Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is investigated and illustrated by example, using a model and corresponding real data of mitochondrial DNA population dynamics. We show that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example. The goal is then for many realizations of the data from parameter values θ′ to help the user choose summary statistics. Specifically, for ABC to lead to good estimation of θ, [θ using some type of data transformation, such as the actual data and moments of the data. The fitted coefficients from the fit are obtained from the pilot simulation runs and can then be used in subsequent runs to estimate the parameter means. Reference [θ and also used auxiliary simulations, but, unlike [θ as in our example in Alternatively, to choose effective summary statistics  aims to on of θ,  shows thon of θ,  to estimeference  also aim, unlike , summaryppost(θ | Sobs) obtained using ABC is to provide an adequate approximation to the full posterior ppost(θ | yobs). A few publications have begun to address the issue of summary statistic selection [ppost(θ | Sobs) obtained using ABC is adequate for model selection [ To summarize this section, the choice of summary statistics is very important if the partial posterior election –21. And, This section presents an example for which the stochastic computer model is relatively simple so we can generate many observations from the model. Neuronal loss in the substantia nigra region of the human brain is associated with Parkinson's disease . DeletioY1 denote the number of healthy (1) mtDNA copies and Y2 denote the number of unhealthy (2) (mutant) mtDNA copies. Following [y1 and y2 refer to an individual cell of type 1 or 2. So, for example, reaction R1 below depicts a single cell of type 1 mutating to type 2 at a rate c1Y1.  R1 : y1 → y2 at rate c1Y1  R2 : y1 → 2y1 at rate c2Y1/(Y1 + Y2) = 1000 c3Y1/(Y1 + Y2)  R3 : y1 → 0 at rate c3Y1  R4 : y2 → 2y2 at rate c4Y2/(Y1 + Y2) = 1000 c3Y2/(Y1 + Y2)  R5 : y2 → 0 at rate c3Y2.   Let ollowing  we assumY2/(Y1 + Y2) > τ for some lethal threshold τ. This simple model can be simulated from exactly using Gillespie's discrete event simulation [Y1, Y2} are available at a sequence of times {t1, t2,…, tn}. The real measurement data will be assumed to be of this form, although the measurement details and number of neurons sampled multiple times from each of 15 patients of varying ages make the measurement process used in [ The time between reactions is assumed to have an exponential distribution. The sum of the five rates is the total reaction rate, which determines exponential parameter (the average time between reactions). Given that a reaction occurs at a specific time, the relative rates determine the probabilities with which the five reactions occur. To model the harmful effects of mutation from type 1 to type 2 cells, it is assumed that a cell dies if its proportion of mtDNA deletions mulation . Referenmulation  gives momulation  and assu used in  somewhat used in  did not  used in  and fromc2 and c4 are assumed to be known multiples of rate c3, so the inference goal is to estimate {c1, c3, τ}. A range of possible values for each of {c1, c3, τ} was based in [c1 was 10−6 to 10−3 per day, for c3 was 3 × 10−5 to 10−3 per day, and for τ was 0.5 to 1. All three of these parameters are of interest not just as model calibration parameters, but for their physical implications. For example, it is not yet known whether neurons can survive with very high levels of mtDNA deletions. As with any Bayesian analysis, an evaluation of the sensitivity to the prior distribution for the model parameters should be included, particularly for informative priors. In our example, we used informative priors, uniform over the accepted ranges. A separate simulation confirmed that the estimated posterior is sensitive to the assumed parameter range. An example comparison using two ranges for the uniform priors is given in Note that rates based in  on previc1, c3, τ} is based on approximating the computer model using a Gaussian Process, which is a common approach in calibrating deterministic computer models. Reference [c1, c3, τ} using ABC. Future work will compare such options. Here, we focus on better understanding of the effect of summary statistic selection on ABC performance.The approach in  to estimeference  mentionsY1, Y2} are available at a sequence of times {t1, t2,…, tn}. The real data we use is in Table 1 of [ Recall that we assume measurements of {ble 1 of , which fT, and the summary statistics. In addition, the user chooses the number of model runs at each value of the parameter vector and the number of values of the parameter vector θ = {c1, c3, τ} presented to the ABC algorithm. The statistical programming language R is among the good choices for ABC implementation; here we use the abc function in the abctools package for R [abc function also requires the user to decide whether to work with transformed parameter values and to select a method to improve estimation of the partial posterior by adjusting the accepted θ values according to the distance between the summary statistics and the observed summary statistics [θ values corresponding to d(S(yobs), S(yM(θ))) < T without any weighting. Results given in θ values. In any implementation of ABC the user must specify the distance measure, the acceptance threshold ge for R . The abcatistics , 13. Theθ to be well calibrated. Well calibrated in this example context means, for instance, that the true θ is contained in approximately 95% of repeated constructions of 95% predictive intervals for θ. That is, the actual coverage is very close to the nominal coverage.  Our goal for this mDNA example is to illustrate an approach to making good choices for the summary statistics when the user wants the estimated partial posterior distribution for nrep = 1000 replications and recorded how often nominal intervals containing 95%, 90%, 80%, 60%, 50%, 40%, 20%, 10%, and 5% of the estimated posterior probability ppost(θ | Sobs) (which serves as an estimate of ppost(θ | yobs)) actually contain the true parameter value for the three parameters in θ = {c1, c3, τ}.To check our ABC “calibration,” we repeated the following simulation procedure using Simulation ProcedureStep A. Simulate data from the SM at many parameter values θ = {c1, c3, τ}. Specifically,c1, c3, τ} from their respective uniform prior distributions  > τ at any step, terminate. Some variations of ABC will repeatedly simulate in step (2) for the chosen {c1, c3, τ} values in step (1). for each selected value of {Step B. Real data :yobs or mimic one realization of real measurement data by repeating Step A once. Here we use simulated measurement data to mimic real data, so that we can know the true value of θ = {c1, c3, τ}. Real data: Either use real measurement data nsim = 1000 simulations from Step A, accept the trial θ = {c1, c3, τ} values from 100 (10%) of the nsim = 1000 simulations on the basis of d(S(yobs), S(yM(θ))) in each of the nsim = 1000 simulations, resulting in an approximation of the partial posterior probability ppost(θ | Sobs) which serves to estimate the full posterior probability ppost(θ | yobs). The accepted trial θ values can be used “as is” to approximate ppost(θ | yobs) or adjusted to account for the actual distance d(S(yobs), S(yM(θ))), for example, as in [Using the e, as in , 13. Step C. (1) Use the 100 accepted trial values of in Step B to tally whether the true parameter values are contained within the estimated 95%, 90%, 80%, 60%, 50%, 40%, 20%, 10%, and 5% posterior intervals. nrep = 1000 replications. Following [n = 1000 cells. We depart slightly from [Y2 = 600 mutant cells (rather than 0 mutant cells), so that each run of up to 100 years tended to conclude in a modest number of years from the starting point due to Y2/(Y1 + Y2) exceeding the lethal maximum τ, so that run times are shorter; this mimics starting with older subjects, nearly all of which do not live close to 100 years beyond their fictitious starting age defined by having 600 mutant cells at the start of the simulation. Such a choice will impact our inference results, analogous to choosing data ranges in calibration experiments. However, our topic is the choice of summary statistics rather than experimental design for choosing effective data ranges (the data ranges are the subjects' ages in our example). This 3-step simulation procedure is repeated for Y1/(Y1 + Y2) fraction was measured with a relative random error standard deviation of 0.20 on the log10 scale. These two effects (observing 300 of 1000 and 0.1% relative error standard deviation on log10(Y1/(Y1 + Y2)) result in a root mean squared error of approximately 0.13 in the measured relative frequency Y1/(Y1 + Y2) on average across the range of Y1/(Y1 + Y2) values. In comparison, [2 scale. Because two measurement techniques are combined in [ Also following  we simulZ1 denote the measured value of Y1, and Z2 denote the measured value of Y2. The first candidate set of summary statistics is the following three: the average rate of change of Z1/(Z1 + Z2), coefficients b1 and b2 in a linear model relating the change in Z1 (the response) to predictors consisting of the current Z1, and the current ratio Z1/(Z1 + Z2). The second candidate set of summary statistics is the same as the first, but also includes the maximum of the observed ratio Z1/(Z1 + Z2) and the number of steps until cell death. The third candidate set of summary statistics is the same as the first, but also includes coefficients b1 and b2 in a linear model relating the change in Z2 (the response) to predictors consisting of the current Z2, and the current ratio Z1/(Z1 + Z2). All three candidate sets of summary statistics were computed for sets of simulated data that was observed at each time step (day), and also much less frequently as in the real data. To mimic the real data, we sampled the simulated data at 13 random times over the duration of each simulation.  Let Z1/(Z1 + Z2) relates directly to parameter c3.  