontal velocityVV  =  vertical velocity


In a forthcoming publication , we findWe study concretely the Liley NPM, which requires 29 different - biologically meaningful - parameters to be specified for bulk activity. In Ref. ), the LiR, governing overall inhibitory synaptic strength, and k, the relative effect of inhibition on excitatory vs. inhibitory subpopulations. Essentially, Family 1 (F1) has two almost parallel saddle-node lines in the R-k plane, whereas Family 2 (F2) sports two cusp points organizing two saddle-node wedges. We then sorted all 73,454 “alpha sets” into F1 and F2, respectively, by an automated procedure using AUTO-07P [By studying bifurcation diagrams of a sub-sample of 405 “alpha sets”, we discovered that without exception all of them could be classified into two distinct dynamical families with regards to a continuation in R was much more extensive for F2.Four important results were obtained by this bifurcation partitioning: 1) Reactions to the modeled application of the anesthetic agent isoflurane differ, with F2 being three times as likely as F1 to show strong, transient spectral power increases. 2) Exogenous stimuli modeled by thalamic input can control transitions between the dynamic families. 3) Distributions of the values of two excitatory population parameters differed strongly between F1 and F2, providing the potential for endogenous control. 4) The repertoire of (multi)stable oscillations for variations of inhibitory strength In summary, we showcase here a new method for partitioning the complex parameter spaces of biologically realistic models according to their bifurcation behavior: “metabifurcation analysis”. For the Liley NPM, we find that one can classify “alpha sets” into two dynamical families that one can speculatively consider as representing “explorative”  and “consolidating”  brain states, where transitions between F1 and F2 are possible via exogenous stimuli.


Nitrogen (N) enrichment resulting from anthropogenic activities has greatly changed the composition and functioning of soil communities. Nematodes are one of the most abundant and diverse groups of soil organisms, and they occupy key trophic positions in the soil detritus food web. Nematodes have therefore been proposed as useful indicators for shifts in soil ecosystem functioning under N enrichment. Here, we monitored temporal dynamics of the soil nematode community using a multi-level N addition experiment in an Inner Mongolia grassland. Measurements were made three years after the start of the experiment. We used structural equation modeling (SEM) to explore the mechanisms regulating nematode responses to N enrichment. Across the N enrichment gradient, significant reductions in total nematode abundance, diversity (H' and taxonomic richness), maturity index (MI), and the abundance of root herbivores, fungivores and omnivores-predators were found in August. Root herbivores recovered in September, contributing to the temporal variation of total nematode abundance across the N gradient. Bacterivores showed a hump-shaped relationship with N addition rate, both in August and September. Ammonium concentration was negatively correlated with the abundance of total and herbivorous nematodes in August, but not in September. Ammonium suppression explained 61% of the variation in nematode richness and 43% of the variation in nematode trophic group composition. Ammonium toxicity may occur when herbivorous nematodes feed on root fluid, providing a possible explanation for the negative relationship between herbivorous nematodes and ammonium concentration in August. We found a significantly positive relationship between fungivores and fungal phospholipid fatty acids (PLFA), suggesting bottom-up control of fungivores. No such relationship was found between bacterivorous nematodes and bacterial PLFA. Our findings contribute to the understanding of effects of N enrichment in semiarid grassland on soil nematode trophic groups, and the cascading effects in the detrital soil food web. Widespread nitrogen (N) enrichment resulting from anthropogenic activities such as N deposition and fertilization has greatly changed ecosystem processes, structure, and functioning Soil nematodes are wide-spread, abundant and highly diverse, both taxonomically and functionally Previous studies have documented inhibition of nematodes after N addition 3−-N and NH4+-N concentrations were found negatively correlated with root herbivores and fungivores Soil acidification