CSF was measured in 9 normal subjects as part of an intensive study of physiological responses stressors in chronic pain and fatigue states. CRHCSF was first correlated with demographic, vital sign, HPA axis, validated questionnaire domains, baseline and maximal responses to pain, exercise and other stressors. Significant factors were used for linear regression modeling.Define covariates of cerebrospinal corticotropin-releasing hormone (CRH) levels in normal humans. CRHCSF with blood glucose and sodium (explained variance = 0.979, adjusted R2 = 0.958, p = 0.02 by 2-tailed testing); (b) CRHCSF with resting respiratory and heart rates (R2 = 0.963, adjusted R2 = 0.939, p = 0.007); and (c) CRHCSF with SF-36 Vitality and Multidimensional Fatigue Inventory Physical Fatigue domains (R2 = 0.859, adjusted R2 = 0.789, p = 0.02).Highly significant correlations were found despite the small number of subjects. Three models were defined: (a) CRHCSF was predicted by lower glucose, respiratory and heart rates, and higher sodium and psychometric constructs of well being. Responses at peak exercise and to other acute stressors were not correlated. CRHCSF may have reflected an overall, or chronic, set-point for physiological responses, but did not predict the reserves available to respond to immediate stressors.Low CRH CSF) and neuropeptide Y (NPY), another important neuropeptide involved in pain, autonomic and stress responses [CSF based on (a) metabolic, (b) autonomic, and (c) psychometric measures.Corticotropin – releasing hormone (CRH) plays a major role in regulating the hypothalamic – pituitary – adrenal (HPA) axis, acute responses to stressors, and other neurological functions . The ceresponses ,3, were esponses . Subjectesponses , chronicesponses , and vetesponses  were incesponses  were defThe mean age for the group was 35.2 yr (95% C.I.: 30.5 to 39.9) years. There were 2 females, 5 African-Americans, 3 Caucasians, and 1 Caucasian Hispanic (TABLE CSF was 121.9 (87.8 to 156.0) pg/ml. NPY did not correlate with any variable.Mean NPYCSF could be grouped into: metabolic, autonomic function, and perceptional and cognitive functions (TABLE CSF (FIGURE 2) were 0.97 and 0.66, respectively. Glucose and sodium were also negatively correlated (R2 = 0.85). Resting norepinephrine levels at 2 time points and heart rate at 4 time points were colinear and positively correlated to CRHCSF.Significant covariates of CRHns TABLE . Serum gF FIGURE . ExplainCSF was negatively correlated with the Holter monitor-derived measure of log heart rate summed for the daytime, and the threshold temperature causing an initial, mild sensation of burning pain (Stressor I). The latter indicated that subjects with higher CRHCSF perceived the burning pain of the cutaneous forearm hyperthermic stimulation at a lower threshold temperature than their peers. This may indicate an increased sensitivity to nociceptive stimuli. However, there was no correlation with deep pressure – induced pain. Negative correlations were also found with the SF-36 Vitality and SES Manage Symptoms domains. High scores were normal for these questionnaires, with lower scores indicating dysfunction.CRHCSF. Perceptions of vulnerability, physical functioning and self-efficacy were more highly related.All parts of the study were completed by at least 5 males. Analysis of the male subgroup gave some information about the role of gender. The pattern of significant covariables was different from the total group TABLE . RespiraCSF. The 3 models had high significance levels  to be so significant (p < 0.02). The explained variances were very high (R2 > 0.85) suggesting that the factors may be causally connected.Sets of the significant metabolic, autonomic, and perceptual variables were grouped and analyzed by 3 linear regression models in order to detect significant relationships between independent variables and CRHls TABLE . A metabCSF did not correlate with variables associated with maximum exercise, heat – and pressure – (dolorimetry) induced pain, or other rapid onset stressors  receptors and the HPA axis . The othCSF were positively correlated. The statistical model indicated that a low CRHCSF was predicted by low respiratory and heart rates. Respiratory and cardiac functions are rigorously controlled by brainstem and other nuclei that integrate incoming signals of plasma O2, CO2 and H+ concentrations, activity needs, anxiety and other stressors. Efferent cardiovascular and other autonomic reflexes are modulated by CRH in man [CSF. This is supported by studies in mice that genetically overexpress CRH. They develop chronic stress – like autonomic and physiological alterations [Resting respiratory and pre-exercise heart rates and CRHH in man . These cerations . Stressoerations  that acterations . An examerations .CSF should be present in persons lacking these stressor states. This was supported by the negative correlations of CRHCSF with scores for the SF-36 Vitality and Change in Health, Self Efficacy Scale Manage Symptoms, MIQ Vulnerability, and MFI Physical Functioning domains. Each scale has an idealized "normal" end of the range of scores. Deviation towards either higher (MFI) or lower (SF-36) ends of the scales provides an estimate of dysfunction. For each of these domains, the lower CRHCSF were associated with more normal scores, while higher CRHCSF was associated with scores that were beginning to shift away from the normal pole of each scale. These trends were further supported by the statistical model where the optimum covariates of CRHCSF were SF-36 Vitality and MFI Physical Fatigue domains. The model predicted that CRHCSF would be in the low normal range when Vitality and Physical Fatigue domain scores were high . Taken together, these results confirm the consistent physical status, mental coping skills, and general health of these subjects.Extrapolation of these concepts suggests that elevated CRH may be related to anxiety, depression, or other disorders associated with chronic stress responses. If so, then lower, but normal, CRHStudies of intraventricular CRH injection in primates support our findings . The CRHCSF and metabolic, autonomic, and psychometric measures. These are novel findings in humans, but are consistent with data on CRH in acute and chronic stress models, and proposed CRH neural circuits. It will now be of great interest to contrast these statistical models identified for normal subjects with the other chronic pain and fatigue patient subsets to determine if CRHCSF correlates with different sets of variables.This small but intensively studied group of normal humans demonstrated surprisingly robust relationships between CRHNine normal, healthy subjects  gave informed consent for this paid, Institutional Review Board – approved protocol. They had a comprehensive screening evaluation to exclude: severe physical impairment, morbid obesity, autoimmune/inflammatory diseases, cardiopulmonary disorders, uncontrolled endocrine or allergic disorders, malignancy, severe psychiatric illnesses (e.g., schizophrenia, substance abuse), factors known to affect the HPA axis or autonomic function , or medication use. Subjects had a history and physical examination, were administered the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) (SCID II) ,20, CompSubjects were admitted to the G-CRC on the evening of Day 1. An 18-gauge catheter was inserted antecubitally and infused with normal saline at 50 ml/hr. A Holter monitor was attached to monitor autonomic regulation of cardiac rhythm -31. Upon125-CRH or -NPY was added, tubes gently vortexed, and again incubated overnight. Goat anti-rabbit antibodies were added, tubes incubated, and immune complexes precipitated by centrifugation. Radioactivity was counted for the standards, and the concentrations for each sample interpolated from the standard curves. Spiking CSF with fixed amounts of I125-peptides and performing the RIA shifted the curves to the left by the anticipated concentrations. Standard curves were reproducible to within 10%.Tubes of CSF were immediately placed on ice and then centrifuged at 4°C. The supernatants were rapidly frozen at -80°C. Tube 2 or 3 was removed from the freezer and thawed at 4°C. Peptides were extracted by precipitating high molecular weight proteins by adding an equal volume of 100% ethanol, 0.1 M acetic acid, 0.2% sodium bisulfite ,37. The Plasma catecholamine levels were measured by HPLC (Mayo Clinic Laboratories, Rochester, MN).All the data from the provocation studies, questionnaire domains, blood work, and neuropeptide analysis were entered into a SAS spreadsheet (SAS, Carey, NC) using sequential hand or scanner entry followed by data checking routines.Means with 95% confidence intervals were reported.CSF) and other objective and subjective patient variables were conducted as an exploratory tool. Given the small sample size (n = 9), both parametric and non-parametric correlations were used to evaluate the robustness of the correlation coefficients. Partial and intra-class correlations examined the structure of the relationships between CRHCSF and the independent variables. Original data were always checked to make sure that correlations were not spurious, due to outliers, colinearity, or sets of virtually identical scores.Simple correlations between our outcome measure (CRHCSF. Independent variables with flat slopes or high scatter that did not generate correlations with CRHCSF, that were highly correlated with each other (collinear), or that had < 5 recorded values were excluded from further analysis.Our data included a very wide range of covariates which spanned the physical and psychological attributes of the patients. Scatter plots were used to determine the response slopes with CRHCSF, the dependent variable. Separate models were required because of the low degrees of freedom available for the analyses, and to ensure that the explained variance (R2) was not inflated due to overloading the model with potentially collinear variables. Variance inflation factors and tolerance values were incorporated to optimize the selection of the most independent combinations of variables that also maximized the R2 and p values. The linear regression procedure took into account the multiple comparisons for each model.The remaining suitable independent variables fell into 3 categories: metabolic, autonomic and psychometric function. Three 3 separate linear regression models  were con2, explained variance; SCID II, Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV); SF-36, Short Form 36; SES, Self Efficacy ScaleCESD, Center for Epidemiologic Studies Depression scale; CFS, Chronic Fatigue Syndrome; CIDI, Composite International Diagnostic Interview; CRH, corticotropin-releasing hormone; CSF, cerebrospinal fluid; MFI, Multidimensional Fatigue Inventory; MIQ, Meaning of Illness Questionnaire; NPY, neuropeptide Y; RJNB directed the RIA studies, correlation of clinical variables, and wrote the manuscript. HM confirmed the accuracy of the study results by double data entry methods, vetted the SAS database, performed the statistical analysis, and co-wrote the manuscript. GW performed the CRH and NPY assays and maintained the repository of these samples. DJC was the Principal Investigator for the overall study and helped review the draft and final manuscript.