Concerning the choice of summary statistics, these three candidate sets are arbitrary but reasonable statistics that clearly relate to the SM and so are informative for the SM parameters. For example, the average rate of change of ppost(θ | yobs) on the basis of the 3-step simulation procedure. Here, the term “well calibrated” means that actual coverage is very close to the nominal coverage. Second, among all sets of such summary statistics, choose the set that has the smallest estimation error for θ. The second criterion is similar to that suggested in [ppost(θ | Sobs). Here we present results for three candidate sets of summary statistics. Our strategy involves two criteria. First, retain for consideration any set of summary statistics that leads to a well-calibrated estimate of ested in , 20. Theested in  describeθ values in abc or not [d(S(yobs), S(yM(θ))) rather than the simple rejection criterion. d(S(yobs), S(yM(θ))) [abc allows the user to compute both adjusted or unadjusted θ values.c or not , 13. RidyM(θ))) , 13, whe1. More formally, to determine whether the smallest RMSE1 among the three (or any number of) candidate sets of summary statistics is significantly smaller than the second smallest RMSE, we can repeat the entire simulation procedure approximately 100 times and rank the candidate sets of summary statistics on the basis of their RMSE1 values across the 100 repetitions of the 3-step simulation. In this example, candidate set 3 has the smallest RMSEs among the three sets of candidate summary statistics. For any set of candidate summary statistics that are acceptable on the basis of RMSE1, the second version of the RMSE defined as c1, c3, and τ should be evaluated. In RMSE2, θi is either c1, c3, or τ, and To quantify the results shown in Figures 2 for τ than the other two choices. As a reviewer has pointed out, such an outcome requires a user choice, and we suggest “majority rule,” meaning that we choose the summary statistic set that has the smallest RMSE1 and/or RMSE2 (depending on user needs) for the most number of parameters. So, in this case we invoke “majority rule” and choose summary statistic set 3.There is no guarantee that the “best” set of candidate summary statistics will dominate the other choices of summary statistics. For example, summary statistic set 3 is our choice in this example, but it has higher RMSE Any application of ABC that does not include a simulation evaluation such as this one or similar ones in –21 is inθ values. However, the real data is observed less frequently than each time step (day), so next we use slightly different summary statistics than described above. Rather than lag-one (one-day) changes, we use the actual times between measurements so that approximate rates of changes can be computed. The resulting posterior for the data in Table 1 of [1 and RMSE2 values . For completeness here, we also use the real data from Table 1 of  in placeble 1 of  is givenc1 from 10−6 to 10−3 per day to 10−7 to 10−2 per day, for c3 from 3 × 10−5 to 10−3 per day to 3 × 10−4 to 10−2 per day, and for τ from 0.50 to 1 to 0.85 to 1. Using summary statistics set 3, the posterior means for {c1, c3, τ} are 0.0007, 0.0001, and 0.90, respectively, for the original prior ranges and are 0.00007, 0.001, and 0.87, respectively, for the modified prior ranges. These posterior means were each calculated twice using 103 simulations and are repeatable to within the number of digits listed. Therefore, the choice of prior does significantly impact the posterior in our example. Reference [τ. However, there are no widely accepted values for any of the three parameters, so we cannot use accepted parameter values as another check to compare ABC summary statistic choices. Instead, we assess the quality of the ABC-based approximation to the posterior using auxiliary simulation as illustrated in Figures Finally, any Bayesian analysis should address the issue of whether the posterior is sensitive to the prior. For example, in our ABC context, we first used the uniform priors for each parameter as described in ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the full data and is therefore computationally feasible. Although ABC is compelling, when the data and/or parameter dimension is large, and is beginning to be used in many application areas, as of 2013, there is no cohesive theory or a consistent strategy for ABC, yet there are many applications in bioinformatics, astronomy, epidemiology, and elsewhere for which a stochastic CM provides an alternative to the likelihood. In addition software to implement ABC is becoming widely available; see  for a pa One technical challenge with ABC is that the quality of the approximation to the posterior distribution of model parameters depends on the user-chosen summary statistics. In this paper, the user requirement to choose effective summary statistics in order to accurately estimate the posterior distribution of model parameters is illustrated by example, using a model and corresponding lab data of mitochondrial DNA population dynamics. The example shows that for some choices of summary statistics, the posterior distribution of model parameters is closely approximated and for other choices of summary statistics, the posterior distribution is not closely approximated. θ are preferred. The disadvantage of this approach is that reliance on auxiliary simulations to choose summary statistics adds to the computational burden. However, the ABC algorithm is easily parallelized so modern desktop computers are fully adequate for many problems, such as our example. The user might consider using criteria other than those used in Figures  A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example. The strategy is to choose the best results from several candidate sets of summary statistics, such as shown in the Results in Figures θ values as in [ Future work will consider the acceptance threshold and variations of ABC such as  that mimes as in , 13. Alses as in –8. Finales as in  would be


Currently, ∼25% of the global N2O emission is ascribed to uncultivated tropical soils, but the exact locations and controlling mechanisms are not clear. Here we present the first study of soil N2O emission from the Pantanal indicating that this South American wetland may be a significant natural source of N2O. At three sites, we repeatedly measured in situ fluxes of N2O and sampled porewater nitrate in situ fluxes of N2O and soil in situ fluxes of N2O were comparable to fluxes from heavily fertilized forests or agricultural soils. An important parameter affecting N2O emission rate was precipitation, inducing peak emissions of >3 mmol N2O m−2 day−1, while the mean daily flux was 0.43 ± 0.03 mmol N2O m−2 day−1. Over 170 days of the drained period, we estimated non-wetted drained soil to contribute 70.0 mmol N2O m−2, while rain-induced peak events contributed 9.2 mmol N2O m−2, resulting in a total N2O emission of 79.2 mmol N2O m−2. At the sites of repeated sampling, the pool of porewater nitrate varied p < 0.05) found in drained soil than in water-logged soil, indicating dynamic shifts between nitrification and denitrification. In the field, O2 penetrated the upper 60 cm of drained soil, but was depleted in response to precipitation. Upon experimental wetting the soil showed rapid O2 depletion followed by N2O accumulation and a peak emission of N2O 2O from these wetland soils is generally representative to the Pantanal, we suggest that this undisturbed tropical wetland potentially contributes ∼1.7% to the global N2O emission budget, a significant single source of N2O.Nitrous oxide (N Microbial processes play a major role in the global cycling of carbon and nitrogen  acts as significant as a source of N2O.The atmospheric concentration of nitrous oxide , 2009 (23 days), and 2010 (14 days), we investigated the Measurements and sampling were carried out along 10–25 m long transects ranging from the retreating water edge to ∼1 m above the initial water edge at sites A, B, and C. At the beginning of each field campaign in 2008 and 2009, the elevated end of transects had been drained for ∼1 month, while the lowermost end was initially water-logged, but drained during the field campaign. Three levels were selected along each transect: Level 1 at the lowest end, Level 2 in the center, and Level 3 at the highest end, thus representing both an increase in elevation and a gradual decrease in soil moisture content from Level 1 to Level 3. Due to an excessive amount of rain on site C during the 2009 field campaign, this site was water-logged during all measurements, while early flooding of site C in 2010 left it inaccessible.in situ flux of N2O was measured at the two lowest levels (Level 1 and 2) along the transect. Flux chambers (n = 5 at each level) made of PVC tubes  were inserted ∼15 cm into the soil. If any litter layer was present on the soil surface prior to inserting the chamber, it was replaced on the soil surface inside the chamber. In situ flux measurements were performed every 2–14 days by placing a lid on the tube and measuring the N2O concentration for 30 min in each chamber with a photo-acoustic gas monitor . Flux chambers were lined with reflective material on the outside and were shaded during measurements to minimize temperature variations. The closed-chamber technique is known to create a bias by altering the diffusion gradient between soil and chamber headspace ] in Sigmaplot , where ppt is the partial pressure of the measured gas at time t, pp0 is the initial partial pressure in the closed-chamber, t is time, and a and b are constants. Integrated emissions of N2O were calculated for each level at each site, assuming linearity between subsequent measurements.At each site, the in situ measurements are thus assumed to represent drained soil fluxes.During the 2010 screening, the sites had to the best of our knowledge not received precipitation in the preceding days, and the n = 3 at each level) on every sample occasion at site A, site B, and site C  and soil porewater immediately extracted in situ by inserting 0.2 μm Rhizon filters  into the side of the whole soil cores at 1.5, 3.5, and 6.5 cm below the soil surface. Samples of ∼0.5 ml of porewater were extracted at each depth by suction with a 60 ml syringe. Additional water samples were taken from rivers and water bodies and filtered . Extracted porewater samples and water samples were immediately transferred to 1.5 ml tubes and stored on ice until return to the field laboratory, where they were stored at −20°C until further analysis. Nitrate analysis (sample size 5 μl) was performed using the vanadium chloride reduction method  at nine different concentrations of Whole soil cores  were collected at all levels (r2 = 0.99) at six different concentrations of n = 1−3) from the mixed upper ∼5 cm soil were collected by inserting a 50 ml screw cap centrifuge tube  directly into the soil. Soil samples were refrigerated up to 48 h until During the third field campaign (2010), soil Means of porewater 2 concentration was measured at site A and C at Level 2 (center of transect) with custom-built fiber-optic O2 optodes . The optical fibers were calibrated in an O2-free solution  and in water equilibrated with