Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis.P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small.A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here. Current computational methods for prediction of protein function rely to a large extent on predictions based on the amino acid sequence similarity with proteins having known functions. The accuracy of such predictions depends on how much information about function is embedded in the sequence similarity and on how well the computational methods are able to extract that information. Other computational methods for prediction of protein function include structural similarity comparisons and molecular dynamics simulations (e.g. molecular docking). Although these latter methods are powerful and may in general offer important 3D mechanistic explanations of interaction and function, they require access to protein 3D structure. Computational determination of a 3D structure is well known to be resource demanding, error prone, and generally requires prior knowledge, such as the 3D structure of a homologous protein. This bottleneck makes it important to develop new methods for prediction of protein function when a 3D model is not available.proteochemometrics was introduced that has several useful features T are the regression coefficients and x = T. The activity y, may be the binding affinity to a receptor but may also be any biological activity e.g., the growth inhibition of cancer cells. In comparison with numerical calculations from first principles and similar approaches, the main advantages of QSAR modelling are that it does not require access to the molecular details of the biological subsystem of interest and that information can be obtained directly from relatively cheap measurements.where The joint perturbation of both the ligand and protein in proteochemometrics yields additional information about the different combinations of ligand and protein properties for an interaction than can be obtained in conventional QSAR modelling where only the ligand is perturbed. In recent years, various other bioinformatic modifications of conventional QSAR modelling have been reported. These include simultaneous modifications of the ligand and the chemical environment (buffer composition and/or temperature) in which the interaction take place -8, and tAlthough proteochemometrics has already proven to be an useful methodology for improved understanding of bio-chemical interactions directly from measurement data, the quantitative proteochemometric models designed so far have not yet been subject to a detailed and unbiased statistical evaluation.K-fold CV for different regression parameters, 2) Select the parameter value that yields the largest estimated performance value, and 3) Report the most promising model found and the associated performance estimate. Although this procedure may seem intuitive and may yield predictive models (as we in fact demonstrate below) the performance estimates obtained in this way may be heavily biased. Interestingly, this problem was recently addressed in the context of conventional QSAR modelling [A key issue in this evaluation is the problem of overfitting. Since the number of ligand and protein properties available is usually very large, to avoid overfitting, one has to constrain the fitting of the regression coefficients. For example, in ridge regression , a penalodelling , and hasodelling ,16.As an alternative or complement to constraining the regression coefficients, one may also reduce the variance by means of variable subset selection (VSS). In QSAR modelling, many algorithms for VSS have been proposed based on various methodologies, for example optimal experimental design ,18, sequAlthough many of the advanced algorithms for VSS are powerful, they are all computationally demanding. Therefore, in order to keep the computing time down in our use of the double loop cross validation procedure employed here, conceptually and computationally simple algorithms for VSS were used instead of the more advanced ones presented, e.g. in -20. MostK separate parts and a conventional K-fold cross validation (CV) was performed. However, since all available examples were used, there were no longer any completely independent test examples available for model evaluation. Interestingly, this problem of introducing an optimistic selection bias via VSS was recently also pointed out in the supervised classification of gene expression microarray data [In previous reported proteochemometrics modelling, all available examples were used in the VSS. These were split into ray data .In this paper we employ a procedure that can be used to perform unbiased statistical evaluations of proteochemometric and other QSAR modelling approaches. An overview of this so-called double loop CV procedure is presented in Figure K1 different variable subset selections are performed, one for each step in the outer CV loop. This avoids optimistic selection bias.1. Q2) found in the inner loop by means of K2-fold CV are computed, but not reported as the model's performance estimate. This avoids the second optimistic selection bias mentioned above.2. The best performance estimates . P2 is the result of different models that are designed and selected in the inner loop. It reflects the performance that one should expect on average.3. An unbiased performance estimate, K1-fold CVs which yield information about the robustness in the results obtained .4. Repeated In addition to these refinements, this work also demonstrates the potential of fast and straight forward alternative methods for VSS and regression in the inner loop. Moreover, it indicates that the performance estimates reported by certain software packages for QSAR may be quite misleading.1-adrenoreceptor variants . As for the first data set, there are not interaction data available for all the 12 × 18 = 216 possible interactions, but for 131, see [amine data set and the alpha data set, respectively.We reanalyzed two of the largest proteochemometric data sets yet reported. The first data set is presented in  and cont131, see  for moreComputer programs were written in MATLAB  to integrate the double loop procedure in Figure K2-fold CV loop was performed with K2 = 5. The different values of ND (the number of molecular descriptors) evaluated were 10, 20, 50, 100, 200, ..., 1000, 1500, 2000, ..., 6000 for the amine dataset and 10, 20, 50, 100, 200, ..., 1000, 1500, 2000 for the alpha data set. The values of NL considered were either the number of latent variables 1, 2, ..., 8, for both the amine and alpha data set or the degree of RR penalty 0, 0.5, 1.0, ..., 3.0 for the amine data set and 10, 50, 100, 150, 200 for the alpha data set. In the outer K1-fold CV loop, the same number of splits (K1 = 5) was used as in the inner loop. On the global level, the complete experiments were performed 100 times using different random partitions of the complete data sets.The joint variable selection and PLS tuning performed in the inner Q2 obtained in the inner loops look quite promising for the PLSfilter method (Q2 = 0.90 for both PLS and RR) and is even higher than the value reported in earlier studies [P2 is much smaller . The Q2 values for the models obtained after variable selection using corrfilter are significantly lower than when using PLSfilter, but the P2 values are almost on the same level for the two variable selection methods when no variable selection at all is used. corrfilter reduces the number of descriptors to about one third of the initial number, but corrfilter still selects more than twice as many descriptors than PLSfilter  is approximately one (1.4 and 0.8) or 15%. In conclusion, the individual models are quite different but essentially all of them yield useful predictions.The estimated performances of the models for the alpha data sets are presented in Table Q2 values for models based on all descriptors built with PLS using between one and ten latent variables for the amine data set. All the Q2 values were computed using the leave out CV method with five random groups and are presented in Figure To verify that our computations using MATLAB are comparable to computations by other programs, such as SIMCA, GOLPE and UNSCRAMBLER, models without variable selection were performed with all four approaches. In the comparison we have compared Q2 values obtained with SIMCA 7.0 are much higher than for the other methods. This is due to the fact that SIMCA does not use the standard formula (Eq. 3) to compute Q2 , for some general information see [Remarkably, the tion see .To study the robustness and interpretability of the set of models obtained using the two data sets considered, two different levels of information were computed and presented. The first level of information consists of two histograms displaying, for each ligand block ), and for each transmembrane region (TM1–TM7), how often different kinds of descriptors are selected. The histograms are based on the 500 observations obtained in the 5-fold cross validation performed 100 times using different, randomly selected, partitions of the data set. The descriptors are divided into receptor descriptors and ligand descriptors that are further subdivided into original descriptors, cross term descriptors, and absolute valued cross terms. In Figure The second level of information displays the average and the standard deviation of the contribution of the different TM regions in the receptors for creation of receptor-ligand affinity according to the 500 different models designed. The contributions were calculated exactly as described in  for a siIn summary, the results reported here confirm earlier reports on the potential of proteochemometrics modelling for prediction of biological activity. It is interesting to note that the VSS did increase the predictivity of the models for the alpha data set, but not for the amine data set. The VSS for the alpha data set did also reduce the number of variables to approximately 4% of the original variables, while for the amine data set 15–38% of the variables remained after VSS. This indicates that for models where many variables receive low weights  the VSS can significantly improve the model, whereas for a data set like the amine data set, with less low weighted variables, the VSS does not improve the model even though it can improve the interpret ability of the model.P2, there is statistical support that a single design will yield useful predictions. In order for a single model to be chemically