This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles.Our tests show that this tool detects more similarities between protein families of distant homology than the previous primary sequence-based method. A very significant improvement in performance is observed when the real secondary structure is used.Integration of primary and secondary structure information can substantially improve detection of relationships between remotely related protein families. Detecting an evolutionary relationship between proteins is the basis for functional inference. Existing methods most often rely on sequence information in an attempt to quantify the evolutionary divergence or similarity between the sequences compared. A significant similarity would suggest that the proteins are related. However, in many cases sequences have diverged to the extent that their similarity is undetectable by standard sequence comparison algorithms. Nevertheless, they may still have similar structures and functions ,2.It has long been postulated that evolutionary pressure acts upon the three-dimensional structure of proteins and intra-protein interactions rather than at the level of the primary sequence ,4. IndeeThere have been many attempts to build algorithms that predict protein structure from amino acid sequence. Unfortunately, this is a hard problem, and existing methods are only partially successful . On the Alternatively, one can use both representations to assess protein similarity. Incorporating secondary structure information into protein comparison is not a new idea. Several researchers have attempted to boost performance and sensitivity of various methods by adding this extra degree of information with some success. Yu et al. encoded functionally conserved sequence patterns into probabilistic structural models (that comprise a family of hidden Markov models) . The modThese and similar studies have indicated that the incorporation of secondary structure information, even if predicted, can increase sensitivity and specificity of a protein comparison model. Here we describe a method that integrates secondary structure information with primary sequence information in a single scoring scheme, using a single statistical representation. The model can be applied to any protein family and does not require the application of expensive threading algorithms. Our method extends our previous work on profile-profile comparison . SpecifiWe use a data set of domain families derived from the SCOP classification of protein structures , releasen-tuple of probability distributions of amino acids, derived from a group of related proteins, where n is the length of the multiple alignment of these proteins. It is represented in software as a two dimensional matrix of 20 rows and n columns, where each column (known as a profile column), is a probability distribution p over the 20 amino acids in one position in the multiple alignment. These profile columns form the basis of profile-profile comparisons.The PSI-BLAST profiles are the basis for our representation of a protein family. Each profile is a true secondary structure information is gleaned from the PDB files of the seed proteins using STRIDE [b, B, C, E, H, I, G, T where b and B stand for Bridge, C = Coil, E = Strand, H = AlphaHelix, I = PiHelix, G = 310Helix and T = Turn. We reduce this set to the three main secondary structures (helix, strand and coil) by mapping H, I, G to H, and b, B, C, T to C.We use two types of secondary structure information in our experiments: true information and predicted information. The g STRIDE . Stride predicted secondary structure information is predicted using PSIPRED [The  PSIPRED . PSIPREDi we have a statistical source that emits amino acid a and secondary structure s with probability Pi(a, s) such thatApriori, it is unclear how one should integrate secondary structure with primary structure in a single model. For example, one might think of a representation over a generalized alphabet, that considers all possible pair combinations of amino acids and secondary structure elements. Assuming independence between positions , then this representation implies that for each position hidden while the other is visible. Noting that Pi(a, s) can be written as Pi(a/s)Pi(s), we can decompose the parameter space into the parameters of the secondary structure distribution, and the parameters of the conditional probability distributions over amino acids. However, the typical amino acid distributions that are available from multiple alignments of protein families differ from these conditional probability distributions by definition. Furthermore, there are other subtleties that one should bear in mind when designing an integrated statistical model for a protein family.and every position can be represented by a vector of 60 probabilities over this pair alphabet. This representation implicitly implies that the amino acid emitted and the secondary structure are two different features of the objects generated by the source, while in reality the secondary structure is not a "character" or an independent property of the emitted objects, but rather a characteristic of the source itself that is usually unknown. This property introduces some special constraints on the distribution of amino acids that are emitted by the source. In other words, the secondary structure and the amino acid distribution in a position are strongly dependent on each other, but one is n. This conformation can be described in terms of the set of 3D coordinates of the n positions, or in terms of the set of distances between coordinates S =  where  is the set of distances from the i-th residue to all other residues – the latter being more amenable for a representation as a statistical source, as it is invariant to translations and rotations. Although there is structural variation across the different instances of the protein family, it is significantly smaller than the sequence variation, and we will assume that a single consensus conformation S reliably describes the protein family. The structural conformation determines the statistical properties of the source distributions. Namely, it induces certain constraints on the sequence space that can be mapped to that conformation, based on the physical properties of its topology. In other words, it induces a probability distribution over the sequence space of O(20n) sequences that can be mapped to that conformationMore precisely, assume we have a protein family, where all proteins adopt a certain structural conformation of length P(a1, a2, ..., an/S).n-dimensional distribution clearly introduces dependencies between remote positions, and the exact probability distribution in a position depends on the amino acids observed in all other positions P(ai/a1, a2, ..., ai-1, ai + 1, ..., an, S). Accurate knowledge of the all-position probability distribution P(a1, a2, ..., an/S) would allow one to compare two sources of protein families theoretically by comparing these high-dimensional distributions. However, because of (limited) data availability and for mathematical simplicity, the marginal probabilitiesNote that due to convergent evolution it is possible that two disconnected regions in the sequence space (two families of homologous proteins) will be mapped to the same conformation  = Pi(a/S)marginal amino acid distributions. For example, the same marginal amino acid distribution can be observed in different secondary structure conformations and on the other hand, even highly similar fragments of secondary structures can be associated with different amino acid distributions.Nevertheless, the secondary structure information can still be useful when comparing protein families. This is because some information is lost if one is to use just the S1 and S2. Indeed, it has been shown that structure comparison is much more effective in detecting remotely related families [S1 and S2 as comparing two sources that induce different probability distributions over the conformation space P1(S) and P2(S). However, characterizing these distributions is very difficult. Moreover, convergent evolution might result in two different sequence sources with structurally similar conformations. These relations are usually perceived weaker than families that are similar both in sequence and structure [ and , where the distributions are again marginalized over all positions other than i, and  is a vector of inter-residue distances. The joint distributions can be rewritten asThe most effective way of comparing two protein families is by comparing their (consensus) structural conformations families ,20,25. IPi(a/S) that are based on all vectors of inter-residue distances .where the last step uses the more accurate marginal probabilities S is difficult . On the other hand, secondary structure (which can be viewed as an approximation of S) is more readily available, and can be predicted quite reliably from sequence information. Therefore we suggest to approximateAs was mentioned earlier, obtaining Pi(s) is the probability to observe a secondary structure s at the i-th position.  Plugging in the empirical distributions  for Pi(a/S) we getwhere , is conditionally independent of the distribution Pi(s). Therefore, to completely describe the source one needs to provide the parameters of the marginal distribution of amino acids, and the parameters of the secondary structure distribution. Since the two distributions are assumed independent, they are amenable to a representation in which their parameters are appended together. I.e. the secondary structure probabilities are appended to the probabilities of the 20 amino acids.i.e., the empirical distribution of amino acids at a position, γ that ranges from 0 to 1. The secondary structure probabilities are normalized such that they sum to γ while the amino acid probabilities are normalized such that they sum to 1 - γ. The higher γ is, the more dependent the profile column is upon secondary structure information. This parameter is optimized as described in section 'Parameter optimization'.Our method is based on an extension of the original profile representation in . Using tand a secondary structure.Note that our normalization maintains the conditional independence of the two types (primary and secondary), as described above. Each component of the extended profile can be viewed as a sub-profile. Since each one of the two components is summed independently to a non-zero probability then two symbols must be "emitted": an amino acid P = p1p2p3...pn and Q = q1q2q3...qm, where n and m are the lengths of the profiles (the number of positions or columns) and pi, qj are probability distributions over the 23 letter alphabet of amino acids and secondary structures, we define the similarity score between two columns pi and qj based on their statistical similarity. The similarity score is composed of two elements: the divergence score and the significance score.In this section we review the main elements of our profile-profile comparison algorithm that was introduced in . We compp and q, for every 0 ≤ λ ≤ 1, the λ-JS divergence is defined asTo estimate the divergence of two probability distributions we use the Jensen-Shannon (JS) divergence measure . Given tDKL[p||q] is the Kullback-Leibler (KL) divergence [where vergence , definedandr = λp + (1 - λ)qp and q, with λ as a prior weight (here set to 0.5). We call the corresponding measure the divergence score and denote it by DJS. This measure is symmetric and ranges between 0 and 1, where the divergence for identical distributions is 0. Besides being symmetric and bounded, an attractive feature of the DJS divergence measure is that it is proportional to the minus logarithm of the probability that the two empirical distributions represent samples drawn from the same ("common") source distribution [ is a metric [can be considered as the most likely common source distribution of both distributions ribution . It has a metric .p and q: their relative distance. However, it does not consider the uniqueness of the two distributions. A match between two distributions that resemble the background distribution is not as significant as a match of two distributions that resemble each other, but are very different from the background distribution. In other words, the more unique the distributions are , the more significant is a match