atmospheric air prior to installation in the soil at 13 fixed depths . The optodes were connected to a four-channel fiber-optic O2 detector system  at each visit to the sites .In 2009, the depth distribution of OPrecipitation, soil temperature, and seasonal changes in volumetric soil water content were measured for a period of 170 days (2009–2010) at site A, Level 2. The volumetric soil water content was logged using soil moisture sensors  installed in four depths; 5, 10, 30, and 60 cm below the soil surface in one profile. Each probe was calibrated in the laboratory using depth-specific soil samples from the site. Precipitation was measured using a “Tipping Bucket” rain gage. Soil temperature and precipitation sensors were logged at 10 min intervals, while the Theta-probes were logged every 6 h .2 microoptodes were constructed according to Klimant et al. . Electrochemical N2O microsensors were constructed according to Andersen et al. , and in water equilibrated with atmospheric air. The N2O sensors were linearly calibrated from a two-point calibration in a 0 and 1% solution of N2O produced by mixing 0.5 ml of saturated N2O water into 49.5 ml water. The sensors were mounted on a motorized micromanipulator and both sensor position and data collection were controlled by a PC running SensorTrace Pro software . Retrieved dry soil cores were wetted from below with aerated river water and concentration profiles (n = 20–30) of O2 and N2O were obtained over the following 56–72 h. Additional profile measurements were done in a soil core retrieved after wetting by a natural rain event. The N2O flux, J, was calculated from the concentration gradient in the water layer on the soil surface using Fick’s first law (J = −D δC/δx), where D is the N2O diffusivity in water at experimental temperature (2.41 × 10−5 cm2 s−1), C is the N2O concentration in μmol l−1 calculated from the measured partial pressure and the experimental temperature according to Weiss and Price  for experimental wetting were collected from level 3 at site A and site B. Further soil cores were collected immediately after a natural, moderate precipitation event of short duration (<15 min). Optical Ot et al.  and mounn et al.  with a fn et al.  and connnd Price , and x iin situ flux of N2O at the sites of repeated sampling varied considerably over time in both 2008  and 1.08 mmol N2O m−2 day−1 , going from wet soil at Level 1 to drained soil at Level 3  at Level 1 as the soil drained, while a significant decrease (p < 0.05) was observed at Level 2 and 3  from wet soil at Level 1 to drained soil at Level 3  over time in porewater At site A (2008) the porewater y p < 0.0, going fy p < 0.0, going fy p < 0.0, going fy p < 0.0, going fp < 0.05) than at Level 1 and Level 2. In addition, a significant increase (p < 0.05) over time in porewater At site B in 2008 Figure B the porr p < 0.0 than at r p < 0.0 than at n = 3) collected on a single occasion showed a soil p < 0.05) than at Level 2, but not compared to Level 3.In 2008 site C Figure C soil sa9 Figure C the ove−3 soil (site K) and 5.38 μmol cm−3 soil (site E).The soil characteristics from the 2010 screening of 10 sites are shown in Table The 2 penetrated to a depth of ∼60 cm (37–97% air sat.) but fluctuated throughout the field campaign in response to precipitation  and less in the deeper soil layers. A subsequent decrease in soil moisture occurred after each rain event, most rapidly at the surface due to evaporation and drainage  and 2.92 mmol N2O m−2 over 2.3 days (soil core from site B) represented only a small fraction (35–38%) of the net production of N2O in the upper 4 cm of the soil matrix, the difference being consumed within the water-logged soil 2–3 days after the wetting event  has also been shown to increase the emission of Nin situ concentrations of 2 profiles  was observed, and 2–3 months after the end of the flooded season most of the soil surface at site A, B, and C was still covered with decaying aquatic macrophytes (largely E. crassipes). In the absence of inorganic nitrogen uptake by plant growth, we suggest that the large pool of nitrogen released from mineralization of the decaying plant residues was largely available for microbial nitrification and denitrification throughout the entire drained period. Such a continued input of nitrogen may explain the high nitrate content found in the Pantanal wetland soil even months after draining.As the wetland soil was draining at site A and B, only sparse plant growth  by the end of the drained period in early 2010. In such non-flooded areas with high plant activity, plant-microbe competition for inorganic nitrogen would likely decrease the availability of inorganic nitrogen for soil microbial N transformations and gaseous N loss.The water level during the 2009 flood was lower than average  were performed from 2008 to 2010. We classified 94 measurements as drained soil fluxes (mean = 0.43 ± 0.03 mmol N2O m−2 day−1), while 22 measurements were considered precipitation-triggered peak events (mean = 1.54 ± 0.24 mmol N2O m−2 day−1). The experimental flooding of soils , this input of carbon must be mineralized. Evidence for such mineralization can be found in studies of CH2O emissions from tropical freshwater wetlands are noticeably scarce. Furthermore, the contribution of N2O from tropical freshwater wetlands has largely been considered negligible (Matson and Vitousek, 2 that is flooded annually (Hamilton et al., 2 (Neue et al., 2O flux from wetland soil we suggest that the Pantanal may be contributing 1.7% to the annual global N2O emission budget during the drained season; this is a significant hitherto ignored single source of N2O.Studies of nitrogen dynamics and N2O budget is not balanced (IPCC, 2O suggesting that South America has a much higher emission of N2O than expected, supporting our observation of the Pantanal as a significant, but hitherto unknown source of N2O.It is currently unknown to what extent the Pantanal wetland system with its dynamic cycling of nitrogen can be compared to other wetlands. The global Ned IPCC, , which hed IPCC, . Kort eted IPCC,  measured2O emission comparable to heavily fertilized agricultural soils. This first study of the dynamics of soil nitrogen pools and emission of N2O from the world’s largest wetland thus emphasizes the current lack of knowledge about nitrogen cycling in undisturbed wetlands, and about how such systems may alter in response to a changing global climate. Lastly, it underscores the paramount importance of varying environmental boundary conditions modulating microbial mineralization processes in the carbon and nitrogen cycle of wetland soils.Our study underscores the direct and indirect importance of flooding and precipitation patterns in tropical watersheds and wetlands, where sudden natural wetting events can cause significant NThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We estimate P nucleotide distributions in a cross section of healthy adults and different T cell subtypes. We show that P nucleotide frequency in all T cell subtypes ranges from 1% to 2%, and that the distribution is highly biased with respect to the coding end of the gene segment. Classification of observed palindromic sequences into P nucleotides using a maximum conditional probability model shows that single base P nucleotides are very rare in VDJ recombination; P nucleotides are primarily two bases long. To explore the role of P nucleotides in thymic selection, we compare P nucleotides in productive and non-productive sequences of CD8+ naïve T cells. The naïve CD8+ T cell clones with P nucleotides are more highly expanded.Diversity of T cell receptor (TCR) genes is primarily generated by nucleotide insertions upon rearrangement from their germ line-encoded V, D and J segments. Nucleotide insertions at V-D and D-J junctions are random, but some small subsets of these insertions are exceptional, in that one to three base pairs inversely repeat the sequence of the germline DNA. These short complementary palindromic sequences are called P nucleotides. We apply the ImmunoSeq deep-sequencing assay to the third complementarity determining region (CDR3) of the β chain of T cell receptors, and use the resulting data to study P nucleotides in the repertoire of naïve and memory CD8 The specificity and affinity of T cell recognition is largely contained in α and β chains of the T cell receptor (TCR). TCR sequence diversity resides primarily in the complementarity determining region 3 (CDR3) loops of α and β chains, which bind to the peptide antigen, conveying specificity. The nucleotide sequence that encodes the CDR3 loops are generated by V(D)J recombination: variable (Vβ), diversity (Dβ) and joining (Jβ) genes in the genome are rearranged to form a β chain, while Vα and Jα genes rearrange to form an α chain.Surveillance of intracellular proteins are primarily mediated by T cells which recognize processed antigens presented by major histocompatibility complex (MHC) molecules on the cell surface. CD8β, Dβ and Jβ genes in the genome and the number of ways Vβ, Dβ and Jβ genes can recombine give rise to combinatorial diversity. Junctional diversity is generated by deletion of nucleotides adjacent to the recombination signal sequences (RSSs) of the Vβ, Dβ and Jβ gene segments and non-templated insertion of random nucleotides (N nucleotides). N nucleotides which increase the diversity by exponential order are inserted by the DNA polymerase terminal deoxynucleotidyl transferase (TdT) at the Vβ−Dβ and Dβ−Jβ junctions in a template independent manner for the TCRβ chain. In this manuscript we will denote the nucleotide insertion at Dβ−Jβ and Vβ−Dβ junctions as N1 and N2 regions, respectively.The large TCR repertoire arises as a consequence of combinatorial and junctional diversity. Existence of multiple VTo express a complete TCR in a developing T cell, an initial DNA break is performed by the recombination activating gene RAG1-RAG2 complex, which binds to the RSS and introduces a DNA double-strand break at the precise border of the coding end and the RSS. The ends bearing the coding sequence are sealed covalently and form transient hairpin loops, which are then cut open and digested to varying extent by an exonuclease. The position of the cut(s) at various points along the hairpins by the Artemis enzyme phosphorylated by a DNA-dependent protein kinase (DNA-PK) plays a key role in the formation of sequence variability in the final joint of the TCR loop. Usually single strand cuts on both strands of the hairpin result in deletion of a few nucleotides at the coding ends. However, for a subset of rearrangements, an