following N addition has been proposed as one of the important factors inhibiting soil nematode abundance after N addition We used a 4-year multi-level N addition experiment in a typical Inner Mongolia grassland in Northern China to investigate the soil nematode community, the plant community No specific permits were required for the described field studies. No specific permissions were required for these locations/activities. The location is not privately owned or protected in any way and the field studies did not involve endangered or protected species.Leymus chinensis (Trin.) Tsvelev and the perennial bunchgrass Stipa grandis P. Smirn are the dominant plant species. Our N manipulation experiment was conducted in Leymus chinensis grassland that had been fenced since 1979 to prevent grazing by large animals.The field experiment was conducted at the Inner Mongolia Grassland Ecosystem Research Station (IMGERS), which is located in the Xilin River Basin, Inner Mongolia Autonomous Region of China . Topographic relief exhibits little variation, with elevation ranging from 1250 to 1260 m at our experimental site. The region has a semi-arid continental climate with a mean annual temperature of 0.7°C and a mean annual precipitation of 346.1 mm with 60–80% of the precipitation falling during the growing season from May to August. The soil is classified as dark chestnut  or loamy sand in terms of texture. Previous experiments have documented that N limitation constrains primary production and regulates plant community composition −2 yr−1; N was added as urea). Hereafter, treatments will be referred to as: Control, N0, N0.4, N0.8, N1.6, N2.8, and N4.0. Each plot, except for Control, also received 0.05 mol P m−2 (added as KH2PO4) to ensure that N was the only limiting nutrient. The fertilizer was thoroughly mixed with sand and then applied to the plot surfaces as a single dose at the beginning of the growing season every year from 2006 to 2009 In 2006, 42 8 m×8 m plots were laid out in a randomized block design. Plots were separated by 1-meter walkways. There were seven treatments with six replicates each, including Control (no nutrient addition), and 6 levels of N addition  based on life forms, including perennial rhizome grass (PR), perennial bunchgrasses (PB), perennial forbs (PF), shrubs and semi-shrubs (SS), and annuals (AS), as in Bai et al. −1 KCl. The concentrations of nitrate N (NO3−-N) and ammonium N (NH4+-N) in the filtrates were determined using a flow injection auto analyzer . The concentrations of soil inorganic N were calculated based on dry soil weight.In 2009, three years after the start of the experiment, from each plot soil samples were first collected on June 10 (when the rain season started and urea dissolved), and then on August 18 and September 19. From each plot, three soil cores  were collected. Per plot, the three soil cores were bulked comprising one soil sample per plot. Bulked samples were placed in a plastic bag and then immediately transported to the laboratory and stored at 4°C. Soil moisture (SM) was determined as mass loss after drying the soils at 105°C for 24 h. Part of the air-dried and sieved samples was prepared for measurement of pH and total organic carbon. For measurement of soil inorganic N, soil samples were extracted using 2 mol LSoil nematodes were extracted from 100 g subsamples collected in August and September 2009 using a modified cotton-wool filter method August and September physicochemical soil properties used in the SEM model and linear regression analyses (see below) were calculated as the mean of July and August, and the mean of August and September, respectively.i(lnpi), where pi is the proportion of individuals belonging to the ith taxon relative to the total number of individuals in the sample; and 3) The maturity index MI = Σv(i)f(i), where v(i) is the c-p value of taxon i indicating their r and K strategies The following nematode community indices were calculated: 1) Nematode taxon richness (S), i.e., the number of genera identified a priori and theoretical knowledge  and Akaike Information Criterion (AIC) were used to judge the model fit to the data. A large P value and a low RMSEA value indicates that the covariance structure of the data does not differ significantly from what would be expected based on the model.Structural equation modeling (SEM) was used to gain a mechanistic understanding of how soil properties and altered plant community composition mediate effects of N