Bacillus subtilis consists of a regular two-component system LiaRS comprising the core Histidine Kinase (HK) LiaS and the Response Regulator (RR) LiaR and additionally the accessory protein LiaF, which acts as a negative regulator of LiaRS-dependent signal transduction. The complete LiaFSR system was shown to respond to various peptide antibiotics interfering with cell wall biosynthesis, including bacitracin.Two-component signal transduction systems are one means of bacteria to respond to external stimuli. The LiaFSR two-component system of liaI promoter. We found that switching into the ‘ON’ state occurred within less than 20 min in a well-defined switching window, independent of the bacitracin concentration. The switching rate and the basal expression rate decreased at low bacitracin concentrations, establishing clear heterogeneity 60 min after bacitracin induction. Finally, we performed time-lapse microscopy of single cells confirming the quantitative response as obtained in the whole population analysis for high bacitracin concentrations.Here we study the response of the LiaFSR system to various concentrations of the peptide antibiotic bacitracin. Using quantitative fluorescence microscopy, we performed a whole population study analyzed on the single cell level. We investigated switching from the non-induced ‘OFF’ state into the bacitracin-induced ‘ON’ state by monitoring gene expression of a fluorescent reporter from the RR-regulated The LiaFSR system exhibits an immediate, heterogeneous and graded response to the inducer bacitracin in the exponential growth phase. Escherichia coli or Bacillus subtilis posses about 30 HKs and RRs Two-component systems (TCS) are a fundamental principle of bacterial signal transduction that enables cells to respond to environmental stimuli Bacillus subtilisliaGFSR operon, which is expressed from the constitutive liaG promoter (PliaG) in the absence of inducing conditions liaI promoter (PliaI) resulting in a strong upregulation of the liaIH operon, but also the complete lia locus  and found it to be narrowly distributed with FIauto 8±1 FU  hardly any fluorescence could be detected within the 120 min observation period, as cells stayed at FIauto = 8±1 FU  all cells switched from FIauto to FImax by 60 min post-induction, this could not be observed at low bacitracin concentrations (1 and 0.3 µg/ml). Here, parts of the population were not induced by bacitracin, as indicated by fluorescence levels in the range of the autofluorescence. Therefore, a clear heterogeneity in gene expression levels was present at 60 min after bacitracin induction at low antibiotic concentrations  and T10 (3 µg/ml bacitracin). We used these intermediate states as seen in experiments with high inducer concentrations (30 and 3 µg/ml bacitracin) to determine the switching threshold by applying a Gaussian fit to the histograms shown in ON(T))  antibiotic concentrations (As we had observed that FItrations . Interes(T)) see , which w(T)) see . Around trations . After tfON) as the first derivative of the fraction of cells in the ‘ON’ state with respect to time (fONmax) was about 10 to 20%/min for high bacitracin concentrations . As GFPmut1 and LiaI represent two different proteins, it is possible that GFPmut1 and LiaI have different proteolysis rates. Therefore, the concentration of GFPmut1 controlled by PliaI is not necessarily a direct measure for the concentration of LiaI. However, the expression rates, i.e. the production rate of LiaI and GFPmut1, are expected to be similar, as the complete native PliaI including all native signals for LiaI expression is present.We observed that the maximal switching rate Pbasal) shifted to higher values with time, saturating at the maximal basal fluorescence value FIbasalmax. This increase of fluorescence values of not-induced cells could be well described by a sigmoid fit function FI(T) (ON(T), indicating that the basal expression rate Pa had a maximum and that the maximum expression rate was shifted to earlier times as compared with the maximum switching rate PfON. The maximal fluorescence values of not-induced cells as obtained at 20 min after bacitracin induction showed significantly higher values as compared to the autofluorescence  , similarrescence , with abrescence .max , at 1 µg/ml was 2.3±0.4 FU/min exceeding the value of 0.3±0.3 FU/min at 0.3 µg/ml bacitracin by a factor of eight  as described in this study, has been used previously to analyze switching of liaI. This is in contrast to other studies, in which maximum RR-regulated mRNA concentrations E. coli revealed a wide span in auto-phosphorylation rates of the HK ranging from about 2 min to 10 min. Phospo-transfer to RRs by phosphorylated cognate HKs took place within less than ½ min liaI promoter is dependent on the bacitracin concentration, indicating that the LiaR concentration is directly affecting gene expression from PliaI. Recently, it was found that even at very high bacitracin concentrations (50 µg/ml) only about 20 molecules of LiaR are present within a single cell liaI is therefore dependent on the bacitracin concentration. The low number of LiaR molecules can explain the observed variations in gene expression, in particular the heterogeneity present at low bacitracin concentrations, as cell-to-cell differences  at T0 = 30 min and applying the cells to the microscopic slides. This way any cross-over from intrinsic stationary phase induction B. subtilis as a negative control. The vector carries a chloramphenicol resistance cassette for selection in B. subtilis, and integrates into the amyE locus by double crossing-over, resulting in a stable integration of the promoter-less-gfp fusion. The plasmid was linearized with PstI and used to transform B. subtilis 168 with chloramphenicol selection (5 µg/ml). Successful integration into the amyE locus was confirmed by starch test.The promoter less vector pGFPamy For flow cytometry experiments, the cultures were grown as described above. Samples were taken every 10 min for 120 min and diluted 1∶100 in PBS . The experiments were performed using a Partec CyFlow Space instrument and the software FlowMax. GFP was excited with a laser at 488 nm and its emission measured at 518 nm. The analysis of the cells was done at a flow-rate of 2 µl/s. In between measurements, the instrument was rinsed with PBS to eliminate cross-contamination. In addition to the different concentrations of bacitracin, not induced samples and PBS alone were analyzed for control purposes. To discriminate dead from healthy cells, appropriate gating procedures have been applied. 50000 cells lying in the appropriate gate have been analyzed for each time point.Cells were sampled throughout growth as indicated in the main text. For image acquisition of the whole cell population, cells were permitted to attach to microscopic slides  and covered with 1% Agarose-patches.For time-series of single cells, cells were allowed to attach to microfluidic chambers coated with 100% 1-[3-(Trimethoxysilyl)propyl]urea (Sigma). Cells were induced already attached to the microfluidic channels and washed with fresh medium in the presence of bacitracin at a flow-rate of 0.3 ml/h.Image acquisition was done using a Zeiss Axiovert 200 M microscope equipped with an Andor Digital Camera and a Zeiss EC Plan-Neofluar 100×/1.3 Oil immersion objective. Andor software was used for image acquisition. The stability of the absolute fluorescence values was verified using a microscope image intensity calibration kit . Microspheres showed a deviation of mean grey value of less than 1% under the experimental conditions used for detection of GFP fluorescence. Homogeneity of illumination was tested using fluorescent slides and the maximum deviation was less than 5%.Images were processed using ImageJ software. Image background was corrected using a rolling ball algorithm with a radius of 50. An intensity threshold tool was used to delimit the boundaries of the cells in the bright field image. The boundaries of the cells were obtained with the wand tool of the ImageJ software and transferred to the fluorescence image using the ROI manager of ImageJ. Only cells that were fully lying within the bright field image and were not in the process of cell division were considered. Furthermore, dead cells as observable by different contrast in the bright field image as compared to healthy cells were excluded from the single cell analysis. The remaining single cells were than analyzed with respect to their mean grey value. Data preparation was performed using the Software IGOR PRO 4.06 and Adobe Illustrator CS4.auto:FI average autofluorescence/fluorescence intensity of cells not induced by bacitracin, given as fluorescence units [FU] as obtained by the mean grey value. The average autofluorescence level of cells prior bacitracin induction was FIauto = 8±1 FU  from cells being in the ‘ON’ state (induced GFP-expression). We used the intermediate states seen in experiments with high inducer concentrations (30 and 3 µg/ml bacitracin) to determine the switching threshold. A Gaussian fit was applied to the histograms shown in ON:f fraction of cells in the ‘ON’ state. We determined the fraction of cells in the ‘ON’ state as a function of time using the switching threshold. The fraction of cells in the ‘ON’ state was well defined by a sigmoid function with fON(T) = fbase+fmax/1+exp). The fit parameter of this function can be found in ONmax) was determined using this fit function  was determined using two different calculation methods: a) Pfonmax = maximum of the 1st derivative of the exact data points of fON. b) by obtaining A of the Gaussian fit applied to the data fON = y0+Aexp (−((x−x0)/width)2) (fON at time point t2 = square root of ((error at (t2))2+(error at (t1))2), with t1 and t2 the time points of the derivated time interval. The individual errors here are the errors of fON as described above. Please note that error propagation has to be taken into account when deriving data points, leading to the high errors in width)2) . The higfONmax):t Thalf of the sigmoidal fit applied to ON(T) = fbase+fmax/1+exp). b) Time point of PfONmax = maximum of the 1st derivative of the exact data points of fON. c) by obtaining x0 of the Gaussian fit applied to the data in fON(T) = y0+A exp (−((x−x0)/width)2) (width)2) .basal:FI average basal fluorescence intensity of cells in the ‘OFF’ state. The error is given as the standard error.basalmax:FI maximal average basal fluorescence intensity as obtained by applying a sigmoidal fit function FI (T) = fbase+fmax/1+exp), with fbase baseline, fmax maximum basal fluorescence intensity, Thalf half time and k rate  has been determined in two different ways: a) Pa = maximum of the 1st derivative of the exact data points of FIbasal. b) by obtaining x0 of the Gaussian fit applied to 0+Aexp (−((x−x0)/width)2) (2 = square root of ((error at (t2))2+(error at (t1))2), with t1 and t2 the time points of the derivated time interval. The individual errors here are the errors of FIbasal as described above. Please note that error propagation has to be taken into account when deriving data points, leading to the high errors in width)2) . The errmax):t Thalf of the sigmoidal fit applied to base+fmax/1+exp)  by obtaining x0 of the Gaussian fit applied to 0+Aexp (−((x−x0)/width)2) ) . b) Pamawidth)2) .fONmax), t(Pamax), PfONmax and Pamax can be found in fONmax), t(Pamax), PfONmax and Pamax are given in A summery of all data described here, as well as the average data obtained from the different calculation methods for tClick here for additional data file.Table S2ON.Fit parameters for the fraction of cells in the ‘ON’ state f(DOC)Click here for additional data file.Table S3fON.Fit parameter for the switching rate P(DOC)Click here for additional data file.Table S4fONmax).Time point of maximal switching rate t(P(DOC)Click here for additional data file.Table S5fONmax.Maximal switching rate P(DOC)Click here for additional data file.Table S6basal.Fit parameters for the basal fluorescence level FI(DOC)Click here for additional data file.Table S7Fit parameter for the basal expression rate Pa.(DOC)Click here for additional data file.Table S8max.Maximal basal expression rate Pa(DOC)Click here for additional data file.Table S9max).Time point of maximal basal expression rate t(Pa(DOC)Click here for additional data file.Table S10Maturation of GFPmut1.(DOC)Click here for additional data file.Figure S1Influence of bacitracin on cell growth. Cells were grown as described in the Material and Methods section in the presence of bacitracin at different final concentrations . At these concentrations bacitracin has no influence on cell growth.(EPS)Click here for additional data file.Figure S2Expression profiles of the Lia response at 0.1 µg/ml bacitracin. At these very low inducing concentration of bacitracin nearly all cells stay in the non-induced ‘OFF’ state. A) Representative image of B. subtilis cells 60 min after bacitracin induction. B) Histograms of GFP expression from the liaI promoter for different time points (T10 = 10 min after bacitracin induction). Red arrows indicate the few cells in the ‘ON’ state at this bacitracin concentration.