asymmetric cut on only one strand of the hairpin transfers nucleotides from one strand to the other and results in a 3′ or 5′ protruding single strand which is complementary with respect to the end of the coding sequence. These short complementary palindromic sequences are called P nucleotides P nucleotides were first discovered in 1993 by Our knowledge of P nucleotides is incomplete due to limited amount of TCR sequence data available. Applying the ImmunoSeq deep-sequencing assay to TCRs, we sequence millions of TCR sequences in parallel from a single sample, creating a large data set of TCRβ CDR3 sequences, which we utilize to study P nucleotides in a unified and coherent manner + and CD4+ T cells that were obtained from the peripheral blood of seven healthy people. We build a linear model to characterize P nucleotide probabilities, and apply this model to each observed palindromic sequence to assign a probability that the sequence is a P nucleotide or a random insertion by TdT. This probability model can be directly used as part of an algorithm to decompose CDR3 junctions into their constituent parts. We elucidate the properties of P nucleotides in different subsets of T cells and across different donors using this model. We show that the distribution of P nucleotides are highly biased with respect to gene segment usages and its coding end and the majority of observed palindromes are not actually P nucleotides. We also show that P nucleotide distribution in productive sequences is different from non-productive sequences, and longer P nucleotides at the specific coding end is biased towards the high average copy number in CD8+ naïve T cell. Additionally, we show that junctions with P nucleotide sequences have fewer non-template insertions and induce a reading frame bias in the β chain of the TCR.In this study, we analyze P nucleotides in the repertoire of TCRβ CDR3 sequences from the genome of naïve and memory CD8+ and CD4+ T cells obtained from the peripheral blood of healthy adults. Genomic DNA was purified from both the naïve  and memory  CD4+ and CD8+ T cells (Table S2). More than 5 million TCRβ CDR3 sequence reads were generated from ∼1 million template genomes in each of the naïve and memory samples. A mean of 400,000 CD8+ naïve, 60,000 CD8+ memory, 340,000 CD4+ naïve, and 170,000 CD4+ memory unique CDR3 nucleotide sequences were observed  data of T cell receptors of ethnically diverse individuals and aims to estimate the P nucleotide probabilities of different length. Unlike the method described in the earlier works At each of the four non-recessed coding ends of the CDR3β chain of T cell receptors either there is no P nucleotide or P nucleotide due to hairpin structure. If P nucleotides arise, they are usually followed by random N nucleotide insertions by the TdT enzyme. Given the rearranged CDR3β chain of T cell receptor, there is no well-defined boundary between the P and N nucleotides to decompose them into disjoint sequences without error. Observed palindrome sequences arise purely by true P nucleotides due to hairpin or purely by N nucleotides due to the TdT enzyme, or by a joint combination of both. We establish a linear system to estimate the P nucleotide probabilities of different length with the assumption that each equation of the system quantifies the observed palindrome length frequency as a mixture combination of P nucleotide and N nucleotide probability distributions.m bases gives rise to an m by m linear system, where the unknowns are the probabilities of P nucleotide from 1 to m base long. The observed frequency fn that the first n nucleotides (n≤m) are self-complementary or palindromic is equal to the sum of the probabilities that these nucleotides are independently added by the TdT or P nucleotides are followed by N nucleotides added by the TdT enzyme or that an observed palindromic sequence has at least an n base P nucleotides. Thus in each equation of the linear system the palindrome length frequency fn can be expressed mathematically as a sum of three terms. These three terms represent the three different biological processes that give rise to palindromes in T cell receptors. The first term represents all n nucleotides are independently added by the TdT enzyme, and thus this term encodes the well-known TdT bias for inserting particular nucleotides. The second term represents the first i base is a P nucleotide, where i≤n−1, and the remaining n−i bases are N insertions; and the third term represents that there is an at least n base P nucleotides. In general, the vector of palindrome length frequency T can be written as a sum of the TdT probability vector, TdT bias, and a matrix A times the vector of unknown P nucleotide probability T. The matrix A encodes the information content of the biological steps that gives rise to P nucleotides. The P nucleotide probability vector is solved by inverting the matrix, A, and multiplying it by the difference of palindrome length frequency vector T and the TdT probability vector.Modeling palindromic sequences up to M = 3 bases because of the following two reasons. First, many of the palindromic sequences in the data set fall in the range of 1 to 3 nucleotides long that gives rise to average palindrome length of 1.62±0.02. The In this manuscript we restrict P nucleotide analysis of palindromic sequences up to β gene segment, N2 segments of seven nucleotides long with no deletion at the 5′ end of the Dβ gene segment are used and nucleotide frequencies in their middle regions  are calculated. We observed that the nucleotide frequencies at the 3rd, 4th and 5th bases from the 5′ of Dβ segment do no vary significantly and are consistent among all donors (data not shown). We took the nucleotide distribution at the 3rd base as an estimate of the TdT probabilities. Similarly the TdT probabilities at the 3′ of Vβ and Dβ and the 5′ Jβ of coding ends are estimated.In order to solve for P nucleotide probability the linear system requires the knowledge of TdT probabilities. To estimate the TdT insertion probabilities based on the assumption that N nucleotides are added independently, we take the middle regions of N2 and N1 segments and calculate the mono nucleotide frequencies. These middle regions must be added by TdT insertions. The regions towards the either end of N1 or N2 segments have potential to be biased due to P nucleotide additions and other unknown factors, thus these regions do not account for a true contribution of TdT insertions. For instance, to estimate the TdT probabilities towards the 5′ end of the DTable S3. The TdT has a strong bias for certain nucleotides like G/C. In addition we also found that nucleotides inserted by the TdT are biased with respect to the coding end. For example, nucleotides towards the 3′Vβ coding end is T rich, while nucleotides towards the 5′ and the 3′ of the Dβ coding ends are G and C rich respectively. We substitute the coding end specific TdT probabilities and empirical frequencies of palindromes into the linear system and solve for the P nucleotide probabilities of different length.Mean estimated TdT probabilities for T cells are given in β, Dβ and Jβ gene segments of TCRs and solved for P nucleotide probabilities. We used these probabilities to calculate the expected number of one, two and three bases P nucleotides in each data set and then normalized by dividing by the total number of unique sequence in the data set. Normalizing this way allows comparison of the results between donors and across T cells subtypes. Several features of P nucleotide distributions were established and characterized.We applied the above linear model to each coding end of the V+ and CD4+ T cells . We have learned from our previous work that the Dβ1 and Dβ2 gene segments are utilized almost uniformly β1 than at the Dβ2 gene segment.Some coding ends have been found to display a high proportion of P nucleotides while the others have few or none  P nucleotides as compared to the 5′Dβ and 5′Jβ, and their distributions were similar in both compartments of the CD8+ and CD4+ T cells. Most of the P nucleotide contribution at 3′Vβ came from the gene segments of the Vβ4 family and the Vβ20 gene segment in the naïve and memory populations of the CD8+ and CD4+ T cells. P nucleotide additions have a different average length depending upon the particular coding end involved. The majority of P nucleotides consist of one nucleotide at 3′Vβ, two nucleotides at the 5′Dβ, and both one and two nucleotides at the 3′Jβ , with two base P nucleotides are dominated by Vβ20-1 and Vβ27 gene segments. We also noticed that there were a few Vβ gene segments  where no palindromes were observed at all. At 5′Dβ end, both the Dβ gene segments were labeled as two-base P nucleotides with high probabilities and the number of sequence with Dβ1 gene segments classified as two-base P nucleotide dominates the Dβ2 by three folds. At 3′Dβ end, conditional probability was too low for both of the Dβ gene segments to be classified as P nucleotide. In Jβ segments, eight of the thirteen Jβ genes  were classified as two base P nucleotides while the remainders of the Jβ segments were classified as no P nucleotide with high conditional probabilities.According to the maximum conditional probability model most of the Vcleotide Table 2β gene segments rarely show any P nucleotide. We saw that many of the two base P nucleotides are restricted to the 5′ of Dβ and Jβ  as compared to sequences with one or no P nucleotide. Using the Student t-test for paired samples, we found that at the 5′ of Dβ, the two groups are statistically different at the significance level of 0.01 in all T cell compartments except the CD8+ memory. On the other hand at the Jβ end, the two groups are statistically different, using Student t-test for paired samples, in each of the naïve T cell subsets at the significance level of 0.01. Therefore, the distribution is more skewed in the naïve population of T cells in the two groups .We observe that sequences bearing receptors with longer than one base P nucleotides at both the Do groups Figure 3We showed that a self-complementary nucleotide sequence of two bases was most likely to be a two base P nucleotide. Since the CDR3 length of the TCRβ is constrained, presence or absence of P nucleotides can affect the number of nucleotides added by TdT. We asked if sequences with P nucleotides correlates with junctional insertion. We assess the distribution of the length of the junctional insertion at N2 and N1 segments with and without the two base P nucleotides at either end of the N2 and N1 segments.To compare the number of junctional insertions at N2 and N1 segments of the TCRβ CDR3 with P nucleotides (consisting of more than one base) and no P nucleotide sequence, we partition the data set from each donor into two groups. We call a sequence a P nucleotide sequence if we observe a palidrome greater than one base at either end of the N2 segment otherwise as no P nucleotide sequence. As mentioned above we observe very few P nucleotides at both ends of N2. For each P nucleotide and no P nucleotide sequence we calculate the length of nucleotides inserted by the TdT enzyme. Note that to calculate the length of nucleotides inserted by TdT in P nucleotide defined sequences, we need to factor out the P nucleotide length from the total insertion, since these P nucleotide bases are not inserted by the TdT enzyme. The calculation was repeated for the N1 segment.