interpretable as well, all the models selected in the inner loop should yield approximately the same number (same set) of variables and the constraints on the regression coefficients  in all models should be approximately equal. With this in mind, the results presented in this work indicate that it is possible to design single proteochemometric models with predictive power based on the two data sets considered but that there is a relatively large variance (from one design set to another) in the variables selected and the constraints put on the regression coefficients. This indicates that although a single proteochemometric model would be useful for predictions, a detailed chemical analysis of such a model would be uncertain. More reliable information should be gained from a careful joint analysis of all the models (and their variables) selected in the inner loops of the different evaluations performed. For example, as briefly discussed in [The basic goal of proteochemometric modelling is to obtain a single quantitative model that can predict biological activities accurately and which can be easily interpreted biochemically. In this context, it is important to stress that the only role of the outer loop employed in this work is to obtain unbiased estimates of the average performance of the design procedure considered in the inner loop. The additional random splitting of the data sets is used on top of this to gain information about the stability in the performance estimates. Thus, for procedures in the inner loop that yield small variances around a high average of ussed in , the varMoreover, we have also presented estimates of the variability (uncertainty) in estimating the contributions to affinity, between various combinations of ligands and receptors, from different transmembrane regions. In Figure Figure Although earlier findings have been confirmed, one should note that there are a number of differences between the present and earlier studies which makes detailed comparisons difficult: 1) In earlier work different variable subset selection methods were employed and in some attempts there were no subset selection at all. 2) The normalization and use of nonlinear cross terms differ between the present and earlier studies of the alpha data set. 3) The limited forms of external predictions attempted earlier e.g., in [This work employs a methodology for unbiased statistical evaluation of proteochemometric modelling and confirms that proteochemometric modelling is a new bioinformatic methodology of great potential. The statistical evaluation performed on two of the largest proteochemometric data sets yet reported indicates that detailed chemical analyses of single proteochemometric models may be unreliable and that a systematic analysis of the set of different proteochemometric models produced in the statistical evaluation should yield more reliable information. Finally, although this work has focused on confirming the potential of proteochemometrics, the kind of systematic unbiased performance estimation employed here is of course also relevant for closely related areas of bioinformatics like microarray gene expression analysis and protein classification.In the amine data set, each of the 23 compounds was described by means of 236 different GRid INdependent Descriptors (GRIND)  computedBefore the proteochemometric modelling step, the amine data set was subjected to preprocessing in order to reduce the dimensionality of the original descriptors. This step should be part of the design procedure, leaving external examples outside. However, this issue is not expected to be critical and was therefore ignored in this study. For the compounds in the amine data set, after mean centering , principal component analysis (PCA) was employed separately to each of six different blocks of GRIND descriptors, each block representing a particular kind of physical interaction. Similarly, each of the seven trans-membrane receptor block descriptors was subjected to PCA. This resulted in 6 × 10 = 60 compound descriptors and 7 × 15 = 105 receptor descriptors. Finally, 12,600 additional "cross-term" descriptors were produced by combining the compound and receptor descriptors nonlinearly. The cross-terms were added to account for non-linearities and they are shown to significantly improve the model predictivity. For each pair of compound and receptor descriptor blocks , the 150 possible products between a compound and receptor descriptor value were computed. In addition, the absolute value of the deviation of each product from the average of the product over the data set available was computed. This resulted in 300 descriptor values for each of the 42 block pairs i.e., 42 × 300 = 12,600 values. For the alpha data set, the cross terms formed were the 2 × 24 × 96 = 4,608 possible products between the descriptors of ligands and receptors. No block-wise PCA was employed to reduce the dimensionality.As a final step before entering the modelling phase, all descriptor values were mean centered and normalized to have unit variance.In PLS regression, first a latent variable modelx = t1b1 + t2b2 + ... + tMbM     (2)x of descriptor values is created where tm is latent variable and bm is the corresponding basis (loading) vector. As few uncorrelated latent variables as possible which have the largest covariances with the response variable y, are selected. Then, a linear model y = a0 + a1t1 + ... + aMtM is obtained from ordinary least squares fitting. Usually, this predictor is transformed back into the original variables yielding y = wT x as in (1). The robustness of PLS comes from the latent variable modelling which eliminates problems caused by strongly correlated variables and few examples. Ridge regression achieves its robustness by adding a penalty term  to the ordinary least squares criterion that reduces the variances in the regression coefficients. In the experiments considered below, the degree of penalty used in the RR and the number of latent variables used in the PLS regression were tuned in the inner CV loop to maximize their corresponding inner K2-fold cross validation performance estimates.of the vector L latent variables, where L is the number of latent variables associated with the model in (2) that explain 99% of the observed variance in y. Then each descriptor is ranked according to the corresponding mean of the squared coefficients, wi, in the regression models (1) from the L different models. For the alpha data set below only PLSfilter is applicable. This is due to the discrete nature of the ligand descriptors.In the PLS modelling, the subsets of molecular descriptors used were selected jointly with the latent variables. Before the joint selection was performed, the molecular descriptors were ranked using two simple and fast methods: A bottom-up algorithm, which we call corrfilter, and a top-down algorithm which we call PLSfilter, corrfilter ranks the molecular descriptors according to the Pearson correlation coefficient between the descriptor and the response variable (the affinity). PLSfilter first builds a PLS model using all available descriptors and between one and K2-fold CV performance estimate. The performance estimates for different combinations of values of ND, the number of top-ranked molecular descriptors, and values of NL, the number of latent variables (PLS) or degree of penalty (RR), were considered. Finally, the pair  of numbers yielding the highest estimated predictive power was selected.After completing the variable ranking, the most promising combination of the number of top-ranked variables and the number of latent variables in the PLS regression modelling or the degree of penalty in the ridge regression modelling was selected as judged by a Q2 defined asThe predictive power of the models was measured by the commonly used dimensionless quantity n is the number of examples, yi is the measured biological activity of example i,  is the corresponding prediction, and  is the arithmetic mean value of all the measured activities. Hence, Q2 is a CV estimate of the fraction of the variance of the response variable explained by the model. In the case of ordinary least squares fitting, Q2 is also a CV estimate of the squared Pearson correlation coefficient between the true (y) and the predicted  response values. Thus, a value of Q2 close to one is traditionally interpreted as a good  model.where K2-fold CV performance estimate is common in conventional chemometrics and is also applied in proteochemometrics. This method of tuning is more complicated and therefore slower than simpler alternatives  but is expected to be less sensitive to overfitting. Although parameter tuning based on CV is attractive, overfitting may still occur and the performance estimate obtained may be too optimistic. Some aspects of this danger were recently pointed out [As already mentioned, selection of a QSAR model that maximizes a nted out  and has nted out . In concK1-fold CV loop, one of the K1 subsets of the whole data set was kept for validation and the rest were used for design of a regression model. The predictions obtained in this outer CV loop were finally used in the formula for Q2 in (3). However, since the predictions used for calculating Q2 were kept outside the whole design procedure, as in earlier work [P2 to indicate that this is an unbiased performance estimate based on external predictions.For each step in the external ier work ,16, we dK1-fold CV are interesting but are sometimes heavily influenced by the particular data partitioning used. In the work reported here, we therefore performed repeated K1-fold CV in the outer loop. For each partitioning selected randomly, the corresponding value of P2 was computed using the procedures described above. Thus, a set of different values of P2 were obtained and used for determination of the variability in the results obtained.The results obtained from a single The main body of programming and computations were performed using MATLAB on standard processors (900 MHz). For comparisons, we also employed the program packages SIMCA , GOLPE  and UNSCE.F. and M.G. devised and implemented the proposed double CV loop procedure, the feature selection algorithms, and a numerically efficient version of ridge regression required. P.P., M.L., J.E.S.W. provided the data sets studied together with experience and insights gained from their earlier work on proteochemometrics. V.M. and M.G. supervised the project. All authors read and approved the final manuscript.