enrichment on soil nematode communities. SEM is based on a simultaneous solution procedure, where the residual effects of predictors are estimated  once common causes from inter-correlations have been statistically controlled for nowledge . In thisAll univariate analyses were performed using SPSS 17.0 . PCA was performed using Program CANOCO Version 4.5 . SEM analyses were performed using AMOS 7.0 .Nitrogen addition greatly increased nitrate and ammonium concentrations in July . However0 to 4 at N4 plots  in September, but the effects were much weaker than in August  of soil nematode community composition. Bac = bacterivores abundance, Fun = fungivores abundance, Her = root herbivores abundance, OP = omnivores-predators abundance. Percentages along the axes correspond to the amount of explained variability in functional group composition.(DOCX)Click here for additional data file.Figure S2Species-sample bi-plot of principal component analysis (PCA) of plant community composition. TolBio = Total aboveground biomass, PR = perennial rhizome grass, PB = perennial bunchgrasses, PF = perennial forbs, SS = shrubs and semi-shrubs. Percentages along the axes correspond to the amount of explained variability in functional group composition.(DOCX)Click here for additional data file.Figure S3Conceptual model of hypothetical interaction pathways in the studied plant-soil-nematode system.(DOCX)Click here for additional data file.Figure S4Relationships between bacterial phospholipid fatty acids (PLFA) and abundance of bacterivorous nematodes, fungal PLFA and fungivorous nematodes. Data are ln transformed.(DOCX)Click here for additional data file.Table S1−2) for Control and N addition treatments. Values shown are mean ± s.d. (N = 6). Significant differences among treatments are indicated by different letter superscripts (P<0.05). PR = perennial rhizome grasses, PB = perennial bunch grasses, PF = perennial forbs, SS = shrubs and semi-shrubs. N0 = 0 mol N m−2 y−1, N0.4 = 0.4 mol N m−2 y−1, N0.8 = 0.8 mol N m−2 y−1, N1.6 = 1.6 mol N m−2 y−1, N2.8 = 2.8 mol N m−2 y−1, N4 = 4.0 mol N m−2 y−1.Plant richness, and shoot, root, and functional group shoot biomass (g m(DOCX)Click here for additional data file.Table S2P<0.001).Results of structural equation modeling of N addition effects on the plant-soil-nematode system as illustrated in (DOCX)Click here for additional data file.Table S3Relative abundance of nematode genera (%) for Control and N treatments in August 2009. Data are mean values (N = 6). H = herbivores, Ba = bacterivores, Fu = fungivores, Om = omnivores, Ca = carnivores.(DOCX)Click here for additional data file.Table S4Relative abundance of nematode genera (%) for Control and N addition treatments in September 2009. Data are mean values (N = 6). H = herbivores, Ba = bacterivores, Fu = fungivores, Om = omnivores, Ca = carnivores.(DOCX)Click here for additional data file.


The aim of this study was to assess the possibility of clinically significant drug–alcohol interactions among home-dwelling older adults aged ≥ 65 years.This study was a cross-sectional assessment of a stratified random sample of 2100 elderly people (≥ 65 years) in Espoo, Finland. The response rate was 71.6% from the community-dwelling sample. The drugs were coded according to their Anatomical Therapeutic Chemical (ATC) classification index (ATC DDD 2012). Significant alcohol interactive (AI) drugs were examined according to the Swedish, Finnish, INteraction X-referencing (SFINX) interaction database, as well as concomitant use of central nervous system drugs, hypoglycaemics, and warfarin with alcohol. “At-risk alcohol users” were defined consuming > 7 drinks/week, or ≥ 5 drinks on a typical drinking day, or using ≥ 3 drinks several times/week, “moderate users” as consuming at least one drink/month, but less than 7 drinks/week, and “minimal/non-users” less than one drink/month.Of the total sample (n = 1395), 1142 respondents responded as using at least one drug. Of the drug users, 715 (62.6%) persons used alcohol. The mean number of medications was 4.2 (SD 2.5) among “at-risk users”, 4.0 (SD 2.6) among “moderate users”, and 5.4 (SD 3.4) among “minimal/non-users” (p < 0.001). The concomitant use of AI drugs was widespread. Among the “at-risk users”, “moderate users”, and “minimal/nonusers” 42.2%, 34.9%, and 52.7%, respectively, were on AI drugs (p < 0.001). One in 10 of “at-risk users” used warfarin, hypnotics/sedatives, or metformin.Use of AI drugs is common among older adults, and