+ and CD4+ T cells. Results are shown for the CD8+ T cells, . It is interesting to note that the two distributions intersect each others when the number of insertions becomes equal to three and this is consistent in each T cell compartment. Thus the probability of up to three insertions is higher in the P nucleotide sequences as compared to the no P nucleotide sequences. On average P nucleotide sequences have three nucleotide insertions in both the naïve and memory compartments while no P nucleotide sequences were found to have four and five nucleotide insertions in naïve and memory compartments respectively. This makes the P nucleotide sequences one to two bases closer to the germ line as compared to the no P nucleotide sequences. And thus the addition of P nucleotides consisting of two bases can reduce the diversity of TCRβ repertoire up to 64 fold.The results show that CDR3 sequences with P nucleotides consisting of more than one nucleotide are likely to have fewer non-template insertions as compared to sequences with no P nucleotide in the naïve and memory compartment of the CD8T cells, Figure 4β gene segments of the TCRβ can be transcribed in all three reading frames. In order to see if the addition of a P nucleotide is biased toward or against a particular reading frame in the Dβ gene segments reading frames 1, 2 and 3 were defined if the 1st undeleted nucleotide of the 5′ end of the Dβ gene segment was at the 1st, 2nd and 3rd base of the codon respectively.Dβ gene segments increased the frequency of the reading frame 1 in all populations of T cells. When a P nucleotide was inserted, 50–60% of T cells used reading frame 1 to transcribe their TCRβ chains. Sequences with the addition of two base P nucleotides in this reading frame, the first position of the codon is occupied by any nucleotide and the two bases of the P nucleotide contribute to the non-synonymous and the synonymous position of the codon. Thus the consequences of two base P nucleotide in this reading frame leads to four canonical amino acid residues, alanine, proline, serine, or threonine in the CDR3β sequences depending upon if the first position of the codon is occupied by G, C, T or A respectively. The respective frequency of the amino acid residues in all T cell subtype is given in Comparing the effects of P nucleotides on the reading frame, we found that the addition of P nucleotides at the 5′ end of the DThe third reading frame with P nucleotides was favored slightly less as compared to no P nucleotide. But note that in this reading frame, sequences with no P nucleotide could add many possible amino acids at this position, while the same reading frame with P nucleotide would only add a proline reside since the two bases of the P nucleotide only affect the non-synonymous positions of the codon. This means that 30–40% of sequences with P nucleotides in this reading will only have proline followed by glycine and thus suggested that these residues might play some important biological role in TCR recognition. In the P nucleotide sequences the combined affect of reading frames 1 and 3 at the amino acid level introduce a strong bias for proline which is the only cyclic amino acid and allows less degrees of freedom.β1 segment in the reading frame 2 was higher than the Dβ2 segment, 25% and 10% in Dβ1 and Dβ2 segments respectively in all T cell populations. Thus, when T cells rearrange their DNA, they are less likely to use Dβ2 segment to encode TCRβ chains in the 2nd reading frame and the addition of P nucleotides further decrease this usage , on average it has a strong well-known bias for inserting G/C nucleotides According to the data shown Table 2+ naïve T cells three donors are related, donors 1 and 3 are siblings and shared the same HLA, they have different age, and are the daughters of donor 2, while the other four donors are completely unrelated in HLA and belong to different age groups. At each coding end we computed the total fraction of palindromes of 1–7 base long in each donor. The fraction of palindromic inserts varied, but no systematic difference emerged (Table S4), though this data set is small to make any general conclusion. We observed that there is no variation in palindromic insertion at the 5′Jβ with regard to the HLA. We saw some HLA-related differences at the 3′Vβ, the two related siblings shows similar fraction of palindromes, donors of African origin show higher fraction of palindrome insertions. One might expect a variation in palindromes due to HLA as the Vβ gene segments interact with the HLA as well as with the peptide for antigen recognition.In the CD8. However, the 30–50% observed is very large. Most other sequence properties, such as CDR3 length and GC content, are indistinguishable between productive and non-productive TCRs, and show little sign of influencing thymic selection. These differences are observed for P nucleotides for Vβ, Jβ, and 5′Dβ.The significant differences between P nucleotide usage in productive and non-productive naïve TCRβ sequences suggests strong thymic selection Figure 2β end in either naïve or memory compartments of CD8+ and CD4+ T cells  With the algorithm presented to assign a probability that an observed palindrome is truly a P nucleotide, we can begin to study the functional role of P nucleotides β−Jβ and Vβ−Dβ junctions as N1 and N2 regions respectively. We denoted the observed and true complementary sequence as palindrome and P nucleotide respectively. The probability of i-base P nucleotide is represented as pi. “Coding” end referred to the sequence directly adjacent to the recombination signal sequences (RSS). TCRβ CDR3 region is defined as a sequence that begins at the nucleotide base that encodes the 2nd conserved cysteine at the 3′ of the Vβ gene and ends at the nucleotide base that encodes the conserved phenylalanine at the 5′ of the Jβ gene.For our purposes, and in keeping with previous works, we introduced some notations and terminologies. We denoted the nucleotide insertion at D+ and CD4+ T cell samples were carried out as described in Isolation and purification of naïve and memory compartments of the CD8β, were done as described in Polymerase chain reaction (PCR) amplification and sequencing of rearranged TCRβ CDR3 regions, in reverse complement from 3′Jβ to 5′Jβ orientation.Raw GA sequence data, 60-nucleotide long, were preprocessed to remove PCR and sequencing errors. Clustering of data into unique TCRβ CDR3 sequences were done using Hamming distance, as described in β, the 3′ end of the Vβ, and both the ends of the Dβ by aligning the solexa sequence with the reference genes. Since somatic hypermutation do not occur in T cell, any difference between the reference gene and solexa sequence is not allowed in the CDR3 region and therefore the first nucleotide difference determine the end of the gene segment.Identification of the TCRβ CDR3 region and the nomenclature were done according to the definition established by the International ImMunoGeneTics collaboration β gene segment has a unique nucleotide motif between the position 12 and 18 from the heptamer signal sequence. The last several bases of solexa sequence are searched for Jβ gene specific J motif, this in turn uniquely determined which Jβ segment was used. If none of the J motif was found in the solexa sequence, the sequence was not considered for P nucleotide analysis. If the J motif is found, solexa sequence is compared base by base, 3′ to 5′ orientation, to the reference Jβ gene segment. Truncated or nontruncated 5′Jβ coding end is respectively determined by the next nucleotide difference or by the end of reference Jβ gene segment.Each of the 13 different Jβ gene segment has a 7-base nucleotide motif which encodes the first four amino acid residues “CASS” of the CDR3. Solexa sequence is searched for Vβ gene specific motif. If Vβ gene specific motif was not found for any of the Vβ gene, the solexa sequence was dropped from the analysis. If the motif is found, solexa sequence is divided into two subsequences: 1st subsequence is the sequence downstream of the motif, containing the motif, and the 2nd subsequence is upstream of the motif. Since the Vβ part of the CDR3 region is too short, Vβ assignment is done in two steps, unlike the original IMGT algorithm. In the first step truncated or nontruncated 3′Vβ coding end, thus the number of undeleted Vβ nucleotides in the solexa sequence, is determined by comparing the 1st subsequence, nucleotide by nucleotide in 5′ to 3′ orientation, to the CDR3 part of the reference Vβ gene segment. In the second step upstream similarity score was calculated for each reference Vβ gene, without gap, using a sliding window between the 2nd subsequence and upstream sequence of the CDR3 part of the reference Vβ gene. The number of undeleted nucleotides calculated in step 1 was added to the upstream similarity score; adding this creates more weight to the CDR3 part of the nucleotides of the Vβ gene segment. If the Vβ segment in the solexa sequence is more likely to be close to the full coding end more likely it is to be uniquely defined. The Vβ gene corresponding to the maximum score is assigned to the solexa sequence. If the maximum score is not unique, Vβ gene cannot be assigned uniquely to the solexa sequence, the solexa sequence is dropped from the analysis.Each of the 54 Vβ and 5′Jβ termini is compared with the reference Dβ gene segments. For each Dβ gene similarity score was calculated, without gap, using a sliding window between the nucleotide sequence and the reference Dβ gene. The Dβ gene corresponding to the maximum score is assigned to the sequence and is used to delimit both the ends of the Dβ segment using the procedure described above for V and J. If the Dβ gene segment in the solexa sequence is not uniquely assigned or the number of undeleted nucleotides in the Dβ part of the solexa sequence is less than 5, the solexa sequence is dropped and not considered for P nucleotide analysis.After parsing V and J, the nucleotide sequence between 3′Vβ, 5′Dβ, 3′Dβ, 5′Jβ, the algorithm search for short palindromic sequences. The algorithm starts the search from the last (first) nucleotide of the gene segment at the 3′(5′) coding end and compared it with the first (last) nucleotide downstream to the 3′(5′) coding end. If the comparison is found to be complementary, the iteration moves to the second last (second) nucleotide of the gene segment and compared it with the second (second last) nucleotide downstream to 3′(5′), and so on. The iteration stops when the next nucleotide comparison is not found to be complementary. Number of iterations determines the length of the palindrome. After palindromic nucleotides are identified they are excluded from contributing to the upstream or downstream gene sequences that remained to be queried. N1 and N2 regions are the part of the sequence that left after the identification and delimitation of the Vβ, Dβ, Jβ and palindromic nucleotides.At each indentified and delimited full-length coding end, 3′VAt the coding ends, P nucleotides are followed by random insertions by the TdT (N nucleotides). There is no well-defined boundary between the P and N nucleotides to decompose them into disjoint sequences. Observed palindromes can be explained purely by true P nucleotides or by N nucleotides, or by a joint combination of both. We establish a linear system to model the P nucleotide probabilities, in which each equation quantifies the frequency of observed palindromic sequence  as a combination of P nucleotide and N nucleotide distributions.p = Let b reflects that excess probability, after subtracting the TdT’s bias for inserting certain nucleotides, which cannot be explained by TdT, and thus it contributes to the true formation of P nucleotides. Given the vector b from the data and TdT probabilities, p is uniquely solvable by inverting the matrix A. Since the TdT probabilities are greater than zero, the matrix A is positive definite and hence invertible.This system suggests that in order for a sequence to have a P nucleotide, we must factor out the TdT likelihood from the observed palindrome frequencies. The column vector b must satisfy the constraint b ≥ 0. 2). If p = 0. This is equivalent to saying that if the first base is added by the TdT to the coding end, it is not possible for the coding end to have a P nucleotide. 3) Since rd base pair is inserted by the TdT. 5) As a special case, if no nucleotide is added by the TdT, P nucleotide probabilities trivially become The solution of the above system must satisfy the following conditions. 1) Since the left hand side of each equation is the sum of probability terms, p, the above system requires the estimation of the entries of the matrix which depend only on the TdT probabilities and the vector b which are dependent upon the TdT probabilities as well as on empirical frequencies of observed palindromes. We calculated the empirical frequencies of the palindromes based on the data. Estimation of the TdT probabilities is described in the section Method Overview. Mean estimated TdT probabilities for the CD8+ and CD4+ T cells are given in Table S3. We substituted the coding end specific TdT probabilities and the empirical frequencies of palindromes into the above system and solve for P nucleotide probabilities of different length.In order to solve for β−Dβ or Dβ−Jβ junctions that are derived in whole or in part by nucleotide insertions from the TdT. Classifying all of the n-base palindromes into n-base P nucleotides overestimate the P nucleotide’s distribution profile and underestimate the TdT distribution in the data set. Here we take an approach of classifying a given palindrome into a P nucleotide of certain length using a maximum conditional probability model. For a Many palindromic sequences appear at VTable S3. In the above equation the denominator captures the number of possible ways a Let In each donor and at the each coding end, the expected number of P nucleotide was calculated using the expression Figure S1β and 5′ Jβ gene segment in memory compartments of CD8+ and CD4+ T cells. Heights represent the mean of the sum of the normalized average copy number over Dβ gene subsets and similarly for Jβ gene segment subsets. Copy numbers were normalized by respective number of total reads in each donor. The error bar indicates one standard deviation.Average copy number with and without P nucleotide. Correlation between average copy number and P nucleotide at 5′ D(TIF)Click here for additional data file.Figure S2+ compartments of every possible pair of individuals as a function of the number of nucleotide insertions at the N2 and N1 segments.Number of insertions distribution in P nucleotide sequence. Insertion distribution of P nucleotide and no P nucleotide CDR3 sequences observed in the naïve and memory CD4(TIF)Click here for additional data file.Figure S3β1 and Dβ2 gene segments in CD4+ naïve and memory T cells. Error bars indicate the standard deviation.Reading frame biased induced by P nucleotide. Heights represent the mean percentage of sequence with a reading frame in P nucleotide and none P nucleotide. Results are shown for D(TIF)Click here for additional data file.Table S1+ naïve and memory data set. Number of in-frame, TCRβ CDR3 nucleotide sequence, total reads and unique number of sequence obtained from the CD8+CD45RO−CD45RAhiCD62L+ (naïve) and CD8+CD45RO+CD45RAlow (memory) T-cell samples from each donor are given in the second and third columns respectively. The corresponding number of sequence with untrimmed coding ends at 3′Vβ, 5′Dβ, 3′Dβ, and 5′Jβ are shown in the last four columns. Each row shows the naïve (above) and memory (below) data.Summary of CD8(DOC)Click here for additional data file.Table S2+ naïve and memory data set. Number of in-frame, TCRβ CDR3 nucleotide sequence, total reads and unique number of sequence obtained from the CD4+CD45RO−CD45RAhiCD62L+ (naïve) and CD4+CD45RO+CD45RAlow (memory) T-cell samples from each of the six donors are given in second and third columns respectively. The corresponding number of sequence with untrimmed coding ends at 3′Vβ, 5′Dβ, 3′Dβ, and 5′Jβ are shown in the last four columns. Each row shows the naïve (above) and memory (below) data.Summary of CD4(DOC)Click here for additional data file.Table S3TdT probabilities estimate for all cell types at each of the four coding ends. Probability estimate of N nucleotide insertion is shown based on independent assumption. In order to avoid any potential bias middle regions of N2 and N1 segments are considered for the calculation of average nucleotide frequency, described in details in Methods Overview.(DOC)Click here for additional data file.Table S4+ naïve T cells. The 5′Jβ does not show any variation while the 3′Vβ shows some fluctuations with regard to the HLA. The two related siblings, donor 1 and donor 3, show the similar pattern of palindromes at 3′Vβ, 5′Dβ and 5′Jβ coding ends.Demographic characteristic of palindromic sequences. In each donor the fraction of palindromic inserts of up to six bases long is shown against the coding ends in CD8(DOC)Click here for additional data file.


The nicotine-evoked overflow of endogenous GLY in hippocampal synaptosomes in superfusion was significantly inhibited by Aβ 1–40 (10 nM) while increasing the concentration to 100 nM the inhibitory effect did not further increase. Both the Choline  and the 5-Iodo-A-85380 dihydrochloride -evoked GLY overflow were inhibited by Aβ 1–40 at 100 nM but not at 10 nM concentrations. The KCl evoked [3H]GLY and [3H]Acetylcholine (ACh) overflow were strongly inhibited in presence of oxotremorine; however this inhibitory muscarinic effect was not affected by Aβ 1–40. The effects of Aβ 1–40 on the administration of nicotine, veratridine, 5IA85380, and PHA543613 hydrochloride  on hippocampal endogenous GLY release in vivo were also studied. Aβ 1–40 significantly reduced (at 10 μM but not at 1 μM) the nicotine-evoked in vivo release of GLY. Aβ 1–40 (at 10 μM but not at 1 μM) significantly inhibited the PHA543613 (1 mM)-elicited GLY overflow while was ineffective on the GLY overflow evoked by 5IA85380 (1 mM). Aβ 40–1 (10 μM) did not produce any inhibitory effect on nicotine-evoked GLY overflow both in the in vitro and in vivo experiments. Our results indicate that (a) the cholinergic modulation of the release of GLY occurs by the activation of both α7 and α4β2 nicotinic ACh receptors (nAChRs) as well as by the activation of inhibitory muscarinic ACh receptors (mAChRs) and (b) Aβ 1–40 can modulate cholinergic evoked GLY release exclusively through the interaction with α7 and the α4β2 nAChR nicotinic receptors but not through mAChR subtypes.Using both  Nicotinic and muscarinic receptors are widely expressed in the brain and implicated in the pathophysiology of many neurological conditions, including Alzheimer’s disease (AD), where typical symptoms include the loss of cognitive function and dementia. The presence of extracellular neuritic plaques composed of β amyloid (Aβ) peptide is a characteristic feature of AD, however, although neurotoxicity is a prominent feature of Aβ, recent data emphasize the existence of synaptic functional roles of this peptide which can modulate the release of several neurotransmitters  is an important aminoacid at hippocampal level which may have a dual role acting as an inhibitory neurotransmitter, when interacting with the strychnine-sensitive receptors, and playing a stimulatory role, when co-activating excitatory in vitro  and in vivo  approaches we investigated whether and to what extent Aβ interferes with the cholinergic modulation of the release of GLY in the rat hippocampus.In the present study using both in vitro in presence of a nanomolar concentration of Aβ 1–40 which is on the contrary ineffective on the inhibitory mAChR receptor subtypes.The results indicate that (a) the cholinergic modulation of the release of GLY occurs by the activation of both α7 and α4β2 (nAChRs) as previously reported  and relative humidity (50%) under a regular light–dark schedule (light 7 a.m. to 7 p.m.). The in vitro experimental procedures were approved by the Ethical Committee of the Pharmacology and Toxicology Section, Department of Experimental Medicine, in accordance with the European legislation  and were approved by Italian legislation on animal experimentation . The in vivo protocol was approved by Ethical Committee of Pavia’s University (registered as 2/2008) according to international regulations for