between them.The divergence score measures one aspect of the statistical similarity of significance score S of a match we measure the JS divergence of the (common) source distribution, r, from the base (background) distribution P0.To assess the S = DJS[r||P0]γ described in section 'Integrating secondary structure with primary structure'. The significance measure reflects the probability that the source distribution, r, could have been obtained by chance. The higher r is, the more distinctive the common source distribution, and the lower the probability that it could have been obtained by chance.In this study the background distribution is composed of two components: the background distribution of amino acids (estimated from a large sequence database) and the background distribution of secondary structure elements . The components are mixed using the same mixing parameter similarity score of two probability distributions p and q as a combination of the divergence score and the significance score:We define the D → 0) whose common source is far from the background distribution (S → 1), tends to one. On the other hand, the similarity score of two dissimilar distributions (D → 1) whose most likely common source distribution resembles the background distribution (S → 0) tends to zero. This scoring scheme also distinguishes two distributions that each are similar to the background distribution (D → 0 and S → 0 giving Score - 1/2) from two dissimilar distributions, but whose common source is similar to the background distribution (D → 1 and S → 0 giving Score = 0). In a recent study [With this expression, the similarity score of two similar distributions (nt study  it has bp over a set X that is obtained by "mixing" two probability distributions over two disjoint subsets: p1 over the subset X1 and p2 over the subset X2 (where X = X1 ∪ X2 and X1 ∩ X2 = θ). Let γ be the mixing parameter, i.e. the total weight of the first distribution p1 in the combined distribution p. Assume q is obtained in a similar manner from q1 and q2. Then,Note that our measures are functionals of the probability distributions, based on variations of the entropy function, and specifically the KL divergence function. One of the nice properties of this function is that it is additive in the following sense. Assume we have a probability distribution X1 (the secondary structure) and X2 (the primary structure).In other words, this measure preserves independence between the two subsets. Therefore, with our extended profile representation, the new functionals are simply a weighted sum of the individual functionals over the subsets γ and (1 - γ) respectively.Note however that this property holds for the divergence and the significance measures but not for the final similarity score that is a combination of the divergence and the significance scores. An alternative would be to compute the divergence, significance and similarity scores independently for the secondary and primary structures, and then combine the two similarity scores into one, with weights i of the seed sequence (see section 'Data sets') as the seed amino acid of the i-th profile column.It is interesting to compare the similarity scores before and after the addition of secondary structure information. To assess the impact of this information, we computed the distribution of similarity scores for five types of profile columns, depending on the type of their seed amino acid. We refer to the amino acid at position Two seed amino acids are defined as similar, neutral, or dissimilar based on their BLOSUM62 scoring matrix , with poidentical columns), (2) different columns that are associated with the same seed amino acid (strongly similar columns) (3) different columns that are associated with similar seed amino acids (similar columns), (4) different columns with mutually neutral seed amino acids , and (5) different columns with dissimilar seed amino acids (dissimilar columns). We repeated this calculation before and after the integration of true secondary structure information  and the results are plotted in Figure The five types of column pairs are: 1) a column with itself .ALLR (Average Log Likelihood Ratio) scoring function that was suggested in [p and q, their ALLR score is defined asWe also tested the ested in . This scnp (nq) is the number of total counts from which p (q) is derived, and P0 is the background distribution.where We computed the correlation scores and ALLR scores for the same sets of columns defined in the previous section and compared it to the information-theoretic scores Figure . Note thprof_ss) depends on several parameters: (1) a shift parameter is introduced to convert the similarity scores to scores that are suitable for local protein comparison . The tradeoff between the primary sequence information and secondary structure probabilities was varied from zero to one. With zero dependence on secondary structure the method is equivalent to prof_sim (profile-profile comparison based on just primary structure). The results are shown in Figure γ = 0.055 (i.e. 0.055 weight on the secondary structure information and 0.945 on the sequence information) gave the best performance. (Note that if each secondary structure was given as much weight as a single amino acid γ would be  or ~0.13).To estimate the best value for γ = 1), the performance was much worse than when only sequence information was used (γ = 0). These corner-case results and the fact that the best results were obtained with γ << 0.5 suggest that for protein family comparison, the coarse-grained secondary structure information is noisier and less reliable than sequence information. However, as the graphs indicate, using both sources of information clearly improves performance. Our tests were done using actual secondary structure information in the profile; however, similar results were obtained when the predicted information was used for one or both of the profiles  Marquardt-Levenberg algorithm.To differentiate true similarity values from those that may be observed by chance, it is essential to establish a baseline empirical distribution for the scores. Here we used the statistical framework of the extreme value distribution (EVD). Although rigorous mathematical proof has not been found for local gapped similarity scores, empirical studies have shown that the distribution of these scores can be approximated by this distribution. An empirically fit EVD also has the benefits of being a true fit to the quirks of a particular protein family. Three such distributions were established to assess the significance of the profile-profile matches. All distributions were fit with the 'fit' function in gnuplot uniform approach (uniform parameters).The first distribution is based upon comparisons between unrelated families . This distribution is useful in that it can be used to assess the significance of a score in comparing any pair of protein families, without further need for computations. Practically, this aggregates all comparisons between non-related families into a single list. This is essentially the distribution of similarity scores of random profiles, as shown in Figure The second distribution is similar to the first, except a correction was made for the length of a profile, similar to the approach employed by FASTA . By chanThe third distribution proves to be the best approach in assessing significance of matches with a particular profile. This distribution is created on a per-family basis. The scores of each family against all (unrelated) SCOP families were fit to an EVD. Since many of the family profiles are unrelated to the query family, the corresponding scores provide a relatively reliable baseline distribution. This approach is a robust method to assessing the significance of matches for a particular profile since it allows for any unusual properties of the query profile  and the parameters are adjusted accordingly  that can be detected with our method. Specifically, each family in our test set is compared with all other protein families and the results are sorted based on the p-value. Given the sorted list we count the number of true family-family relationships that are detected before the orted in ).true relationship if both families belong to the same superfamily, a possible relationship if both families belong to the same SCOP fold, a weak relationship if they belong to the same class, suspicious if they belong to different classes  and an error if one family is all-alpha and the second is all-beta. We repeat the procedure described above, each time using a different definition of a false positive. The results are summarized in Table Usually a false positive is defined as a relation between families that do not belong to the same superfamily. This popular criterion, however, is somewhat strict as relations between families that belong to the same fold can also be considered as positives. We use the following terminology to distinguish between the different types of "false positives". We define a relationship between two protein families to be a prof_ss improves over prof_sim  although the improvement is smaller compared to the one reported in Table γ was set to 0.055), the new algorithm almost doubles the number of pairwise relationships that are detected.The second measure we use is the receiver operator characteristic (ROC) measure, a common measure in assessing sensitivity and selectivity. Given a sorted list of results, the ROC index measures the area under the curve that plots the positives versus the negatives. Maximal performance translates to a perfect separation and a maximal normalized ROC score of 1. The ROC-N measure is a variation over the ROC measure, where the plot is truncated at N negatives. In other words, the ROC-N measure is the number of true positives detected up to N false positives. Here we used the popular ROC-50 measure. To obtain the ROC-50 scores for each method we pool together all pairwise comparisons for all protein families, and sort them by their normalized e-value. The number of true positives is aggregated until 50 false positives occur. As before, we repeated this procedure with different definitions of false positives, and the results are summarized in Table In this section we give several interesting examples of alignments between remote protein families that exemplify the differences between sequence-based profile-profile alignments and the new generalized profile alignments.prof_sim is able to roughly match up the helices but not the beta strands with a rms of 11.96. The predicted secondary structure does not improve the alignment in this case, however, when the true secondary structure is used, prof_ss is able to completely align the helices as well as most of the strands with a much better rms of 4.45 , and the replication terminator protein (family a.4.5.7, seed scop domain dlbm9a_). Although designated as all-alpha, proteins in this superfamily contain a small beta-sheet at the core. The similar substructures have three alpha helices and a couple beta strands, 5 Figure .This superfamily belongs to the concanavalin A-like lectins/glucanases fold, characterized by a sandwich structure with 12–14 strands in 2 sheets. We compared two families in this superfamily: the beta-Glucanase-like family (b.29.1.2, seed domain dlcpm__) and the vibrio cholerae sialidase, N-terminal and insertion domains (b.29.1.8, seed domain dlkit_2).prof_ss both when using the predicted information and the true secondary structure information. On the other hand, prof_sim is unable to align the sheets at all  and the bromoperoxidase A2 family (c.69.1.12, seed domain dlbrt__). These are large and complex proteins with many helices and strands. This paper presents a simple method to improve remote homology detection between protein families. We use statistical models of protein families in the form of profiles, and by incorporating secondary structure information within that model, we can reuse existing comparison methods for comparing profiles. It is shown that this method improves over the previous method that is based only on primary sequence information.As opposed to other methods that compare single proteins, our method compares models of protein families. Instead of summing over different models, our model combines structural and primary sequence information within the profile itself. Our method allows us to explore a wide range of scenarios, between purely sequence-based representation and a purely secondary-structure based representation. The optimization of the single mixing parameter shows that the slight incorporation of predicted secondary structural information is invaluable. Since predicted structure information in PSIPRED comes from neighboring profile columns, this proves that each profile column confers extra information that is relevant to its neighbors and is useful to inferring protein relationships.Furthermore, it is shown that if true secondary structure information is used, performance improvements are very significant and the number of relationships that can be detected is almost doubled. We conclude that despite the high overall accuracy of the secondary structure prediction method, its imperfect nature can greatly affect the performance. However, our method can be generalized to any secondary structure prediction method that produces estimated probabilities for secondary structure, so should a new prediction method be found that performs better than the current methods, the model presented here is expected to reflect the improved performance and consequently improve homology detection.prof_sim program and integrated secondary structure information, optimized the model, ran experiments, and analyzed the result sets. GY conceived of the study, designed the model and analyzed the results.RC extended the