This report describes a patient counseling approach and non-surgical management of a dichorionic-diamniotic twin pregnancy where delivery of the second twin followed the delivery of the first by 110 days.in utero. Reassuring fetal status was observed for twin B without evidence of contractions or chorioamnionitis. A viable male infant (2894 g) was delivered vaginally at 35 1/2 weeks.An early transvaginal sonogram at 19 1/2 weeks suggested cervical dilation with protruding amniotic membranes. Tocolytic and antibiotic therapy was initiated; no cerclage was placed. Spontaneous rupture of membranes and cord prolapse occurred 48 h later, resulting in delivery of a stillborn female infant. Conservative management was offered after counseling for possible risks associated with maternal sepsis, need for extended hospitalization, potential for hysterectomy and death. The cervix appeared closed after delivery and the umbilical cord was ligated, with subsequent spontaneous cord retraction This report outlines a counseling approach useful for patients with premature delivery of one twin, and presents application of conservative obstetrical management principles for the aftercoming twin even when delivery interval is extreme. The incidence of twin gestation has increased in recent years, a trend influenced in part by greater utilization of the assisted reproductive technologies. In this report, we describe a conservative approach culminating in vaginal delivery of a viable second twin 110 days after delivery of a preterm stillbirth.3P1011 presented for initial prenatal assessment at six weeks gestation. The conception was established without medical assistance. The patient had no significant medical or surgical history. She underwent an uncomplicated curettage for missed abortion four years before presentation and an uneventful term vaginal delivery occurred two years later.A 30 year-old non-smoking Caucasian GTransvaginal ultrasound at seven weeks gestation revealed a dichorionic-diamniotic twin pregnancy. At 18 weeks gestation no growth discordance was noted, but cervical length was two cm with funneling. Based on these findings, the patient was counseled about maternal and neonatal risks associated with twin pregnancy, particularly the risk of preterm labor due to cervical shortening. Although rescue cerclage was offered to the patient, this option was declined. She was therefore placed on bedrest for two weeks with follow-up ultrasonography for assessment of cervical length.2/7 weeks gestation the patient experienced abdominal cramping and non-purulent blood-tinged vaginal discharge. The patient remained afebrile. She was hospitalized and placed on bed rest in Trendelenburg position after sterile speculum exam found the cervix two cm dilated with protruding "hourglass membranes".At 19One day later, amniotic membranes had fully retracted and were no longer visible above a closed cervix. Microscopic examination of vaginal fluid found occasional clue cells. External monitoring identified occasional uterine contractions; heart rates at ~150/min were measured for both twins. The patient was again counseled about the implications of preterm labor at this early stage, and the uncertainty of preventing further cervical dilation. After consideration of all therapeutic options (including cerclage), the patient elected tocolysis with a view to save her pregnancy. A 4 g loading dose of magnesium sulfate was administered intravenously, followed by a maintenance dose of 2 g/h. Oral metronidazole (500 mg) was given every 8 h, and 500 mg ampicillin was given intravenously every 6 h after a 2 gm loading dose according to hospital protocol. Additionally, oral indomethacin (50 mg) was given every 6 h for 3 days. Just as the magnesium sulfate was initiated, the patient experienced spontaneous rupture of membranes and prolapse of umbilical cord of twin A was noted several hours later. Fetal demise was confirmed approximately 1 h later, but there was no evidence of labor or infection over the next 24 h. After discussing the potential dangers of prolonged rupture of membranes, retention of dead fetus, maternal sepsis, the potential for prolonged hospitalization, need for hysterectomy and risk of death, the patient elected to continue limited oxytocin augmentation in an attempt to deliver twin A and salvage twin B. The risk of losing both fetuses was carefully discussed, and the patient agreed with this management approach despite the acknowledged uncertainty of outcome.in situ and the umbilical cord of twin A was divided and ligated near the cervix with 3-0 chromic gut suture. Oxytocin was immediately discontinued. Monitoring of twin B confirmed stable heart tones and appropriate fetal movement throughout delivery of the non-viable twin. Intravenous ampicillin and metronidazole were continued postpartum but MgSO4 was not reinitiated. Immediately following delivery of twin A, sterile speculum exam found a closed cervix with the umbilical cord completely retracted in utero. Maternal vital signs remained stable and she was discharged home one week later on full bed rest, preterm labor precautions, and oral amoxicillin-clavulanate (875 mg) twice daily × 5 d.After 8 h of oxytocin therapy, a stillborn female fetus (319 g) was delivered. The placenta remained At 25 weeks gestation, 12 mg betamethasone was administered intramuscularly with an additional dose 24 h later. There was no evidence of infection or coagulopathy at biweekly clinical evaluations, which included serial ultrasounds until 34 weeks to assess cervical length. Formal biophysical profiles (BPP) began at 28 weeks, when the estimated fetal weight was 1250 g and the BPP score was 8/8. At this time mild uterine irritability was detected and 5 mg oral terbutaline was given every 4 h until 34 weeks. Uterine activity was reduced following oral terbutaline therapy.Spontaneous labor began at 35 1/7 weeks. At readmission, the cervix was 5 cm dilated with intact membranes and vertex presentation. Epidural anesthesia was established, an amniotomy was performed, and the patient had a normal progress of labor. She delivered a viable male infant weighting 2894 g  over an intact perineum. Approximately 5 min later, two placentas were delivered spontaneously  are more likely to appear in the medical literature than those describing an unsatisfactory result, a publishing bias may favor the former.i.e., conceptions following use of the advanced reproductive technologies), some authors have suggested that attempts to prolong the pregnancy following spontaneous abortion or extremely premature birth of one fetus is efficacious and justified [While the longest known intertwin delivery interval with cerclage is 153 d , our expustified .The author(s) declare that they have no competing interests.LMG and SAW were the resident physicians associated with the case. SAS was the lead obstetrician and primary physician at delivery. ESS conceived of the research, directed the residents, and coordinated manuscript revisions.The pre-publication history for this paper can be accessed here:


In most cases the success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that E. coli genetic network, the S. cerevisiae protein interaction network, and the C. elegans neuronal network, out of a set of network models including a linear preferential attachment model and a small-world model.We present a method to assess systematically which of a set of proposed network generation algorithms gives the most accurate description of a given biological network. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional  "word space". This map defines an input space for classification schemes which allow us to state unambiguously which models are most descriptive of a given network of interest. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work. We show that different duplication-mutation schemes best describe the Our method is a first step towards systematizing network models and assessing their predictability, and we anticipate its usefulness for a number of communities. Moreover, much of the coding regions of the chromosomes (the subsequences which are directly translated into proteins) are highly homologous. The complexity comes then, not from a larger number of parts, or more complex parts, but rather through the complexity of their interactions and interconnections.The post-genomic revolution has ushered in an ensemble of novel crises and opportunities in rethinking molecular biology. The two principal directions in genomics, sequencing and transcriptome studies, have brought to light a number of new questions and forced the development of numerous computational and mathematical tools for their resolution. The sequencing of whole organisms, including P(k) over nodes of given connectivity or degree k, which was found to be completely unlike that of a "random" or Erdös-Rényi graph. Instead, the distribution obeyed a power-law P(k)~kγ-. As a consequence many mathematicians concentrated on (i) measuring the degree distributions of many technological, sociological, and biological graphs  and (ii) proposing various models of randomly-generated graph topologies which could reproduce these degree distributions , other times as Barabasi , Klemm (duplication-mutation), Kim , or Flammini (duplication-mutation) depending on the subset of words chosen. This clearly indicates that different features support different models. Therefore the confidence in classifying S. cerevisiae to be Sole is limited. The statistical significance of individual words in different models is investigated using kernel density estimation (see Methods) by finding words which maximize ηij ≡ pi(x0)/pj(x0) for two different models (i and j) at a word value of the real data set x0. Figure C. elegans network: the MZ model ij = Aijδij     (1)[U = I - D. (Note that we do not use Einstein's summation convention. Indices i and j are not summed over.) We define the primitive alphabet {A; T, U, D} as the adjacency matrix A and the operations T, U, D with the transpose operation T(M) ≡ MT, for any matrix M . T(A) and A distinguish walks "up" the graph from walks "down" the graph. From the letters of this alphabet we can construct words (a series of operations) of arbitrary length. A number of redundancies and trivial cases can be eliminated  leading to the operational alphabet {A, AT, AU, AD, AUT}. The resulting word is a matrix representing a set of possible walks, which can be enumerated. An example is shown in Figure and its complement Each word determines two relevant statistics of the network: the number of distinct walks and the number of distinct pairs of endpoints. These two statistics are determined by either summing the entries of the matrix (sum) or counting the number of nonzero elements (nnz) of the