(EPS)Click here for additional data file.Figure S3Maturation of GFPmut1. Arrow indicates the addition of 400 µg/ml erythromycin at 80 min leading to immediate translation inhibition. Therefore any fluorescence development arising after erythromycin addition can be attributed to the maturation of the GFP fluorophore. Grey: cells grown in the absence of erythromycin. Black: Cells grown in the presence of erythromycin. Solid lines: best fit to an exponential function (function .(EPS)Click here for additional data file.Figure S4Flow cytometry analysis of the PliaI response of LiaFSR to bacitracin. Flow cytometry analysis verified the results obtained by quantitative fluorescence microcopy as shown in main 60 all cells reached their maximum fluorescence intensities. While at high bacitracin concentrations all cells shifted to high fluorescence values, at low bacitracin concentrations (1 and 0.3 µg/ml) a fraction of cells did not express GFP and stayed at the autofluorescence value. As low fluorescence intensities of induced cells were hard to distinguish from the background fluorescence of not induced cells using flow cytometry, we chose quantitative fluorescence microscopy for detailed analysis of the LiaFSR response. Data shown here represent the mean grey value of each single cell: mean FI [FU]. A) Background signal of the buffer PBS in the gated area. B) Autofluorescence of not induced Bacillus subtilis cells C)–F) Histograms of GFP expression from the liaI promoter for different time points, at C) 30 µg/ml bacitracin (T30 = 30 min after bacitracin induction), D) 3 µg/ml bacitracin, E) 1 µg/ml bacitracin, and F) 0.3 µg/ml bacitracin.(EPS)Click here for additional data file.Figure S5fONmax and maximal basal expression rate Pamax for various bacitracin concentrations.Maximal switching rate P As switching into the ‘ON’ state took place in a very short time period of less than 10–15 min, only few data points between the ‘OFF’ and the ‘ON’ state could be obtained. As cells treated with high bacitracin concentrations, although not showing any fitness defects, tended to lyse when stored on ice, a shorter experimental time resolution was not possible. Therefore, to reduce the error by simply fitting to the data, the maximal switching rate as well as the maximal basal expression rate was determined using several calculation methods as described in the Material and Methods section. This Figure gives an overview of the data obtained by the various methods used. A) Time point of maximum switching rate t(PfONmax); Black, grey and light grey bars represent data obtained as described in fONmax: Black, and light grey bars represent data obtained as described in fONmax. C) Time point of maximum basal expression rate t(Pamax); Black, grey and light grey bars represent data obtained as described in max). D) Maximum basal expression rate Pamax; Black and light grey bars represent data as described in max.(EPS)Click here for additional data file.


Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening.There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework.An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects.A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies. In epidemiological studies two main measures of interest are the risk of an event occurring (probability) and the rate at which it occurs (hazard). PatientThere are two main approaches to modelling competing risks. The firCompeting risks analyses are being increasingly carried out in epidemiological studies. However, the methodology applied varies and is not always optimal. Often, separate analyses will be carried out for each competing event and only the cause-specific hazard ratios will be reported for each-9. This In many research papers, the model used to estimate the cause-specific hazards will be different from the model used to estimate the cumulative incidence functions. For example, the cause-specific hazard ratios are reported from a Cox proportional hazards regression model but the cumulative incidence functions are estimated non-parametrically and separately for different subgroups of patient-14. WhilMany regression models used to estimate cumulative incidence functions will assume proportional hazards. In large epidemiological studies the assumption of proportional hazards is often unreasonable. Therefore, a model that can easily incorporate time-dependent effects is desirable.In summary, we would like to be able to model competing risks scenarios using the approach that estimates both the cause-specific hazards and the cumulative incidence functions as we believe both to be useful measures. We would like to obtain smooth estimates for both of these measures rather than considering a step function. Finally, we want to be able to incorporate time-dependent effects for one or all of the competing events. Whilst the majority of the above can be addressed within a Cox modelling framework, we feel that parametric models have the advantage of directly estimating cause-specific hazard rates in the model as well as handling non-proportional hazards with ease. For these reasons, we advocate the use of the flexible parametric survival model to obtain both the cause-specific hazards and the cumulative incidence function in a competing risks framework.K different causes, the cause-specific hazard for the kth cause, hk(t) is the rate of failure at time t given that no failure from cause k or any of the K-1 other causes has occurred[If we assume that a patient is at risk from occurred. When thk for a patient with covariates xk can be calculated using the equationAssuming proportional hazards, the cause-specific hazard rate for cause hk,0(t) is the baseline cause-specific hazard for cause k and βk are the covariate effects (log hazard ratios).where Sk(t), through the following transformationOnce the cause-specific hazard has been estimated, many researchers will transform to obtain a survival function, k in a hypothetical world where it is not possible to die from anything else[Under the assumption that the competing events are independent , the complement of the cause-specific survival function can be interpreted as the probability of dying from cause ing else. In manyCk(t), gives the proportion of patients at time t who have died from cause k accounting for the fact that patients can die from other causes.The cumulative incidence function, The cumulative incidence function is not only a function of the cause-specific hazard for the event of interest but also incorporates the cause-specific hazards for the competing events. PreviouWe could apply Equation (4) to any standard parametric model; however, there are very few real world examples where all of the competing events can be adequately captured using a Weibull or exponential model for example. The flexible parametric survival model was first proposed by Royston and Parmar for use Transforming this to the log cumulative hazard scale givest) the flexible parametric model uses restricted cubic splines for ln(t)[This is now a linear function of log-time. However, rather than assuming linearity with ln(or ln(t). The logor ln(t). Finallyor ln(t).Regression splines are piecewise polynomial functions that are forced to join at predefined points on the x-axis. These joining points are known as knots. In order to obtain a smooth function the regression splines are also forced to have continuous first and second derivatives. For restricted cubic splines a further restriction forces the splines to be linear beyond the boundary knots.s(ln(t)|γ, n0), with N knots, a vector of knots n0 and parameters γ0,⋯,γN - 1 can be written asA restricted cubic spline function, 1 ⋯ zN - 1 are calculated as followsThe derived variables zwhere+ = u if u > 0 and 0 if u ≤ 0. Thus, a model with N knots for the baseline log cumulative hazard uses N-1 degrees of freedom.and (u)s(ln(t)|γ, n0), with knot locations n0, and covariates x and can be written asThe baseline log cumulative hazard in a proportional hazards model incorporates the restricted cubic spline function of Covariate effects can be interpreted as log hazard ratios here under the assumption of proportional hazards. The survival and hazard functions can be obtained through a transformation of the model parametersnj, at centiles of the event times. If there are D time-dependent effects, then we can extend Equation (11) as follows:One of the main advantages of the flexible parametric approach is the ease with which time-dependent effects can be fit. Time-denj[The number of spline variables for a particular time-dependent effect will depend on the number of knots, nj.K causes simultaneously. This approach is described in further detail later in the paper.As shown in Equation (4), the cumulative incidence is a function of the cause-specific hazard functions. The cause-specific hazard function can be obtained from the flexible parametric model through Equation (13) by only considering one cause of death at a time and censoring competing events. Alternatively, we can stack the data and fit one model for all The integral in Equation (4) can be obtained numerically. The integration is performed using similar methods to those proposed by Carstensen and LambTwo user-friendly commands have been written in Stata that implement the methodology described in this paper. The command stpm2 will fit a flexible parametric survival model and the Once the cause-specific hazards and the cumulative incidence function have been estimated it is possible to obtain other useful measures through some simple manipulation of the estimates. The relative contribution to the total mortality can be derived as:k given that a death has occurred by time t.This can be interpreted as the probability of having died from cause The relative contribution to the overall hazard can be derived as:k given that a death has occurred at time t.This can be interpreted as the probability of having died from cause One research area that is increasingly making use of competing risks methodology is population based cancer studies. Here we use data obtained from the SEER public use dataset on surviCause of death was categorised into breast cancer, other cancer, diseases of the heart and other causes. Age at diagnosis was categorised into the groups 18–59, 60–69, 70–79 and 80+. Staging of the cancer was classified as localised, regional or distant. Diagnosis of breast cancer was considered as the time origin and follow-up was restricted to 10 years. Table It is possible to fit 4 separate models, one for each cause, to obtain 4 cause-specific hazards. However, to allow for potential shared covariate effects over two or more causes we can fit one model for all 4 causes simultaneously. In order to do this the data needs to be stacked so that each individual patient has 4 rows of data, one for each of the 4 causes. Table k,0(t), therefore, when obtaining the cumulative incidence functions the Breslow method for the cumulative baseline hazard needs to be substituted into Equation (4). However, if the cause-specific hazard rates were required then the baseline hazards would need to be estimates through post-estimation using, for example, kernel smoothing[th, 50th and 75th centiles of the event times for each of the four causes.Both a Cox-proportional hazards model and a flexible parametric proportional hazards model were fitted in order to make a comparison of the two models in terms of both the cause-specific hazard ratios and the cumulative incidence function. The Cox proportional hazards model does not directly estimate the baseline hazard, hmoothing. For theAs shown in Table Table Figure Figure Previous studies have shown a relationship between radiation therapy and cardiovascular mortality-29 and aFigure For the remaining analyses we only considered a flexible parametric non-proportional hazards model. This model included time-dependent effects for age groups 60–69, 70–79 and 80+ for breast cancer and other causes and also for regional and distant stages for breast cancer, other cancer and other causes. These were selected using likelihood ratio tests . All the time-dependent effects were fitted using 4 degrees of freedom and had the same knot locations as those used in the proportional hazards model.Figure Figure Figure Figure Figure All the non-proportional hazard analyses in this paper were carried out using 4 degrees of freedom for both the baseline effects and the time-dependent effects. As a sensitivity analysis, four further models were fitted that compared the number and locations of the knots for the baseline effects and the time-dependent effects of age group and stage. Table We have shown how to estimate both the cause-specific hazards and the cumulative