However, knockout in the mouse of cftr-/- mouse. Lungs showed decreased compliance and increased airway resistance in young animals as compared to cftr+/+ littermates. These changes were noted in animals less than 60 days old, prior to any long term inflammatory effects that might occur, and are consistent with structural differences in the cftr-/- lungs. Surprisingly, the cftr+/- animals exhibited a lung phenotype distinct from either the homozygous normal or knockout genotypes. The heterozygous mice showed increased lung compliance and decreased airway resistance when compared to either homozygous phenotype, suggesting a heterozygous advantage that might explain the high frequency of this mutation in certain populations.Using measurements of pulmonary mechanics, a definitive lung phenotype was demonstrated in the cftr results in distinct differences in pulmonary mechanics of the adult. Distinct phenotypes were demonstrated in each genotype, cftr-/-, cftr +/-, and cftr+/+. These results are consistent with a developmental role for CFTR in the lung.In the mouse the gene dosage of  CF is one of the most common autosomal recessive diseases in Caucasians with a carrier rate of 3–4% [1), functional residual capacity (FRC), and other parameters of lung function prior to the onset of recurrent infection [Cystic fibrosis (CF) is a progressive disease primarily affecting the intestines, lungs, and pancreas. The gene responsible for CF was identified in 1989 [ of 3–4% , and is nfection .cftr knockout mouse does not develop overt lung disease, which has severely limited its usefulness. However, the availability of new methods for pulmonary testing in rodents [cftr knockout mouse for functional lung changes. In the present study, therefore, we examined pulmonary function in young adult cftr -/-, cftr +/-, and cftr+/+ S489x mice in an effort to establish a lung phenotype.Soon after the CF gene was discovered, a knockout mouse was developed. This mouse demonstrates subtle changes in epithelial cell phenotype, including alterations in secretory glycoconjugates and changes in secretory vesicles . Monocyt rodents ,9 now prPV) curve which is dependent upon both lung structure and interfering pathology. PV curves were measured in triplicate, starting from positive end-expiratory pressure (PEEP) values of 0, 3, and 6 cmH2O in S489X mice at 30–60 days of age following genotyping for the normal and mutant cftr alleles. The 3 cmH2O PEEP curves obtained for each genotype are presented in Figure PV curves all begin at V = 0 ml, which is the FRC defined by the 3 cmH2O PEEP. The PV curves obtained at PEEP levels of 0 and 6 cmH2O were similar.Routine evaluation of dynamic lung function employs the stepwise variation in air volume on both the inflation and deflation phases of a single breath. Measurement of airway pressures at each step results in the classic pressure-volume (Cst) of the lungs, which reflects elastic recoil at a given pressure, was calculated from the slopes of the PV curves. As shown in Figure Cst was significantly reduced in both cftr+/+ (p < 0.001) and cftr-/- (p < 0.001) as compared to age-matched cftr+/- mice. Hysteresis was altered among the 3 genotypes  and cftr+/+ mice (p < 0.05) compared to cftr-/- mice. These data suggest the presence of a gene dosage effect in which an altered lung structure in the heterozygous animals leads to an elevated compliance relative to the two homozygous animals. Lung weights were measured and no statistically significant differences were observed among the three genotypes  and determined values for airway resistance (Raw), tissue damping (G), and tissue elastance (H). Figure Raw, G, and H were significantly reduced in the cftr+/- mice as compared to both cftr+/+ and cftr-/- animals (Panels A, C, & D) at all PEEP levels. Raw also decreased with PEEP in a similar fashion in all three genotypes. In contrast, Raw, H and G were significantly increased in the cftr-/- mice compared with cftr+/+ and cftr+/-, and showed a greater dependence on PEEP (Panels A, C & D). The ratio G/H, termed hysteresivity (both the low and high frequency), was not significantly affected by either genotype or PEEP (Panel E). Thus, the absence of either 1 or 2 copies of the cftr gene had significantly different effects on the phenotype of the lung. Paradoxically, the absence of only one cftr copy resulted in a greater lung compliance (lower elastance) than if neither or both copies were present.We applied the forced oscillation method to the mice and determined respiratory mechanical input impedance ,11. We fcftr knockout mouse as a model of cystic fibrosis has been severely limited by its failure to demonstrate readily measurable lung disease, the primary cause of morbidity and mortality in humans [cftr in the lung of young adult animals resulted in decreased Cst and η and increased Raw, G and H as compared to normal littermate controls.The usefulness of the n humans . HoweverCst and hysteresis in cftr+/- mice was significantly higher than in cftr+/+ animals while G and H were decreased. As this was not associated with any pathology such as emphysema, we conclude that it represents a functionally different lung from that of the cftr+/+. Our data thus reveal a remarkable inverse correlation between the effect of one and two non-functional copies of the cftr gene.A particularly intriguing further observation was that η[cftr is a chloride channel and is thought to be involved in water balance, a change in surface tension in the lung, and consequently in η, might be expected. However, as shown in Figure G and H both increase, they do so in the same proportion so there is no significant change in η between the three cftr genotypes. On the other hand, lung weights were not different among the different groups of mice, so the decreased compliance and increased resistance of the cftr-/- animals was not simply due to their having smaller lungs than control animals. This suggests that the parenchymal structure in the lungs of the homozygous and heterozygous animals were organized differently, in a manner that affected G and H similarly.What do these data mean in terms of lung structure? The knockout animal has cystic fibrosis by definition, and our data now show it to also have lung disease manifest as a reduced compliance and increased resistance. These changes could reflect changes in the intrinsic mechanical properties of the parenchyma, or simply a reduction in lung volume. The former effect could include alterations in the biophysical properties of the air-liquid interface in the lungs, and would be expected to result in a change in η. Indeed,cftr levels are highest in the developing lung and decrease 75-fold at birth. In utero over-expression of cftr has also been shown to affect lung growth and development[cftr may affect the early development of the lung in a manner that is affected by the interaction of other genes.As documented in numerous publications, the mouse strain used in the present study does not develop chronic inflammatory disease up to the age (30–60 days) used in this study (for review see ). On thevelopment, and thevelopment. These dcftr animals? Interestingly, there is no decrement in lung function in human heterozygotes [cftr, something we term a "Goldilocks Effect". That is, while two defective copies of the gene are detrimental and two normal copies are satisfactory, one normal and one defective gene may results in an optimal dosage for lung development.Are there any functional consequences for increased lung compliance in the heterozygous ozygotes -21. Alsoozygotes ,23. Indeozygotes . When onozygotes  is perhaozygotes . The rescftr knockout mice should reveal additional genetic loci that modulate the influence of cftr on lung growth and development. Corresponding studies in humans should be useful in evaluating the effect of therapies on reversing altered pulmonary function in the CF patient.Further studies of pulmonary mechanics in cftr knockout mouse. In addition, the cftr+/- mouse had a distinguishable pulmonary function phenotype from that observed in either the homozygous normal or mutant genotype mice. These data are consistent with CFTR-dependent, physiologic changes in the structure and function of the lung.Using sophisticated techniques to a evaluate rodent pulmonary function; a distinct, readily quantifiable lung phenotype was identified in the th generation backcross to C57Bl/6 has been maintained by random mating for the past 8 years. This colony has a 100% mortality rate among cftr knockouts by 45 days of age unless the animals are placed on an elemental liquid diet and corncob bedding upon weaning [cftr alleles. Age and litter matched cftr+/+ and cftr+/- were used for each cftr-/- mouse examined. Six animals were included in each group. All experiments were approved by the animal care and use committee.The S489X mouse 5 weaning . Mice 30flexiVent, SCIREQ Inc. Montreal, PQ, Canada) and ventilated with a tidal volume of 10 ml/kg; inspiratory:expiratory ratio of 66.67%, respiratory rate of 150 breaths/minute, and maximum pressure of 30 cmH20. PEEP was controlled by submerging the expiratory limb from the ventilator into a water trap. Each animal was paralyzed with pancuronium bromide (0.5 mg/kg) and allowed to equilibrate on the ventilator until spontaneous breathing ceased (5 minutes).The mice were anesthetized with intra-peritoneal pentobarbital (90 mg/kg) and the trachea was dissected free of surrounding tissue and cannulated with a 20-gauge cannula. The animals were then connected to a small animal ventilator , i is the imaginary unit, α links G and H, and f is frequency. We also calculated a quantity known as hysteresivity (η = G/H), which is believed to increase when regional heterogeneities develop in the lung [Raw, G and H were all normalized by multiplication by lung weight.where g airways, Iaw is the lung . Raw, G FRC defined by the PEEP, the flexiVent was programmed to deliver seven inspiratory volume steps for a total volume of 0.8 ml followed by seven expiratory steps, pausing at each step for 1 s. Plateau pressure (P) at each step was recorded and related to the total volume (V) delivered to produce a quasi-static PV curve. Cst was calculated from the slope of each curve [Starting at the ch curve , and wasZrs measurements at each PEEP level and PV curves were obtained in triplicate. Data were statistically evaluated using paired t-test with p < 0.05 being taken as significant