matrix, respectively. Thus the two operations sum and nnz map words to integers. This allows us to plot any graph in a high-dimensional data space: the coordinates are the integers resulting from these path-based functionals of the graph's adjacency matrix.The coordinates of the infinite-dimensional data space are given by integer-valued functionalsF(L1L2...LnA)     (2)Li is a letter of the operational alphabet and F is an operator from the set {sum, sumD, sumU, nnz, nnz D, nnz U}. We found it necessary only to evaluate words with n ≤ 4  to construct low test-loss classifiers. Therefore, our word space is a 6  = 4680-dimensional vector space, but since the words are not linearly independent , the dimensionality of the manifold explored is actually much smaller. However, we continue to use the full data space since a particular word, though it may be expressed as a linear combination of other words, may be a better discriminator than any of its summands.where each transitivity of a network can be defined as 3 times the number of 3-cycles divided by the number of pairs of edges that are incident on a common vertex. For a loopless graph (without self-interactions), this can also be calculated as a simple expression in word space: sum(D A A A)/sum(U AA). Note that this expression of transitivity as the quotient of two words implies separation in two dimensions rather than in one. However, there are limitations to word space. For example, a similar measure, the clustering coefficient, defined as the average over all vertices of the number of 3-cycles containing the vertex divided by the number of paths of length two centered at that vertex, cannot be easily expressed in word space because vertices must be considered individually to compute this quantity. Of course, the utility of word space is not that it encompasses previously studied metrics, but that it can elucidate new metrics in an unbiased, systematic way.In , we discsupport vector machine, or SVM [A standard classification algorithm which has been used with great success in myriad fields is the , or SVM . This teWe rely on a freely available C-implementation of SVM-Light , which uyi(w·xi + b) ≥ 1 - ξi; i = 1,..., m where f(x) = w·x + b is the equation of the hyperplane, xi are training examples and yi ∈ {-1, +1} their class labels. Here, C is a fixed parameter determining the trade-off between small errors ξi and a large margin 2/|w|. We set C to a default value . We observe that training and test losses have a negligible dependence on C since most test losses are near or equal to zero even in low-dimensional projections of the data space.with Our objective is to determine which of a set of proposed models most accurately describes a given real data set. After constructing a classifier enjoying low test loss, we classify our given real data set to find a 'best' model. However, the real network may lie outside of any of the sampled distributions of the proposed models in word space. In this case we interpret our classification as a prediction of the least erroneous model.robust classifier as one which consistently classifies a test datum in the same class, irrespective of the subset of features chosen. And we measure robustness as the ratio of the number of consistent predictions over the total number of subspace-classifications. In this paper we consider robustness for a subspace dimensionality of 500, a significantly small fraction of the total number of dimensions 4680.We distinguish between the two cases by noting the following: Consider building a classifier for apples and oranges which is then faced with a grapefruit. The classifier may then decide that, based on the feature size the grapefruit is an apple. However, based on the feature taste the grapefruit is classified as an orange. That is, if we train our classifier on different subsets of words and always get the same prediction, the given real network must come closest to the predicted class based on any given choice of features we might look at. We therefore define a A generative model, in which one estimates the distribution from which observations are drawn, allows a quantitative measure of model assignment: the probability of observing a given word-value given the model. For a robust classifier, in which assignment is not sensitively dependent on the set of features chosen, the conditional probabilities should consistently be greatest for one class.p(C = c|Xj = x), the probability of being assigned to class c given a particular value x of word j. By comparing ratios of likelihood values among the different models, it is therefore possible, for the case of non-robust classifiers, to determine which of the features of a grapefruit come closest to an apple and which features come closest to an orange.To identify significant features we perform density estimations with Gaussian kernels for each individual word, allowing calculation of x0 from the training data xi  asWe compute the estimated density at a word value λ by maximizing the average log-probability Q of a hold-out set using 5-fold cross-validation. More precisely, we partition the training examples into 5-folds , where {fi(j)}j is the set of indices associated with fold i (i = 1...5). We then maximizewhere we optimize the smoothing parameter λ. In all cases we found that Q(λ) had a well pronounced maximum as long as the data was not oversampled. Because words can only take integer values, too many training examples can lead to the situation that the data take exactly the same values with or without the hold-out set. In this case, maximizing Q(λ) corresponds to p having single peaks around the integer values, so that λ tends to zero. Therefore, we restrict the number of training examples to 4Nv, where Nv is the number of unique integer values taken by the training set. With this restriction Q(λ) showed a well-pronounced maximum at a non-zero λ for all words and models.as a function of The simplest scheme to find new metrics which can distinguish among given models is to take a large number of training examples for a pair of network models and find the optimal split between both classes for every word separately. We then test every one-dimensional classifier on a hold-out set and rank words by lowest test loss..Additional figures, more detailed description of the network models, and detailed results can be found at .Source code was written in MATLAB and is downloadable from our our website MM, EZ, and CW had the original ideas for this paper. CW and LC guided the project. Most of the coding was done by MM and EZ. CA, JH, RK, CL, and GW coded most of the network generation agorithms. The final manuscript was mainly written by MM, EZ, CW, and LC.


There are many different sex-determining systems in plants and animals with separate sexes (dioecious species). In some species, environmental factors activate sex-determining genes that trigger expression of genes leading to male or female development. Other species have evolved specialized sex chromosomes. In the well-known X-Y system of mammals, individuals inheriting a Y chromosome become males, and XX individuals become females.Sex chromosomes have arisen independently in many taxonomic groups. It is an interesting question whether the same mechanisms were involved each time. Similarities in sex chromosome evolution have been reported between birds and mammals . In a new study, Michael Nicolas and colleagues uncover striking parallels in the details of sex chromosome evolution between mammals and a far more distant group: plants.Silene, which includes the White Campion, includes both dioecious and hermaphrodite species. The authors focus on three dioecious species, Silene dioica, S. latifolia, and S. diclinis, which share an X-Y sex-determination system where Y specifies maleness.Sex chromosomes are an oddity in flowering plants. They are limited to dioecious species, a subset of plants that carry male and female organs  on separate individuals (most flowering plants are hermaphrodites). The genus The theory of sex chromosome evolution holds that sex chromosomes were once homologs  that evolved different morphology and gene content because they lost their ability to recombine. Suppression of recombination is thought to start around the sex-determining region, but may eventually affect much of the sex chromosomes. Recombination is a key genetic process in which two chromosomes pair and exchange their sequences. In the absence of recombination, the two chromosomes of a pair evolve separately.Silene species are morphologically distinct, like those of mammals, and they also have a PAR and a nonrecombining region. Nicolas and colleagues' results shed some light on how recombination suppression evolved on the Silene sex chromosomes.In the case of mammals, whose sex chromosomes evolved about 320 million years ago, loss of recombination led to widely diverged X and Y chromosomes that pair only over a very small region, the pseudoautosomal region . The X and Y chromosomes of dioecious Silene X chromosomes that are also present on their Y chromosomes. They mapped the genes relative to the PAR and compared the nucleotide sequences of the X and Y version of each gene in each species. As expected of sequences that no longer recombine, the X and Y versions of each gene have diverged. Strikingly, the extent of nucleotide divergence increases with the gene's distance from the PAR.The authors studied four genes outside the PAR on the Silene data is one of a progressive suppression of recombination, gradually diminishing the PAR. A similar scenario has been proposed in mammals and birds. However, the authors estimate that the Silene sex chromosomes started diverging only 10 million years ago. The Silene chromosomes might therefore offer a better chance to observe recombination suppression in its early stages, and perhaps to get at its mechanisms.Evolutionary biologists use sequence divergence as a clock: the longer two originally identical sequences have been isolated from one another, the more independent mutations they accumulate. The picture that emerges from the Silene Y chromosome genes. Y degeneration is well documented in mammals, in which most X-linked genes have no Y-linked counterparts. Understanding X-Y divergence in Silene species may thus shed light on the evolution of sex chromosomes in vertebrates as well.The authors also report evidence for some degeneration of the