this increases the potential risks related to the use of alcohol. The concomitant use of drugs with potential interactions with alcohol was common.Among the “at-risk users” and “moderate users”, 42.2% and 34.9% were on drugs potentially causing significant interactions with alcohol.It is important that clinicians discuss medication and alcohol consumption with their patients and warn patients who are prescribed alcohol interactive drugs about alcohol–drug interactions.Over the past few decades, the use of drugs has increased among older adults ,2. ApproAlthough epidemiological studies have shown a decline in alcohol drinking along with age, the number of older adults drinking will increase when the age cohort born in the 1950s with their heavier drinking habits reach old age . Use of Furthermore, many drugs interact adversely with alcohol . ConsistThere are only a few epidemiological studies investigating the concomitant use of alcohol and drugs presenting a potential risk for older adults ,19,22–24The data were gathered in May–September 2007. A postal questionnaire was sent to a stratified random sample of 2100 older individuals (≥ 65 years) from the Espoo Population Register. A stratified random sample of 350 was retrieved from each five-year age cohort . The questionnaire was re-sent after three months to those who had not yet responded.The questionnaire consisted of socio- demographic variables, health-related variables, and drinking patterns. Respondents were invited to list their medical diagnoses and medications prescribed by their doctors. The drugs were coded according to their Anatomical Therapeutic Chemical (ATC) classification index . The SweAlcohol consumption was charted with several questions developed from the clinical guidelines for alcohol use in older adults  and the We counted the amount of alcohol consumed by the respondent by taking into account the frequency of use and the portions consumed on one occasion. We defined “at-risk users” as those consuming > 7 drinks/week, or ≥ 5 drinks on a typical drinking day, or using ≥ 3 drinks several times/week to be in line with the guidelines for persons ≥ 65 years . “ModeraFalls related to alcohol use were inquired about as follows: “Have you fallen or injured yourself when you have used alcohol ”.We used the Statistical Package for the Social Sciences (SPSS) version 15.0 for Windows, and NCSS statistical analysis and graphics software to compute descriptive statistics. The “at-risk users”, “moderate users”, and “minimal or non-users” groups were compared with the chi-squared test in their categorical variables and the Kruskal–Wallis test was used for the non-normally distributed continuous variables.Of the random sample, 31 had a native language other than Finnish or Swedish (inquiry form languages), 16 were deceased, 92 lived in permanent institutional care, and the postal address of 14 individuals had changed and was therefore unknown. Thus, the number of potential respondents was 1947, of whom 1395 returned the questionnaire. The response rate for the community-dwelling older persons was 71.6%.Of the total sample (n = 1395), 1142 persons reported using at least one drug and had data available concerning their alcohol consumption. Their mean age was 78.7 years and 64.5% were females. By definition, 90 could be defined as “at-risk users”, 625 as “moderate users”, and 427 as “minimal/ non-users”. The “at-risk users” were more often males and married; they were younger and had higher education than the “non/minimal users”. The mean number of medications in these groups was 4.2 among “at-risk alcohol users”, 4.0 among “moderate users”, and 5.4 among “minimal/non-users” (p < 0.001) .Participants had many diseases . The “miOf the drug users, 62.2% (715) persons also used alcohol. The use of drugs with potential alcohol interactions among participants is presented in Among the “at-risk users” on AI drugs, 13.8% reported they had fallen or injured themselves when using alcohol, whereas the respective figure among drug-users was 4.1% (p < 0.001).The concomitant use of drugs with potential interactions with alcohol was common. Of the drug users, 62.2% (715) persons also used alcohol. Among the “at-risk users”, “moderate users”, and “minimal/nonusers”, 42.2%, 34.9%, and 52.7% were on drugs potentially causing significant interactions with alcohol. Of the “at-risk users”, 11% were on warfarin, 11% on hypnotics/sedatives, and 13% on metformin, which is a serious concern for