Caenorhabditis elegans retroviruses, are class II viral fusion proteins. Comparisons of divergent viral fusion proteins can reveal features essential for virion:cell fusion, and suggest drug and vaccine strategies.The Bunyaviridae family of enveloped RNA viruses includes five genuses, orthobunyaviruses, hantaviruses, phleboviruses, nairoviruses and tospoviruses. It has not been determined which Bunyavirus protein mediates virion:cell membrane fusion. Class II viral fusion proteins (beta-penetrenes), encoded by members of the Alphaviridae and Flaviviridae, are comprised of three antiparallel beta sheet domains with an internal fusion peptide located at the end of domain II. Proteomics computational analyses indicate that the carboxyl terminal glycoprotein (Gc) encoded by Sandfly fever virus (SAN), a phlebovirus, has a significant amino acid sequence similarity with envelope protein 1 (E1), the class II fusion protein of Sindbis virus (SIN), an Alphavirus. Similar sequences and common structural/functional motifs, including domains with a high propensity to interface with bilayer membranes, are located collinearly in SAN Gc and SIN E1. Gc encoded by members of each Bunyavirus genus share several sequence and structural motifs. These results suggest that Gc of Bunyaviridae, and similar proteins of Tenuiviruses and a group of  Two classes of viral envelope proteins that mediate virion:cell fusion have been described. Class I and II fusion proteins (aka α-and β-penetrenes) are distinguished, in part, by the location of the "fusion peptide," a cluster of hydrophobic and aromatic amino acids that appears critical for fusing viral and cell membranes. The fusion peptides of class I fusion proteins are located at or near the amino terminus, whereas fusion peptides of class II fusion proteins are internal. The overall structures of these two classes of viral fusions proteins are also distinct. Class I fusion proteins have a pair of extended α helices that are separated by sequences variable in length, but usually containing one or more dicysteine linkages. Several otherwise disparate viruses, including orthomyxoviruses, paramyxoviruses, retroviruses, arenaviruses, filoviruses and coronaviruses encode class I fusion proteins -4. ClassCaenorhabditis elegans retroviruses, are class II viral fusion proteins (β-penetrenes).The Bunyaviridae family of enveloped RNA viruses includes five disparate genuses. Orthobunyaviruses, phleboviruses, nairoviruses and tospoviruses are spread by insect vectors, whereas hantaviruses are spread by rodent vectors . MembersCaenorhabditis elegans, including Cer13 . We also compared Bunyavirus M sequences to structural proteins of Flaviviruses, including members of the flavivirus genus tick-borne encephalitis virus, strain Neudoerfl (TBEV, P14336); Japanese encephalitis virus, strain JaOARS982 (JEV, P32886), yellow fever virus, strain 17D-204 (YFV, P19901), dengue virus type 2, strain PR-159/S1 (DEB, P12823), and West Nile virus, strain NY 2000-crow3356 (WNV, AF404756). The prototype hepaciviruses, strain H (subtype 1a) of hepatitis C virus (HCV, P27958), and several pestiviruses, including the Alfort 187 strain of classical swine fever virus, aka hog cholera virus (CSFV, CAA61161), bovine viral diarrhea virus genotype 1 aka pestivirus type 1, stain NADL (BVDV, CAB91847) and border disease virus, strain BD31 (BVD, AAB37578), were used in other comparisons.For sequence and structural comparisons of Bunyavirus M encoded proteins representatives of the five genuses were used, including pleboviruses Sandfly fever virus, Sicilian strain (SAN, accession number: AAA75043) and Rift Valley fever virus (RVF, P03518), orthobunyavirus Bunyamwera virus (BUN, NP047212), hantavirus Hantaan virus, strain 76–118 (HAN, P08668), nairovirus Crimean-Congo hemorrhagic fever virus, strain IbAr (CCHF, NP950235), and tospovirus tomato spotted wilt virus, ordinary strain (TSWV, NP049359). Additional phlebovirus M sequences compared included those of Uukuniemi virus (UUK, NP941979) and Punta Toro virus (PTV, VGVUPT). Bunyavirus M sequences were compared to sequences encoded in Alphavirus subgenomic RNA, including structural proteins of Sindbis virus (SIN, P03316), Semliki Forest virus (SFV, NP463458), Venezuelian equine encephalitis virus, strain TC-83 (VEE, P05674), Western equine encephalitis virus, strain McMillan (WEE, AAF60166), O'nyong-nyong virus, strain GULU (ONN, P22056), Mayaro virus, strain TRVL4675 (MAY, AA033335), Barmah Forest virus strain BH2193 (BFV, AA033347) and Ross River virus, strain NB5092 (RRV, NP740686). Comparisons were also made with proteins encoded by RNA2 of the HZ isolate of Rice stripe virus, a Tenuivirus (pvc2, AA031607), and with certain retroviruses of ) was the preferred method of secondary structure prediction [). TMpred is based on a statistical analysis of TMbase, a database of naturally occurring transmembrane glycoproteins [Xplorer version 2.2a from the Stephen White laboratory using default settings [ was used to predict mucin type GalNAc O-glycosylation sites. RasMac , developed by Roger Sayle, was used to render a 3D model of SFV E1, which was extrapolated to SIN E1 and SAN GC.Methods to derive general models of surface glycoproteins have been described previously . PRSS3, ediction . PHDsec proteins . Sequencsettings . The NetSimilar sequences or common structural/functional motifs are located collinearly in the carboxyl terminal glycoprotein of Sandfly fever virus and Sindbis virus envelope glycoprotein E1.Previously, Gallaher and coworkers modeled the structure of the retroviral transmembrane glycoprotein (TM)  onto thePrior X-ray crystallographic studies have demonstrated that SFV E1 is a class II viral fusion protein (β-penetrene) . BecauseTo provide support for the proposed alignment of SAN Gc and SIN E1, another proteomics computational tool was used to compare potential membrane interactive domains in the glycoproteins. Besides fusion peptides, a motif that can be important in virus:cell fusion and is present in many class I and class II viral fusion proteins is an aromatic aa rich domain proximal to the transmembrane anchor . The preRecently, Gibbons and coworkers determined the structure of a fragment of the SFV E1 ectodomain  after exposure to low pH and liposomes . Under tThere are many possible alternatives to the cysteine linkages and secondary structures of SAN Gc drawn in Figure -18). As noted previously [-8). As with phlebovirus Gc, the prototype of the hantavirus genus, Hantaan virus (HAN), showed a modest sequence alignment (p < 0.05) with SIN E1, further supporting the proposed similarities between Bunyavirus Gc and Alphavirus E1. Significant alignments were not detected between Bunyavirus Gc or Alphavirus E1 and TBEV E or other flavivirus class II viral fusion proteins. Limited local similarities were observed between some Bunyavirus Gc and pestivirus E2. It is noteworthy that the significance of the overall sequence similarities between certain phlebovirus Gc and Alphavirus E1 is higher than some similarities among Gc of some prototypic members of the Bunyaviridae . These results further validate the use of the PRSS3 algorithm to identify limited similarities amongst viral proteins. Alignment of the carboxyl terminal portion of the pvc2 protein of rice stripe virus (RiSV), a Tenuivirus, and the envelope protein encoded by Cer13 retrovirus with two phlebovirus Gc reveals a collinear arrangement of fusion peptide consensus sequences . HAN Gn also showed a significant alignment with Gn of RVF, a phlebovirus. Both HAN Gn and Gn of TSWV, a tospovirus, also showed significant alignments with envelope protein 2 (E2) of SIN. SIN E2 has been implicated as the virion protein responsible for binding to the cell surface receptor [The longest open reading frames of M segments of all members of the Bunyaviridae are antisense to the virion RNA. mRNAs transcribed from Bunyavirus M segments are translated into large polyproteins that are subsequently cleaved by into functional proteins ,43. Gc oreceptor . These rThe simplest M polyprotein, encoding only Gn and Gc, is that of hantaviruses Fig. . In addiC. elegans retroviruses previously shown to have remarkable sequence similarities to phlebovirus Gc. These results also indicate that Gallaher's "Rosetta Stone" strategy can be used to identify potential class II viral fusion proteins, as demonstrated previously for class I fusion proteins [Proteomics computational analyses suggest that Bunyavirus Gc proteins are class II viral fusion proteins (β-penetrenes), with a structure similar to the fusion proteins of Alphaviruses and Flaviviruses. Similar sequences or common structural/functional motifs are collinearly located in Bunyavirus Gc and Alphavirus E1. Features common to other class II fusion proteins, including an internal fusion peptide, a carboxyl terminal transmembrane domain and regions with a high propensity to interface with bilayer membranes, are conserved and in similar locations in Gc of viruses in each genus of the Bunyaviridae. These features are also present in glycoproteins encoded by nonenveloped Tenuiviruses of plants, and a group of proteins ,49. The Many viral fusion proteins fit neither class I or II and it is likely that other classes of viral fusion protein also exist. However, among major classes of enveloped RNA viruses, there are at least six, myxoviruses, retroviruses, paramyxoviruses, filoviruses, arenaviruses and coronaviruses, that encode class I viral fusion proteins -4. AlphaAlphaviruses appear to use separate envelope proteins for fusion (E1) and attachment (E2) . BecauseThe remarkable similarities in both the pre- and post-fusion forms of the fusion proteins of SFV E1, an Alphavirus, and DEN and TBEV, members of the flavivirus genus of the Flaviviruses, in the absence of detectable sequence similarities, suggest that Alphavirus and Flavivirus class II fusion proteins may have diverged from a common progenitor. Alternatively, there may have been convergent evolution towards the common structure. Likewise, the sequence similarities detected between phlebovirus Gc and SIN E1 are consistent with divergent evolution from a common progenitor, but are insufficient to directly establish a phylogenic relationship. The results presented here suggest that Gc of members of the Bunyaviridae may have a common ancestor. Gn and Gc are in analogous locations in the polyproteins encoded by the five genuses of the Bunyaviridae. The simplest Bunyavirus M polyprotein, that of hantavirus members, encodes only Gn and Gc, whereas M of members of other Bunyavirus genuses encode several additional proteins. Therefore, divergence of Bunyavirus M segments may have occurred either through acquisition of sequences and/or lose of sequences in a cassette manner constrained in part by the locations of the major glycoproteins.Comparisons of divergent viral fusion proteins with internal fusion peptides can reveal features essential for virion:cell fusion. Regions of high membrane interfacial propensity including the fusion peptide and the transmembrane anchor, appear in similar locations in Bunyaviruses, Alphaviruses and Flaviviruses. The presence of several additional sequences with the propensity to interact with bilayer membranes in class II viral fusion proteins has not been considered in previous virion:cell fusion models ,11,54. CCurrent fusion models do not consider that the transmembrane domain and fusion peptide, while anchored into the viral and cellular membranes, would still be free to move laterally without distorting the membranes. More importantly, the virion is quite small compared to the cell, and would be freely mobile. Rearrangement of the fusion proteins may simply draw the virus closer to the cell without distorting either the viral or cellular membranes. An alternative to the models involving apposing membrane nipple formation is suggested by the observation that sequences of class II viral fusion proteins, including the fusion peptide, the transmembrane anchor and other sequences with high WWIHS scores, potentially form a nearly continuous track of membrane interactive regions that could channel the movement of lipids during virion:cell fusion Fig. , black. In the absence of structural determinations by X-ray crystallography, models such as proposed here can provide useful hypotheses to guide experimental strategies for development of vaccines or drugs to prevent or treat infection by viruses with class II fusion proteins. Prior to the availability of X-ray structural data, several potent HIV-1 TM inhibitors were developed ,57 basedThe authors declare that they have no competing interests.CEG performed the sequence alignments and assisted in the preparation of figures. RFG supervised the work and wrote the manuscript.