Inflammation is believed to play an important role in the pathology of Alzheimer's disease (AD) and cytokine production is a key pathologic event in the progression of inflammatory cascades. The current study characterizes the cytokine expression profile in the brain of two transgenic mouse models of AD (TgAPPsw and PS1/APPsw) and explores the correlations between cytokine production and the level of soluble and insoluble forms of Aβ.Organotypic brain slice cultures from 15-month-old mice  were established and multiple cytokine levels were analyzed using the Bio-plex multiple cytokine assay system. Soluble and insoluble forms of Aβ were quantified and Aβ-cytokine relationships were analyzed.Compared to control littermates, transgenic mice showed a significant increase in the following pro-inflammatory cytokines: TNF-α, IL-6, IL-12p40, IL-1β, IL-1α and GM-CSF. TNF-α, IL-6, IL-1α and GM-CSF showed a sequential increase from control to TgAPPsw to PS1/APPsw suggesting that the amplitude of this cytokine response is dependent on brain Aβ levels, since PS1/APPsw mouse brains accumulate more Aβ than TgAPPsw mouse brains. Quantification of Aβ levels in the same slices showed a wide range of Aβ soluble:insoluble ratio values across TgAPPsw and PS1/APPsw brain slices. Aβ-cytokine correlations revealed significant relationships between Aβ1–40, 1–42 (both soluble and insoluble) and all the above cytokines that changed in the brain slices.Our data confirm that the brains of transgenic APPsw and PS1/APPsw mice are under an active inflammatory stress, and that the levels of particular cytokines may be directly related to the amount of soluble and insoluble Aβ present in the brain suggesting that pathological accumulation of Aβ is a key driver of the neuroinflammatory response. Alzheimer's disease is a progressive neurodegenerative disorder characterized by intra-cellular abnormally phosphorylated tau protein and extra-cellular beta amyloid plaques. It has been suggested that inflammation may be a key player in the pathophysiology of AD as evidenced by epidemiological studies which have revealed that the long term use of non-steroidal anti-inflammatory drugs reduces the risk of developing AD -3. TransThere are numerous reports of increased levels of cytokines in the brains of Alzheimer's disease patients, and in transgenic mouse models of Alzheimer's disease -12. HoweBy examining different mouse models of AD using multiplex technology, it is possible to more clearly characterize the particular cytokines which maintain the inflammatory environment and to relate them to particular forms of Aβ .There is considerable debate over which length of Aβ and which conformations are most potently toxic. Recently, specific oligomeric forms have been shown to be most toxic to neurons. These soluble species of Aβ differ from the higher-molecular-weight aggregated insoluble forms that are found precipitated in the AD patient and mouse brain. This study sought to determine whether soluble or insoluble Aβ fractions were most closely related to cytokine levels.Mouse brain slice cultures were prepared as previously described . BrieflyMulti-plex cytokine array analysis was performed using the Bio-plex protein multi-array system, which utilizes Luminex-based technology . For theBrain slices were washed with PBS , and 300 μl of lysis buffer was added. Lysis buffer consisted of mammalian protein extraction reagent  with 1X protease inhibitor cocktail XI , 100 μM Sodium Orthovanadate, and 1 μM Phenylmethylsulfonyl Fluoride (PMSF) . The resulting mixture was sonicated using a sonic dismembrator Protein content in each slice was determined using the bicinchoninic acid (BCA) protein reagent kit , as per the manufacturers protocol. Insoluble Aβ was extracted using 70% formic acid as previously published .Aβ content in brain slices was determined using human Aβ 1–40 and Aβ 1–42 ELISA detection kits , as per the manufacturers protocol.. A correlation matrix was constructed using the raw data and subsequently converted to a distance matrix by subtracting each element in the correlation matrix from 1. The distance matrix was used as the dissimilarity matrix for building an hierarchical cluster using the averaging method. The resulting dendrogram consists of closely related members under the same node. The farther one needs to traverse across the tree to reach another member, the higher the dissimilarity represented. The distance from the base in the y-axis represents dissimilarity or 1-r, where r is the correlation co-efficient.For statistical analyses, ANOVA and t-tests were performed where appropriate using SPSS for Windows release 10.1. Hierarchical cluster analysis of Aβ-cytokine data from brain slices were performed with the R program Cytokine production was evaluated by multi-plex cytokine array analysis using the cell culture supernatant of organotypic brain slice cultures from control, PS1 (Presenilin 1 mutant heterozygotes), TgAPPsw, and TgPS1/APPsw mice at 15 months of age. We chose non-transgenic littermates as controls for the TgAPPsw mice and the PS1 animals as controls for the PS1/APPsw mice as the PS1 animals were the littermates of the PS1/APPsw mice. There were no significant differences in cytokine production between control slices and PS1 slices showing that PS1 over-expression does not directly induce inflammatory events. Compared to control slices, production of IL-1α, TNF-α, GM-CSF and IL-6 was increased in TgAPPsw slices figs. . CompareQuantification of amyloid levels in brain mouse slices revealed that PS1/APPsw mice produce significantly more total Aβ as compared to TgAPPsw mice at the same age, and levels of insoluble and soluble Aβ (both 1–40 and 1–42) correlated well with each other  mutation does not have a significant impact on cytokine production. No significant change in the production of IL-4 and IL-10 was observed in the brains of these transgenic mice compared to their respective controls, indicating the absence of an anti-inflammatory response. All of the cytokines that were increased in the TgAPPsw brain slices  were further increased in the PS1/APP brain slices. This suggests that the presence of these inflammatory molecules is related to the amount of β-amyloid protein present, in agreement with a pro-inflammatory effect of Aβ -29. A reIn order to further understand the correlation between the amount of Aβ and cytokine levels in the brains of transgenic mice, levels of both soluble and insoluble (formic acid-extracted) Aβ1–40 and 1–42 were quantified in the same slices from which cytokine production was measured, allowing a direct correlation of Aβ-cytokine levels.Levels of soluble and insoluble Aβ1–40 correlated well with each other, and the same was observed for Aβ1–42. As expected, quantification of Aβ levels generally revealed significantly higher amyloid levels in the PS1/APPsw mouse brain slices compared to TgAPPsw  but there was considerable slice-to-slice variation in soluble and insoluble Aβ levels within and between genotypes. The TgAPPsw and PS1/APPsw mice express equal levels of the APPsw molecule, but the PS1/APPsw model produces greater levels of Aβ and develops plaques at an earlier age (10 weeks) -33. ThisThe Aβ data in the current report found a significant range of values for soluble:insoluble Aβ ratios between brain slices. This broad spread of values allowed correlation with equally wide ranges of cytokine production. This approach of examining Aβ-cytokine correlations within the same slices in the same aged animals eliminated the confounding factor of age related changes in cytokine production. Both Aβ1–40 and 1–42 correlated closely with all the cytokines that changed in the brain slices, but the correlation was particularly striking with IL-12p40. IL-12 is a hetero-dimeric cytokine which can comprise two subunits; IL-12p40 and IL-12p35. It is produced mainly by monocytes and macrophages and is a crucial factor in directing the T-cell response to infection, by inducing a Th1-type cytokine response. Our data agrees with that of previous reports showing that IL-12p40 is strongly up-regulated in-vitro (in response to an inflammatory stimulus) and in-vivo in the cerebral cortex of TgAPPsw mice ,38.IL-1, which was increased in the transgenic brain slices, is a major immune-response molecule functioning in the periphery and brain. The family comprises three related proteins ). IL-1α and IL-1β are two different isoforms of IL-1 that have similar affinities for their receptor IL-1R, and therefore have similar activities. Both are capable of inducing inflammatory cascades in-vivo and in-vitro, and it has been shown that they are capable of up-regulating expression of astrocyte-derived S100B and APP ,40. It hThe Aβ 1–42:40 ratio is also of considerable interest in relation to cytokine levels and although there are currently no studies correlating Aβ 1–42:40 ratio with cytokine levels in-vivo, certain reports have suggested that cytokines can modulate Aβ production -55. PS1 It is important to consider the nature of the exact form of Aβ that may be most responsible for the inflammatory events seen in AD brains. Aβ can exist in various forms , but it is not yet clear which of these forms are most potent in inducing inflammatory cellular responses ,60,61. TAD: Alzheimer's diseaseAPP: Amyloid precursor proteinAPPsw: Amyloid precursor protein Swedish mutationPS1: Presenilin 1Aβ: Beta-amyloidTg: TransgenicTNF: Tumor necrosis factorIL-x: Interleukin-xIL-1ra: Interleukin-1 receptor antagonistGM-CSF: Granulocyte macrophage colony stimulating factorPBS: Phosphate buffered salineCOX-2: Cyclo-oxygenase-2PGE2: Prostaglandin E2IFN: InterferonNSAID: Non-steroidal anti-inflammatory drugThe author(s) declare that they have no competing interests.NP carried out the in-vitro brain slice assays, processed brain tissues, performed the Bio-plex assay, ELISAs and drafted the manuscript. DP conceived the design of the study, carried out Bio-plex assays, performed statistical analyses and aided in manuscript preparation. VM analyzed data and constructed dendrograms. AQ aided in ELISA and Bio-plex assays and collected mouse brain tissues. FC oversees management of the mouse colonies. MM aided in manuscript preparation and gave critical analysis of the manuscript.Correlation table of levels of different β-amyloid species with cytokines in transgenic mouse models of Alzheimer's disease.Click here for file