incidence functions using a flexible parametric survival model. This approach provides smooth estimates of the cause-specific hazard and the cumulative incidence function, both of which we consider to be measures of interest. The flexible parametric model can easily incorporate time-dependent effects for one or more of the competing events. We have also illustrated two other useful measures that can be obtained with some simple manipulation of the cause-specific hazard and cumulative incidence estimates.The flexible parametric proportional hazards model produces very similar estimates to the Cox proportional hazards model in terms of both the cause-specific hazard ratios and the cumulative incidence functions. A further alternative is to use a mixture model for competing risks data as proposed by Larson and Dinse,33. HoweThe confidence intervals obtained through the delta method have been shown to be very similar to those obtained through bootstrapping but have the added advantage of taking considerably less time to compute.The assumption of proportional hazards is often unreasonable in epidemiological studies. It is important to understand the changing effect of a covariate over the time period rather than just assuming a constant hazard. For example, a treatment may have a large impact on mortality early on in the follow-up period but this effect could diminish as time goes on. It is, In this paper we have grouped age into four categories for simplicity whilst illustrating the method. However, it may be preferable to model age continuously using regression splines as has been done in previous papers,38.The main advantages of the flexible parametric model are in large studies where time-dependent effects will often play a prominent role. In much smaller studies where there are fewer events there may not always be sufficient information to adequately estimate the underlying hazard using this model.This paper describes modelling cause-specific hazards and using these to obtain the cumulative incidence function. Alternatively, the cumulative incidence function can be modelled directly using, for example, Fine and Grays subdistribution approach. This maUnlike measures of net survival, the cumulative incidence function allows us to present “real world” probabilities where a patient is not only at risk of dying from their cancer but also from any other cause of death. We can also estimate these “real world” probabilities using relative survival. The advFinally, a user friendly program has been written in Stata to enable users to implement the methodology described in this paper. This command is called stpm2cif and is available from the Statistical Software Components (SSC) archive,39.The integral in Equation (4) can be obtained numerically. Using similar methods to those proposed by Carstensen and Lambm, of small intervals.1. The time scale is split into a large number, x0 at each of the m time intervals, tm.2. The integrand of the cumulative incidence function,G be the m × p matrix of observation-specific derivatives then the variance-covariance matrix can be estimated using the equation3. The variance-covariance matrix for the integrandwherem time intervals. In order to do this, a triangular matrix L needs to be created. For example, for three intervals this looks like4. The cumulative incidence function can then be calculated by summing the values of the integrand for the l is the interval length.where kth cause is then calculated using5. The variance-covariance matrix for the cumulative incidence function of the ***Expand the data so that each patient has 4 rows – one for each cause of death***expand 4bysort id: gen cause = _n***Generate indicator variables for each cause of death along with an overall indicator ***gen breast = cause==1gen cancer = cause==2gen heart = cause==3gen other = cause==4gen event = (cause==cod)***Create interactions between age group and causes***gen agebreast = agegrp*breastgen agecancer = agegrp*cancergen ageheart = agegrp*heartgen ageother = agegrp*other***Create dummy variables for each age cause interaction***tab agebreast, gen(agebreast)tab agecancer, gen(agecancer)tab ageheart, gen(ageheart)tab ageother, gen(ageother)***Re-name age cause dummy variables ***foreach var in breast cancer heart other {rename age`var'2 age`var'1rename age`var'3 age`var'2rename age`var'4 age`var'3rename age`var'5 age`var'4}*** Create interactions between stage and causes***gen stagebreast = seerhistoricstage*breastgen stagecancer = seerhistoricstage*cancergen stageheart = seerhistoricstage*heartgen stageother = seerhistoricstage*other***Create dummy variables for each stage cause interaction***tab stagebreast, gen(stagebreast)tab stagecancer, gen(stagecancer)tab stageheart, gen(stageheart)tab stageother, gen(stageother)*** Re-name stage cause dummy variables ***foreach var in breast cancer heart other {rename stage`var'2 stage`var'1rename stage`var'3 stage`var'2rename stage`var'4 stage`var'3}***stset the data to tell Stata we are dealing with survival data***stset exit, origin(dx) failure(event) scale(365.24) exit(time dx + (10*365.24))*** Fit a flexible parametric proportional hazards model using stpm2 command***stpm2 breast cancer heart other agebreast? agecancer? ageheart? ageother? ///stagebreast? stagecancer? stageheart? stageother?, ///scale(hazard) rcsbaseoff nocons ///tvc(breast cancer heart other) initstrata(cause) ///knotstvc(breast 1.37 2.62 4.70 ///cancer 1.00 2.95 5.87 ///heart 1.79 3.87 6.37 ///other 1.95 3.95 6.46) ///bknotstvc(breast 0.038 9.96 ///cancer 0.04 9.96 ///heart 0.04 9.96 ///other 0.04 9.96)***Predict the cumulative incidence functions, the cause-specific hazard rates, the contribution to the total mortality and the contribution to the overall hazard for each covariate pattern using stpm2cif command***forvalues j = 1/4 {forvalues l = 1/3 {if `j'! = 1 {if `l'==1 {stpm2cif breast`j'`l' cancer`j'`l' heart`j'`l' other`j'`l', ///cause1(breast 1 agebreast`j' 1) ///cause2(cancer 1 agecancer`j' 1) ///cause3(heart 1 ageheart`j' 1) ///cause4(other 1 ageother`j' 1) haz conthaz contmort}if `l'! = 1 {stpm2cif breast`j'`l' cancer`j'`l' heart`j'`l' other`j'`l', ///cause1(breast 1 agebreast`j' 1 stagebreast`l' 1) ///cause2(cancer 1 agecancer`j' 1 stagecancer`l' 1) ///cause3(heart 1 ageheart`j' 1 stageheart`l' 1) ///cause4(other 1 ageother`j' 1 stageother`l' 1) haz conthaz contmort}}if `j'==1 {if `l'==1 {stpm2cif breast`j'`l' cancer`j'`l' heart`j'`l' other`j'`l', ///cause1(breast 1) ///cause2(cancer 1) ///cause3(heart 1) ///cause4(other 1) haz conthaz contmort}if `l'! = 1 {stpm2cif breast`j'`l' cancer`j'`l' heart`j'`l' other`j'`l', ///cause1(breast 1 stagebreast`l' 1) ///cause2(cancer 1 stagecancer`l' 1) ///cause3(heart 1 stageheart`l' 1) ///cause4(other 1 stageother`l' 1) haz conthaz contmort}}}}The authors declare that they have no competing interests.SRH and PCL conceived the project. SRH carried out the analysis and extended the software to enable use of the method. Both authors participated in the interpretation of the results. SRH drafted the paper, which was later revised by both authors. Both authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/13/13/prepub


Surprisingly, we found that duration was resilient to spatial and numerical interferences whereas space and number estimation were affected by time. Specifically, and counter-intuitively, results revealed that longer durations lead to smaller number and space estimates whether participants knew before (prospectively) or after (retrospectively) a given trial which quantity they had to estimate. Altogether, our results support a magnitude system in which perceptual evidence for time, space and numbers integrate following Bayesian cue-combination rules.Perceptual interferences in the estimation of quantities  have been interpreted as evidence for a common magnitude system. However, if duration estimation has appears sensitive to spatial and numerical interferences, space and number estimation tend to be resilient to temporal manipulations. These observations question the relative contribution of each quantity in the elaboration of a representation in a common mental metric. Here, we elaborated a task in which perceptual evidence accumulated over time for all tested quantities  in order to match the natural requirement for building a duration percept. For this, we used a bisection task. Experimental trials consisted of dynamic dots of different sizes appearing progressively on the screen. Participants were asked to judge the duration, the cumulative surface or the number of dots in the display while the two non-target dimensions varied independently. In a  Time, space, and numbers can be encoded through all sensory modalities. As such, these dimensions provide a first level of abstract quantification in mental space. Specifically, mental magnitudes can be defined as the neural realization of quantities which afford computational operations akin to arithmetic Space , the longer the estimated duration irrespective of the nature of these events 33 participants were recruited from local universities and compensated for their time. Participants provided their written consent in accordance with the Declaration of Helsinki (2008) and the study was approved by the Ethics Committee on Human Research review boards at NeuroSpin  and UCL . Each participant only took part in one of the two experiments. Each experiment consisted in two sessions which took place on different days within the same week. Taking both experiments together, 3 participants' data were excluded from the study due to poor performance after the first session, , and 1 participant's data were excluded because he did not complete the second session. Data from 2 participants in experiment 1, and 3 participants in experiment 2 were excluded due to poor performance in the second session . Thus, 24 participants were considered in the study .The experiment was coded using Matlab 7.0 and Psychtoolbox 3.0 0.75, X0.9, X0.95, Xmean, X1.05, X1.1 and X1.25, with dimension X being D, S or N; 0.75) and maximum (X1.25) values for each dimension (see Procedure section). In the subsequent tests, participants made a categorical judgment on one of the dimensions: ‘closer to the minimum (−)’ or ‘closer to the maximum (+)’ by pressing one of two keys. Magnitude estimation could be prospective , orthogonal dimensions were set to their mean ; in condition 1 (c1), to their minimal values  and in condition 2 (c2), to their maximal values . In conditions 3 and 4 , one orthogonal magnitude value was maximal (Ymax) whereas the other was minimal (Zmin). The last two conditions allowed us to evaluate the relative weight of each orthogonal dimension.The paradigm was a bisection task . Each taStimuli were displayed on a 1024×768 monitor screen with a 75 Hz frame rate. Participants were seated 60 cm away from the display. Response keys were ‘h’ and ‘j’ keys on the computer keyboard. Each experiment was carried out in two sessions taking place on different days within the same week.mean = 800 ms, Smean = 900 mm2 and Nmean = 28 dots. Three blocks of a short bisection task were then performed independently on each dimension X while orthogonal dimensions were held constant (set to Ymean and Zmean). At the end of each block, for each participant, the WR was extracted and Smean and Nmean were increased or decreased to calibrate task difficulty, resulting in identical WR all three dimensions. Dmean was kept constant (800 ms) for all participants. The final Smean and Nmean were 878±105 mm2 and 27±3 dots respectively.In the first session, stimuli were adjusted individually using the measured participant's Weber Ratios  to equate task difficulty for all three magnitudes. For this, participants were first familiarized with the minimum (−) and maximum (+) values in each dimensions (pre-test), based arbitrarily on TIn the second session, participants performed a pre-test again to recalibrate the minimum and maximum values in each dimensions. They then performed a bisection task in which trials were pseudo-randomized across dimensions and conditions. A total of 900 trials were collected  in 100-trial blocks.The instruction ‘Duration’, ‘Surface’ or ‘Number’ was displayed centrally on the monitor screen either before f using Psignifit 3.0.8 Proportions of ‘+’ responses were computed separately per experiment, dimension and condition. Values were individually fitted to a logistic function PSE, WR and RT data for which values were negative or outside ±3 standard deviations of the mean in each condition were replaced by the mean of the other values in the same condition. Participants for whom more than half the measures failed the criterion were excluded from the analysis. There was no more than one value replaced per condition.Repeated-measure Analyses Of Variance (ANOVAs) were performed on PSEs, WRs and RTs using the IBM SPSS software (Version 19.0). A Greenhouse-Geisser correction