potential adverse events.The strength of this study lies in the large and representative sample of older home-dwelling persons in bigger cities in Finland. The high response rate also supports its validity. However, relying on self-reporting measures imposes a limitation on the study since self-reported alcohol consumption is likely to be underestimated to some extent in drinking surveys . The croThe “at-risk users” and “moderate users” were younger, healthier, and more often males and more educated than the “non-users”. The cross-sectional nature of our study does not allow us to evaluate whether older people decrease their consumption of alcohol when they get older, have more illnesses and receive more medications, or whether these findings reflect changing trends in lifestyle of older and younger cohorts. Some prior studies have suggested that the cohorts born later actually use more alcohol than the older cohorts .Falls and injuries were more common among “at- risk users” using AI drugs concomitantly than among others. Both alcohol and drugs with central nervous system effects can increase the risk of falls ,31. AlcoThe metabolism of warfarin is influenced by alcohol. The use of warfarin may expose individuals to higher risk of serious bleeding ,18, espeAlcohol can potentiate the effects of metformin, which was the most commonly used oral hypoglycaemic in our study. Of the “at risk-users” 13.3% were on metformin, 6.7% used sulfonylureas, and 1.1% insulin. The use of metronidazole, tinidatzole, disulfiram, griseofulvin, prazosin, and tacrolimus was negligible.The concomitant use of medications with potential interactions with alcohol was widespread. It is important that clinicians are screening the alcohol consumption (e.g. by using AUDIT) and discuss medication and alcohol consumption with their patients and warn patients who are prescribed alcohol interactive drugs about alcohol–drug interactions.


Retinoic acid-related orphan receptors RORα and RORγ play a regulatory role in lipid/glucose homeostasis and various immune functions, and have been implicated in metabolic syndrome and several inflammatory diseases. RORα-deficient mice are protected against age- and diet-induced obesity, hepatosteatosis, and insulin resistance. The resistance to hepatosteatosis in RORα-deficient mice is related to the reduced expression of several genes regulating lipid synthesis, transport, and storage. Adipose tissue-associated inflammation, which plays a critical role in the development of insulin resistance, is considerably diminished in RORα-deficient mice as indicated by the reduced infiltration of M1 macrophages and decreased expression of many proinflammatory genes. Deficiency in RORγ also protects against diet-induced insulin resistance by a mechanism that appears different from that in RORα deficiency. Recent studies indicated that RORs provide an important link between the circadian clock machinery and its regulation of metabolic genes and metabolic syndrome. As ligand-dependent transcription factors, RORs may provide novel therapeutic targets in the management of obesity and associated metabolic diseases, including hepatosteatosis, adipose tissue-associated inflammation, and insulin resistance. In the past 50 years, the occurrence of obesity has greatly increased worldwide in both adults and children and has become a major health-care concern in many countries. In the United States 30% of the population is considered obese, while more than 66% of adults and almost 17% of children and adolescents are overweight . ObesityThe RORs alpha, beta, and gamma (RORα–γ or NR1F1–3) constitute a subfamily of nuclear receptors that function as ligand-dependent transcription factors . RORs exIl17 promoter -associated inflammation and the development of insulin resistance in obese mice and patients . WhetherStaggerer (sg/sgRORα) mice, a natural mutant strain containing a deletion in the RORα gene that results in loss of RORα expression, indicated that RORα plays a critical role in the control of lipid metabolism and the development of various aspects of metabolic syndrome. These investigations showed that sg/sgRORα mice are protected against age- and diet-induced obesity and the development of several obesity-linked pathologies, including adipose tissue-associated inflammation, hepatosteatosis, and insulin resistance (sg/sgRORα mice fed a high fat diet (HFD) gain relatively less weight and exhibit a significantly lower total body fat index compared