Gab1 is a docking protein that recruits phosphatidylinositol-3 kinase (PI-3 kinase) and other effector proteins in response to the activation of many receptor tyrosine kinases (RTKs). As the autophosphorylation sites on EGF-receptor (EGFR) do not include canonical PI-3 kinase binding sites, it is thought that EGF stimulation of PI-3 kinase and its downstream effector Akt is mediated by an indirect mechanism.We used fibroblasts isolated from Gab1-/- mouse embryos to explore the mechanism of EGF stimulation of the PI-3 kinase/Akt anti-apoptotic cell signaling pathway. We demonstrate that Gab1 is essential for EGF stimulation of PI-3 kinase and Akt in these cells and that these responses are mediated by complex formation between p85, the regulatory subunit of PI-3 kinase, and three canonical tyrosine phosphorylation sites on Gab1. Furthermore, complex formation between Gab1 and the protein tyrosine phosphatase Shp2 negatively regulates Gab1 mediated PI-3 kinase and Akt activation following EGF-receptor stimulation. We also demonstrate that tyrosine phosphorylation of ErbB3 may lead to recruitment and activation of PI-3 kinase and Akt in Gab1-/- MEFs.The primary mechanism of EGF-induced stimulation of the PI-3 kinase/Akt anti-apoptotic pathway occurs via the docking protein Gab1. However, in cells expressing ErbB3, EGF and neuroregulin can stimulate PI-3 kinase and Akt activation in a Gab1-dependent or Gab1-independent manner. Ligand stimulation of the epidermal growth factor receptor (EGFR) and the three other members of the EGFR family of receptor tyrosine kinases  results in tyrosine autophosphorylation, recruitment of signaling proteins, and activation of distinct complement of signaling pathways that regulate a great variety of cellular responses ,2.One of the signaling pathways that is activated by the EGFR is the phosphatidylinositol-3 kinase (PI-3 kinase)/Akt anti-apoptic signaling pathway . Unlike EGF stimulation of PI-3 kinase may also be mediated by the docking protein Gab1 (Grb2-associated binder-1). EGF stimulation leads to tyrosine phosphorylation of Gab1 enabling recruitment and activation of PI-3 kinase by the three canonical pYXXM motifs on Gab1 . Gab1 waCells over-expressing a mutant Gab1 protein containing tyrosine to phenylalanine mutations at the three p85 binding sites have been shown to be defective in EGF-induced JNK activation, and treatment of cells over-expressing wild type Gab1 with PI-3 kinase inhibitors interferes with MAPK signaling in response to EGF treatment, thus revealing a link between Gab1 and PI-3 kinase in EGF-induced mitogenic signaling ,14. FurtAlthough the role of the Shp2 protein in the control of EGFR/Gab1 interactions is not well understood, several studies have suggested that Gab1-associated Shp2 may influence EGF-induced PI-3 kinase signaling. Previous work has shown that Gab1 is not a global substrate of Shp2, as complex formation between Gab1 and Shp2 does not reduce the total EGF-induced tyrosine phosphorylation levels of Gab1 . HoweverIn the experiments presented here we utilized fibroblasts isolated from Gab1-/- mouse embryos in order to examine the role of Gab1 in EGF-mediated activation of the PI-3 kinase/Akt cell survival pathway. We also address the question of whether ErbB3 recruitment of PI-3 kinase is dependent on or independent of Gab1. Our results demonstrate a clear requirement for Gab1 in recruitment and activation of PI-3 kinase in response to EGF stimulation. Additionally, we show that while Shp2 does not mediate global dephosphorylation of Gab1, it does appear to negatively regulate the EGF-induced activation of PI-3 kinase through an undefined mechanism. Finally we demonstrate that ErbB3 is capable of recruiting PI-3 kinase in the absence of Gab1, but Gab1 functions as the major mediator of PI-3 kinase activation in response to EGF stimulation.Previous studies have suggested that ErbB3 and Gab1 can function as links between EGFR and PI-3 kinase. In this report we use MEFs derived from Gab1 -/- embryos  to exploMEFs derived from Gab1-/- or wild type (WT) embryos were stimulated with EGF and assayed for Gab1 tyrosine phosphorylation, for activation of PI-3 kinase and for Akt stimulation. As shown in Figure The cDNA encoding the wild type murine Gab1 sequence was cloned into a retroviral vector, and the virus was used to infect Gab1 -/- MEFs. Stable cell lines were selected for co-transduction of a puromycin resistance gene and pools of selected cells were used for further analysis. As shown in Figure F446/472/589), was cloned into a retroviral vector and used to generate pools of stable MEF cell lines as described above. We first assayed the cells for Gab1 expression, and for the ability of the mutant Gab1F446/472/589 protein to become tyrosine phosphorylated in response to EGF treatment. As shown in Figure F446/472/589 undergo tyrosine phosphorylation in response to EGF treatment (upper left panel). Quantitation of multiple experiments by densitometry reproducibly demonstrates that Gab1F446/472/589 is tyrosine phosphorylated following EGF treatment to similar levels when normalized for Gab1 expression levels. We next subjected lysates from unstimulated or EGF stimulated cells to immunoprecipitation with anti-Gab1 antibodies followed by immunoblotting with anti-p85 antibodies. As has been demonstrated previously  ATP) per sample. Samples were incubated at 30°C for 15 minutes, and the reaction was stopped by addition of 100 μl 1N HCl. Samples were extracted by addition of 200 μl CHCl3/CH3OH (1:1). The samples were vortexed and centrifuged, and the lower organic phases containing phospholipids were dried at 27°C for two hours. Samples were resuspended in 10 μl of PI-4-P standard ) and subjected to thin layer chromatography (TLC plates – VWR) in CHCl3/CH3OH/NH4OH/H2O (45:35:7:3). The TLC plates were exposed in a PhosphorImager cassette for four days.The PI-3 kinase assay was performed essentially as previously described -24. BrieBL carried on the initial biochemical experiments before leaving the laboratory. DM continued this study and carried out the biochemical studies, the PI-3 kinase assays, and drafted the manuscript. IL participated in the PI-3 kinase assays. JS initiated and supervised the project. All authors read and approved the final manuscript.


A time course of gene expression at the onset of labor reveals transcriptional networks associated with activation of the uterine muscle and identifies targets for drugs to prevent premature labor. Early transition to labor remains a major cause of infant mortality, yet the causes are largely unknown. Although several marker genes have been identified, little is known about the underlying global gene expression patterns and pathways that orchestrate these striking changes.We performed a detailed time-course study of over 9,000 genes in mouse myometrium at defined physiological states: non-pregnant, mid-gestation, late gestation, and postpartum. This dataset allowed us to identify distinct patterns of gene expression that correspond to phases of myometrial 'quiescence', 'term activation', and 'postpartum involution'. Using recently developed functional mapping tools  and GenMAPP 2.0), we have identified new potential transcriptional regulatory gene networks mediating the transition from quiescence to term activation.These results implicate the myometrium as an essential regulator of endocrine hormone (cortisol and progesterone synthesis) and signaling pathways (cyclic AMP and cyclic GMP stimulation) that direct quiescence via the transcripitional upregulation of both novel and previously associated regulators. With term activation, we observe the upregulation of cytoskeletal remodeling mediators (intermediate filaments), cell junctions, transcriptional regulators, and the coordinate downregulation of negative control checkpoints of smooth muscle contractile signaling. This analysis provides new evidence of multiple parallel mechanisms of uterine contractile regulation and presents new putative targets for regulating myometrial transformation and contraction. The initiation of mammalian labor is a complex physiological process that requires the expression and secretion of many factors, both maternal and fetal ,2. The mThe transition to labor results in synchronous contractions of high amplitude and high frequency by the myometrium. Factors previously associated with the regulation of myometrial activation include the oxytocin receptor, gap junction protein connexin-43, voltage-gated calcium channels, prostaglandin receptor subtypes, estrogen, cortisol and transcription factors c-Jun and c-Fos. Most of these proteins participate in pathways that stimulate calcium release  and the formation of intracellular junctions, leading to stimulation of contractions. Although several important components that regulate the initiation of labor have been identified, the mechanisms that guide this transition are poorly understood.A difficult challenge in identifying the regulatory events that control the switch from myometrial quiescence to activation has been developing tools for examining whole-genome expression profiles in the context of known biology. Recent efforts to identify transcriptional changes from laboring and non-laboring human myometrium have proved valuable in identifying putative physiological regulators -8; howevTo identify molecular mechanisms involved in the transition from myometrial quiescence to labor, we analyzed gene-expression changes in mouse myometrium at mid-gestation, throughout late gestation, and during the postpartum period. Our results reveal several novel patterns of expression occurring along the phases of myometrial quiescence to term activation and postpartum involution. Analysis of putative quiescence and term activation regulators in the context of well defined biological pathways revealed new putative functional roles for several previously unassociated genes in the suppression of contraction throughout gestation and activation of phase-dependent contractions at labor. This analysis further implicates the regulation of several novel pathways, including smooth muscle-extracellular matrix interactions throughout late gestation and cell junction-cytoskeletal interactions immediately before the onset of labor.p < 0.05 and a change in level of expression of more than 20% (fold-change 0.2).Messenger RNA transcript levels were measured from isolated myometrium of 35 time-mated mice at four time-points of late gestation (14.5-18.5 days), at postpartum (6 and 24 hours after labor), and from a non-pregnant control group. In all, approximately 13,000 probe sets corresponding to around 9,000 unique cDNAs and expressed sequence tags (ESTs) were probed with oligonucleotide microarrays. About 35% of these transcripts were regulated throughout gestation and postpartum (14.5 days through 24 hours postpartum) using the criteria of Analysis of these probe sets with HOPACH -12 reveap-value) of Gene Ontology (GO) biological categories associated with each cluster, from which the most significant nonsynonymous groups are listed .To characterize the major biological processes, molecular functions, and cellular components associated with the HOPACH pattern groups, we used MAPPFinder (a component of GenMAPP version 2.0) -16. MAPPGene expression groups associated with the maintenance of pregnancy (quiescence) or induction of labor (activation) were confirmed by mapping lists of previously associated regulators of uterine quiescence and activation onto our HOPACH cluster map. Extensive literature searches for such regulators identified 