was applied when appropriate. Post-hoc Bonferroni-corrected paired t-tests were performed to explore significant main effects or interactions.Repeated-measure ANOVAs with WR as dependent variable and factors of magnitude  and condition (5) were conducted separately for the prospective and retrospective experiments. No main effects or interactions were found. This strongly suggests that participants’ sensitivity to the tested magnitudes did not vary across tasks and conditions, indicating that task difficulty was successfully matched across magnitudes .12,4 = 5.327, p = .015, ηp2 = .326) and a marginal interaction of magnitude with condition . Overall, manipulating orthogonal magnitudes significantly influenced the target magnitude estimation but this effect was not consistently observed for each target magnitude  and condition (5) revealed a main effect of condition  were significantly higher than PSE1  : surfaces were surprisingly overestimated when few dots were presented for a short duration. Additionally, surfaces were judged to be smaller when duration and number were maximal than when they were minimal . Similarly, in numerosity estimation, PSE0  were significantly higher than PSE1  : numerosity was overestimated when the surface and the duration of dots were smallest. PSE1  were also significantly lower than PSE2  , suggesting that numerosity was estimated to be largest when duration and surface were smallest. PSE1  were significantly smaller than PSE4  , showing that with longer durations, numerosity was underestimated. Unexpectedly, neither surface nor numerosity significantly interfered with duration estimation. Hence, two surprising observations were that duration estimation appeared resilient to changes in other dimensions  and a significant interaction of magnitude with condition  suggested that manipulating orthogonal dimensions significantly affected target magnitude estimation  and condition (5) were conducted. A main effect of condition . As can be seen in 1 , c4 , c0 , c3  to c2 . Specifically, surfaces were overestimated when presented with few dots for a short duration but underestimated when presented with many dots for a long duration (PSE1<PSE0 and PSE2>PSE0 respectively). Consistent with the prospective experiment, combined duration and numerosity negatively interfered with surface estimation. Additionally, these results suggest that numerosity interfered more with surface estimation than duration did: when the number of dots was minimal (PSE1 and PSE4), surfaces were overestimated in comparison to PSE0 irrespective of duration; when the number of dots was maximal (PSE2 and PSE3), surfaces were underestimated relative to PSE0 irrespective of duration.In number estimation, PSE0  were lower than PSE2   and PSE1  were lower than PSE2   and PSE4  . In comparison to c0, numerosity was underestimated when surface was maximal over the longest duration (PSE2>PSE0); numerosity was smallest when surface and duration were largest (PSE2>PSE1). Duration had a stronger influence than surface on numerosity: a change in duration produced a significant change in estimates of numerosity (PSE1<PSE4) whereas a change in surface alone did not significantly interfere with numerosity. Overall, both surface and duration negatively interfered with numerosity estimations with a predominant effect of duration.Similarly in duration estimation, PSE1  were significantly higher than PSE3  : for the smallest surface, duration estimation increased with number of dots. Thus numerosity influenced duration in the same direction . However, the absence of any other difference between conditions (in particular none involving conditions c1 and c2) indicates that orthogonal magnitudes interfered very little with duration estimation, in agreement with results in the prospective task.In Overall, the trends in PSE changes were comparable in both experiments: surface and numerosity showed little-to-no interference with the estimation of duration see also  whereas Importantly, overall performance was not affected by task manipulation: a 2 (prospective vs. retrospective) × 3  × 5 (conditions) mixed-design repeated-measure ANOVAs with PSE and WR as dependent variables revealed no main effect or interaction involving the factor experiment (prospective vs. retrospective). This negative finding suggests that increasing the cognitive load by attending to all three rather than one magnitude did not impact participants' performance or pattern of responses.Below, we report the analysis of reaction times in both experiments . RT meas12,5 = 8.743, p≤.001, ηp2 = .443). Paired Student t-tests across tasks showed that participants responded significantly faster to X0.75 than to X0.9 , and that X1.25 was responded to significantly faster than X0.9, X0.95, X1.05 and X1.1 . This is consistent with the distance effect A repeated-measure ANOVA with RT as dependent variable and factors of magnitude  and magnitude quantity  revealed a main effect of quantity  and of quantity . Paired Student t-tests across tasks showed that participants responded significantly faster to X0.75 than to X0.9 . No other differences were observed indicating that RTs were little affected by quantity. However, paired Student t-tests across quantities showed that participants were significantly faster to estimate surface than duration  suggesting a magnitude effect in which duration is longest to retrieve when all three dimensions are held in memory (A repeated-measure ANOVA with RT as dependent variable and factors of magnitude (3) and quantity (6) revealed a main effect of magnitude  compared to space and numbers . Third, a leaky loss of information over time would predict that the distance effect for short durations should be more pronounced than for larger durations: we computed the distance effect on space and number separately for minimal and maximal duration trials and did not observe any significant differences as a function of duration . HoweverInterestingly, the increased cognitive load introduced in the retrospective experiment did no significantly interfere with any of the magnitude estimations as compared to prospective judgments. These results were unusual in light of time perception research: previous results showed that during a prospective time estimation task, increasing the cognitive load or driving attention away from temporal monitoring systematically leads to time compression How then can we reconcile the lack of interference on time estimation with an automatic magnitude mapping? Recent computational advances have successfully addressed the problem of multiple cues combination using Bayesian principles de facto dominant considering that full evidence was provided as soon as a stimulus was displayed. As such, most interactions were dominated by either space or number but seldom by time. By introducing a task in which time naturally dominated, the opposite direction was observed. We thus suggest that our results converge with a Bayesian principle for dimension integration in a common magnitude representation. For instance, negative interference of number on surface judgment was observed between space and numbers in the retrospective experiment. In our design, for a given number of dots, larger surfaces contained on average bigger dots yielding to an underestimation of cumulative surface; conversely, for a given surface, a large number of smaller dots on average were displayed yielding to an overestimation of surface. This compensatory mechanism could be predicted when evidence from multiple dimensions has to be integrated over time. Additional research could further explore the directionality of space and number interactions when they accumulate over time, for example by maintaining the duration constant and manipulate spatial and numerical information independently.Applying an analogous principle to the magnitude system, magnitude representation could be estimated based on the integration of all quantities estimates. The weight of information provided by each magnitude dimension could depend on two factors: the precision of the estimate in the corresponding dimension, and the mental distance between dimensions – determined by how strong the system believes that information provided by one magnitude dimension is related to another. The smaller the mental distance, the more integration (or interference) across dimensions should be observed whereas the larger the mental distance, the more dominant a dimension should become in the representation of magnitude. In our task, the weights of time, space and number were equated by design (same Weber Ratios) but the belief was skewed by time as surface and number strongly depended on the time over which the evidence accumulated. Hence, this paradigm enabled to strengthen the belief of the system that duration inversely predicted other dimensions . Here, it is unclear whether the pattern of results fit an interpretation as cue integration (i.e. cue integration increases with the belief that duration predicts other dimensions) or as an “all-or-none” time-dominant effect (i.e. integration occurs when the strength of belief reaches a certain threshold). Irrespective, our results are compatible with the observation that time is rarely observed to affect spatial and numerical judgments yet can, under certain conditions, dominate quantity estimations. By far, most studies have used paradigms in which space or number were By imposing evidence accumulation on all magnitude estimations, the present study showed that time can become resilient and in fact strongly interfere with space and numbers. It is here proposed that the encoding of dimensions rely on cue-combination mechanisms ultimately leading to an integrated magnitude representation Figure S1Scatterplots illustrating the effect of duration on spatial and numerical judgments. Data points show individual PSE in the surface and number tasks in a condition where duration is maximal against a condition in which duration is minimal while surface or number are held constant. (A) Influence of duration on surface judgments in the prospective (top) and retrospective (bottom) experiments. On the left panels, number is maintained at maximal value (Nmax = 1.25×Nmean) whereas duration is either minimal (c3: Dmin = 0.75×Dmean) or maximal (c2: Dmax = 1.25×Dmean). On the right panels, number is maintained at minimal value (Nmin = 0.75×Nmean) whereas duration is either minimal (c1: Dmin = 0.75×Dmean) or maximal (c4: Dmax = 1.25×Dmean). (B) Influence of duration on number judgments in the prospective (top) and retrospective (bottom) experiments. On the left panels, surface is maintained at maximal value (Smax = 1.25×Smean) whereas duration is either minimal (c3: Dmin = 0.75×Dmean) or maximal (c2: Dmax = 1.25×Dmean). On the right panels, surface is maintained at minimal value (Smin = 0.75×Smean) whereas duration is either minimal (c1: Dmin = 0.75×Dmean) or maximal (c4: Dmax = 1.25×Dmean).(TIF)Click here for additional data file.Figure S2Scatterplots illustrating the absence of spatial and numerical effects on duration judgments. Data points show individual PSE in the duration task in a condition where surface (resp. number) is maximal against a condition in which surface (resp. number) is minimal while number (resp. surface) is held constant. (A) Influence of surface on duration judgments in the prospective (top) and retrospective (bottom) experiments. On the left panels, number is maintained at maximal value (Nmax = 1.25×Nmean) whereas surface is either minimal (c3: Smin = 0.75×Smean) or maximal (c2: Smax = 1.25×Smean). On the right panels, number is maintained at minimal value (Nmin = 0.75×Nmean) whereas surface is either minimal (c1: Smin = 0.75×Smean) or maximal (c4: Smax = 1.25×Smean). (B) Influence of number on duration judgments in the prospective (top) and retrospective (bottom) experiments. On the left panels, surface is maintained at minimal value (Smin = 0.75×Smean) whereas number is either minimal (c1: Nmin = 0.75×Nmean) or maximal (c3: Nmax = 1.25×Nmean). On the right panels, surface is maintained at maximal value (Smax = 1.25×Smean) whereas number is either minimal (c4: Nmin = 0.75×Nmean) or maximal (c2: Nmax = 1.25×Nmean).(TIF)Click here for additional data file.Figure S3Influence of trial duration on distance effect in the surface (left) and number (right) tasks. Distance effects have been computed separately for trials in which duration is Dmin and trials in which duration equal Dmax. No significant difference was found between duration conditions for either task. Error bars show standard error of the mean.(TIF)Click here for additional data file.Table S1Post-hoc t-tests comparing PSE in different conditions within each modality, for the prospective (A) and retrospective (B) experiments. Results are Bonferroni-corrected (significance threshold  = .005). Values in the table are t values .(DOCX)Click here for additional data file.