to wild-type (WT) littermates on a HFD. Similarly, male sg/sgRORα mice were also protected against age-induced obesity. Adipose tissue is the main site of storage of excess energy that is stored in the form of triglycerides in single large lipid droplets. The reduced adiposity in sg/sgRORα mice was largely related to smaller adipocyte size due to diminished deposition of triglycerides.Study of sistance . RORαsg/RORα, particularly the RORα4 isoform, has been shown to be highly expressed in WAT and to be induced during differentiation of D1 and 3T3-L1 preadipocytes . Overexpsg/sgRORα mice was found not to be due to reduced food intake or increased fecal lipid excretion. Indirect calorimetric analysis showed that VO2, VCO2, and heat generation were significantly enhanced in sg/sgRORα mice on a HFD  M2 macrophages to proinflammatory “classically activated” (CD11c+CD206-) M1 macrophages .In addition to functioning as the main site of storage of extra energy in the form of triglycerides derived from food intake, white adipocytes produce a variety of endocrine hormones, including leptin, adiponectin, resistin, and retinol-binding protein 4 (RBP-4) which regulate food intake, lipid metabolism, and inflammation . Leptin rophages , which i obesity . In addi+ T lymphocytes in WAT of sg/sgRORα mice fed a HFD. This was further supported by the significant reduction in the formation of CLS and the expression of several macrophage markers, such as F4/80, Mac-2, macrophage expressed 1 (Mpeg1), and macrophage scavenger receptor 1 (Msr1), in WAT of sg/sgRORα mice. Moreover, the relative percentage of proinflammatory M1 macrophages was significantly diminished in sg/sgRORα WAT. This was supported by flow cytometric analysis and the much lower levels of Cd11c expression. The reduced inflammation in sg/sgRORα WAT is further indicated by gene expression profiling showing a greatly reduced expression of a large number of chemokines, including Ccl2, Ccl8, Ccl3, and Ccl7, the chemokine receptors Ccr3, Ccr5, and Ccr7, the proinflammatory cytokines Tnfα and IL-6, the interleukin 1 receptor antagonist (Il1rn), osteopontin (Opn), CD44, serum amyloid 3 (Saa3), and several TLRs and metalloproteinases in WAT of sg/sgRORα mice compared to their WT counterparts . Future studies have to determine what the primary effects are by which RORα regulate the expression of these genes.Deficiency of RORα greatly inhibits diet-induced adipose tissue-associated inflammation in mice . This isterparts . The expterparts . CD44, aterparts . CD44 anterparts . These osg/sgRORα mice fed a HFD or aging male sg/sgRORα mice , key regulators for lipogenesis, was reduced in liver of sg/sgRORα mice. In addition, the expression of several genes involved in the main pathway of triglyceride synthesis, including glycerol-3-phosphate acyltransferase (Gpam or Gpat1) and acyl-glycerol-3-phosphate acyltransferase 9 (Agpat9) and Mogat1, which is part of an alternative pathway of triglyceride synthesis, were significantly diminished in sg/sgRORα liver (Cidea and Cidec) and perilipin 2 (Plin2 or Adfp), which play a critical role in the regulation of lipid storage, lipid droplet formation, and lipolysis (RORα-deficient mice. RORα has been reported to activate Plin2 transcription directly through interaction with ROREs in the Plin2 promoter (Fgf21), an important regulator of lipid/glucose metabolism, was found to be directly regulated by RORα in hepatocytes .Obesity is associated with increased prevalence of NAFLD, which is characterized by elevated lipid accumulation in hepatocytes . NAFLD d/sg mice . These osg liver . The hepipolysis , was alspromoter . Recentlatocytes . Togethesg/sgRORα mice fed a HFD are consistent with this correlation. sg/sgRORα mice, which are protected against obesity, hepatosteatosis, and WAT-associated inflammation, exhibited a significantly reduced susceptibility to diet-induced insulin resistance and glucose intolerance compared to obese WT mice .Many inflammatory and lipogenic genes, including ckground . Il1rn, /sg mice , has bee/sg mice . Similarsistance . Deficiesistance  suggestisistance . Thus, tIn addition to adipose tissue and liver, the pancreas and the skeletal muscle also play important roles in energy homeostasis and insulin resistance. The pancreatic islets produce a number of hormones, including insulin and glucagon, that are critical in the regulation of lipid and glucose homeostasis . RORα wasg/sgRORα mice and this correlated with an increase in the level of