Grafting is a powerful but complex means to study the spread of RNA silencing Dactylosphera vitifoliae devastated European grapewine varieties over the course of the late 1800s and early 1900s, the varieties were saved by grafting them onto resistant rootstocks from the New World. Since then, these rootstocks have been used to maintain the susceptible Old World cultivars. But grafting is also an excellent tool for scientists studying systemic signals traveling between the rootstock and distal parts of the plants, and vice versa. For example, two important studies  refers to the phenomenon whereby specific gene transcript levels are reduced in the presence of a related RNA. From studies of RNA silencing in several systems, much is now known about the mechanisms involved , but theAs expected, the discovery of this process triggered a quest for the “systemic inducer” of the process: a signal that travels through the plant and is able to initiate silencing in a remote location within the plant. Grafting was an obvious tool to use in the quest for this signal, as it allowed silencing source and sink tissues to be of different origin.Nia. Some of the transgenic lines generated always showed higher levels of Nia transcripts than the wild type—as expected from the presence of an additional gene—and were termed class I lines. However, other transgenic lines had undergone silencing for both the endogenous and exogenous Nia genes and those were termed class II lines. Nia transgene, which has an endogenous counterpart in the wildtype plant, green fluorescent protein has no homolog in nontransgenic lines. Therefore, silencing spreading in the wild-type “spacer” in the sandwich grafts could not be assisted by an endogenous sequence. Rather, the systemic signal must have traveled all the way to the scion and induced gene silencing there. The establishment of systemic silencing took 4 wk in the “direct” grafts and 6 wk in the sandwich grafts. However, silencing spread to the scion only in some of the grafts: in ten out of 16 direct grafts and five out of 11 sandwich grafts.Related experiments by I found these papers were very important not only for what they proved—the existence of a systemic signal of silencing—but also because they gave an unequivocal answer to the scientific questions they posed, using relatively simple methodology. Although excited by these successful examples of the transmission of silencing, I kept coming back to two questions: (1) what prevents transmission in some of grafts, and (2) why does it take longer to transmit silencing to the scion than it takes systemic silencing to reach the most remote parts of an intact plant?When we started working on the silencing signal ourselves, we repeated some of the above experiments but found somehow lower efficiencies in the initiation of silencing in the scions. We soon realized that our results were influenced by the developmental stage of our scions. A paper from Jeff Meins's laboratory in Switzerland shed light on some aspects of the grafting puzzle. Researchers there introduced additional chitinase genes using bolistics, in sense or antisense orientation under the control of a strong promoter (35S) into chitinase transformant lines of tobacco that never exhibited spontaneous gene silencing . In lineSurprisingly, only top grafting resulted in scions that were systemically silenced by a rootstock signal. Furthermore, even transmission after top grafting was less effective than expected; in one stock/scion combination only 27 out of 71 grafts exhibited transmission of the silencing signal. The authors also found that antisense-induced silencing was never transmitted to the scion. These findings do not answer the many questions about the mechanisms underlying systemic silencing, but they point us in certain directions.The individual parts of a whole plant are, in terms of import and export, in an equilibrium that changes with development. When grafting takes place, how this equilibrium is altered depends on the individual “parts” that contribute to the “new” whole plant. In addition, there is now indirect evidence  that whaFrom the grafting experiments to date, it is now evident that the transporting capacity of the vascular tissue bypass that is formed at the graft junction does not fully reach the level of the original vascular tissue. The basis for these restrictions is not known. In a way, the graft interface functions as an unintentional filter. If the specificities of this “filter” were known, it would help us comprehend some transmission inconsistencies. Keeping in mind these limitations, grafting remains an invaluable tool in the search for the systemic silencing signal.


Tamoxifen is being used successfully to treat breast cancer. However, tamoxifen also increases the risk of developing endometrial cancer in postmenopausal women. Raloxifene also decreases breast cancer in women at high risk and may have a lower risk at developing cancer of the uterus. Tamoxifen has been shown to stimulate arachidonic acid release from rat liver cells. I have postulated that arachidonic acid release from cells may be associated with cancer chemoprevention.3H] arachidonic acid. The release of the radiolabel from these cells during incubation with tamoxifen and the raloxifene analog LY117018 was measured. The prostaglandin I2 produced during incubation of the rat liver cells with μM concentrations of tamoxifen and the raloxifene analog was quantitatively estimated.Rat liver, rat glial, human colon carcinoma and human breast carcinoma cells were labelled with [2 production and that induced by lactacystin and 12-O-tetradecanoyl-phorbol-13-acetate. LY117018, however, blocks the tamoxifen stimulated prostaglandin production. The stimulated prostaglandin I2 production is rapid and not affected either by preincubation of the cells with actinomycin or by incubation with the estrogen antagonist ICI-182,780.Tamoxifen is about 5 times more effective than LY117018 at releasing arachidonic acid from all the cells tested. In rat liver cells only tamoxifen stimulates basal prostaglandin ITamoxifen and the raloxifene analog, LY117018, may prevent estrogen-independent as well as estrogen-dependent breast cancer by stimulating phospholipase activity and initiating arachidonic acid release. The release of arachidonic acid and/or molecular reactions that accompany that release may initiate pathways that prevent tumor growth. Oxygenation of the intracellularly released arachidonic acid and its metabolic products may mediate some of the pharmacological actions of tamoxifen and raloxifene. The successful treatment and prevention of estrogen-dependent breast cancer in women by tamoxifen is attributed to its estrogen receptor (ER) occupancy [reviewed in ,2]. In tTamoxifen and raloxifene have several properties in common; e.g. prevention of tumors in the DMBA induced rat mammary model, maintenance of bone density in the ovariectomized rat and reduction of low density lipoprotein cholesterol. The partial estrogen agonist activity of tamoxifen on uterine tissue, however, increases the risk of developing endometrial cancer. This does not appear to occur with raloxifene.2 production by the rat liver cells. Although both compounds release AA from these cells, LY117018 is less effective. Only tamoxifen stimulates both basal and PGI2 production induced by incubation of rat liver cells with lactacystin in the presence of 12-O-tetradecanoyl-phorbol-13-acetate (TPA). LY117018, however, inhibits the PGI2 production stimulated by tamoxifen.Tamoxifen stimulates arachidonic acid release from rat liver cells . In thisThe intracellular release of AA and/or the cellular reactions that accompany that release may initiate pathways that prevent tumor growth. The tissue specific effects of tamoxifen and LY117018 may be associated with the AA or with cyclooxygenase (COX) activity and/or one of the many bioactivities resulting from oxygenation and metabolism of the released AA.3H]AA (91.8 Ci/mmol) was purchased from NEN Life Science Products, Inc. (Boston, MA, USA); ICI-182,780 from Tocris Cookson, Inc. ; tamoxifen and 4-OH-tamoxifen were from Sigma Chemical Co. (St. Louis, MO, USA). LY117018 was obtained from Dr. David A. Cox, Eli Lilly and Co. (Indianapolis, IN, USA). Raloxifene was extracted from EVISTA® tablets with dimethylsulfoxide.The C-9 rat liver and BT-20 human breast carcinoma cells were purchased from the American Type Culture Collection (Manassas, VA, USA) and maintained in MEM supplemented with 10% fetal bovine serum. The C-6 rat glial cell line was obtained from Dr. Elaine Y. Lai in the Department of Biology, Brandeis University and maintained in medium 199. The human colon carcinoma cells (HT-29) were obtained from Dr. Basil Rigas, American Health Foundation, Valhalla, NY and maintained in McCoy's medium. [5 cells/35 mm dish. The freshly seeded cultures were incubated for 24-h to allow for cell attachment. After decantation of incubating media, 1.0 ml fresh media  containing 10% fetal bovine serum and [3H] AA (0.2 μCi/ml) was added and the cells incubated for 24-h. The cells were washed 4 times with MEM and incubated for various periods of time with 1.0 ml of MEM, medium 199 or McCoy's containing 1.0 mg bovine serum albumin (BSA)/ml and different concentrations of each test compound. The culture fluids were then decanted, centrifuged at 2000 × g for 10 min, and 200 μl of the supernate counted for radioactivity. Radioactivity recovered in the washes before the 6-h incubation was compared to input radioactivity to calculate the % radioactivity incorporated into the cells [Two days prior to experiments, the cells were treated with 0.25% trypsin-EDTA and, after addition of minimum essential medium (MEM), medium 199 or McCoy's medium containing 10% fetal bovine serum, the floating cells were seeded onto 35 mm culture dishes. The plating densities varied from 0.1 to 0.5 × 103H] released from radiolabelled methylcholanthrene transformed fibroblasts (about 20% after a 3-h stimulation by serum), 92% was AA, 4% was PGE2, 0.6% and 1% were PGF2α and phospholipids respectively [3H] AA had been incorporated into phosphatidylcholine (50%), phosphatidylethanolamine (36%), phodphatidylserine (9%) and triglycerides (10%) [3H] AA label released from human colorectal cancer cell lines (HCT116 and SW180) was AA as measured by TLC [As measured by thin layer chromatography (TLC), of the [es (10%) . Such dies (10%) . The [3H2 production by the rat liver cells, 1.0 ml of MEM supplemented with 10% fetal bovine serum, lacking [3H] AA, was added after the first 24-h incubation. The cells were incubated for another 24-h, washed three times with MEM, then incubated with lactacystin and TPA in the presence and absence of the test compounds in MEM/BSA for various periods of time. The culture fluids were decanted and analyzed for 6-keto-PGF1α, the stable hydrolytic product of PGI2, by radioimmunoassay [4 or less per dish) only the major COX product of rat liver cells (97% 6-keto-PGF1α) could be measured. The other two COX products, PGE2 (2%) and PGF2α (1%) could not be measured.For PGI3 H] AA is presented as a percentage of the radioactivity incorporated by the cells. Except for the time-course experiments, which used duplicate dishes  would be 0.25 nM (the specific activity of the [3H] AA is 92 Ci/mmole). Since TLC or HPLC analyses were not done, the concentration of the total released AA could not be quantitatively estimated. However, even treatment of a colorectal cancer cell line with 200 μM AA for 48-h leads to apoptosis [The highest level of [The release of AA is neither species nor tissue specific. Both drugs also release AA from human breast carcinoma (BT-20) and human colon carcinoma (HT-29) cells Fig.  and 2-B.2 production by these cells were determined. Tamoxifen stimulates basal PGI2 production as well as that induced by lactacystin in the presence of TPA. LY117018 and raloxifene , inhibits the PGI2 production stimulated by lactacystin in the presence of TPA  ng 6-keto-PGF1α per ml in the absence and presence of tamoxifen, respectively]. Similar to AA release [2 production by tamoxifen is not affected by preincubation of the cells for 2-h with 1 μM actinomycin D. The induction obtained by incubation with lactacystin in the presence of TPA is inhibited  of basal activity is shown in Fig.  μM of ba release , stimulated Fig. . Stimula780 Fig. . This es, GW7845 . The rap2 production stimulated by tamoxifen  or Aramidex ® (anastrozole) do not release AA from cells in culture (unpublished data). These two drugs inhibit estrogen synthesis by blocking aromatase enzymes and also prevent estrogen-dependent breast cancer [These studies demonstrate that, at μM concentrations, tamoxifen and the raloxifene analog, LY117,018 stimulate the release of AA from cells in culture. At nM levels, the release is not observed. The effectiveness of these two compounds at prevention of estrogen-dependent breast cancer reflects competition for the ER . In addit cancer . In view2+ ionophore A-23187 reflects phospholipase activities. It is regulated by phosphorylation of the enzyme [A mechanism that most simply explains the release of AA by tamoxifen and LY117018 is the ability of such compounds to intercalate into cell membranes and affect phospholipase activities. The release of AA from endothelial cells by the Cae enzyme . The enze enzyme  and woulThe AA release by tamoxifen and other reagents studied in my laboratory occurs with μM concentrations ,19,23,24When measured after a 6-h incubation, 10 μM tamoxifen stimulates deesterification of membrane phospholipids as measured by extracellular AA release, Fig. . The 6-h2 produced by the rat liver cells can be quantitatively determined. The effects of tamoxifen and LY117018 on the COX activity of the rat liver cells may be similar to their effects on other cells that express COX. Tamoxifen and LY117018 affect COX activity differently; only tamoxifen stimulates PGI2 production. AA, which regulates the production of lipoxygenase, COX and cytochrome P-450 epoxygenase products could impact many of the pharmacological actions of these two selective estrogen receptor modulators. AA can be oxygenated by COX isoforms, lipoxygenases, and cytochrome P-450 epoxygenases and their products converted to the prostaglandins, leukotrienes, epoxyeicostetranoic acids [2 to the major physiologically active products, PGD2, PGE2, PGF2α, PGI2 and thromboxane A2 [Some tissue specific effects of tamoxifen and raloxifene, e.g. on endometrial cells of the uterus, may be related to COX activity. At the low cell densities used in the present studies, the PGIic acids  and AA tic acids . In the oxane A2 . They, ioxane A2 ,31.None declared.L.L. is the sole author and all of the experiments, with the exception of the western blots referred to as Levine L and Tashjian A (unpublished data), were conducted by L.L.The pre-publication history for this paper can be accessed here:


Cervical cerclage is a surgical procedure involving suturing the cervix with a purse type stitch to keep it closed during pregnancy. This procedure has been used widely in the management of pregnancies considered at high risk of preterm delivery. Several observational studies into the efficacy of cervical cerclage have claimed high rates of successful pregnancy outcome in women with a poor obstetric history attributed to cervical incompetence. However, a recent aggregate data Cochrane review found no such conclusive evidence from seven included randomised studies. Current data suggests that cervical cerclage is likely to benefit women considered to be 'at very high risk' of a second trimester miscarriage due to a cervical factor, however identifying such women remains elusive and many women may be treated unnecessarily. Undertaking an individual patient data (IPD) meta-analysis of the studies will allow us to investigate whether treatment is more effective in particular subgroups. Such an analysis will also provide a more powerful analysis of the predictors of preterm delivery and pregnancy loss, including ultrasound measurement of cervical length, and will allow a more complete analysis of 'time to event' outcomes.The analysis will include data from randomised trials comparing the intervention of elective cerclage versus no cerclage or bedrest to prevent miscarriage or pre-term labour. A specific list of data will be requested for each trial, including demographic and obstetric history data. The primary outcomes of interest will be neonatal mortality/morbidity. Attention will also be given to secondary outcomes such as time from randomisation to delivery, preterm delivery before 32 weeks and maternal morbidity. An intention to treat analysis will be performed, with attention paid to assessing clinical and statistical heterogeneity. Multilevel models with patients and trials as the two levels will be explored to investigate treatment effect on various outcomes. Patient-level covariates will be incorporated into the models in an attempt to account for statistical heterogeneity as well as to investigate interactions with treatment effect.Predictive models generated from our analysis should lead to more effective counselling of women at risk and a more cost effective use of cerclage. Cervical cerclage is a surgical procedure carried out during pregnancy. The operation involves suturing the neck of the womb (cervix) with a purse type stitch to keep the cervix closed. This surgical procedure has been used widely in the management of pregnancies considered to be at high risk of preterm delivery.Several observational studies in the last 50 years have claimed high rates of successful pregnancy outcome in women that had a poor obstetric history attributed to cervical incompetence. However, a recent Cochrane review found no conclusive evidence from seven included randomised studies that inserting a cervical stitch in women perceived to be at risk of preterm birth or second trimester pregnancy loss attributed to cervical factors, reduces the risk of pregnancy loss, preterm delivery or morbidity associated with preterm delivery (Drakeley 2003).In the Cochrane review, the data for important clinical outcomes including preterm delivery and maternal infection showed significant heterogeneity due to inconsistency in clinical definitions used, including the cut off gestational age defining preterm delivery, and different patient populations studied.Practically, methods of undertaking a meta-analysis of several studies may involve collecting either aggregate data, or data on each patient individually. The advantages of the latter approach, described as the 'yardstick'  include One of the main concerns regarding current evidence related to cervical cerclage and other interventions for preventions of preterm delivery is a possibility that the 'primary outcomes' may have been selected to give results in greatest accord with the a priori beliefs of the authors. The evidence to support this phenomenon of within-study selective reporting comes from empirical research, which demonstrates discrepancies between research protocols and subsequent publications . A regreThe aim of this project is to undertake an IPD meta-analysis of randomised trials of cervical cerclage. Specific objectives are as follows.1. To estimate the effect of cervical cerclage on gestational age at delivery.2. To investigate whether cervical cerclage is more likely to prevent extreme prematurity (<28 weeks) or delivery at later gestations.3. To investigate risk factors for preterm delivery.4. To investigate interactions between risk factors and cervical cerclage.5. To model the effect of cervical cerclage and other risk factors on neonatal and maternal morbidity.The types of studies considered for inclusion in the analysis will be all randomised trials comparing cervical cerclage with expectant management or no cerclage during pregnancy. The previous Cochrane review (Drakeley 2003)  identifiThe data collected in the studies will relate to women with confirmed, or suspected of having, cervical incompetence who desire future pregnancies and women who present as an emergency and are thought to have a diagnosis of cervical incompetence. The intervention investigated in the studies will be elective cerclage by whichever method , versus no cerclage or bed rest