Akkermansia muciniphila, was about 4-fold higher in CRC (p<0.01). Proportionately higher amounts of butyrate were seen in stool of healthy individuals while relative concentrations of acetate were higher in stools of CRC patients. GC-MS profiling revealed higher concentrations of amino acids in stool samples from CRC patients and higher poly and monounsaturated fatty acids and ursodeoxycholic acid, a conjugated bile acid in stool samples from healthy adults (p<0.01). Correlative analysis between the combined datasets revealed some potential relationships between stool metabolites and certain bacterial species. These associations could provide insight into microbial functions occurring in a cancer environment and will help direct future mechanistic studies. Using integrated “omics” approaches may prove a useful tool in identifying functional groups of gastrointestinal bacteria and their associated metabolites as novel therapeutic and chemopreventive targets.In this study we used stool profiling to identify intestinal bacteria and metabolites that are differentially represented in humans with colorectal cancer (CRC) compared to healthy controls to identify how microbial functions may influence CRC development. Stool samples were collected from healthy adults (n = 10) and colorectal cancer patients (n = 11) prior to colon resection surgery at the University of Colorado Health-Poudre Valley Hospital in Fort Collins, CO. The V4 region of the 16s rRNA gene was pyrosequenced and both short chain fatty acids and global stool metabolites were extracted and analyzed utilizing Gas Chromatography-Mass Spectrometry (GC-MS). There were no significant differences in the overall microbial community structure associated with the disease state, but several bacterial genera, particularly butyrate-producing species, were under-represented in the CRC samples, while a mucin-degrading species,  Helicobacter pylori has been established A healthy gastrointestinal system relies on a balanced commensal biota to regulate processes such as dietary energy harvest Streptococcus infantarius (formerly S. bovis) became diagnostically important after it was recognized that bacteremia due to this organism was often associated with colorectal neoplastic disease Prevotella group was over-represented in both stool and mucosa samples from individuals with colon cancer compared to their cancer-free counterparts. They also found that Bifidobacterium longum, Clostridium clostridioforme, and Ruminococcus bromii were underrepresented in samples from these individuals and concluded that a lack of correlation between tumor stage/size with the over-represented populations suggested a contributory role of the bacteria in tumor development. Two additional studies, published concurrently, examined the microbiota present in the tumor mucosa and adjacent healthy tissue of individuals with colon cancer and both studies revealed an overrepresentation of Fusobacterium sppCoriobacteria and other probiotic species An association between colorectal cancer (CRC) and commensal bacteria has been suspected for decades. For example, The question remains whether over-representation of particular microbial species in stool and mucosal samples is indicative of a contributory role in the development of CRC or a consequence of the tumor environment. Although a causal role of intestinal biota in CRC development has not been demonstrated, there is evidence to suggest that induction of pro-inflammatory responses by commensals contribute to tumor initiation and development To clarify the role of intestinal biota in the development of CRC, it will be necessary to move beyond taxonomic over-representation and examine changes in the CRC associated microbiome in a more functional context. One important functional parameter is how commensal organisms contribute to the flux of metabolites and the breakdown of dietary components. Thus, metabonomics, the study of global changes in metabolites in response to biological stimuli All individuals provided written informed consent prior to participating in the study. All study protocols were approved by Colorado State University (Protocol numbers 10-1670H and 9-1520H) and Poudre Valley Hospital-University of Colorado Health System’s Institutional Review Boards (Protocol numbers 10-1038 and 10-1006).Stool samples were collected from healthy individuals (n = 11) and recently diagnosed colon cancer patients (n = 10) prior to surgery for colonic resection  footnoteAmplification of the V4 region of the bacterial 16S rRNA gene was performed in triplicate using primers 515F and 806R labeled with 12-bp error correcting Golay barcodes http://www.mothur.org); (iii) filtered to remove vertical gaps; (iv) screened for potential chimeras using the uchime method; (v) classified using the Green Genes database (http://www.mothur.org) and the naïve Baysian classifier obsS), Chao1 estimates of total species richness (ChaoS), Shannon’s diversity (H’) and evenness (HE), and Simpson’s diversity (1-D) and evenness (ED) were determined by fitting a 3-parameter exponential curve [y = y0+ a(1-e−bx)] to rarified parameters over a range of 100 to 1250 sequence reads, where the asymptotic maxima is equal to the sum of y0 and a. Effective number of species were calculated as SH = exp (H’) for the Shannon’s index and SD = 1/D for Simpson’s. All sequence data is publicly available through the Sequence Read Archive (SRA) under study accession number ERP002217, which is available at the following link: http://www.ebi.ac.uk/ena/data/view/ERP002217.All sequence read editing and processing was performed with Mothur Ver. 1.25 m/z were scanned at 5 scans/sec after electron impact ionization. For each sample, a matrix of molecular features as defined by retention time and mass (m/z) was generated using XCMS software www.nist.gov) and Golm (http://gmd.mpimp-golm.mpg.de/) metabolite databases for identifications.One hundred milligrams of lyophilized stool sample were extracted two times with 1 ml of 3∶2∶2 isopropanol:acetonitrile:water spun at 14,000 rpm for 5 minutes and the supernatants were combined. The extract was dried using a speedvac, resuspended in 50 µL of pyridine containing 15 mg/mL of methoxyamine hydrochloride, incubated at 60°C for 45 min, sonicated for 10 min, and incubated for an additional 45 min at 60°C. Next, 50 µL of N-methyl-N-trimethylsilyltrifluoroacetamide with 1% trimethylchlorosilane  was added and samples were incubated at 60°C for 30 min, centrifuged at 3000×g for 5 min, cooled to room temperature, and 80 µL of the supernatant was transferred to a 150 µL glass insert in a GC-MS autosampler vial. Metabolites were detected using a Trace GC Ultra coupled to a Thermo DSQ II (Thermo Scientific). Samples were injected in a 1∶10 split ratio twice in discrete randomized blocks. Separation occurred using a 30 m TG-5MS column  with a 1.2 mL/min helium gas flow rate, and the program consisted of 80°C for 30 sec, a ramp of 15°C per min to 330°C, and an 8 min hold. Masses between 50–650 SCFA determination. Stool samples were extracted for short chain fatty acids by mixing 1 g of frozen feces with acidified water (pH 2.5) and sonicated for 10 min. Samples were centrifuged and filtered through 0.45 µM nylon filters and stored at −80°C prior to analysis. The samples were analyzed using a Trace GC Ultra coupled to a Thermo DSQ II scanning from m/z 50–300 at a rate of 5 scans/second in electron impact mode. Samples were injected at a 10∶1 split ratio, and the inlet was held at 22°C and transfer line was held at 230°C. Separation was achieved on a 30 m TG-WAX-A column  using a temperature program of 100°C for 1 min, ramped at 8°C per minute to 180°C, held at 180°C for one minute, ramped to 200°C at 20°C/minute, and held at 200°C for 5 minutes. Helium carrier flow was held at 1.2 mL per minute. Peak areas were integrated by Thermo Quan software using selected ions for each of the short chain fatty acids, and areas were normalized to total signal.Differences in bacterial phylotypes and global metabolites between samples from healthy individuals and colon cancer patients were determined using AMOVA and student’s t-tests with a significance cutoff of <0.01. Phylotypes and metabolites that were significantly different between groups were further refined by removing markers that had fewer than 25 total reads (bacteria) or borderline background signals (metabolites) or that were present in fewer than 3 individual samples. Short chain fatty acid concentrations were determined in two separate chromatographic runs, so a weighted mean was calculated for each quantified compound and statistical differences between stool samples from healthy individuals and colon cancer patients were determined using a mixed model ANOVA with experiment representing a random effect and disease status as a fixed effect . Correlations between metabolites and bacteria were determined using Pearson’s r with a moderate correlation denoted by an r≥0.50 and a strong correlation denoted by an r≥0.70.Helicobacter pylori), there are marked decreases in estimates of diversity and evenness H = 63, SD = 20) compared to those from CRC patients ; however, the inter-individual variation was too great to achieve statistical significance. Based on these data, we suggest that alpha diversity descriptors of stool microbiota are not indicative of disease state in CRC; although a limitation of this study is that only stool samples and not tissue mucosa were analyzed. However, despite inherent differences in stool and mucosal microbial communities our findings are consistent with other published reports of total bacterial diversity and evenness estimates between CRC and healthy stool and tissue/mucosasamples Typical community descriptors of alpha diversity for molecular microbial data include actual and estimated OTU richness, and indices of population diversity and evenness. In systems where pathogens are introduced (e.g. class) to compare community membership (YC) to compare community structures  and Ruminococcus obeum , both members of the Clostridium XIVa group, suggesting that differences between cohorts with regard to these two species may be a result of differences in the mean age of participants in each group rather than CRC disease status. To our knowledge, a decline in the population of Clostridium XIVa group members has not been previously associated with aging, but has been associated with dysbiosis related to intestinal inflammatory conditions such as Crohn’s disease Age and BMI represent other factors that play a role in shaping the intestinal microbial communities. Several reports have demonstrated a correlation between the ratio of