the care and treatment of laboratory animals, to the Italian Act  and to EEC Council Directive . All efforts were made to minimize animal suffering and to use the minimal number of animals necessary to produce reliable results.Adult male Wistar rats  were used for both ® gradients  essentially according to  to remove nuclei and debris; the supernatant was gently stratified on a discontinuous Percoll® gradient  and centrifuged at 33,500 × g for 5 min at 4°C. The layer between 10 and 20% Percoll®  was collected, washed by centrifugation, and resuspended in physiological HEPES-buffered medium having the following composition (mM): NaCl 128, KCl 2.4, CaCl2 3.2, KH2PO4 1.2, MgSO4 1.2, HEPES 25, pH 7.5, glucose 10, pH 7.2–7.4 , with antagonists being added 8 min before agonists. The evoked overflow was calculated by subtracting the corresponding basal release from each fraction and was expressed as pmol/mg of synaptosomal proteins. We previously demonstrated that in our superfusion system the indirect drug effects exerted by other mediators in the monolayer of synaptosomes in superfusion are absolutely minimized  hippocampal synaptosomes were incubated for 20 min at 37°C with [3H]GLY  in the presence of the selective GLY transporter 1 transporter blocker N-[(3R)-3--3-(4-fluorophenyl)propyl]-N-methylglycine hydrochloride  or with [3H]Choline . The K+-induced overflow from synaptosomes was estimated by subtracting the neurotransmitter content into the first and the third fractions collected  from that in the 6-min fraction collected during and after the depolarization pulse . The amount of radioactivity released into each superfusate fraction was expressed as a percentage of the total synaptosomal tritium content at the start of the fraction collected .When studying the release of Choline (specific activity: 60–90 Ci/mmol) and [3H]Glycine (specific activity: 15 Ci/mmol) were purchased from Perkin Elmer SpA.Beta amyloid , percollin vivo by Western Blot procedure  analyzed in vivo, we also do not expect to observe aggregation at the lower concentrations and shorter times used in vitro in light of the fact that aggregation is a concentration and time-dependent process.It is known that Aβ peptides structure and aggregation properties depend on several factors. It is therefore important when using Aβ peptides to specify both the concentration of the soluble Aβ but also the methods used to prepare Aβ solution and to verify the presence of Aβ aggregates . In presence of oxotremorine also the KCl evoked release of [3H]ACh from hippocampal nerve endings was significantly inhibited  on hippocampal GLY release in vivo. In order to test whether the administration of Aβ 1–40 through the dialysis probe allowed the delivery of the peptide to the tissue we performed an immunohistochemical analysis. Figure in vivo (1 and 10 μM) within the hippocampus. Despite the fact that we do not know the exact amounts of Aβ reaching the tissue, there was a visible positive correlation between the concentration administered and the signal of Aβ immunoreactivity in the tissue. Moreover, immunohistochemical analysis shows that no evident signs of apoptosis were observed within the area of amyloid diffusion as shown by Hoechst staining.Based on the in vivo was derived from previous data demonstrating that the administration by microdialysis of 50 mM nicotine was able to significantly increase the levels of GLY in hippocampal extracellular compartment  as it may happen because of the disease may dysregulate the cholinergic modulation of hippocampal activity leading to a disproportionate inhibition since it leaves unaffected the muscarinic inhibitory control and impairs the nicotinic stimulatory one. All this occurs in absence of Aβ acutely induced neuronal damage.We here report that the cholinergic modulation of GLY release at the presynaptic level on hippocampal nerve endings was modulated not only by stimulatory α4β2 and α7 nAChRs as previously reported  produced full effect while did not produced any effect at 1 nM concentration. Ten nanomolars Aβ beyond the estimated normal concentrations in human CSF but may be caused by the altered precursor protein processing as it occurs along with the disease  of both nicotine and PHA543613 stimulated GLY release. On the other hand in vivo important differences were noticed at the baseline in the ability of the two specific nAChRs agonists to elicit GLY release as compare to in vitro. Indeed in vitro both α7 and α4β2 nAChRs agonists were able to stimulate to the same extent GLY release; while in vivo the release elicited by 5IA85380 was less than a half that obtained in response to PHA543613. In vivo to many variables may take place, and in spite of the above described discrepancy, there is a general good agreement of the effects in vivo and in vitro of Aβ on the release of various neurotransmitters (present work and references) including the observation that so far Aβ has never been able to modify the depolarization (veratridine or potassium)-elicited release of neurotransmitters, but only the one modulated by presynaptic receptors.Our findings In conclusion our findings show that Aβ can modulate cholinergic evoked GLY release exclusively through the interaction with α7 and the α4β2 nAChR nicotinic receptors, acting probably through different mechanisms of action, but not through mAChRs. The present study may therefore provide further insight into the mechanism by which AB impairs synaptic plasticity and cholinergic function in AD brain.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


However, it is unknown whether this occurs in the brain, as there are few research tools to adequately address this question. As the α4*-containing nAChRs are highly expressed in the ventral tegmental area (VTA) we assessed the molecular, functional and pharmacological roles of α5 in α4*-containing nAChRs in the VTA. We utilized transgenic mice α5+/+(α4YFP) and α5-/-(α4YFP) that allow the direct visualization and measurement of α4-YFP expression and the effect of the presence (α5+/+) and absence of α5 (-/-) subunit, as the antibodies for detecting the α4* subunits of the nAChR are not specific. We performed voltage clamp electrophysiological experiments to study baseline nicotinic currents in VTA dopaminergic neurons. We show that in the presence of the α5 subunit, the overall expression of α4 subunit is increased significantly by 60% in the VTA. Furthermore, the α5 subunit strengthens baseline nAChR currents, suggesting the increased expression of α4* nAChRs to be likely on the cell surface. While the presence of the α5 subunit blunts the desensitization of nAChRs following nicotine exposure, it does not alter the amount of ethanol potentiation of VTA dopaminergic neurons. Our data demonstrates a major regulatory role for the α5 subunit in both the maintenance of α4*-containing nAChRs expression and in modulating nicotinic currents in VTA dopaminergic neurons. Additionally, the α5α4* nAChR in VTA dopaminergic neurons regulates the effect of nicotine but not ethanol on currents. Together, the data suggest that the α5 subunit is critical for controlling the expression and functional role of a population of α4*-containing nAChRs in the VTA.Human genetic association studies have shown gene variants in the α5 subunit of the neuronal nicotinic receptor (nAChR) influence both ethanol and nicotine dependence. The α5 subunit is an accessory subunit that facilitates α4* nAChRs assembly  The α5-/- mice were found to have normal transcript levels for all nAChRs subunits, including α4 and β2 in all brain areas including the VTA ,50. Althent line . Hence i2+ permeability [2+ permeability through nAChR is thought to increase the excitability of the neuron and modulate neurotransmitter release [We find that greater number of α4*-containing nAChRs in the presence of α5 strengthens nicotinic receptor currents in VTA dopaminergic neurons. Nicotinic currents in both α5 +/+ and α5 -/- mice were almost fully inhibited by the α4* nAChR antagonist DHβE, suggesting that the ACh-induced nAChR currents in VTA dopaminergic neurons were predominantly mediated by α4*-containing nAChRs, and that the presence of the α5 subunit in the α4* nAChR assembly boosted receptor currents. A caveat in our study is that electrophysiological recordings were done in animals between postnatal p21-p28 and the western blot analysis was done in animals between p35-p70. However previous studies indicate it is unlikely that there would be any difference in the expression or current between these two age groups ,54. The eability ,49. Our  release . Hence, The α5 subunit is clearly an important accessory component of the α4* nAChR assembly in the brain. Moreover, human genetic association studies have indicated that the minor allele of rs16969968 in CHRNA5, encoding a single nucleotide polymorphism in the α5 subunit of the nAChR, to be associated with increased risk of nicotine dependence ,56 and aIn vitro studies have shown nicotine at high concentrations (or prolonged exposure at low concentrations) can cause desensitization of nAChRs on dopaminergic neurons [The human genetic studies have been complemented well with behavioral animal studies to show that α5*-containing nAChRs are important for nicotine ,36,57. A neurons ,61 and t neurons . We foun neurons ,63 induc neurons  and incr neurons –66.in vivo and during in vitro brain slice electrophysiology [Xenopus oocytes expressing different subunit compositions nAChRs [The behavioral role of α5 in ethanol’s effect has been shown to modulate the sedative effects but not ethanol consumption in mice . Previouysiology ,68. The ysiology . Ethanolysiology ,70 and s nAChRs ,71. Heres nAChRs ,17. Toge2+ permeability of human neuronal α3* nAChRs in recombinant assays and non-neuronal expression systems [Our observation that α4α5* nAChR appear to not play a regulatory role in ethanol’s effect is not completely surprising. In behaving animals studies using null mutant mice of the β2 nAChR subunits , α4 nACh systems ,77. None systems ,44,47.In conclusion, we have shown the α5 subunit is critical for maintaining the expression of α4* nAChR protein levels of the VTA neurons and strengthening nicotinic currents in dopaminergic neurons. The presence of α5 causes resistance to nicotine desensitization but does not regulate ethanol enhancement of ACh currents in VTA dopaminergic neurons. The α5 nAChR subunit is an important component of the α4* containing nAChRs and plays a vital role for nicotine’s effect in the brain. The α5α4* nAChR appears to be a promising target for at least the treatment for nicotine dependence.