66 genes, of which 23 were regulated in our dataset  revealed a number of biological categories associated with uterine quiescence. These categories contain a large number of highly regulated genes coupled to the inhibition of prostaglandin and cortisol synthesis, stimulation of cAMP and cGMP signaling pathways, extracellular matrix remodeling, cytolysis and regulation of cell growth Figure , Table 1Adm, Cgrp, Hsd11b2, Gnas, Cnn1 and Utg; see Tables Guca2b and Cmkor1), genes for calcium-dependent phospholipid binding proteins , and for the Anxa2 dimerization partner S100A10 , and transcriptional regulation (Sfrp4 and Klf4). Several of these and other genes were found to have highly reproducible patterns of expression using quantitative real-time PCR (TaqMan), with typically larger fold-changes produced by TaqMan than by GeneChip  normalization) .Besides well established quiescence regulators  or myometrial postpartum involution . These results are in accordance with the inhibition of contraction and suppression of cell death in late gestation. Unlike term-related biological processes, categories shared between the decreased quiescence and 'increased postpartum involution' group appear to be largely the result of a common transcript expression profile Figure .Myl4; the Myl4 protein is the primary target for oxytocin-induced phosphorylation leading to uterine contraction at term. Several additional putative components of the oxytocin contractile signaling pathway  were also present in this expression group  , very few genes were downregulated more than twofold at 14.5 days of gestation (Table ) Figure . AnotherA large percentage of genes regulated with quiescence continued to be highly regulated at term. This result emphasizes the importance of expression changes immediately before labor to counteract the effects of quiescence. Consistent with the number of upregulated genes, MAPPFinder analysis of the increased term activation group identified a smaller set of GO terms and pathways. Prominent among these were genes associated with the formation of cell junctions, kinesin complexes and endopeptidase inhibitors. In addition, functionally related transcription factors (members of the basic helix-loop-helix (bHLH) family), ion transport proteins and ion transport regulators were coordinately upregulated at term.Within these biological categories, several contractile regulators, both associated and unassociated with parturition, were highly upregulated. These genes include those for cell junction proteins , the pulmonary smooth muscle contractile regulator and complement component C3, the estrogen signaling regulator Hsp70, the chloride conductance regulator Fxyd3 and the ryanodine receptor regulator Gsto1 Table  and collRgs2), a potent inactivator of Gαq-GTP bound activity, and inhibitor of DNA binding 2 (Idb2), a bHLH factor that heterodimerizes with other HLH proteins to inhibit their function. Rgs2 is one of the most downregulated genes throughout the gestation-postpartum time-course, in addition to being highly expressed in non-pregnant myometrium and throughout gestation. Additional term-downregulated G-protein signaling proteins that act to antagonize calcium-calmodulin signaling are illustrated in Figure HOPACH analysis with a metric that disregarded the direction of fold-change  revealed a small number of downregulated genes at term that mirror the increased term activation group. Among these, we observed two highly downregulated genes: regulator of G-protein signaling 2  during the phase of quiescence. Genes within this multigene cluster undergo tight coordinate regulation in response to cell stimulation [One of the most prominent observations in this dataset is the highly significant correlation in the expression and genomic position of genes for eight serine-type endopeptidases ), we identified 11 clusters of genes that are co-localized and co-regulated (the same HOPACH cluster) . Among tThis time-course analysis provides the first global view of gene-expression changes in mouse myometrium from uterine quiescence through the activation of the myometrium before labor and to its postpartum involution. Examination of multiple time points, the use of replicates, robust array normalization and powerful clustering tools enabled us to delineate and characterize unique patterns of gene expression throughout this physiological process. In addition to partitioning clusters of genes, analysis with the program HOPACH also provides us with a continuum of expression changes that reveals an overall transition in the expression of genes from one cluster group to another Figure . AnnotatHsd11b, which encodes an enzyme that converts cortisol to the inactive cortisone, and Cyp11a1, encoding an enzyme that promotes the synthesis of progesterone. Conversely, Hsd11a, coding for an enzyme that catalyzes the synthesis of cortisol, increased expression from 11- to 18-fold throughout gestation, suggesting that local regulation of cortisol levels are important for myometrial activation. While we observed the upregulation of the estrogen signaling regulator Hsp70, with term activation, downstream markers of estrogen action are among the most highly upregulated genes with term activation, supporting the role of the fetus in myometrial activation.A number of studies emphasize the importance of fetal regulation of the switch from quiescence to term activation, particularly increased cortisol and estrogen output from the fetal adrenal gland ,2. InterExamination of highly upregulated putative quiescence and term activation genes revealed several novel changes within important associated pathways for quiescence and activation . Proteins encoded by these genes include Guca2b (uroguanylin), Anxa3, and Anxa8 with quiescence, and C3, Edg2, Gsto1 and Fxyd3 during activation  and bZip transcription factors, upregulated during quiescence, and endopeptidase inhibitors and bHLH factors, upregulated with term activation. In addition to its role in cytolysis, granzyme expression and secretion by T lymphocytes has been associated with the breakdown of extracellular matrix proteins in the uterus during pregnancy ,23,24. IAlthough the myometrium is considered to be relatively homogeneous, many of the largest changes in gene expression at term occurred in genes that are not normally associated with muscle, such as the keratins, tight junction and desmosome junction proteins. Indeed, altered gene expression due to changes in cell-type distribution or the invasion of the myometrium by the decidua and endometrium would not be distinguished if those changes occur consistently between gestational myometrium preparations. Further inspection of the literature reveals that the cytokeratins, which compose the bulk of this group, are expressed within smooth muscle and probably function as components of intermediate filaments of the cytoskeleton -28. FurtIn addition to the capability to group and annotate clusters of genes, pattern analysis with HOPACH can be used to interrogate gene clusters in the context of genomic location. For this analysis, we developed a program to isolate gene clusters that are likely to be co-regulated on the basis of genomic location, similar to other reported methods -32. UsinWe have identified several highly regulated genes not previously associated with myometrial quiescence or activation, in addition to families of genes co-regulated at different phases of the myometrial time-course. In addition to providing new hypotheses about how the switch from quiescence to term activation may be facilitated Figure , these dn = 3), 16.5 (n = 4), 17.5 (n = 5), or 18.5 days (n = 7) after timed mating, and 6 (n = 4) or 24 h (n = 4) after delivery. Control myometrium was harvested from non-pregnant littermate females (n = 8) 1 day after timed mating with a vasectomized male. After dissection of both uterine horns, the tissue closest to the cervix was removed. Each horn was washed with PBS and opened longitudinally. Pups and placenta were discarded, and the decidua was removed by blunt dissection. The myometrium from each horn was then immediately frozen in liquid nitrogen and stored at -80°C.FVB/N mice (Jackson Laboratory) were sacrificed in the morning (10 to noon) at 14.5  setting of 100%. Resulting .cel files were generated with Affymetrix Microarray Suite 5.0 and analyzed with RMA .p-values were calculated from a permutation test with the F-statistic function from the multtest package of Bioconductor [2 expression values of each time-point group versus the mean of non-pregnant controls. For cluster analysis, the dataset was filtered for probe sets with a p < 0.05 across the full expression time-course and a greater than 20% change in level of expression (positive or negative) for at least one time-point group versus non-pregnant controls. Additional filters were used downstream of clustering for genes related to uterine quiescence and term activation. For clusters related to quiescence and term activation, a change of more than 20% was required for the midgestation (14.5 days) and term (18.5 days) time points, respectively, versus non-pregnant controls.To identify transcripts differing in mean expression across the seven experimental groups, onductor ,34. Foldp-value was calculated by MAPPFinder 2.0 to adjust for multiple hypothesis testing . Because of the highly redundant nature of the oligonucleotide arrays used, redundant probe sets corresponding to a single gene were identified from the Affymetrix NetAFFX website [Gene expression clustering for 4,510 significant probe sets was performed using the program HOPACH , with uncentered correlation distance . HOPACH  website .n = 18) using Primer Express software (Perkin Elmer) and based on sequencing data from the National Center for Biotechnology Information (NCBI) databases and purchased from Biosearch Technologies. Sequence data for all oligos are available online [PPIA, GAPDH, PGK1 and S9. Moreover, using GeNorm [PGK1 and GAPDH as the two most stable housekeeping genes across all 12 specimens and used their geometric means for normalization. Normalized data were graphed and compared to the data generated on similar specimens via microarrays. Genes could be broken down into the following groups: 13 genes with concordant microarray-TaqMan patterns; one false-negative result by microarray (Acta2); three genes with high TaqMan variability ; and one gene with evidence of alternative splicing (Csb) . It should be noted that Acta2 baseline expression was relatively high for both microarray and TaqMan results. As both of these techniques probed different regions of the Acta2 gene, we cannot exclude the possibility of alternative splicing.Real-time reverse transcription PCR (RT-PCR) was used to validate the expression patterns of several highly regulated genes associated with specific phases of myometrium gestation. Gene-specific primers for multiplex real-time RT-PCR were designed for each gene of interest Click here for additional data file