Bacteroidetes to Firmicutes and obesity Ruminococcus spp. and Pseudobutyrivibrio ruminis, were lower in stool samples from CRC patients compared to healthy controls. Therefore, we quantified several short chain fatty acids from frozen stool samples. The three major SCFAs produced as microbial metabolites, acetate, propionate, and butyrate, were all detected as were valeric, isobutyric, isovaleric, caproic, and heptanoic acids. Among these, acetic and valeric acids were significantly higher in stool samples from CRC patients (p<0.0001 and p = 0.024 respectively) while butyric acid was significantly higher in the feces of healthy individuals , particularly butyrate, are widely studied microbial metabolites reported to have anti-tumorigenic effects Stool samples allow for evaluation of bacteria residing in the intestinal lumen, and therefore, stool small molecules are considered to result from co-metabolism or metabolic exchange between microbes and host cells Akkermansia muciniphila, a mucin-degrading bacteria, observed in CRC stool samples; although we saw no strong correlations between the relative proportion of these bacteria and specific amino acid concentrations.Compared to healthy controls, stool metabolome analysis revealed 11 amino acids that showed a 41–80% increase in stool samples of individuals with CRC . ReasonsThere were higher levels of glycerol as well as several unsaturated fatty acids detected in the stool samples of healthy individuals. Human cancer cells have a known transport system for the uptake of glycerol, suggesting stool glycerol may be lower in CRC because it is being taken up by the tumor cells. Alternatively, bacterial lipases present in healthy individuals may facilitate the metabolism of dietary and endogenously produced triacylglycerols, resulting in the final degradation products of glycerol and free fatty acids. In addition to glycerol, fatty acids most closely matching metabolomic signatures for linoleic acid, and stereoisomers of oleic acid were also higher in controls . FinallyBacteroides finegoldii, two Dialister spp., and P. ruminis were strongly correlated, and Bacteroides intestinalis and Ruminococcus obeum were moderately correlated with increased stool free fatty acids and glycerol Click here for additional data file.Table S2Linear regressions of selected bacterial taxa with participant BMI.(DOCX)Click here for additional data file.Table S3Linear regressions of selected bacterial taxa with participant age.(DOCX)Click here for additional data file.


Saccharomyces paradoxus across wide swaths of their natural genotypic and phenotypic space. Surprisingly, environment-specific influences of ploidy on reproduction were found to be the rule rather than the exception. These ploidy–environment interactions were well conserved across the 2 billion generations separating the two species, suggesting that they are the products of strong selection. Previous hypotheses of generalizable advantages of haploidy or diploidy in ecological contexts imposing nutrient restriction, toxin exposure, and elevated mutational loads were rejected in favor of more fine-grained models of the interplay between ecology and ploidy. On a molecular level, cell size and mating type locus composition had equal, but limited, explanatory power, each explaining 12.5%–17% of ploidy–environment interactions. The mechanism of the cell size–based superior reproductive efficiency of haploids during Li+ exposure was traced to the Li+ exporter ENA. Removal of the Ena transporters, forcing dependence on the Nha1 extrusion system, completely altered the effects of ploidy on Li+ tolerance and evoked a strong diploid superiority, demonstrating how genetic variation at a single locus can completely reverse the relative merits of haploidy and diploidy. Taken together, our findings unmasked a dynamic interplay between ploidy and ecology that was of unpredicted evolutionary importance and had multiple molecular roots.The number of chromosome sets contained within the nucleus of eukaryotic organisms is a fundamental yet evolutionarily poorly characterized genetic variable of life. Here, we mapped the impact of ploidy on the mitotic fitness of baker's yeast and its never domesticated relative  + transporter to another completely altered the relative merits of having one or two chromosome sets when exposed to high Li+ concentrations. This complex and dynamic interplay between the number of chromosomes sets and the fluctuating environment must be taken into account when considering organismal form and behavior.Organisms vary in the number of chromosome sets contained within the nucleus of each cell, but neither the reasons nor the consequences of this variation are well understood. We designed yeasts that differed in the number of chromosome sets but were otherwise identical and mapped the consequences of such ploidy variations during exposure to a large palette of environments. Contrary to commonly held assumptions, we found ploidy effects on the mitotic reproductive capacity of yeast to be the rule rather than the exception and to be highly evolutionarily conserved. Furthermore, our data rejected previously contemplated hypotheses of generalizable advantages of haploidy or diploidy when cells face nutrient starvation or are exposed to toxins or increased mutation rates. We also mapped the molecular processes mediating ploidy–environment interactions, showing that cell size and mating type locus composition had equal explanatory power. Finally we show that ploidy effects can be mechanistically very subtle, as a designed shift from one plasma membrane Li Ploidy varies throughout the tree of life, with ancient polyploidization events close to the angiosperm Saccharomyces cerevisiae, reproduces asexually in stable haploid, diploid and polyploid forms and has emerged as a key model for ploidy research. Here, we exhaustively mapped the impact of ploidy on the mitotic fitness of S. cerevisiae and its never domesticated relative Saccharomyces paradoxus across wide swaths of their genotypic and phenotypic space. Influences of ploidy on asexual proliferation in different ecological contexts were found to be the rule rather than the exception with the majority of ploidy effects being well conserved over the 2 billion generations separating the two species The unicellular baker's yeast, S. cerevisiae and 27 S. paradoxus natural isolates  and 14.5%  of the variance in strain pair similarity. However, the species divergence had essentially no impact on the effect of ploidy on traits, explaining only 2.5%  of the similarity between strains with regards to ploidy-environment interaction , is almost completely determined by recent gene amplifications of the copper chelating metallothionein CUP1 and Li+ exporter ENAENA mediated lithium efflux, the three S288c ENA genes ENA1,2 and 5, which derive from a single non-ancestral ENA variant recently introgressed from S. paradoxus into the European S. cerevisiae population and later amplified in tandem in S288c MATα haploids and MATa/α diploids. Deletion of the ENA genes rendered both haploids and diploids hypersensitive to LiCl. Remarkably, when measuring the growth efficiency of Li+ exposed ena1Δ2Δ5Δ cells at 30 mM LiCl, causing a roughly similar trait reduction as that of WT cells exposed to 225 mM LiCl, we found the ploidy effect on efficiency to be not only obliterated but actually reversed by removal of the ENA genes , 1n (a) and 1n (α) strains, these were cultivated in environments favoring either S288c haploidy or diploidy  in yeast are naturally heterozygous at the mating-type locus (α/a), whereas haploids (1n) contain only one type of genetic information at this locus, either diploidy . Signifiposition . 12.5% oposition . The supposition . Thus, aS. cerevisiae lab strain gametes of complementary mating types mate and diploidize after only a few rounds of haploid clonal reproduction, thereafter maintaining diploid mitosis until nutrients in the local environment are exhausted S. cerevisiae and S. paradoxus during periods of small population sizes. Furthermore, the routine approximation of yeast asexual reproduction to fitness S. cerevisiae and S. paradoxus suggests such a decoupling to be unlikely to explain the bulk of the observed effects. In fact, it implies strong selection to have acted on the ploidy-environments interactions in both these species since the time of their divergence. This leaves the alternative explanation; that the tendency towards diploidization is not a universal feature of S. cerevisiae in natural habitats. The recent emergence of yeast population genomics S. cerevisiae properties vary within surprisingly wide boundaries. Ploidy preference, varying enormously between yeast species but unstudied over a wider section of the genotypic and ecological space of S. cerevisiae, may be similarly fleeting, as supported by a surprisingly large natural variation in ploidy at micro-ecological scales Mutation rates are thought to be independent of ploidy state MATa or MATα to MATa/MATα. This mediates a shift from haploid to diploid specific transcription programs and from preparedness to mate to readiness to pass through meiosis and later sporulation MATa/MATα S288c diploids through the a1-α2 repression of the NEJ1 transcription factor A natural shift from haploidy to diploidy also alters mating type locus composition, from +, we traced these effects to the presence of the Ena lithium pumps. Given the enormous influence of the ENA locus on asexual reproductive efficiency in lithium environments + by Ena proteins completely controls Li+ efflux at intermediate [H+] + exposure was not only obliterated by removal of the ENA genes, but reversed, now favoring diploids. This suggests that ploidy has reverse impacts on the Nha1 and the Ena systems, illustrating how a simple molecular shift can completely alter the relative merits of haploidy and diploidy in a particular environmental context. This is consistent with a recent finding that adaptive mutations emerging and driving towards fixation in evolving laboratory populations have different effect sizes when reconstituted individually in haploid and diploid genomic contexts +, appears to be largely unregulated and expressed at basal levels An increase in ploidy often has similar effects on cell size through repression of the G1 cyclin linking cell cycle progression to cell size Although alterations in cell volume-to-surface area ratios may mediate many cell size dependent ploidy-environments interactions, it should be noted that also organelle volume-to-surface area ratios fluctuate as a function of cell size and environmental context. Expansions and fragmentations of yeast vacuoles S. cerevisiae and 27 Saccharomyces paradoxus isolates, corresponding to known yeast populations, geographic origins and source environments (URA3 (KanMX) and HO (HygMX), mating and sporulation, haploid (MATa and MATα) and autodiploid (MATa/MATα) were obtained 24 ronments , were is2 transformed ij, were calculated by normalization of each measurement to an internal (WT) standard  as:kjwt is the trait measure of the kth measurement of the wild type for trait j, ijx is the measure of strain i for trait j and r indicates the run. To maintain directionality between the mitotic fitness components, the measure for proliferation efficiency was inverted. Note that the lag measures generally should be treated with caution due to its higher sensitivity to bias. For example, it cannot be excluded that some early growth is misclassified as a lag, due to the cell density increase being below the threshold of detection.The rate (population doubling time), lag (population adaptation time) and efficiency  of asexual reproduction were extracted from high density growth curves and logMATα haploids (n = 32), MATa haploids (n = 16) and diploids (n = 16) were tested. Ploidy effects were calculated as the mean difference between haploid and diploid phenotypes. Statistical significance of trait differences between haploids and diploids was tested using a two-tailed homoscedastic Student's t-test False Discovery Rates (α = 0.05) were applied to account for multiple hypotheses testing To compare haploid and diploid asexual proliferative capacity, a mean of the two mating types (each n = 2) was used to derive a single measure of haploid performance. This was compared to that of the diploid (n = 4). For S288c, a substantially higher number of Hierarchical clustering, as outlined in 2) between strains, after omitting missing values and after scaling phenotype values across strains to unit variance. In order to quantify the contribution of species divergence, population structure and source environments to the observed variation in such strains we performed one-way analysis of variance (ANOVA) with the function lm in R. In three separate analyses, we assumed equal variances and tested (F-test) for differences in the means for pairs of strains (i) between and within species, (ii) between and within population, (iii) and between and within source environment. For significant effects, we used adjusted r2 values to quantify the explained variance.Similarities between pairs of yeast strains was calculated similarly for ploidy effects and haploid and diploid phenotypes. The similarity between two strains was calculated as the Pearson correlation coefficients (rgene x::kanMX6) in the S288c derivatives BY4741 and BY4743 (http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html) and were previously determined as being cell size extremes Large (n = 10) and small (n = 10) haploid strains and large (n = 29) and small (n = 20) diploid strains  were culMATa and MATα respectively were individually deleted in a/α diploid S288c, start to stop, using a KanMX6 cassette as described MATa and MATα haploids and the MATa, MATα and MATa/MATα diploids  were pre-cultivated and cultivated in a subset of environments  and MATa/MATα diploids  were pre-cultivated and cultivated in conditions with and without LiCl. As the deletion of ENA1,2 and 5 renders cells hypersensitive to, [LiCl] was reduced to 30 mM in order to obtain a reduction of mitotic fitness roughly comparable to that observed in WT cells exposed to 0.225M LiCl.A haploid derivate of S288c, BY4741, lacking 7 cells were recovered by centrifugation and washed with 1 mL of PBS buffer . Cells were fixed with 1 mL of cold 70% ethanol and incubated 1 h at room temperature. After washing with PBS buffer, cells were resuspended in 300 µL of 50 mM sodium citrate containing 0.1 mg/mL RNAse A and incubated overnight at 37C. Finally, cells were washed and resuspended in 500 µL of PBS buffer and sonicated to disrupt aggregates (3×10 s). 2 µL of 1 µg/µL PI was added to each sample and incubated at 37C for 20 min. DNA content was determined using a FACSAria cytometer (BD Bioscience). Counting in total 10.000 events (cells), the number of events (cells) as a function of signal intensity (DNA content) was determined using the 488 nm line of an argon-ion laser for PI excitation and reading the PI emission at 578 nm.Quantification of DNA content by flow cytometry was carried out using propidium iodide (PI) staining as described Figure S1Ploidy–environments interactions are as likely to favor haploidy and diploidy, independent of population or source environment. The overall asexual performance of haploids and diploids from distinct populations and source habitats was compared. All environments were considered but each mitotic fitness component was investigated separately. No significant general difference between the two ploidy states  were found considering any population or source environment. Note that data is shown on a log(2) scale. Error bars represent SEM. A) Population B) Source habitat.(PDF)Click here for additional data file.Figure S2Ploidy–environments interactions are as likely to favor haploidy and diploidy, independent of strain. The overall asexual reproductive performance of haploids and diploids from distinct genetic backgrounds was compared. All environments were considered but each mitotic fitness component was investigated separately. No significant general difference between the two ploidy states  were found considering any strain. Note that data is shown on a log(2) scale. Error bars represent SEM.(PDF)Click here for additional data file.Figure S3S. cerevisiae (blue) and S. paradoxus (red) strains in an environment with nutrient excess and no stress where the yield is limited by the amount of glucose (2%) that is present. Broken lines represent a 1∶1 correlation (null hypothesis expectation). Note that data is shown on a log(2) scale.Trade-offs between yeast ploidy states in optimal environments. Performance of haploid (n = 4) and diploid (n = 2) versions of individual (PDF)Click here for additional data file.Figure S4S. cerevisiae strains L-1528, Y12, DBVPG6765 and UWOPS83-783.3 and S. paradoxus strain N-44, after cultivation in absence and presence of 20 µg/mL doxorubicin, 100 µg/mL cisplatin or 15 mg/mL hydroxyurea. DNA of stationary phase cultures were stained with propidium iodide (PI) and analyzed by FACS cytometry, counting 10.000 events (cells). The number of events (cells) as a function of signal intensity (DNA content) was determined. Peak positions of haploid and diploid populations, corresponding to G1 and G2 phases with replicated and non-replicated DNA respectively, are indicated (arrows). Note that DBVPG6765 cells are highly sensitive to doxorubicin and largely arrested in G2, explaining the absence of G1 peak. Correspondingly, N-44 cells are highly sensitive to cisplatin. Partial arrest in different phases can also be seen for other populations, accounting for much of strain variations in relative heights of G1 and G2 peaks for different strains.Short-term proliferation in presence of DNA damage inducing agents does not alter ploidy states of haploid or diploid populations. The relative number of cells with a particular DNA content was quantified for haploid and diploid populations of (PDF)Click here for additional data file.Figure S5S. cerevisiae universal reference strain S288c. Note that data is shown on a log(2) scale. Error bars represent SEM.Ploidy–environment interactions in the S288c reference strain. Fitness traits with a significant  difference between haploids (n = 48) and diploids (n = 16) of the (PDF)Click here for additional data file.Figure S6+ exposure is independent of the main Li+ exporter Ena. The three tandemly amplified ENA genes, ENA1,2 and 5, was deleted in the S288c derivative BY4741. The haploid deletion strain was autodiploidized through mating type switching. The resulting ena1Δ2Δ5Δ haploids and diploids showed a vast increase in population doubling time relative the WT during Li+ exposure, necessitating a substantial reduction in [LiCl]. WT haploids (n = 16) and diploids (n = 16) and ena1Δ2Δ5Δ haploids (n = 8) and diploids (n = 56) were microcultivated in 0.225M and 0.03M LiCl respectively and population doubling times were extracted. Note that data is shown on a log(2) scale. Error bars = SEM. P-values correspond to a homoscedastic, two-tailed Student's t-test. The ploidy dependence of the doubling time during Li+ exposure was unaffected by removal of the ENA genes, diploids growing significantly faster than haploids. Hence, the ploidy effect of Li+ on mitotic growth rate is independent of the extrusion of Li+ via the ENA genes.The ploidy dependence of the mitotic growth rate during Li(PDF)Click here for additional data file.Table S1S. cerevisiae or three S. paradoxus clean populations strain belongs to. For mosaic strains (containing genetic information from more than one population), the population donating the majority of the genetic information is indicated. “Source” refers to the source environment from which the strain was originally isolated. For YS9 (MATa), and 322134S (MATa), only one mating type was tested.Natural yeast isolates used in the study. “Population” refers to which of five (DOC)Click here for additional data file.Table S2Environments used in the screen. “Carbon source” indicates that 2% glucose was substituted with the indicated concentration of the relevant carbon source. “Nitrogen source” indicates that 0.5% ammonium sulfate was substituted with the relevant nitrogen sources at nitrogen limiting concentrations (corresponding to 29 µg N/mL). Except for the addition of 20 mg/L uracil, which cannot be used as nitrogen source by any of the strains, no other nitrogen was supplied. Furthermore, two consecutive pre-cultures were performed to deplete internal storages of nitrogen; the first was performed using nitrogen limiting amounts of ammonium sulfate, the second using nitrogen limiting amounts of the indicated nitrogen source. “Nutrient depletion” indicates that experiments were performed in medium completely lacking the indicated nutrient.(DOC)Click here for additional data file.Table S3Gene deletion strains used to screen for cell size effects on mitotic performance. Single gene deletions in the BY4741 (haploid) or, as homozygotes or heterozygotes, in the BY4743 (diploid) background used to screen for effects of cell size on mitotic performance in different environmental contexts see .(DOC)Click here for additional data file.Table S4Environments tested in cell size and mating type locus experiments.(DOC)Click here for additional data file.


Stylophora pistillata. Bleaching occurred from April to September with a 66% decline in chlorophyll a concentration, while recovery began in October. Using aquarium and transplantation experiments, we explored environmental factors such as temperature, photon flux density and heterotrophic food availability. Our experiments and observations did not yield one single factor, alone, responsible for the seasonal bleaching. The dinoflagellate symbionts (of the genus Symbiodinium) in shallow (5 m) Stylophora pistillata were found to have a net photosynthetic rate of 56.98–92.19 µmol O2 cm−2 day−1. However, those from mesophotic depth (60 m) during months when they are not bleached are net consumers of oxygen having a net photosynthetic rate between −12.86 - (−10.24) µmol O2 cm−2 day−1. But during months when these mesophotic corals are partially-bleached, they yielded higher net production, between −2.83–0.76 µmol O2 cm−2 day−1. This study opens research questions as to why mesophotic zooxanthellae are more successfully meeting the corals metabolic requirements when Chl a concentration decreases by over 60% during summer and early fall.Coral bleaching occurs when environmental stress induces breakdown of the coral-algae symbiosis and the host initiates algae expulsion. Two types of coral bleaching had been thoroughly discussed in the scientific literature; the first is primarily associated with mass coral bleaching events; the second is a seasonal loss of algae and/or pigments. Here, we describe a phenomenon that has been witnessed for repeated summers in the mesophotic zone (40–63 m) in the northern Red Sea: seasonal bleaching and recovery of several hermatypic coral species. In this study, we followed the recurring bleaching process of the common coral  Symbiodinium that reside in the animal's gastrodermal cells. The corals' success in shallow, tropical, oligotrophic marine environments is attributed to their association with these symbiotic dinoflagellates; evidence of this relationship exists for the past ∼251 million years Most scleractinian corals engage in an obligate mutualistic symbiosis with “zooxanthellae,” which are photosynthetic dinoflagellates, belonging predominantly to the genus Two types of coral bleaching have been thoroughly discussed in scientific literature. The most prominent type is associated with the exodus of the symbiotic algae from the host tissue via several triggering mechanisms With the onset of technical diving in the Gulf of Eilat, northern Red Sea in 2004, there have been increased investigations into the Mesophotic Coral Ecosystem (MCE) areas that extends from 30 m to beyond 100 m and is characterized by the low availability of light for photosynthesis. In this MCE, we began observing a surprising phenomenon that occurs every summer: seasonal bleaching and recovery of several hermatypic coral species. The bleaching was most pronounced between 40 m and 63 m, the lower edge of the reef slope. Mesophotic coral bleaching has only been noted once before, in the Cayman Islands, down to a depth of 84 m Stylophora pistillata as observed in the northern Red Sea, ranging from the shallow reef down to 63 m. We present a description of the dynamics of this phenomenon over the course of a year and examine the environmental factors surrounding the mesophotic bleaching. We measured main physiological parameters, describing photosynthesis and respiration rates seasonally. This is a first attempt to separate out the primary factors that possibly lead to seasonal “bleaching” in a deep coral ecosystem, using both tank and transplantation studies in conjunction with long-term environmental monitoring data.This investigation is aimed at characterizing deep water bleaching of the key coral Stylophora pistillata colonies at depth 55–60 m. These were revisited every month for the following year, and the percent of colony surface area that was bleached was measured by placing a 50×50 cm2 frame, subdivided by nylon string to 5×5 cm2 squares and counting bleached/unbleached squares according to visual estimation.All research described here was conducted at the Gulf of Eilat, Israel (29°30′06.8″ N 34°55′02.8″ E). Survey and replicate collection was carried out along the continuous slope (0–63 m) in front of the Inter-University Marine Institute (IUI) using open water SCUBA (TRIMIX protocol). In October 2010, we marked 10 S. pistillata fragments of ∼5 cm length were collected on five different occasions from the deep reef at 55–60 m  and twice from shallow reef at 5 m (October 2010 and May 2011) for several experiments, as detailed below. The collection was conducted under permit number 31944 from the Israel Nature and Parks Authority. Each month, at each depth, fragments were collected from five different colonies in a black collection basket that prevents exposure to sunlight. Fragments were then transferred to outdoor water tables  with PFD sets that corresponded to those found at 60 m and 5 m at the month of collection. Ambient PFD  was calibrated from data from the International Monitoring Program in the Gulf of Eilat (http://www.iui-eilat.ac.il/NMP/Default.aspx) as well as other local measurements S. pistillata from 60 m and were conducted between September-October, 2011 High temperature (29–30°C); 2) Low temperature (21°C); 3) High PFD (∼135 µmol quanta cm−2 s−1); 4) Low PFD (∼20 µmol quanta cm−2 s−1); both measured with Li-Cor (LI-190) quantum sensor at surface; 5) starvation (0.2 µm filtered seawater); and 6) heterotrophically-fed . Over the next 12 days, fragments were assessed every three days for photosynthetic and respiration rates in a metabolic chamber as described below. On day 23, tissue and algae of the coral fragments were removed as described below.Aquarium experiments were performed in 10 liter tanks on fragments of partially bleached ber, 2011. Five coS. pistillata fragments (15 cm length) taken from the same colonies used in the tank experiments , were transplanted the following day after being taken out of the reef (September 2011) to 30 m on an underwater table positioned on the reef slope. Transplants were visited 10 days following transplantation and appeared completely bleached, though still living. On the 23rd day, coral fragments were collected for algae and chlorophyll analysis as described below, one fragment from each original colony (five fragments).Large In September and October 2010 and in March and May 2011, fragments were taken for a simultaneous continuous measurement of chlorophyll fluorescence and oxygen production and consumption . Measurements were taken within 24 hours of collection. Fragments were measured in a metabolic chamber, submerged in 0.2 µm filtered seawater and stirred continuously. Temperature was 22–26°C, similar to the reef at the time and depth of collection; it was documented by a thermometer mounted on the oxygen optrode . PFD was measured with a spherical micro quantum sensor  connected to the DUAL-PAM/F analyzer and software (WALZ) along with an optical fiber supplying measuring light and saturating pulses, and documenting the fluorescence signal at 685 nm. Actinic light was provided by a double fiber light source .−2 s−1 to 1800 µmol quanta cm−2 s−1). Continuously increasing the actinic light over 2 hours aimed to imitate the conditions on the reef. After the experiment, each fragment was placed in liquid nitrogen and placed in −80°C until further analysis: Coral tissue was removed with an air brush and tissue was analyzed for algae concentration via direct counts under a light microscope using a C-ChipTM hemocytometer; chlorophyll a concentration and fragment surface area were recorded as described by Each fragment was subjected to one measurement conducted as follows: 10 min dark acclimation+measurement of oxygen followed by continuously increasing actinic PFD  and “ITS2CLAMP” (5′ GGGATCCATATGCTTAAGT TCAGCGGGT-3′) Escherichia coli cells (strain DH5) and cultured on Fast Media Lab Agar IPTG/X Gel plates . We sequenced the ITS2 rRNA gene (337 bp) from 23 deep colonies  and 5 shallow colonies  . Sequences were viewed and analyzed with BioEdit software.Zooxanthellae DNA was extracted using a protocol described by Nir a concentration and coral surface area, by using the hyperbolic tangent function in Excel: respiration (R), maximum rate of photosynthesis (Pmax), maximum rate of photosynthesis under limited light conditions (α), and PFD at photosynthetic- respiration compensation point (Ek). The parameter Effective PSII quantum yield (YII) was calculated from chlorophyll fluorescence data according to Genty et al. http://www.iui-eilat.ac.il/NMP/database/database.aspx and analyzed with SigmaPlot software.The following photosynthetic parameters were calculated from oxygen measurements, normalized both to chlorophyll Stylophora pistillata in the mesophotic reef for one year (September 2010–September 2011). S. pistillata colonies present a range of pigment shades during the months of June to November, consisting of unbleached  losing almost all its color, and the polyps' tentacles and inter-polyp areas (coenosarcs) showing little to no loss of color . Algae numbers did not change significantly between the seasons (2 production/consumption (µmol O2 cm-2 Day-1) yields negative results during March and May and positive results in September and October  since 2004 (http://www.iui-eilat.ac.il/NMP/Default.aspx). In the winter months (December-March), the water column cools to 21–22°C, leading to deep 300–700 m mixing and upwelling of nutrients. Summers are warm (∼27°C at surface ∼26°C at 60 m and ∼24°C at 140 m) with strong thermal stratification of the water column and exhibit a depletion of bionutrients in the photic zone  and algae concentration  after 23 days  being a deep dwelling coral (EU703806.1). The shallow algae community of a bleaching and recovery of the hermatypic coral Stylophora pistillata. We describe its dynamics over a course of a year, revealing pigment bleaching that occurs along the spring and summer (April–September) and begins to recover during October  in the northern Red Sea: seasonal chlorophyll  October .S. pistillata corals were compared to fed corals in the same light regime, chlorophyll a declined after 10 days in low PAR intensity a in S. pistillata starved and exposed to elevated temperature for 15 days. Starvation as a cause for mesophotic bleaching was tested in our aquarium experiment, but yielded no significant results , extreme temperature, and fresh water lances, as well as to investigate yearly environmental changes that may induce bleaching. The temperature at 60 m depth in the Gulf of Eilat ranges during the year between 21°C–26°C . These t results . The mes−2 s−1 at 60 m to 162 µmol quanta cm−2 s−1 at 30 m) and spectrum (from 395–520 nm at 60 m to 340–589 nm at 30 m) (data not shown) caused a significant loss of pigments and algae. As the change in temperature is only 1°C, the dramatic increase in PFD (vs. 20 µmol quanta cm−2 s−1) did not cause pigment bleaching or bleaching associated with algae loss. Also, the UVA and UVB intensity in 30 m and 60 m is small . The intensity described in the literature does not test affects of such low intensities. However, this bleaching behavior was composed of losing both chlorophyll and algae, unlike the mechanism of mesophotic bleaching.In the 60 m to 30 m transplantation experiment, a change of temperature (1°C increase from 25.8°C to 26.8°C), light intensity (from 14 µmol quanta cme in PFD  likely phttp://www.iui-eilat.ac.il/NMP/Default. aspx) to produce an assessment of daily O2 production/consumption. Nighttime respiration was not included since it is an order of magnitude lower than daytime respiration. For example, in October, for 60 m colonies, we found average consumption of −0.0036±0.0011 µmol O2h−1chl a−1 at night vs. −0.066±0.048 µmol O2h−1chl a−1 of day respiration. This calculation has its limitations, mainly due to the approximated conditions of the metabolic chamber experiment, resembling, but not identical to the conditions in the reef. Still, we believe the trends presented in Characterization of some photosynthetic parameters of the zooxanthellae reveals that the pigment bleaching decreases their photosynthetic rates . With pia concentration is low .Although the photosynthetic efficiency (α) is greater in March (0.0054±0.0007) than in September and October , the light intensity at that depth never exceeds the compensation point during March. The same applies to May. In September and October the compensation point is lower than in May and March due to reduced respiration rate. Combined with higher maximum PFD during these months, the zooxanthellae satisfy or are close to satisfying the basic metabolic demands of the coral, even thou Chl a concentration is high , but succeed in meeting its metabolic requirements when Chl a concentration is 60% lower in the summer and early fall. This indicates that seasonal bleaching of these mesophotic corals can be energetically beneficial for the coral holobiont and perhaps may be a part of a mesophotic coral's acclimation response to a seasonally changing environment Our results show that mesophotic zooxanthellae are not meeting the corals metabolic demands when Chl SymbiodiniumThe coral-algal relationship is considered mutualistic relationship, however, it has been suggested that it can exist in a form where the coral is parasitic