For pH<5, the Zn isotopic compositions of the various parts of the plants are expected to be similar to those of groundwater. In the neutral to alkaline region, the calculations correctly predict that 66Zn is enriched over 64Zn in roots, which concentrate phosphates, with respect to leaves, which concentrate malates and citrates, by about one permil. It is proposed that Zn isotope fractionation represents a useful tracer of Zn availability and mobility in soils.Stable Zn isotopes are fractionated in roots and leaves of plants. Analyses demonstrate that the heavy Zn isotopes are enriched in the root system of plants with respect to shoots and leaves as well as the host soil, but the fractionation mechanisms remain unclear. Here we show that the origin of this isotope fractionation is due to a chemical isotope effect upon complexation by Zn malates and citrates in the aerial parts and by phosphates in the roots. We calculated the Zn isotope effect in aqueous citrates, malates, and phosphates by  Arabidopsis halleri and to malate and citrate in the aerial parts. The importance of this observation was strengthened by the remarkable discovery 66Zn in the root system of plants grown in a controlled environment is enriched over 64Zn by 0.6 permil (‰) with respect to the aerial parts of the plant, an observation replicated and confirmed on plants collected in their natural habitat ab initio methods Using XAFS microspectroscopy X and ([A]/[A′])Y are the isotopic ratios A/A′ measured in the complexes AX (and A′X) and AY (and A′Y), respectively. The isotope enrichment factor is defined as αm−1. Since α is close to 1, α−1 can be approximated as ln α.Isotopic exchange in chemical reactions can be represented by two half-reactions,X such that an approximation expression δ≈103 ln α is suitable.Deviations of isotopic ratios from a reference value in parts per 1000 are conventionally defined asm and m′ the masses of two isotopes (prime represents the light isotope).The standard theory of chemical isotope fractionation is based on mass-dependent isotopic differences in vibrational energies of isotopologues s/s′)f defined asi stands for vibrational frequencies, s for the symmetry number of the molecule, and iu = hνi/kT. The isotope enrichment factor due to the molecular vibration can be evaluated from the frequencies summed over all the different modes. The partition function ratio (s/s′)f for isotopologues A′X and AX  is noted βX . In the isotopic exchange reaction 3, isotope fractionation can be estimated from the relation ln α≈ln βY−ln βX. Contribution of other isotope effects, such as the nuclear field shift effect, to ln β is less than 10% for the Zn chloride system 66Zn/64Zn ratio.The isotope enrichment ln α due to intramolecular vibrations can be evaluated from the reduced partition function ratio (RPFR) β =  was determined by substituting νi into Eq. (7). Then 64Zn was replaced by 66Zn and the vibrational frequencies were calculated again for the same molecular structures to obtain ln b(iu), from which ln β was then determined.In the present study, the optimized structures of Zn species were first determined for −9.Orbital geometries and vibrational frequencies of aqueous Zn(II) species were computed using density functional theory (DFT) as implemented by the Gaussian09 code File S1. The hydrated Zn2+ ion possesses six H2O molecules in its first coordination shell and complexation with large anions such as Cl− decreases the coordination number from six to four 0, Zn(cit)24−, and Zn2H−2(cit)24−, were found in a titration measurement 2O)n−, have been estimated by the ab initio method 24−, hydrated ZnH(cit), and hydrated Zn2H−2(cit)24−. Since citrate ion (cit)3− is a tridentate ligand, Zn(cit)(H2O)3− and Zn(cit)2(H2O)04− were calculated as hydrated Zn(cit)− and Zn(cit)24−, respectively. The monoprotonated citrate ion H(cit)2− showed a bidentate character, such that four H2O were arranged in the empty coordination sites of ZnH(cit). The deprotonated citrate ion H−1(cit)4− is a strong anionic ligand. Calculations show that the Zn2+coordination number for the dimeric species Zn2H−2(cit)24− must be reduced to four. Zn2H−2(cit)2(H2O)44−, in which two H2O bind to Zn2+ and another two bridge the H−1(cit)4− ligands, was optimized.Four citrate species, Zn(cit)22+, ZnH+, and Zn0 emerge as the potential malate species. A higher-order complexation of Zn22− may exist 2− and its protonated species were treated as bidentate ligands, with H2O molecules arranged in the empty coordination sites of Zn malates.Several species of Zn malates were found in titration studies 2PO4+, ZnH4(PO4)2, ZnH3(PO4)2−, ZnHPO4, and Zn2H2(PO4)2. Except for strong acidic/basic conditions (pH<3 and pH>11), the hydrogen phosphate ions, H2PO4− and HPO42−, are the major anionic species in orthophosphate solutions. Cadmium ion, Cd2+, a congener ion of Zn2+, in orthophosphate solutions shows a sixfold coordination, in which five coordination sites are occupied by H2O and one by an oxygen atom of the hydrogen phosphate ion 2PO4− and HPO42− are treated as monodentate ligands. Since the bonding power of HPO42− is stronger than that of H2PO4−, HPO42− attracts H+ from a H2O molecule in ZnHPO4(H2O)5 and the optimized structure resembles that of ZnH2PO4(H2O)4OH. For ZnH4(PO4)2, cis- and trans- configurations of Zn(H2PO4)2(H2O)4 were tested and the trans-configuration was energetically stable. Calculations of ZnH3(PO4)2− as Zn(H2PO4)(HPO4)(H2O)4 deformed six coordination to smaller values. Dehydrated H2O molecules bridged H2PO4− and HPO42−. Configuration of dimeric species such as Zn2H2(PO4)2 has been suggested in the literature Complexation of Zn in orthophosphate solutions has been studied in a pioneering work using titration method total = 0.005 mol dm−1 (M) and [cit] total = 0.01 M. Similar conditions were chosen for malates. For the phosphate system, we kept [Zn]total at the same level as in the other systems and used a rather high phosphate concentration of [P]total = 1 M. In 66Zn (‰) is the difference between the ln β values of the different species. We calculated δ66Zn between Zn2+, Zn citrates, malates, and phosphates , ln β values of all species are tightly grouped between 3.26 and 3.47‰. In the neutral region, ln β is 4.3–4.4‰ for the major phosphate species and ∼3.5‰ for Zn citrates and malates. The difference is therefore about 0.8–0.9‰. Fractionation with respect to the original solution with positive δ66Zn in phosphates and negative δ66Zn in citrates and malates therefore is expected. In the basic region (pH>8), the major phosphate and citrate species show ln β∼4.4–4.5‰, while Zn malates and Zn2+ show ln β<3.5‰. The difference is about 0.9–1‰. Negative δ66Zn in malates and positive δ66Zn in phosphates and citrates are again expected. Overall, phosphates preferentially concentrate isotopically heavy Zn, whereas Zn2+ and malates concentrate the light Zn isotopes. Isotope fractionation in citrates varies from positive to negative depending on pH but does not seem to alter either the phosphate/malate or the phosphate/Zn2+ systems. Zinc isotope fractionation between phosphate and Zn2+ and between phosphate and malate therefore is of the order of 1‰ at pH>5. The presence of citrates may affect both Zn speciation and δ66Zn, but for pH<8, phosphate isotopically remains the heaviest Zn compound.Mole fractions of Zn citrates, malates, and phosphates are shown in osphates Figure 3Arabidopsis species, Zn is mostly distributed among phosphates, malates and citrates, with phosphates being present notably in the root system 66Zn of herbaceous plants with respect to nutrient solutions 66Zn 66Zn Using EXAFS and x-ray microfluorescence, it was pointed out that for a number of Arabidopsis halleri has much higher δ66Zn than the root system of the nonaccumulator Arabidopsis petraea. A simple explanation of this observation is that the pH is regulated around a neutral value by the root system of Arabidopsis halleri, which promotes phosphate dissociation and Zn complexation. In contrast, in the root system of Arabidopsis petraea, pH drops to values pH<5 Zn, with the consequence that phosphate complexation is minimal and Zn isotope fractionation is greatly reduced.Nevertheless, pH at the spot of root hair initiation can drop below 4.5 66Zn of ferromanganese nodules In general, the fates of Zn and phosphate seem to be strongly connected and Zn isotopes should provide a new perspective on the chemistry of soils and groundwater. Phosphates are adsorbed on iron hydroxide precipitating in seawater 66Zn and 64Zn between phosphates, malates, and citrates, three Zn compounds abundant in plants, using ab initio techniques for a broad range of pH conditions. We found that, for pH>5, Zn phosphate is 1‰ heavier than Zn2+ and Zn malate and, for pH<8, the heavy character of Zn phosphates is not greatly affected by the presence of citrates. This result accounts for the high δ66Zn found for roots and herbaceous plants with respect to leaves and sprouts.We investigated fractionation of File S12+ ion, citrates, malates, and phosphates (see Optimized structure Cartesian coordinates of hydrated Znates see .(DOC)Click here for additional data file.