as interventions to prevent miscarriage or pre-term labour as defined in the original Cochrane review (Drakeley 2003).The methods of trial identification described in the original Cochrane review (Drakeley 2003)  were excluded in the original review.The following data for each woman/infant pair will be requested from all trials: date of randomisation and gestational age, maternal demographics and obstetric characteristics at randomisation including cervical length on ultrasound, fibronectin and bacterial vaginosis data if available, treatment allocated, complications during pregnancy including ruptured membranes, maternal pyrexia or chorioamnionitis, date of delivery, gestational age at delivery and all neonatal data including birthweight, length of stay at NICU and morbidity related to prematurity.The following methodological data will also be requested for all trials: method of generation of randomisation list, method of concealment of randomisation, stratification factors and blinding methods.Data will be accepted either in electronic (floppy disk/CD/internet) or paper form. A desired format and coding will be specified but trialists may supply data in the most convenient way open to them, providing details of coding are sent with the data.A copy of the original data sent (before checking) will be held in a separate file. The following procedures will then be performed and documented for all trial data supplied. Trial details will be crosschecked against any published report of the trial. Range and consistency checks will be applied – missing data, errors and inconsistencies will be followed up with a nominated individual. The chronological randomisation sequence will be reviewed. The balance of prognostic factors will be checked, taking into account of factors stratified for in the randomisation procedure.The primary outcome of interest will be neonatal mortality/morbidity. Choice of primary outcome is about what should determine clinical decision-making. However it is recognised that trials to date may have insufficient power and there is a need to consider secondary outcomes of time from randomisation to delivery, preterm delivery before 32 completed weeks (<32+0 weeks) and maternal morbidity as defined in the Cochrane Review (Drakeley 2003). We willReporting of these outcomes in the original trial report is not an eligibility requirement for this review.Data on all randomised patients will be requested to perform an intention-to-treat analysis as far as possible. Clinical heterogeneity will be assessed by reviewing the differences across trials in characteristics of randomised patients.Initially, an aggregate data analysis will be undertaken although treatment effect estimates will be obtained from the individual patient data. Binary outcomes will be summarised in terms of odds ratios or relative risks, depending on the degree of heterogeneity observed. Time-to-event outcomes will be summarised in terms of the log (hazard ratio). The I square statistic and chi-square test for statistical heterogeneity will be applied to these summary data.Regression models, stratified by trial, will be used to explore the effects of treatment, risk factors and treatment-covariate interactions on the various outcomes of interest. These will include Cox and accelerated life models for time-to-event outcomes  treatment effect across trials. Patient-level covariates (as listed above) will then be incorporated into the model in an attempt to account for some of the remaining statistical heterogeneity. An attempt will be made to incorporate these covariates first of all by assuming their effect to be constant across all trials and subsequently by assuming some heterogeneity in the covariate effect across trials by modelling them either as fixed or random effects. Finally, treatment-covariate interactions will be investigated by including additional variables and adopting a similar approach.If IPD are not available for some trials, the potential for bias will be investigated as follows. The reasons for not being able to obtain the data will be assessed for the potential for bias. Results using aggregate data from these trials will be compared with results using aggregate data from trials where IPD have been supplied, and any difference investigated. The analysis plan will be reviewed in light of the availability of IPD but prior to any comparative analyses.Predictive models generated by our analysis should allow more effective counselling of women at risk of preterm delivery and thus more cost effective use of cerclage.ZA and PRW were authors of a paper that will be included in the IPD meta-analysis (To, 2004). ZA was an author of the non-IPD systematic review on this topic (Drakeley, 2003). The autPRW conceived the idea, CTS drafted the initial protocol, and all authors commented on and approved this final version.The pre-publication history for this paper can be accessed here:


A 38-year-old woman presented with acute hematological toxicity from her anticonvulsants, even though she had been taking them for many years Six months prior to admission, her weight was 175 lbs, and her body mass index was 36. On admission, her complete blood count was normal, but over a 2-wk period she developed acute pancytopenia . During 6/l; and WBC, 10.7 × 106/l. After starting carbamazepine, her WBC dropped to 4.5–5.5 × 106/l, and her absolute neutrophil count dropped from 9 × 106/l to about 2.5 × 106/l. When the sodium valproate was added, her MCV increased to 112 fl, her platelet count fell to 100–150 × 106/l, and her Hb dropped to 110–120 g/l.She had been taking carbamazepine for 17 y and sodium valproate for 13 y for a mixed seizure disorder. At age 22 y, before starting any anticonvulsants, her baseline hematological parameters were as follows: Hb,123 g/l; MCV, 106 fl; platelets, 296 × 10The patient's other medications on admission were carnitine and low-dose L-thyroxine for hypothyroidism; she had been taking both for several years. She had not been recently exposed to any new medications, environmental toxins, or over-the-counter dietary supplements. There was no family history of aplastic anemia. The patient lived at home with her mother, who cares for her and has legal guardianship.A bone marrow biopsy  showed a6/l and her platelet count to 248 × 106/l. Serial complete blood counts showed worsening anemia over the next 15 wk, requiring two packed red blood cell transfusions at week 6 and week 14, when her Hb was 31 and 53 g/l, respectively. Throughout this period, her reticulocyte count was low, at about 0.18% , while her WBC and platelet count were normal. We made a diagnosis of pure red cell aplasia (PRCA). A second bone marrow biopsy under minimally conscious sedation was unsuccessful, and the patient declined further attempts. After the first transfusion she was started on prednisone and erythropoietin for her PRCA. Meanwhile, because of her worsening seizures, we increased her sodium valproate dose, increasing her serum valproate level from 70 mcg/ml to 110 mcg/ml. After the second transfusion, we discontinued her sodium valproate and started her on clonazepam and oxcarbazepine as alternative anticonvulsants.The patient's carbamazepine was discontinued. Within 10 d, her WBC had risen to 6.7 × 10The patient had a brisk reticulocyte response. Her reticulocyte count rose from 0.36% to 0.78% in the second week and to 6.61% in the fourth week after stopping the valproate. Six weeks after stopping the drug, her Hb was 125 g/l and her MCV was 106 fl, suggesting replacement of transfused red blood cells with newly formed red blood cells (which have a higher MCV than older transfused cells). Over the next 30 mo of follow-up, she had no relapse of her aplastic anemia.We were unable to identify a specific cause for the patient's anorexia and weight loss. We found no evidence of malignancy on admission or subsequent follow-up. Within 1 wk of stopping her sodium valproate, her appetite improved and she put on 15 lbs over the next 6 wk. By the fourth month after stopping the drug, she had gained 45 lbs.6/l; Hb, 147 g/l; MCV, 104.9 fl; and platelets, 262 × 106/l.We last saw the patient in August 2004. Her seizure control had worsened, and she had developed signs of early dementia. Her current anticonvulsants are clonazepam, oxcarbazepine, and zonisamide, and she continues taking synthroid. Her last complete blood count was stable: WBC, 6.4 × 10Sodium valproate and carbamazepine are associated with rare but potentially lethal hematological complications. There are five case reports of PRCA shortly after initiation of sodium valproate, with the longest interval between the initiation of therapy and the onset of aplasia being 2 y ,2,3,4,5.Our investigations ruled out most of the known causes of acute bone marrow suppression, making the anticonvulsants the most likely cause. Malnutrition was an unlikely cause: her WBC and platelet counts had recovered before any increase in appetite or weight gain; her body mass index was normal despite her weight loss; and her bone marrow iron stores were adequate and her serum folate and vitamin B12 levels were high, suggesting adequate nutrient supply.We considered and rejected the possibility of Down syndrome–associated aplastic anemia. There have been six case reports of this condition, and in all cases it occurred in young children, suggesting a genetic predisposition . Half ofThe brisk return of her WBC and platelet counts upon discontinuing carbamazepine, and her brisk reticulocytosis upon discontinuing sodium valproate, were both consistent with previous reports of hematological toxicity due to these drugs ,4,5,6,7.We found no cause for the patient's anorexia and weight loss, but her appetite returned and she gained weight after stopping the sodium valproate. There is a known association between this drug and anorexia .Acute hematological complications of anticonvulsant therapy can still occur after many years of therapy, so continued vigilance is warranted.Although sodium valproate is generally associated with increased appetite and weight gain, it can also be associated with anorexia, nausea, vomiting, and weight loss.