insulin-induced Akt phosphorylation, Glut4 expression, and glucose uptake. This stimulation in Akt signaling might at least in part account for the improved insulin sensitivity observed in sg/sgRORα mice.In muscle, insulin stimulates glucose uptake by stimulating the translocation of Glut4 (Slc2a4) to the plasma membrane . This inRORγ -/- mice, the number of adipocytes was increased (hyperplasia), while adipocyte size was reduced. Fasting blood insulin levels were shown to be significantly lower in diet-induced obese RORγ-/- mice and in RORγ-/-ob/ob double knockout mice and mice displayed improved insulin sensitivity. In addition, RORα-/- adipocytes were highly insulin sensitive leading to improved control of circulating FFA. These observations are consistent with a recent study showing that, opposed to adipose hypertrophy, obese patients with adipose tissue hyperplasia  exhibit better glucose and lipid profiles and might be less susceptible to developing insulin resistance , that are expressed in a highly tissue-specific manner . Expresssistance  and withsistance . Up to ndiposity . These oIn addition to adipose tissue, regulation of lipid and glucose metabolism in other tissues, including liver, pancreas, and skeletal muscle might be part of the mechanism by which by RORγ modulates insulin sensitivity. In skeletal muscle, RORγ has been reported to regulate the expression of genes associated with lipid and carbohydrate metabolism as well as the production of reactive oxygen species . A recenIt has been well established that many behavioral and physiological activities display circadian rhythms that are regulated by endogenous clocks . At the db/db mice  signaling pathways are constitutively activated resulting in elevated levels of circulating TNFα, Il-1β, and Il-6 . Bmal1, Npas2, E4bp4, and Cry1 transcription are directly regulated by RORγ and RORα in several peripheral tissues through their interaction with ROREs in their regulatory regions . Data demonstrating that the circadian pattern of expression of a number of metabolic genes are regulated by clock proteins and RORs and observations showing that circadian expression of RORγ1 is controlled by the clock machinery suggested that RORs might function as downstream mediators in the mechanism by which clock proteins regulate the circadian expression of metabolic genes , which exhibit roles in lipogenesis, glycogenolysis, and/or cholesterogenesis .A number of studies demonstrated that RORs play a role in the modulation of circadian behavior and clock gene expression (  regions , 2012. R regions . The ROR regions . Recent ic genes , 2012. Togenesis . Thus, Rtrans retinoic acid and the synthetic retinoid, ALRT 1550 were reported to bind and function as antagonists for RORβ and RORγ, but not RORα (in vitro and in vivo and to suppress the development of experimental autoimmune encephalomyelitis (X-ray crystallography studies of the LBD of RORα identified the presence of cholesterol in the ligand-binding pocket of RORα . Subsequnot RORα . Recentlnot RORα , while 2not RORα . The LXRnot RORα . The RORmyelitis . Therefomyelitis . BecauseThe study of ROR-deficient mice has clearly demonstrated that RORα and RORγ are important in several physiological processes, including the regulation of several immune responses, lipid/glucose homeostasis, and circadian rhythm. These studies revealed that loss of RORα protects against the development of diet- and age-induced obesity, hepatosteatosis, glucose intolerance, and insulin resistance, while loss of RORγ protects against insulin resistance. These protective effects have been linked to suppression of the expression of multiple proinflammatory and metabolic genes. RORs regulate expression of some of these genes directly by binding ROREs in their regulatory region and in certain cases involves changes in their circadian pattern of expression. Although much progress has been made, what event or which ROR target genes are the primary driving force by which RORs influences WAT-associated inflammation, hepatosteatosis, and insulin resistance needs further study. With the increasing evidence for an interrelationship between the controls of lipid/glucose metabolism, inflammation and circadian rhythm, RORs might functions as intermediaries between the controls. With the discovery of ROR antagonists, RORs may provide a novel therapeutic target in the management of various aspect of metabolic syndrome.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.