The objective of this study was to examine the Minimum Data Set (MDS) and Geriatric Depression Scale (GDS) as measures of depression among nursing home residents.The data for this study were baseline, pre-intervention assessment data from a research study involving nine nursing homes and 704 residents in Massachusetts. Trained research nurses assessed residents using the MDS and the GDS 15-item version. Demographic, psychiatric, and cognitive data were obtained using the MDS. Level of depression was operationalized as: (1) a sum of the MDS Depression items; (2) the MDS Depression Rating Scale; (3) the 15-item GDS; and (4) the five-item GDS. We compared missing data, floor effects, means, internal consistency reliability, scale score correlation, and ability to identify residents with conspicuous depression (chart diagnosis or use of antidepressant) across cognitive impairment strata.The GDS and MDS Depression scales were uncorrelated. Nevertheless, both MDS and GDS measures demonstrated adequate internal consistency reliability. The MDS suggested greater depression among those with cognitive impairment, whereas the GDS suggested a more severe depression among those with better cognitive functioning. The GDS was limited by missing data; the DRS by a larger floor effect. The DRS was more strongly correlated with conspicuous depression, but only among those with cognitive impairment.The MDS Depression items and GDS identify different elements of depression. This may be due to differences in the manifest symptom content and/or the self-report nature of the GDS versus the observer-rated MDS. Our findings suggest that the GDS and the MDS are not interchangeable measures of depression. Depression is common among residents of nursing homes . Of the The Minimum Data Set (MDS), created in response to the US Omnibus Budget Reconciliation Act of 1987, aims to provide a uniform, standardized, and comprehensive assessment of residents in nursing homes . The MDSThis study compares the measurement properties of two measures of geriatric depression: the GDS and the MDS. The GDS is evaluated via a 15 and five item form. The MDS is evaluated by the items located in Section E in total and as a subset included in the Depression Rating Scale.Participants were nursing home residents living in nine Massachusetts nursing facilities participating in a research study between July 1994 and March 1998. Details of the study are described elsewhere [Residents were evaluated with the Minimum Data Set (MDS) Resident Assessment Instrument version 2.0  as well We also examined the depression symptom scales with regard to indicators of clinically recognized depression: chart diagnoses of depression and recent use of antidepressant medication. These data were also obtained by research nurses using structured chart review forms keyed to data elements collected with the MDS. MDS assessors are instructed to note the presence of a disorder related to the resident's current functional, cognitive, and behavioural status, medical treatments, and risk of death . Among tWe stratified the sample into two groups based on the severity of cognitive impairment. The impaired group comprised residents who were comatose (MDS 2.0 item B1 = 1), and/or with a short-term memory problem (B2a = 1) and those who only rarely/never make themselves understood (C4 = 3). All other residents were assigned to the "cognitively intact" group. This decision rule matches a screening rule for the MDS versus GDS depression symptom assessment proposed in the US Centers for Medicare & Medicaid Services' (CMS) working draft of the MDS version 3.0 .We compared sample statistics and psychometric properties for each of the four depression scales across strata defined by cognitive impairment. We evaluated missing data by examining the proportion of residents with complete data on all assessment items, and also by the proportion of residents with complete data on a majority of items in the scale. For all other analyses we substituted missing values with the mean of the resident's non-missing items if a majority of the scale items were not missing. We examined means, standard deviations (SD), proportion of residents scoring at the floor, the internal consistency reliability  and areThe very high frequency of missing data for the GDS encouraged us to examine the frequency of missing data at the item level. We present this information in Table The means for the GDS- and MDS-derived scales were different between the cognitively intact and impaired groups Table . Of noteFigure The GDS-15 had the highest internal consistency reliability coefficient, and the lowest was observed for the DRS. However, this difference does not substantially exceed that expected due to the greater scale length of the GDS-15. Using the Spearman-Brown prophecy formula , the relThe correlation between the GDS- and DRS- derived measures were not statistically different from zero. All of the differences in correlation coefficients are similar among the cognitively impaired and cognitively intact and not statistically different, except for the correlation of the E1SUM and DRS (P < .001).The comparison of mean depression symptom scale scores among residents that received antidepressants and/or had a chart diagnosis of depression, within and across cognitive impairment strata, is reported in Table For the MDS-derived depression scales, the differences in means associated with antidepressant use and depression diagnoses are much more dramatic than for the GDS-derived scales, but only among the cognitively impaired. For both the E1SUM and DRS, the marginal differences for depression diagnoses and antidepressant use describe moderate to large effect sizes. These differences are statistically significant (both P < .001). On the other hand, among those residents not identified as cognitively impaired, the marginal differences associated with antidepressant use and a depression diagnosis were trivial to small and not statistically significant.In this study, we found that MDS- and GDS-derived depression measures were not correlated with one another, but were apparently adequately reliable measures of their intended construct. Thus, we infer that the MDS and the GDS measure different aspects of nursing home residents' depression. Each scale has specific strengths and limitations. The practical utility of the GDS is undermined by a very high frequency of missing data. The proportion of GDS responses missing differs greatly by level of cognitive functioning. The floor effect limits both instruments. While the internal consistency reliability is apparently greater for the GDS, this may simply be due to the greater number of items on the GDS. We observed a weak relationship between GDS-5 scale scores and clinical indicators of depression , but the strong association between MDS-derived scales and clinical indicators was only seen among cognitively impaired residents.Other investigators have found a low correlation between the DRS and the GDS and other instruments for assessing depression, but these findings vary according to data collection strategies. For example, Anderson et al found a low correlation between the MDS DRS abstracted from residents' charts and symptom data collected with the GDS (r = .13) and the Hamilton Depression Rating Scale  using research interviewers . SimilarIn our study, the assessment methods followed more closely how they were designed to be used. The GDS was used as a direct interview, and the MDS was used as instructed in the MDS manual , and incThe fact that the MDS- and GDS-derived scales do not correlate implies that the two scales evaluate different aspects of a resident's depression. For positive MDS depression symptom ratings, residents must visibly act by making negative statements, be easily angered, and display unrealistic fears to trigger MDS symptoms. The GDS asks residents if they are satisfied with their life, feel helpless or worthless, and are often bored. It is conceivable that GDS-derived measures capture a brooding mental set, reflective of a dysphoric personality trait or adjustment disorder (for example in response to a recent change in living situation) rather than the presence of major depression.RAI include a provision for self-assessment of depression [The lack of correlation between MDS depression measures has implications for proposed revisions to the MDS. Until more is known about the phenomenology and clinical validity of syndromes measured by these and other depression measures used in long-term care settings, adding self-report of symptoms of depression to the MDS is supported by our findings. We note that both CMS's draft revision of the MDS and new pression . HoweverThe Geriatric Depression Scale and Minimum Data Set mood items measure different aspects of the depression syndrome among nursing home residents. The two measures cannot be treated as exchangeable or equivalent, and each has it's own strengths and limitations. The GDS uses self-report, but as a consequence suffers from a high frequency of missing data. The MDS relies upon informant ratings and therefore provides information about most residents. Although the GDS has higher internal consistency reliability than MDS, this is not beyond that expected due to greater scale length. The MDS measures were more strongly related to antidepressant use and record of a diagnosis of depression than were GDS measures. These results highlight the fact that more psychometric research is needed to better understand and improve the measurement of depression among frail nursing home residents.GDS Geriatric Depression ScaleMDS Minimum Data SetDRS Depression Rating ScaleCSDD Cornell Scale for Depression in DementiaHDRS Hamilton Depression Rating Scale.No financial competing interests. TR, JH, GIC, BEF and JNM are fellows of InterRAI, a non-profit group dedicated to improving the health care for persons who are elderly, frail or disabled. InterRAI owns the copyright to the MDS for nursing homes and many other care settings. InterRAI offers free licenses for the use of assessment forms of which it owns the copyright to. Therefore, there are no financial competing interests for members of the writing group. For more information please visit MK and RNJ conceived of the study. MK drafted the manuscript. RNJ performed the analyses and provided critical review of and completed the manuscript. JNM obtained the data, participated in the analysis and provided critical review of the manuscript. JH, MS, GIC and BEF provided critical review of the analyses and manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: