Dense time series of metabolite concentrations or of the expression patterns of proteins may be available in the near future as a result of the rapid development of novel, high-throughput experimental techniques. Such time series implicitly contain valuable information about the connectivity and regulatory structure of the underlying metabolic or proteomic networks. The extraction of this information is a challenging task because it usually requires nonlinear estimation methods that involve iterative search algorithms. Priming these algorithms with high-quality initial guesses can greatly accelerate the search process. In this article, we propose to obtain such guesses by preprocessing the temporal profile data and fitting them preliminarily by multivariate linear regression.The results of a small-scale analysis indicate that the regression coefficients reflect the connectivity of the network quite well. Using the mathematical modeling framework of Biochemical Systems Theory (BST), we also show that the regression coefficients may be translated into constraints on the parameter values of the nonlinear BST model, thereby reducing the parameter search space considerably.The proposed method provides a good approach for obtaining a preliminary network structure from dense time series. This will be more valuable as the systems become larger, because preprocessing and effective priming can significantly limit the search space of parameters defining the network connectivity, thereby facilitating the nonlinear estimation task. The intriguing aspect of profiles is that they implicitly contain information about the dynamics and regulation of the pathway or network from which the data were obtained. The challenge for the mathematical modeler is thus to develop methods that extract this information and lead to insights about the underlying pathway or network.The rapid development of experimental tools like nuclear magnetic resonance (NMR), mass spectrometry (MS), tissue array analysis, phosphorylation of protein kinases, and fluorescence labeling combined with autoradiography on two-dimensional gels promises unprecedented, powerful strategies for the identification of the structure of metabolic and proteomic networks. What is common to these techniques is that they allow simultaneous measurements of multiple metabolites or proteins. At present, these types of measurements are in their infancy and typically limited to snapshots of many metabolites at one time point , to swith NMR ,4, 2-d gwith NMR  or protewith NMR ), or to with NMR  that peret al. [i.e., the maximum deviation from steady state) as well as the slopes of the traces at the initial phase of the response. Torralba et al. [in vitro glycolytic system. Similarly, by studying a large number of perturbations, Samoilov et al. [In simple cases, the extraction of information can be accomplished to some degree by direct observation and interpretation of the shape of profiles. For instance, assuming a pulse perturbation from a stable steady state, Vance et al.  present a et al.  recentlyv et al.  showed tFor larger and more complex systems, simple inspection of peaks and initial slopes is not feasible. Instead, the extraction of information from profiles requires two components. One is of a mathematical nature and consists of the need for a model structure that is believed to have the capability of capturing the dynamics of the underlying network structure with sufficient accuracy. The second is computational and consists of fitting this model to the observed data. Given these two components along with profile data, the inference of a network is in principle a regression problem, where the aim is minimization of the distance between the model and the data. If a linear model is deemed appropriate for the given data, this process is indeed trivial, because it simply requires multivariate linear regression, which is straightforward even in high-dimensional cases. However, linear models are seldom valid as representations of biological data, and the alternative of a nonlinear model poses several taxing challenges.canonical forms, which are nonlinear structures that conceptually resemble the unalterable linear systems models, but are nonlinear. Canonical models have in common that they always have the same mathematical structure, no matter what the application area is. They also have a number of desirable features, which include the ability to capture a wide variety of behaviors, minimal requirements for a priori information, clearly defined relationships between network characteristics and parameters, and greatly enhanced facility for customized analysis.First, in contrast to linear models, there are infinite possibilities for nonlinear model structures. In specific cases, the subject area from which the data were obtained may suggest particular models, such as a logistic function for bacterial growth, but in a generic sense there are hardly any guidelines that would help with model selection. One strategy for tackling this problem is the use of The best-known examples of nonlinear canonical forms are Lotka-Volterra models , their The strict focus on two-component interactions in LV models has substantial mathematical advantages, but it has proven less convenient for the representation of metabolic pathways, where individual reaction steps depend on the substrate, but not necessarily on the product of the reaction, or are affected by more than two variables. A simple example of the latter is a bi-substrate reaction that also depends on enzyme activity, a co-factor and possibly on inhibition or modulation by some other metabolite in the system. These types of processes have been modeled very successfully with GMA and S-systems. Between these two forms, the S-system representation has unique advantages for system identification from profiles, as was shown elsewhere -24 and wcf. [The inference of a nonlinear model structure from experimental data is in principle a straightforward "inverse problem" that should be solvable with a regression method that minimizes the residual error between model and data. In practice, however, this process is everything but trivial (cf. ) as it acf. , similarcf. , mutual cf. , and gencf. . An indicf. . The alget al. [Nonlinear estimation methods have been studied for a long time, and while computational and algorithmic efficiency will continue to increase, the combinatorial explosion of the number of parameters in systems with increasingly more variables mandates that identification tasks be made easier if larger systems are to be identified. One important possibility, which we pursue here, is to prime the iterative search with high-quality starting conditions that are better than naïve defaults. Clearly, if it is possible to identify parameter guesses that are relatively close to the true, yet unknown solution, the algorithm is less likely to get trapped in suboptimal local minima. We are proposing here to obtain such initial guesses by preprocessing the temporal profile data and fitting them preliminarily by straightforward multivariate linear regression. The underlying assumption is that the structural and regulatory connectivity of the network will be reflected, at least qualitatively, in the regression coefficients. D'haeseleer et al.  exploredet al. [et al. [e.g., [et al. [Several other groups have recently begun to target network identification tasks with rather diverse strategies. Chevalier et al.  and Diazet al. ,34 propo [et al.  recently. [e.g., ,36) that. [e.g.,  used a n [et al.  used neun species can often be represented by a system of nonlinear differential equations of the generic formThe behavior of a biochemical network with X is a vector of variables Xi, i = 1, ..., n, f is a vector of nonlinear functions fi, and μ is a set of parameters. If the mathematical structure of the functions fi is known, the identification of the network consists of the numerical estimation of μ. In addition to the challenges associated with nonlinear searches mentioned above, this estimation requires numerical integration of the differential equations in (1) at every step of the search. This is a costly process, requiring in excess of 95% of the total search time; if the differential equations are stiff, this percentage approaches 100% [n differential equations, and if measurements are available at N time points, the decoupling leads to n × N algebraic equations of the formwhere hes 100% . A simplhes 100% ,17. Thusμij are the "unknowns" that need to be identified.It may be surprising at first that it is valid to decouple the tightly coupled system of nonlinear differential equations. Indeed, this is only justified for the purpose of parameter estimation, where the decoupled algebraic equations simply provide numerical values of variables (metabolites or proteins) and slopes at a finite set of discrete time points. The experimental measurements thus serve as the "data points," while the parameters a priori not trivial. However, we have recently shown [The quality of this decoupling approach is largely dependent on an efficient and accurate estimation of slopes from the data. Since the data must be expected to contain noise, this estimation is ly shown ,39 that f in Eq. (1) about one or several reference states. As long as the system stays close to the given reference state(s), this linearization is a suitable and valid approximation. We consider four options: (I) linearization of absolute deviations from steady state; (II) linearization of relative deviations from steady state; (III) piecewise linearization; and (IV) Lotka-Volterra linearization.The smoothing and decoupling approach reduces the cost of finding a numerical solution of the estimation task considerably. Nonetheless, algorithmic issues associated with local minima and the lack of convergence persist and can only be ameliorated with good initial guesses. To this end, we linearize the model zi = Xi - Xir, where Xir denotes the value at a reference state of choice. If the reference state is chosen at a stable steady state, the first-order Taylor-approximation is given byOption (I) is based on deviations of the type A is the n × n Jacobian with elements aij = (dfi / dXj) calculated at Xr .where  Xr cf. -34). If . If A isui = zi/Xir. At a steady state, this yields the linear systemFor option II, we define a new variable A' is an n × n matrix in which a'ij = (Xjr / Xir)·aij.where f, a perturbation of this magnitude may already lead to appreciable approximation errors. While this is a valid argument, it must be kept in mind that the purpose of this priming step is simply to detect the topological structure of connectivity and not necessarily to estimate precise values of interaction parameters. Simulations (see below) seem to indicate that this detection is indeed feasible in many cases, even if the deviations are relatively large.A general concern regarding linearization procedures is the range of validity of sufficiently accurate representation, which is impossible to define generically. From an experimental point of view, the perturbations from steady state must be large enough to yield measurable responses. This may require that they be at the order of 10% or more. Depending on the nonlinearities in ai0, which is equal to fi(Xr). The choice of subsets and operating points offers further options. In the analysis below, we use the locations of extreme values (maximum deviation from steady state) of the variables as the breakpoints between different subsets. Thus, a variable with a maximum and a later minimum has its time course divided into three subsets.In order to overcome the limitation of small perturbations, a piecewise linear regression (option III) may be a superior alternative. In this case, we subdivide the dataset into appropriate time intervals and linearize the system around a chosen state within each subset. Most  reference states are now different from the steady state, with the consequence that Eq. (3) has a constant term Xi and Xj is assumed to be proportional to the product XiXj [Xi isThe fourth alternative (option IV) is a Lotka-Volterra linearization. In a Lotka-Volterra model, the interaction between two species uct XiXj . FurtherXi, which is usually valid in biochemical and proteomic systems, because all quantities of interest are non-zero. Thus, the differentials are again replaced by estimated slopes, the slopes are divided by the corresponding variable at each time point, and fitting the nonlinear LV model to the time profiles becomes a matter of linear regression that does not even require the choice of a reference state. The quality of this procedure is thus solely dependent on the quality of the data and ability of the LV model to capture the dynamics of the observed network. It is known  that thatXi, i.e., vij's and wi's, or αij's). The response variable is the rate of change of a metabolite, while the predictors are the concentrations of each metabolite in the network. The different linearization models (I-IV) differ in the transformations of the original datasets, which are summarized in Table yi =  /Xir, and the predictor variables are transformed as xi = (Xi - Xir)/Xir.No matter which option is chosen, the next step of the analysis consists of subjecting all measured time traces to multivariate linear regression and solving for the regression coefficients (Xj affects the dynamics (slope) of another metabolite Xi. In particular, a coefficient that is zero or close to zero signals that there is no significant effect of Xj on the slope of Xi. By the same token, a coefficient that is significantly different from zero suggests the presence of an effect, and its value tends to reflect the strength and direction of the interaction. In either case, the coefficients computed from the linear regression provide valuable insight into the connectivity of the network. Furthermore, the estimated coefficients provide constraints on the parameter values of the desired nonlinear model f. Indeed, if f consists of an S-system model, the coefficients estimated from the regression can be converted into combinations of S-system parameters, as is demonstrated in the following theoretical section and illustrated later with a specific example.The result of the regression is a matrix of coefficients that indicate to what degree a metabolite f in Eq. (1) if this model has the form of an S-system. To determine the relationships between the regression coefficients and the parameters of the S-system, it is convenient to work backwards by computing the different types of linearizations discussed before for the particular case of S-system models. This derivation is simply a matter of applying Taylor's theorem.The regression analysis yields coefficients that offer information on the connectivity of the network of interest. It also provides clues about the parameter values of the underlying nonlinear network model In the S-system formalism, the rate of change in each pool (variable) is represented as the difference between influx into the pool and efflux out of the pool. Each term is approximated by a product of power-law functions, so that the generic form of any S-system model isn is the number of state variables [gij and hij are called kinetic orders and describe the quantitative effect of Xj on the production or degradation of Xi, respectively. A kinetic order of zero implies that the corresponding variable Xj does not have an effect on Xi. If the kinetic order is positive, the effect is activating or augmenting, and if it is negative, the effect is inhibiting. The multipliers α i and β i are rate constants that quantify the turnover rate of the production or degradation, respectively.where ariables ,14. The zi = Xi - Xis, where the subscript s denotes the value of the variable at steady state, then leads directly toIf the Taylor linearization is performed at a steady state, the production term of the S-system model equals the degradation term. The absolute deviation of the first option, wherecij = gij - hij,cf. [Fij are always non-negative, while cij may be either positive or negative depending on the relationship between Xi and Xj. A common scenario is that a variable Xj influences either the production or degradation of variable Xi, but not both. In this case, a positive (negative) cij implies activation (inhibition) of production or inhibition (activation) of degradation. The special case of cij = 0 permits two possible interpretations: 1) gij = hij = 0, which implies that Xj has no effect on either production or degradation of Xi; or 2) gij = hij ≠ 0, which means that Xj has the same effect on both production and degradation of Xi. The former case is the more likely, but there are examples where the latter may be true as well, and this is indeed the case in the small gene network in Figure (cf. ). The soaij in Eq. (3) corresponds to the product of Fij and cij:Comparing the expression in Eq. (6) with the linear regression results, one sees immediately that each coefficient aij = Fijcij.     (7)aij have been estimated, the parameters of the corresponding S-system are constrained – though not fully determined – by Eq. (7). In particular, Eq. (7) does not allow a distinction between various combinations of gij and hij, as long as the two have the same difference. For instance, re-interpreting the regression coefficients as S-system parameters does not differentiate between the overall absence of effect of Xj on Xi (gij = hij = 0) and the same effect of Xj on both the production and degradation of Xi (gij = hij ≠ 0). This observation is related to the observation of Sorribas and Cascante [Thus, once the regression has been performed and the coefficients Cascante  that steui = (Xi - Xis) / Xis, in option II, are assessed in an analogous fashion. In this case one obtainsRelative deviations from steady state, wherecij = gij - hij,Fi are positive, while cij may be either positive or negative.. Again, The piecewise linear model for an S-system is easily derived as well. It is given asXjr denotes the value of the variable at the reference state. This case also includes the situation of a single approximation, which however is not necessarily based on a steady-state operating point.where Xi and then linearizing around an operating point. The resulting expressions become especially simple if this point is chosen as the steady state. In this case, the relationship between the parameters of the LV system and the S-system areIn the case of the Lotka-Volterra linearization, the correspondence between computed regression coefficients and S-system parameters is determined most easily by dividing the S-system equations by the corresponding cij = gij - hij.where et al. [We applied the methods described in the previous sections to simulated time profiles obtained from the small gene network in Figure et al.  used it et al. ,39.et al. [To generate time profiles, the system was implemented with the parameter values published by Hlavacek and Savageau , and as et al. , the modet al. [i.e., the maximum deviations from steady state) of the various variables both in time and size is in accordance with their "topological distance" from the perturbed variable, and variables not directly affected by the perturbed variable have zero initial slopes. As an example, the effect of a perturbation in X3 is shown in Figure X1 and X4 reaching their maximal deviation from steady state before X2 and X5, suggesting that X1 and X4 precede X2 and X5 in the pathway. The value of the initial slope is different from zero for X1 and X4, implying that these variables are directly affected by X3, whereas X2 and X5 have zero initial slopes suggesting that their responses are mediated through other variables.Quasi as a pre-analysis, we examined the guidelines proposed by Vance et al. . Indeed,X2 on X3. This result is not surprising, because the effect of X2 is the same on both the production and degradation of X3, which leads to cancellation. It is noted that this analysis does not necessarily distinguish between transfer of mass and a positive modulation, because both result in a positive effect on a variable. In a realistic situation, biological knowledge may exclude one of the two options, as in this case, where modulation is the only possibility for the effect of X3 on both X1 and X4, because the former is a protein and the latter are RNA transcripts. For the mathematical model in the S-system form, this is not an issue, as both types of influence are included in the equations in the same way (as a positive kinetic order).Maximal information about the network is obtained when every variable is perturbed sequentially. Experimentally, such perturbations could be implemented with modern methods of RNA interference  or, for While Vance's method works well in this simple noise-free system, it is not scalable to larger and more complex systems. The next step of our analysis is therefore regression according to the four options presented above and with a number of simulated datasets of the gene network that differ in the variable to be perturbed and the size of the perturbation. Because the illustration here uses a known model and artificial data, it is easy to compute the true regression coefficients through differentiation of the S-system model. These coefficients can be used as a reference for comparisons with coefficients computed from the entire time traces, which mimics the estimation process for (smoothed) actual data.The results for three of the options  can be summarized in the following three points, while the piecewise linear model will be discussed afterwards.The network connectivity is reflected in the values of the regression coefficients. The values of the estimated coefficients provide strong indication as to which variables have a significant influence on the dynamics of other variables. A comparison between computed and estimated coefficients is shown in Table a12 and a24) are not estimated as exactly zero, but their values are at least one order of magnitude smaller than the coefficients that are in actuality not zero. Table X3 and X4. A possible explanation for X3 is that the effect of X2 is present in the non-linear system, but not in the linear system, and thus the behavior of X3 must be explained by the other variables. Overall, of the 25 theoretically possible connections, 76% are correctly identified, while 24 % are false positives.(1) The different linear models give  the same results. A comparison of the results of the three models reveals that the values of the regression coefficients are very similar  The greater the perturbation, the less accurate is the estimation of the regression coefficients. The deviation between the estimated and computed coefficients increases as the size of the perturbation increases (see Table (3) t = 0 to the time of the first extreme value for a given variable . For the perturbed variable  the first limit point was given by the smallest of the limit points of the other variables. The second interval contained the data points from the first to the second extreme value (a minimum), while the third interval included the remaining data points. The midpoint of each interval was taken to be the reference state. The result of the piecewise linear regression for a 5% deviation in X3 is given in Table X3 in the two last subsets reflect the variable's connectivity to a much greater extent than the other linearization approaches. As the reference state is different from the steady state, the effect of X2 is present in the linear system as well, and thus there is no compensation through the other variables. Another benefit is that the piecewise model tolerates larger perturbations. Even for a two-fold perturbation, the fraction of correctly identified coefficients in the last subset is 84%.The piecewise linear model was obtained by dividing the whole dataset into three smaller subsets for each variable. The first interval contained the data points from X3 is identified correctly from comparing the six models. The classification of the remaining four connections varies greatly among the different models, and it is therefore impossible to deduce a type of interaction with sufficient reliability.If we compare the results of all four linearized models, the degree of similarity may provide a measure of how reliable the estimated coefficients are, assuming that an interaction identified in all models is more reliable than an interaction identified in only one or few of the models. Considering the piecewise linear model as three models, yielding a total of 6 models from one dataset, one may thus determine the most likely connectivity for the small gene network. The result is presented in Table X1. Table X1, X3 and X5. If so, the linear model in Eq. (8) suggests the following:In addition to reflecting the connectivity, the coefficients provide likely parameter ranges or likely constraints on parameter values of the true model. As an example, consider variable  and the regression coefficients (aij) are taken from the model in Eq. (4). The values of the variables at steady state are known. Because the kinetic orders may be positive or negative and the cij may result from different combinations of gij's and hij's, it is not possible to deduce directly which exponent is greater than the other. However, in many cases one may have additional information on the system, which further limits the degrees of freedom . In addIdentifying the structure of metabolic or proteomic networks from time series is a task that most likely will require large, parallelized computational effort. The search space for the algorithms is typically of high dimension and unknown structure and very often contains numerous local minima. This generic and frequent problem may be ameliorated if the search algorithm is provided with good initial guesses and/or constraints on admissible parameter values. Here, we have shown that linear regression may provide such information directly from the types of data to be expected from future experiments. For illustrative purposes, we used artificial data from a known network, but all methods are directly applicable to actual profile data and scaleable to large systems.The coefficients estimated from the different regressions reflect the effect of one variable on another surprisingly well and thus provide a simple fashion of prescreening the connectivity of the network. In addition, the estimated coefficients provide constraints on the parameter values, if the alleged nonlinear model has the form of an S-system. To explore the pre-assessment of data as fully as feasible, we studied four linearization strategies: using an absolute deviation from steady state; a relative deviation from steady state; piecewise linearization; and Lotka-Volterra linearization. Interestingly, all models gave qualitatively similar results for the analyzed example, and this degree of similarity may provide a measure of how reliable the identified connections are. Specifically, of the 25 possible connections in the small gene network studied, 19 were identified correctly in at least 83 % of the regression analyses.A concern of any linearization approach is the validity of the linear approximation. However, as long as the perturbation from steady state remains relatively small, the estimated linear model is likely to be a good fit of the actual nonlinear model, at least qualitatively. This limitation may furthermore be alleviated by fitting the profile data in a piecewise linear fashion. As most reference states in this case are different from the steady state, this strategy has the added benefit that more of the true relationships within the nonlinear model are likely to be preserved. As an alternative, one could explore the performance of the so-called "log-linear" model, which is linear in log-transformed variables .The Lotka-Volterra linearization did not perform as well as expected with regard to large perturbations. This may be a consequence of the particular example, which was originally in S-system form rather than in a form more conducive to the LV structure, which emphasizes interactions between pairs of variables. Since it is easy to perform the LV analysis along with the other regressions discussed here, it may be advisable to execute all four analyses.The illustrative model used for testing the procedure consisted of a relatively small system with only five variables and relatively few interactions. Nonetheless, one should recall that this very system required substantial identification time in a direct estimation approach . In ordeNone declared.SRV performed the analysis and prepared the results. JS developed and implemented the neural network for computation of slopes. EOV developed the basic ideas and directed the project.universal function which smoothes the data with predetermined precision and also allows the straightforward computation of slopes that can be used for network identification purposes. This appendix briefly outlines the procedure; details can be found in Almeida [It was recently shown that good parameter estimates of S-system models from metabolic profiles might be obtained by training an artificial neural network (ANN) directly with the experimental data. The result of this training is a so-called  Almeida  and Voit Almeida . The ANN Almeida .Noise and sample size do not have a devastating effect on the results of the ANN-method, as long as the true trend is well represented . In factThe use of the entire time course is in stark contrast to earlier methods of parameter estimation and structure identification in metabolic networks. Mendes and Kell  applied et al. [Chevalier and co-workers  first fiet al.  suggestez(tk + 1) = z(tk)exp(h·A),     (A1)h is the step size. The problem is thereby reduced to a mulitilinear regression in which the matrix Φ = exp(h·A) is the output. Instead of estimating the slopes, they obtain the Jacobian directly by  expanded in its Taylor-series. This approach yields a faster convergence to the elements of the Jacobian than the one suggested by Chevalier et al. [where r et al. , but theOur approach takes advantage of the entire time course and is therefore less sensitive to the particularities of assessing a system at a single point. The ANN itself does not provide much insight, because it is strictly a black-box model, but it is a valuable tool for controlling problems that are germane to any data analysis, namely noise, measurement inaccuracies, and missing data.


To investigate potential mechanisms for telomere capture the spatial arrangement of telomeres and chromosomes was examined in G1 (non-cycling) mitotic cells with diploid or triploid genomes. This was examined firstly by directly labelling the respective short arm (p) and long arm subtelomeres (q) with different fluorophores and probing cell preparations using a number of subtelomere probe pairs, those for chromosomes 1, 3, 4, 5, 6, 7, 9, 10, 12, 17, 18, and 20. In addition some interstitial probes  and whole chromosome paint probes (e.g. WCP12) were jointly hybridised to investigate the co-localization of interphase chromosome domains and tethered subtelomeres. Cells were prepared by omitting exposure to colcemid and hypotonic treatments.In these cells a specific interphase chromosome topology was detected. It was shown that the p and q telomeres of the each chromosome associate frequently (80% pairing) in an intrachromosomal manner, i.e. looped chromosomes with homologues usually widely spaced within the nucleus. This p-q tethering of the telomeres from the one chromosome was observed with large , medium sized , or small chromosomes . When triploid nuclei were probed there were three tetherings of p-q subtelomere signals representing the three widely separated looped chromosome homologues. The separate subtelomere pairings were shown to coincide with separate chromosome domains as defined by the WCP and interstitial probes. The 20% of apparently unpaired subtelomeric signals in diploid nuclei were partially documented to be pairings with the telomeres of other chromosomes.A topology for telomeres was detected where looped chromosome homologues were present at G1 interphase. These homologues were spatially arranged with respect to one-another independently of other chromosomes, i.e. there was no chromosome order on different sides of the cell nuclei and no segregation into haploid sets was detected. The normal function of this high frequency of intrachromosomal loops is unknown but a potential role is likely in the genesis of telomere captures whether of the intrachromosomal type or between non-homologues. This intrachromosomal tethering of telomeres cannot be related to telomeric or subtelomeric sequences since these are shared in varying degree with other chromosomes. In our view, these intrachromosomal telomeric tetherings with the resulting looped chromosomes arranged in a regular topology must be important to normal cell function since non-cycling cells in G1 are far from quiescent, are in fact metabolically active, and these cells represent the majority status since only a small proportion of cells are normally dividing. The reason why the telomeres attach to the nuclear membrane in meiosis is not dependent on the presence of normal numbers of TTAGGG repeats and, in fact, still occurs in late generation Terc-/- mice without detectable pantelomere repeats [et al. [et al. [et al. [et al. [et al. [et al. [In both plants and animals, during early meiosis in normal cells there is a clustering of all or most of the telomeres of the entire chromosome set to a single region on the nuclear membrane -3. This  repeats . In plan repeats  but a po repeats  but has  repeats ,8 but th [et al. ,10 using [et al. ,12 but N [et al. ,10 propo [et al.  where th [et al. ,15. From [et al.  studied  [et al.  were abl [et al.  utilisin [et al. ,19. We rall p-q signals tethered was 46–72% as compared to 33–58% in the triploid cells. This would be expected with more opportunity for interhomologous tethering in the triploid nuclei with an extra chromosome.Fluorescence in situ hybridization (FISH) performed with the various subtelomere probes Table  gave disThe triploid cells were used to test the likelihood that intrachromosomal pairing of subtelomeric signals was occurring rather than the pairing of p and q signals with the q and p signals of the other homologue(s). As can be seen in figure Further confirmation that the p-q tetherings in figure This study shows that the pairings of red/green signals from the subtelomeres of the short-arm and long-arm respectively occur at high frequency in these non-cycling diploid nuclei. In many cases the association is so close that the subtelomeric signals are superimposed  no such p-q telomeric tethering pattern is present in our observations of lymphocytes (not shown) and the only associations are of sister chromatids. That most lymphocytes are at G2 can be observed by the doubled signals representing sister chromatids (not shown) which is in contrast to the single (chromatid) signals in the unreplicated G1 nuclei , reviewed in Kolodner et al. [et al. [et al. [et al. [et al. [telomere transposition by Daniel et al. [reciprocal translocation was unproven. This translocation relocated the telomeres to the opposite chromosome arm and then by recombination the result was a duplication of one telomere on both chromosome ends and a deletion of the other. For this model to be correct a high frequency of balanced telomeric translocations would have to occur. These would be observed as large pericentric inversions and are rarely reported – see review in Daniel, 1988 [et al. [As reviewed by Ballif et al.  there aret al.  or by  in an intrachromosomal manner which was confirmed by studying chromosome domains with interstitial probes (chromosome arms) and WCP probes. It was found that homologues were regularly arranged without any defined relationship to non-homologous signal groups; i.e. there was no apparent chromosome order on different sides of the nuclei and no segregation into haploid chromosome sets was detected. The normal function of this high frequency of intrachromosomal telomeric pairings is unknown but a potential role is likely in the genesis of telomere captures whether of the intrachromosomal type or between non-homologues. In our view, these intrachromosomal telomeric tetherings with the resulting looped chromosomes arranged in a regular topology must be important to normal cell function since non-cycling cells in G1 are far from quiescent, are in fact metabolically active, and these cells represent the majority status since only a small proportion of cells are normally dividing.Cell lines were retrieved from liquid nitrogen, washed in Dulbecco's phosphate buffer (DPB) and reconstituted in Hams F10 medium. The following lines were used: a diploid skin fibroblast line CG04-0743BBRS with karyotype 46,XX derived from fetal cartilage and a triploid 69,XXX cell line CG01-2042YA of diandric origin derived from mesodermal cells of a chorionic villus biopsy. These were cultured until they reached confluence via contact inhibition. At this stage the cells exhibit a number of swirls of closely packed cells in parallel. They were then severally prepared for trypsin harvest usually 48 hours after the last media change without colcemid/colchicine treatment and without the usual 0.075 M KCl hypotonic treatment. The cells were trypsinised off and fixed three times in 3:1 methanol to glacial acetic acid and were stored at room temperature (R.T.) in fixative for 1–3 days. This period allowed some mild acidic digestion of the chromatin and spreading of the nuclei when slides were prepared. At the end of the storage period, cells were rewashed once with fresh fixative and dropped onto glass slides as per routine techniques, and stored on trays in a low humidity cabinet until used for FISH.The identity of the probes used in the study is shown in Table Slides were pretreated with a combined Pepsin/Rnase step. This was performed by prewarming RNAse and pepsin to 37°C, 200 μl of RNAse  was dispensed onto each slide, coverslipped and incubated at 37°C for 40 minutes in a humidified chamber. Coverslips were removed and slides washed twice for 5 minutes in 2xSSC at room temperature (RT). Slides were briefly drained and 200 μl of pepsin (0.2% in 0.01 M HCl) was placed on the slides, coverslipped and incubated at 37°C for 30 seconds. Coverslips were removed and slides were washed twice for five minutes in phosphate buffered saline (PBS) at RT. Fixation was with 6% paraformaldehyde in PBS, by dispensing 200 μl/slide, and adding a coverslip for 2 minutes at RT. Slides were then washed twice for five minutes in PBS at RT, dehydrated through 70, 90 and 100% ethanol for 3 minutes/wash at RT, and air dried. Probes in hybridisation mix were stored at -20°C, removed and thawed for 30 minutes; dispensed onto slides, covered with 15 mm diameter coverslips, and sealed with liquid rubber – art cement. Joint denaturation was achieved at 75°C for 5 minutes on a Omnigene hot plate, transferred to a humidified hybridization chamber at 37°C and hybridised overnight. After this the coverslips were removed. Post-hybridization washes were 0.4 SSC/0.3% NP40 at 73°C for 2 minutes then quickly transferred to 2xSSC/0.1% NP40 at RT for 1 minute. Slides were counterstained in DAPI and then rinsed and air dried. When ready, slides were mounted in antifade (2.3% DABCO in 40% glycerol/0.02 M TRIS-HCl) and covered until fluorescence examination. Slides were examined on a Zeiss Axioscop 20 fitted with a Zeiss fluoarc light source and images captured on an Applied Imaging Cytovision station using the false colours that are attributed by the software.Initially, the subtelomere probes were labelled in Spectrum Orange for all short arms and Spectrum Green for all chromosome long arms. Cells were separately probed with the two subtelomeric probes for a single chromosome at the one time. Probe pairs were used for the subtelomeres of chromosomes 1, 3, 4, 5, 6, 7, 9, 10, 12, 17, 18, and 20. Images were captured for a large number of cell groups for each chromosome and pairings were scored on the captured images. Signals were interpreted as paired if the distance between signals was 10% or less of the greatest diameter of the nucleus . In addition to the above subtelomeric probe pairs, other probes were used to investigate the frequency of non-homologous tetherings (subtelomeric probes 1p and 9q, see Table Two such experiments were performed in the current study see fig . These cAD designed the study, captured and analysed all FISH signals, and drafted the manuscript.LH performed all growing of probes, labelling of probes, and most probe hybridizations.


Its function is unknown, but it is considered to be a major structural component of metazoan nuclear pores. Yet, our previous findings showed pronounced differences in expression levels in embryonic mouse tissues and cell lines. In order to identify factors regulating GP210, the genomic organization of human GP210 was analyzed The human gene was mapped to chromosome 3 and consists of 40 exons spread over 102 kb. The deduced 1887 amino acid showed a high degree of alignment homology to previously reported orthologues. Experimentally we defined two transcription initiation sites, 18 and 29 bp upstream of the ATG start codon. The promoter region is characterized by a CpG island and several consensus binding motifs for gene regulatory transcription factors, including clustered sites associated with Sp1 and the Wilms' tumor suppressor gene zinc finger protein (WT1). In addition, distal to the translation start we found a (GT)n repetitive sequence, an element known for its ability to bind WT1. Homologies for these motifs could be identified in the corresponding mouse genomic region. However, experimental tetracycline dependent induction of WT1 in SAOS osteosarcoma cells did not influence GP210 transcription.Although mouse GP210 was identified as an early response gene during induced metanephric kidney development, and WT1 binding sites were identified in the promoter region of the human GP210 gene, experimental modulation of WT1 expression did not influence expression of GP210. Therefore, WT1 is probably not regulating GP210 expression. Instead, we suggest that the identified Sp binding sites are involved. Nuclear pore complexes (NPCs) provide the only known gateway for transport of RNAs to the cytoplasm and bidirectional transport of proteins between the nucleus and the cytoplasm. The NPC in vertebrates has an estimated mass of approximately 125 Mda. Structural studies suggest an octagonal rotational symmetry framework, from which 50–100-nm long fibrils extend into the nucleoplasm and cytoplasm. A comprehensive inventory of all NPC constituents has been made for yeast .In the list of metazoan nucleoporins, there are only two integral membrane proteins, gp210 -6 and POIn contrast to POM121, gp210 has an inverted topology with its main bulk residing in the lumen of the NE and only a short 58 residue C-terminal portion facing the NPC . The amiThe primary sequence of gp210 is known for rat  and mouscis-acting elements in the human promoter and its mouse counterpart. Several putative Wilms tumor suppressor binding 1 sites were found. Nevertheless, experimental overexpression of WT1 in SAOS osteosarcoma cells did not influence GP210 mRNA expression.In the current study we present the genomic structure of the human integral membrane glycoprotein 210 gene (GP210), the open reading frame sequence and a promoter region analysis. This was done in silico by taking advantage of the available human genomic sequence. Transcription start sites were determined experimentally by RNA ligase mediated rapid amplification of cDNA ends. Computer-assisted searches of the promoter sequence indicated putative consensus binding sites for transcription factors involved in tissue specific gene regulation. We also identified of shared putative We initially assumed that mouse gpP210 was a member of a yet undiscovered large family of tissue-specific nuclear pore membrane proteins, and initially named it POM210 . Since cThe deduced amino acid sequence of human GP210 contains 1887 residues, predicting a molecular mass of 205 198 Da and a pI of 6,41 of the non-processed protein. Alignment to the corresponding mouse and rat sequences displayed a high degree of homology . One insertion, an alanine at position 1858, makes the GP210 one residue longer compared to rat and mouse GP210. A signal peptide cleavage consensus site could be defined between residues 25 and 26 using the SignalP algorithm  elements (consensus Py Py C A N T/A Py Py) . In ordeAnalysis of the sequence surrounding the translation start site with the GRAIL program predicted a 1236 bp long CpG island, with a GC content of 75.3% starting 434 bp upstream of ATG, covering the first exon and extending into the first intron. We used a variety of promoter and transcription factor binding site algorithms to analyse the region upstream the start site for GP210, including TESS and Matinspector. By selecting for perfect matching and human consensus motifs a restricted number of putative transcription factor binding sites were found. Using these criteria, 22 motifs recognized by 14 different factors were defined on the sense strand, evenly distributed within 1000 bp proximal to the translation start codon  was found in the Mouse Genome Sequencing Consortium (MGSC) database. This sequence mapped to mouse chromosome 6 and contained the first exon, part of the first intron, and 3 kb of the upstream promoter region of mouse GP210. Similar to human, a GPC island containing 644 bp and with a GC content of 74% was found starting 268 nt upstream of the start codon and extending into the gene. Pairwise ClustalW alignment of this genomic clone showed 54% homology to its human counterpart within the first 500 bp upstream of the translation start  suppressor protein binding sites, four Sp1 biding sites, and one ETS binding site in a range of 315 bases just upstream of the translation initiation site. Some of these were conserved in the mouse promoter region.Mouse GP210 was initially identified as an early response gene to induction of embryonic kidney tubule development , suggestWT1 is a zinc finger transcription factor known to exist in different isoforms due to alternative pre-mRNA splicing. DNA binding specificity is determined by insertion or removal of three amino acids between zinc finger III and IV (referred to as WT1(+KTS) and WT1(-KTS)). The -KTS isoform have been reported to repress or activate target genes containing variations of an EGR1 related, GC-rich motif (consensus GXGXGGGXG) in their promoter . Other bcis-elements widely distributed in different types of transcription control regions  dCTP by random priming using Megaprime DNA labeling system kit (Pharmacia). Filters were hybridized in 20 mM Na2HPO4 (pH 7.2), 7% SDS at 65°C for 18 hours. After washing in 20 mM Na2HPO4 (pH 7.2), 5% SDS at 65°C for 2 × 60 min followed by 2 × 60 min in 20 mM Na2HPO4 (pH 7.2), 1% SDS at 65°C the filters were exposed to Hyperfilm-MP films (Amersham) for 5 days at -70°C in the presence of intensifying screens. Band intensities were quantified using a PhosphoImager 400S .WCCS-1 Wilms tumor cells  and HeLaescribed . To visuescribed . Probes 2 atmosphere. For the induction, cells (at 70% confluence) were washed twice with PBS and refed with fresh media in the absence or presence of 1 μg/mL of Tetracycline. After 18 hours, the cells were washed twice with PBS and total RNA was isolated using TRIzol reagent  according to the manufacture's instructions. 4 μg of the RNA were resolved on formaldehyde-containing agarose gels and transferred to Nytran membranes . Hybridization was performed in ULTRAhyb buffer  at 42°C. Briefly, filters were prehybridized in ULTRAhyb buffer for 6 hours followed by an overnight hybridization at 42°C with the 935 bp hGP210cDNA probe. A WT1 cDNA probe (exons 5 to 10) and a cDNA probe for actin were used as controls. Membranes were exposed to BIOMAX MS films  at -80°C in the presence of intensifying screens. Probes were stripped by boiling the membranes in 0.1% sodium dodecyl sulfate/ standard saline citrate solution for 10 minutes.WT1-A SAOS cells were constructed from a cell line harboring the tetracycline-repressor-VP16 fusion protein , transfeTo determine transcription start, the RNA Ligase Mediated Rapid Amplification of cDNA Ends (RML-RACE) kit (Ambion) was used according to the instructions of the manufacturer's. Briefly 5 μg of Hela cell RNA or 2–5 μg of poly-A+ RNA from partially differentiated human G6 satellite cells  was treaSequencing was performed using ABI PRISM and the Dye Terminator cycle sequencing kit according to the manufacturer's directions (Perkin Elmer) and analysed by an automated ABI-310 fluorescent-dy sequencer (Applied Biosystems).. Open reading frame finding and all sequence analyses were done using the MacVector 6.5.3 sequence analysing software (Oxford Molecular Group). The BLAST  server of the National Center of Biotechnology Information (NCBI)  and The Mouse Genome Sequencing Consortium (MGSC) database  was used for sequence alignments. Signal peptide prediction was done using the SignalP v1.1 at WWW Prediction Server (Center for Biological Sequence Analysis, and membrane topology predictions at the HMMTOP server using the TopPred2 program [ and Matinspector [. Prediction of GPC islands and detection of repeats in sequences analysed were done with the GRAIL program [Exon-intron boundary predictions were done manually and using the Genscan web server at MIT  program . Transcrnspector  search p program .Table 1. Organization of the human GP210 gene including exon/intron sizes, splice acceptor consensus sequences and intron phases.Click here for filecis-acting elements of the human GP210 promoter. The start sites of the elements are indicated as 5' end of consensus sequence and relative to translation start as +1. The compilation was made using the TESS and MatInspector analyze programs. Results are restricted to human species and perfect match except for four WT1 binding sites indicated as a boxed nucleotide. Some abbreviations: EGR2, Early growth response gene 2; WT1, Wilms' tumor zinc finger protein 1; PuF, c-myc purine-binding transcription factor; IL-6. RE-BP, IL-6 Response element-Binding protein; NF-1, Nuclear factor 1; Ets-2, proto-oncoprotein; USF2, upstream stimulating factor.Table 2. Predicted Click here for file


Detection of Loss of Heterozygosity (LOH) is one of the most common molecular applications in the study of human diseases, in particular cancer. The technique is commonly used to examine whether a known tumour suppressor gene is inactivated or to map unknown tumour suppressor gene(s). However, with the increasing number of samples analysed using different software, no tool is currently available to integrate and facilitate the extensive and efficient data retrieval and analyses, such as correlation of LOH data with various clinical data sets.An algorithm to identify prognostic disease markers is devised and implemented as novel software called LDMAS. LDMAS is a software suite designed for data retrieval, management and integrated analysis of the clinico-pathological data and molecular results from independent databases. LDMAS is used in stratification of disease stages according to clinical stage or histological features and correlation of various clinico-pathological features with molecular findings to obtain relevant prognostic markers such as those used in predicting the outcome of cervical intraepithelial neoplasia (CIN). This approach lead to the identification of novel prognostic cervical cancer markers and extraction of useful clinical information such as correlation of Human Papilloma Virus (HPV) status with CIN lesions.® GeneChip Mapping arrays.A novel software called LDMAS is implemented and used to extract and identify prognostic disease markers. The software is used to successfully identify 4 novel prognostic markers that can be used to predict the outcome of CIN. LDMAS provides an essential platform for the extraction of useful information from large amount of data generated by LOH studies. LDMAS provides three unique and novel features for LOH analysis : (1) automatic extraction of relevant data from patient records and reports (2) correlation of LOH data with clinico-pathological data and (3) storage of complex data in flexible format. The first feature automates the creation of database of clinically relevant information from huge amount of data, the second feature extracts useful biomedical information such as prognostic markers in CIN and the third feature simplifies the statistical analyses of the data and allows non-statisticians to carry out the analysis. Additionally, LDMAS can be used to extract clinically useful markers from other diseases and interface to high throughput genotyping analysis software such as GDAS used to generate LOH data from Affymetrix Detection of LOH is one of the most common molecular applications in the study of human diseases, in particular cancer. It is commonly used to examine whether a known tumour suppressor gene is inactivated or to map unknown tumour suppressor gene(s). Detection of LOH not only helps in understanding the molecular mechanisms underlying the development of cancer, but also provides important information useful for disease diagnosis and prognosis. LOH detection is commonly carried out by the analysis of microsatellite markers using an automated DNA sequencer. With the raw data from the sequencer being stored in one file per lane together with corresponding clinical information and patient follow up data, each LOH study ,2 generaLDMAS package is composed of three modules:(1) MRES  which parses patient report files, extracts the information of interest and organises it into a structured format, applicable to LDAS(2) LDAS  which obtains LOH data from Genotyper  or GDAS  software and correlates it to clinical data obtained from MRES(3) LDMS (LOH Data Management Software) which is used to gather patients' clinico-pathological data and extract significant relationship between the various data sets® Windows 2000 or Windows XP operating system. Figure LDAS and LDMS work synergistically to manage and analyse LOH data. The MRES source code for automatic parsing of patient reports is written in C++ using C++ Builder 5.0 , LDAS is written in Visual Basic for Application as an Excel 2000 add-in and LDMS is written as Visual Basic for Application modules embedded within Access 2000 as fully functional software. These modules can be run independently and used for applications other than LOH. LDMAS runs on MicrosoftThe MRES module takes its input from any patient report file containing clinical details such as diagnosis, stage of the disease, treatment and follow up results, parses and formats patient's data into a structured format that can be saved as Excel spreadsheet. In this case, data were taken from SunQuest patient record system and MRES converted and produced the data as:(1) Hospital Number(2) Hospital ID(3) Patient Name(4) Date of Birth(5) Pathological specimen Number(6) Date of Diagnosis(7) Histological diagnosis.The user can manually check the data and use it as template for analysis. Data analysis is carried out using LDAS which obtains LOH data from Genotyper and correlates it to the clinical data obtained from MRES. LDAS obtains data in plain text format and can thus be easily interfaced to any LOH platform generating software such as Genotyper and GDAS. Finally all data are entered into LDMS for storage and intelligent mining of data. Database query results can be exported back to LDAS allowing correlation between LOH and various clinico-pathological parameters such as age, histological grade, treatment modality and their responses, and HPV status as well as carry out multivariate analysis to determine the sensitivity and specificity of the markers involved in the LOH study. A more detailed description of all the modules is provided in LDMAS user's guide [Additional file 1] and the example below.LDMAS offers several advantages to users. It is user friendly and its architecture is modular allowing versatility of use. It enforces the standardisation of procedures for studies involving large cohorts of individuals. The data is well organized since LDMAS systematically assigns LOH results of each case to its corresponding clinical information. Additionally, LDAS standardizes LOH data analysis implicitly and allows the user to edit the data manually if needed. Microsoft Excel has been chosen to implement LDAS because of its wide use, versatility and convenient statistical analysis features facilitating the implementation of multivariate analysis and correlation testing between LOH and clinico-pathological parameters.We divided the CIN groups into disease free indicating cases that become CIN free after treatment, and disease persistence/progression indicating cases that develop show progression or persistence of CIN despite treatment. We used LDMAS to retrospectively examine the prognostic value of LOH at 12 microsatellite markers including 10 from 3p14, 3p22-21, 6p21 and 11q23 which are frequently deleted in cervical cancer [Initially MRES was used to automatically parse 4300 patient records and extract clinico-pathological data including age, diagnosis, method of treatment and treatment response during follow up. Out of those, 164 cases with follow up of 3 or more years were chosen for the study and their clinico-pathological information was imported into LDAS. Initially, 71 out of the 164 selected cases were examined for LOH using 12 fluorescent microsatellite markers ran on ABI377 DNA Sequencer. LDAS was then used to identify the microsatellite markers for which LOH was significantly associated with disease persistence/progression of CIN using two tailed student t-test. Figure To validate this finding, LOH at these four markers was investigated in a further series of 93 cases. Compatible results were obtained from these additional cases.The two sets of data were combined and further compared using LDMS. Methodologies included :2 (chi-squared) test of LOH at each of the four microsatellite markers with age, various methods of treatment, different subtypes of HPV infection and between CINs showing disease free or disease persistence/progression.1) comparison using χ2) correlation of LOH data with histological grade of CIN, treatment response and various HPV subtypes.Through such complex analysis, we showed that concurrent LOH at two of the four microsatellite markers could identify 47% of CINs that showed disease persistence/progression with 100% specificity . FurtherBased on the above example, an algorithm can be developed to extract prognostic markers for other diseases. The algorithm can be summarised in the following pseudocode :(1) Divide the disease in groups according to the pathology staging(2) Parse patient data from clinical records and use the groups defined in part (1)(3) FOR each microsatellite markercarry out a two tailed student t-test between the disease groups using LOH dataIF t-test p ≤ 0.05Marker is significant in prognosis of the diseaseELSEMarker is not significant in prognosis of the disease2 (chi-squared) test of LOH with clinico-pathological data and correlation of LOH data with histological grade of CIN, treatment response and various HPV subtypes.(4) Validate the prognostic markers using χLDMAS has been implemented using the above pseudocode.® GeneChip Mapping arrays and fingerprinting studies. Modules such as MRES can be used independently to parse medical records facilitating extraction of specific clinical information of interest. Additionally, LDMAS can be used to extract clinically useful markers for other diseases.We have devised an effective algorithm to identify and extract useful markers that can be used to predict the outcome of disease and used the algorithm to successfully identify 4 novel prognostic markers that can be used to predict the outcome of CIN. The algorithm was implemented in a novel software called LDMAS which provides an essential platform for the extraction of useful information from large amount of data generated by LOH studies. Furthermore, LDMAS is used to efficiently store, manage and track the data. Its flexible nature allows the easy manipulation of data facilitating complex analysis as demonstrated in the current study. The various modules of LDMAS can be easily adapted and used with other applications such as high throughput LOH and genotyping using SNPs on AffymetrixThe source code and executable files for LDMAS modules as well as user manual including examples from real study data are freely available and can downloaded from our website at : Additionally examples of input files are provided from our website for users to test the software and assess its functionality.RH designed and developed and implemented LDMAS software and the web site. AE and RH did the experimental work to generate the data necessary to test and validate LDMAS. MD supervised the study and designed the experimental work using CIN biopsies and smears. All authors read and approved the final manuscript.


Little is understood about the conceptual relationship of depression and quality of life (QoL). Judgments concerning both, implicitly or explicitly, involve a time perspective. The aim of this study was to test de Leval's theoretical model linking depression and QoL with a time perspective. The model predicts that changes in cognitions about one's past, present and future QoL, will be associated with changes in depressive symptomatology.Eighteen psychiatric in-patients with a clinically confirmed diagnosis of depression were assessed on commencing treatment and 12 weeks later. QoL was assessed by the Schedule for Evaluation of Individual Quality of Life (SEIQoL), depression by the Beck Depression Inventory (BDI-II) and hopelessness by the Beck Hopelessness Scale (BHS). Time perspective was incorporated by asking QoL questions about the past, present and future.Depression and hopelessness were associated with a poorer present QoL. Depression lowered present QoL but did not alter future QoL, as these remained consistently high whether participants were depressed or recovering. However, depressed individuals had a larger gap between their actual present QoL and future (aspired to) QoL. Changes in QoL were influenced by depression and hopelessness. Contrary to the model, perception of "past" QoL was not affected by depression or hopelessness.de Leval's model was largely confirmed. Thus depression and hopelessness influence a person's present and future QoL. The analysis of a temporal horizon was helpful in understanding the link between depression and QoL. Assessment of quality of life (QoL) has become increasingly important in health care, particularly as an evaluative method to measure outcomes of the impact of disease and interventions. To date it is unclear how research on QoL relates to other psychological constructs such as depression and anxiety. Many clinical studies assess a number of related psychosocial dimensions but without a theoretical basis for the unique contribution of each. On an intuitive level, QoL and depression can appear as opposing phenomena – crudely representing all the positive and negative aspects of well-being. Poor QoL is sometimes seen as a consequence of depression -3. On thA theoretical approach developed by de Leval ,5,8 trieThe "three-time-dimension" theory describes the dislocated temporal horizon of the depressed patient. It situates both depression and quality of life as part of a continuum in time rather than as independent phenomena. De Leval proposes that, for the depressed individual, time passes slowly, the present is dissociated from the past and the potential for the future is lost or viewed with hopelessness . During In de Leval's theory, QoL is perceived as the gap between actual experience and future aspirations. Whereas depression is placed on the past-present timeline, QoL is placed along the timeline using present and future. QoL according to this model is defined as being "the appropriateness of future aspirations to the present" or "the making present of the future". The larger the gap, the lower the QoL of the individual Figure . AlthougIn a cross-sectional study, de Leval  examinedpresent ill-being' in the case of depression) and anticipated status (i.e 'therapeutic future') and was measured by the discrepancy between present and future actual QoL scores. The level of phenomenological depression – i.e. the gap between a person's perceived past and present "ill-being" was measured by the discrepancy between the past and present actual QoL scores. These gaps are referred to as time comparisons gaps. The full model is displayed in Figure The present study sought to advance the understanding about the conceptual relationship of depression and QoL by empirically testing de Leval's model in a longitudinal study. It also sought to assess the model using a previously developed individualised system for assessing QoL. If de Leval's proposals concerning the centrality of the temporal assessment are correct, then any QoL assessment instrument which measures the present can be adapted to measure the past and the future as aspired to. In this study, the model was operationalised as follows: scores on standardised depression measures were taken to demonstrate an individual's level of depression, i.e., 'present ill-being'. QoL was the gap between an individual's present status  from time 1 to time 2 predicts the change in the gap between actual and aspirational QoL scores.Hypothesis 3: The change in hopelessness scores from time 1 to time 2 predicts the change in the gap between Hypothesis 4: A reduction in depression narrows the gap between the past and present, and enhances QoL by narrowing the gap between present and future QoL.Consecutive psychiatric in-patients N = 27) were approached to participate in a longitudinal study of depression. Study inclusion criteria were age ≥ 18 years, diagnosis of moderate to severe uni-polar depression (DSM-IV-criteria), a moderate or severe depression rating on the Beck Depression Inventory II [ were appQoL was assessed by the Schedule for the Evaluation of Individual Quality of Life (SEIQoL) , a well-The final assessment step in SEIQoL involves quantifying the relative importance (weight) of each cue to the judgment of participants' overall QoL. This is obtained by using a weighting disk, consisting of five interlocking coloured disks that can be rotated around a central point to form a pie chart. These disks are labelled with the five nominated cues and are adjusted by participants until they are satisfied that the proportion of the pie chart displayed by each life area accurately reflects the relative importance they attach to these life areas. The SEIQoL Index score is calculated by multiplying each cue weight with the relevant level of functioning. These five scores then are summed. The scale of the SEIQoL Index score ranges from worst possible (0) to best possible (100). This was done for each time anchor and point.To measure the temporal nature of the gap in terms of perceptions of their actual and aspirational past, present and future QoL according to the Leval's theory and Calman's definition of QoL, participants were asked to rate the status of each nominated SEIQoL cue (i.e. how were you doing?) based on the following questions:Past:- Where were you before you got depressed? - Where would you have liked to have been? Present: - Where are you now? - Where would you like to be? Future:- Where do you expect to be in a year’s time? - Where would you like to be in a year’s time? Thus scores were taken for actual present, aspirational present, actual past, aspirational past and actual future, aspirational future.The Beck Depression Inventory (BDI), a 21 item self-report instrument for measuring the severity of depression in adults, was developed for the assessment of symptoms corresponding to criteria for diagnosing depressive disorders based on the DSM-IV. In this study an updated version is used (BDI-II) [The Hopelessness Scale  is a 20 Of 27 patients approached, 24 gave written consent and took part in the study (89% response rate). Over the three month follow-up period, six patients dropped out (75% follow-up rate). Information on the complete sample of 18 patients was analysed. Sociodemographic and clinical details are presented in table Baseline scores of the depression measure (BDI-II) showed high levels of severity, which were reduced significantly after three months  was significantly higher at baseline and three months than actual past and this did not change over time table . Actual pd1 = .468, rfd1 = .475, both p < .05, rpd2 = .815, rfd2 = .730, both p < .01). In addition, correlations between the QoL preference comparison gaps for present, past and future assessment and hopelessness (h) were statistically significant at baseline for the present (p) gap and at three months (2) for present (p) and future (f) gaps . There was no difference between actual past and actual future QoL scores at baseline and three months . This confirms hypothesis 1a.The gap between actual and aspirational QoL is presented in table Multiple regression was conducted to investigate whether depression and hopelessness contribute to the change in the size of the gap of actual and aspirational QoL over time. Changes in depression and hopelessness scores over time were entered as independent variables in a multiple regression, while the change in gap scores was entered as the dependent variable. A significant amount of variance was explained by changes in the present and future gaps for actual and aspirational QoL . Change in depression contributed to change (reduction) in the gap between present actual and aspirational Qol (β =.676). Change in hopelessness (β =.589) was the significant contributing factor for change (reduction) in the gap between actual and aspirational future QoL . This partly disconfirms hypothesis 4To investigate how depression and hopelessness may influence the change in the size of the gaps proposed by de Leval, multiple regression analysis was conducted. Change in depression and hopelessness scores over time were entered as independent variables in a multiple regression, while the change in the gap over time, was entered as the dependent variable. Changes in the size of the two time comparison gaps  were not influenced by change in depression or hopelessness scores . Secondly, the time comparison gap was assessed, i.e. where patients see themselves now in comparison to the past, and in comparison to the future. Both approaches were carried out on the basis of de Leval's model linking depression to all three time dimensions.Our findings are consistent with previous results, showing that people use their current affective state as a basis for making judgments of how happy and satisfied they are with their lives. A depressed person will usually see his or her well-being, social functioning and living conditions as worse than they appear to an independent observer or to patients themselves after recovery – the so called "affective fallacy" . ClinicaOur findings suggest the larger the present preference comparison gap, the greater the depression. While the preference comparison gap of the past did not change over time with the decrease of depression, there was a trend for the future preference comparison gap that might become statistically significant with a larger sample size. However, aspirations were not affected by depression, since they remained the same for past, present and future from baseline in hospital to three months later. Overall these findings may indicate that a reduction in the present preference gap should be the main goal for therapeutic actions for depressed patients. An achievable target may be to keep the same preference comparison gap between and over time points to maintain a healthy homeostasis and a good QoL figure . InteresSuccessful treatment of depression, indicated by a significant change of BDI-II and BHS scores, was accompanied by a significant reduction in the gap between present actual and aspirational QoL. A reduction in the gap between actual present QoL with the aspirational QoL ("where one would like to be right here and now") reflects a more positive view of the present and is accompanied by significant improvements in QoL. Depression was consistently correlated with the time comparison gap between present and future QoL assessments. However, depression was more highly associated with the present and future preference comparison gap at three months. This suggests, that depression may be more highly associated with the actual/aspirational gap when people are less depressed. In contrast, hopelessness was consistently correlated with the preference comparison future gaps and only at baseline with the gap of present actual/aspirational QoL. The gap between the actual past and aspirational past QoL was not explained by either depression or hopelessness. Change in depression or hopelessness was important in reducing the preference comparison gap for present and future QoL assessments. Change in depression was important in reducing the size of the present gap. Change in hopelessness significantly contributed to reducing the size of the future gap. Given that present "ill-being" is seen as depression, while hopelessness is future orientated and directed, this finding makes sense. This has implications for therapy: to improve the QoL of patients with depression, relief from depression must be provided. Hopelessness needs to be tackled in addition to achieving a realistic future QoL perspective.To a large extend this study confirms de Leval's theory. The assessment of de Leval's model: present and future QoL  and past and present  showed interesting results. The theoretical time comparison gap between a "healthy past" and present "ill-being" was confirmed by the significant difference between patients' perceptions of actual present and future QoL, at baseline. Over time, following appropriate treatment, this time comparison gap should close and QoL should improve, if the treatment is considered to be successful. The significant reduction of the past-present gap over time, and the loss in significance in size of this gap three months later, further confirms de Leval's model. This reduction of the time comparison gap between the present "ill-being" and a "healthy past" resulted in an improvement in QoL. The reduction was achieved by a change in the actual view of the present, leaving the view of the past unchanged figure .According to de Leval's model, recovery from depression is achieved when the appraisal of present and past QoL are very close, i.e. a very narrow gap. This means that a reduction of the gap between present and future QoL should improve current QoL. Although this study showed a decrease in depression over time by standard measures of depression and the size of the time comparison gap was significantly reduced between the two time points, the size of the gap did remain significant between the present and future at three months follow up. This finding may either indicate that the patients were still depressed at three months, or this part of the model could not be confirmed figure . de LevaSince we tested the model in a small and specialised sample, the findings are not generalisable. However, our study did test de Leval's model in a clinically depressed sample which is the focus of the most likely benefit of understanding the interplay of the two phenomena of depression and quality of life. It is also a challenging group to recruit, engage with and maintain in a research project.This study showed that depression influenced individual QoL by lowering the person's actual QoL. Thus depression was associated with a larger gap between current reality  and patient aspirations and realistic expectations. Aspirations for past, present and future QoL remained the same over time. However, patients actual appraisal of their present QoL improved with successful treatment of depression, reflected by a closure of the present preference gap.de Leval's model was largely confirmed. Thus depression and hopelessness influence a person's present and future QoL. The analysis of a temporal horizon was very helpful in understanding the link between depression and QoL. Therapeutic interventions can be considered as closing the gap between a person's present QoL and their present aspirations and realistic expectations for the future. This could be achieved, according to the data presented here, by changing the view of the present and not necessarily by changing the level of expected future QoL. Knowing the persons aspirational QoL level may be a helpful guide for the therapist in that it provides important information about a depressed person's position in the progress towards what he or she considers to be recovery.Margaret Moore contributed to conception and design, acquisition, analysis and interpretation of the data and revised the article. Stefan Höfer drafted the article and contributed to the analysis and interpretation of the data. Hannah McGee contributed to the conception and design and interpretation of the data and revised the article. Lena Ring contributed to the interpretation and revised the article.All authors have given final approval of the version to be published


Plexiform neurofibromas (PNF) are benign tumors of the peripheral nerve which mostly develop in patients with neurofibromatosis type 1 (NF1). Surgical interventions are usually not applied to children with small tumors. These are rather restricted to debulking of larger tumors in adults that cause clinical complications or aesthetic disfigurement. In most cases, a total resection of PNF is not possible due to the network-like growth of the tumors.Early surgical intervention was carried out for 9 small PNFs in 7 NF1 children. Tumor resection was performed following the graphical delineation of the affected skin and according the MRI findings.Total resection was achieved for all 9 PNF without causing any neurological or organic deficit. Annual magnetic resonance tomography over a period of four years did not reveal any relapse of the tumors.Early surgical intervention for small superficial PNFs in NF1 children have various advantages and may especially be considered a strategy to prevent progression. NF1 on 17q [Plexiform neurofibromas (PNF) are benign tumors originating from nerve sheath cells, subcutaneous, or visceral peripheral nerves and can involve multiple fascicles . PNF occ1 on 17q -5. At le1 on 17q ,6,7, whi1 on 17q -10.Previously we reported that PNF can be distinguished into three growth categories using magnetic resonance imaging (MRI): superficial, displacing and invasive. Superficial PNF arise from subcutaneous or cutaneous nerves and may remain within the upper layer of the skin – usually not involving major nerves . SubtotaIn this study, we present the results of early intervention for 9 PNFs in 7 NF1 children aged 3 to 15 years.The 7 children were examined in our NF-Clinic in the Department of Maxillofacial Surgery, University Hospital Eppendorf, Hamburg. Diagnosis of NF1 was based on the NIH criteria . InformeSeven NF1 children, five boys and two girls, aged 3 to 15 years were included in this study. All of them met the NIH diagnostic criteria for NF1 . PNF wasIn 2001, all seven children underwent surgical intervention for their nine PNF. During the mobilization of the underlying skin layers, three patients showed enlarged nerves running from subcutis to superficial muscle layers, which were identified and additionally resected. These enlarged nerves were proven to be PNF in all cases. Primary wound closure was achieved following the mobilization of the marginal skin. Healing followed without complications in all cases. The scars are relatively small and no hypertrophy was observed. The intervention was well tolerated by all children and recovery occurred within a few days.Postoperative MRI was done for three children. Six children underwent follow-up examinations annually by MRI for three years. One child was only examined in 2003, two years after the operation. MRI revealed successful total resection for all 9 tumors Figs. , 5, 6, 7In this study we demonstrated that small superficial PNF can be completely resected, leaving scars without hypertrophy. Operation is relatively uncomplicated, allows healing by primary intention, requires only a few days of hospitalization and is thus no burden for even the youngsters. Within the clinical and radiological follow-up period of three years there was no re-growth of the tumors. However, it cannot be excluded that all tumor cells have been resected. Therefore those patients warrant further annual clinical examination.The scars in some children may seem more obvious and disfiguring after the operation. However, in comparison to the complications the tumors may cause later, they are only of minor concern. Furthermore, these scars may be corrected later surgically.Without resection, these small PNF are likely to grow continuously to a large or even very large size which often causes aesthetic disfigurement, functional deficits and pain. Resection of large tumors is much more difficult and total resection is usually not possible any more. Currently it is not clarified whether or not growth patterns of PNF might possibly change from superficial to displacing or to invasive types over time. Early surgical intervention of small superficial PNF may thus be considered as a preventive strategy for later disfigurement and functional deficits.Adequate diagnosis of NF1 and thus small PNF in the pediatric age group is challenging. Children usually do not complain about subtle pain or discomfort in the affected skin region. Even in the presence of some symptoms parents are often not aware of the origin of the tumor. Only small PNF of the face and neck causing disfigurement in children tend to be recognized early. On the other hand, small PNF of the trunk and extremities which present with subtle hyperpigmentation, hair excess and palpable tumor tissue are frequently not noticed by the physicians. These tumors are rarely diagnosed correctly as PNF. In our cohort only one child received the diagnosis of a PNF correctly by the referring family physician. For better management and adequate treatment, efforts should be made to diagnose NF1 and PNF in their early stage.Early surgical intervention of small superficial PNFs is uncomplicated, without burden for even the youngsters and enables total resection of the tumors. It may be considered as a preventive strategy for later disfigurement and functional deficits.The authors declare that they have no competing interests.REF and RS carried out the surgical intervention for the tumors and post-operational care of the patients.CF carried out the MRI and was involved in the diagnosis of PNF.MH and VFM did the diagnosis and were responsible for management and consulting of the patients.REF and VFM designed the study and prepared the manuscript.All authors read and approved the final version of the manuscript.


Microbes are arguably the most adaptable organisms on Earth, inhabiting nearly every crevice of nearly every corner of the globe. Some invade the cavities of a wide variety of insects and other invertebrates while others colonize the skin, blood, eyes, and internal organs of animals. Still others thrive in such inhospitable places as the hydrothermal vents of the ocean floor and the Dry Valleys of Antarctica. These “simple” single-celled organisms have evolved unique molecules and strategies over some 3.5 billion years that suit life on the edge. With the sequenced genomes of nearly 140 microbial species in hand, scientists are gaining valuable insights into the nature of this adaptive diversity.Adapting to such radically different niches, it appears, has produced genes with diverse functions that evolve at very different rates. Genes that code for molecules essential for fundamental cellular functions like maintaining cell metabolism and structure tend to evolve rather slowly, while genes that make proteins charged with mediating cellular responses to internal or external changes often evolve relatively quickly. Pathogenic microbes in particular rely on a flexible genome to keep a step ahead of their hosts' similarly evolving defenses in the never-ending struggle to gain adaptive advantage. This adaptability underlies the increasing antibiotic resistance of diseases like tuberculosis, as selective pressures favor the expansion of resistant bacterial populations.Combating such problems requires a molecular understanding of bacterial infections, yet function has been ascribed to only a fraction of the genes found in microbial genomes. One approach to improve functional analyses of genome sequences combines bioinformatics with experimental methods. With such collaborations in mind, Yu Zheng, Richard Roberts, and Simon Kasif have developed a computational approach to help filter out the genetic noise and home in on genomic regions likely to contain clues to gene function. Their method relies on a novel way of classifying genes that flags sequences likely to reward biochemical and genetic efforts to analyze gene function.Many comparative genomic studies have focused on looking for sequence “motifs” that correlate with well-characterized protein sequences  and predicting function based on their similarity to the known protein sequences. Zheng, Roberts, and Kasif took a different approach, classifying genes based on their sequence variation. The researchers analyzed 43 fully sequenced microbial genomes and, after determining the degree of conservation or divergence among similar genes in different species, divided the genes into three broad categories: rapidly evolving genes unique to a particular species; highly conserved genes; and “segmentally variable,” or mosaic, genes. Stipulating that the boundaries between the categories are somewhat blurred, Zheng et al. define segmentally variable genes as regions that show a mosaic pattern of one or more rapidly evolving, variable regions interspersed with conserved regions. Based on evidence suggesting that retained variable regions tend to serve a function, the researchers predicted that these mosaic genes, with their highly variable, fast-evolving regions, would shed light on the forces that shape genome diversity and adaptation.For most of the microbes analyzed, mosaic genes accounted for about 8–20% of their genomes. Selecting several large families of mosaic genes, Zheng et al. explored the relationship between genes with known function and the structure of their variable regions. Noting an overabundance of particular functional categories in different species—such as signaling proteins that come into either direct or indirect contact with the cell's environment—the researchers speculate that the variable regions may constitute an adaptive layer for the microbe, as they not only “play a key role in mediating interactions with other molecules” but also support a microbe's ability to adapt to its particular niche. Several bacteria species, for example, contain roughly 40% more mosaic sensor genes involved in cell motility, which the authors attribute to the microbes' “expanded ability to detect different chemical signals and find favorable environments.”This regional variability appears to reflect the influence of selective pressures that fuel diversity through ongoing interactions with other rapidly evolving molecules in the environment, adding another source of genetic adaptability as cells adjust to new environments and outmaneuver pathogenic threats. While many of the mosaic genes identified encode proteins involved in host-pathogen interactions, defense mechanisms, and intracellular responses to external changes, their function is only broadly understood. While Zheng et al. cannot say to what extent variability affects function—Is extreme variability required for diversity or can modest variation suffice?—they are refining their classification of segmentally variable genes to address such questions. Until then, the authors' “mosaic” approach to understanding gene function promises to improve efforts to annotate the volumes of sequenced genomes on hand, offering biologists a much-needed tool to sift through the mountains of genomic datasets and identify promising targets for further study.


Obesity, the most common cause of insulin resistance, is increasingly recognized as a low-grade inflammatory state. Adipocyte-derived resistin is a circulating protein implicated in insulin resistance in rodents, but the role of human resistin is uncertain because it is produced largely by macrophages.The effect of endotoxin and cytokines on resistin gene and protein expression was studied in human primary blood monocytes differentiated into macrophages and in healthy human participants.Inflammatory endotoxin induced resistin in primary human macrophages via a cascade involving the secretion of inflammatory cytokines that circulate at increased levels in individuals with obesity. Induction of resistin was attenuated by drugs with dual insulin-sensitizing and anti-inflammatory properties that converge on NF-κB. In human study participants, experimental endotoxemia, which produces an insulin-resistant state, causes a dramatic rise in circulating resistin levels. Moreover, in patients with type 2 diabetes, serum resistin levels are correlated with levels of soluble tumor necrosis factor α receptor, an inflammatory marker linked to obesity, insulin resistance, and atherosclerosis.Inflammation is a hyperresistinemic state in humans, and cytokine induction of resistin may contribute to insulin resistance in endotoxemia, obesity, and other inflammatory states. Inflammatory stimuli affect resistin expression in human macrophages and raise serum resistin levels in healthy volunteers Dietary and lifestyle changes during the last century have entailed an unprecedented epidemic of obesity and associated metabolic diseases, including type 2 diabetes and atherosclerosis . Many inWe originally identified and characterized resistin as a circulating mouse adipocyte gene product that is regulated by antidiabetic drugs . In rodeHere we show that the endotoxin lipopolysaccharide (LPS), a potent inflammatory stimulant, dramatically increases resistin production by inducing secretion of inflammatory cytokines such as TNFα. This increase in resistin production is blocked by both aspirin and rosiglitazone, drugs that have dual anti-inflammatory and insulin-sensitizing actions and have been shown to antagonize NF-κB. Indeed, activation of NF-κB is sufficient to induce resistin expression, and loss of NF-κB function abolishes LPS induction of resistin. Resistin serum levels are increased dramatically by endotoxemia in humans, and correlate with a marker of inflammation in patients with type 2 diabetes. Thus, systemic inflammation leads to increased resistin production and circulating levels in humans. The increased level of resistin in humans with obesity is likely an indirect result of elevated levels of inflammatory cytokines characteristic of states of increased adiposity. Hence, obesity and acute inflammation are both hyperresistinemic states associated with insulin resistance.6 cells per well, allowed to adhere for 4 h, then washed with Dulbecco's Modified Eagles Medium and further cultured in 10% FBS in Dulbecco's Modified Eagles Medium supplemented with 5 ng/ml GM-CSF  to promote macrophage differentiation. All experiments were performed after overnight equilibration with macrophage serum-free medium  supplemented with 5 ng/ml GM-CSF. Cells were treated with LPS (Sigma), aspirin (Sigma), SN50, and/or control peptide , MG132, PD98059, SB20358 , and TNFα . Neutralizing antibodies to TNFα, IL-6, and anti-IL-1β, as well as control IgG, were obtained from R&D Systems. Adenovirus expressing activated IKK in pAD easy with GFP and control vector was a generous gift from Steven Shoelson.Peripheral blood mononuclear cells were isolated from whole blood of healthy donors following apheresis and elutriation. Greater than 90% of these monocytes expressed CD14 and HLA-DR. Cells were plated in 24-well plates at a density of 10RNA was isolated using RNeasy Mini Kit , then subjected to DNase digestion followed by reverse transcription (Invitrogen). mRNA transcripts were quantified by the dual-labeled fluorogenic probe method for real-time PCR, using a Prism 7900 thermal cycler and sequence detector . Real-time PCR was performed using Taqman Universal Polymerase Master Mix (Applied Biosystems). The primers and probes used in the real-time PCR were the following: Sense-Resistin, 5′-AGCCATCAATGATAGGATCCA-3′; Antisense-Resistin, 5′-TCCAGGCCAATGCTGCTTAT-3′; Resistin Probe, 5′-Fam-AGGTCGCCGGCTCCCTAATATTTAGGG-TAMRA-3′; Sense human 36B4 sense, 5′-TCGTGGAAGTGACATCGTCTTT-3′; Antisense 36B4, 5′-CTGTCTTCCCTGGGCATCA-3′; and 36B4 Probe, 5′-FAM-TGGCAATCCCTGACGCACCG-TAMRA-3′.−deltadeltaCT, in which deltadeltaCT equals the deltaCT of the compound-treated group minus the deltaCT of the chosen control group, which was normalized to 1.Primer and probe for TNFα were obtained from Applied Biosystems. The cycle number at which the transcripts of the gene of interest were detectable (CT) was normalized to the cycle number of 36B4 detection, referred to as deltaCT. The fold change in expression of the gene of interest in the compound-treated group relative to that in the vehicle-treated group was expressed as 2p < 0.001), except that the Biovendor values were approximately 30% lower than those determined with the Linco assay. This appeared to be related to the standards used for calibration. Discrepant absolute values among different assays, including the Biovendor assay, were recently described by others , a, a28], aMouse resistin, produced exclusively by adipocytes, is down-regulated by antidiabetic thiazolidinediones, including rosiglitazone . ConsistNext, we asked whether our findings from ex vivo studies of human macrophages would translate into in vivo observations in humans. Six healthy volunteers were injected with LPS, using a protocol similar to that shown to produce insulin resistance . BaselinPatients with type 2 diabetes and insulin resistance, many of whom are obese, have elevated levels of several inflammatory markers, including IL-6, TNFα, and sTNFR2 . LPS admWe have demonstrated that, in human macrophages, an inflammatory cascade with secretion of cytokines, including TNFα and IL-6, is sufficient and necessary for the induction of resistin. Insulin sensitizers that have anti-inflammatory properties, including a synthetic PPARγ agonist as well as aspirin, suppress macrophage resistin expression, as does direct inhibition of NF-κB. Experimental endotoxemia in healthy volunteers, based on the well-established gram-negative bacterial inflammatory response in humans ,39,40, iIn rodents, resistin is produced exclusively by adipocytes, regulates normal glucose homeostasis, and causes insulin resistance at high circulating levels ,13. TranR = 0.31, p < 0.001) was found in a cohort of 879 non-diabetic individuals, in whom resistin levels independently correlated with coronary atherosclerotic disease .Clearly the relationship between obesity, inflammation, and resistin expression is complex, and needs to be systematically studied in larger and varied patient populations. Intriguingly, we found a strong correlation between plasma levels of resistin and sTNFR2, the soluble cleavage product of the activated TNFα receptor, in diabetic patients. A comparable correlation between resistin and sTNFR2  are linked to diabetes, one possibility is that obesity in humans, by being similar to inflammation, causes immune cells to make lots of resistin and hence promotes diabetes that way.More research is necessary to confirm these findings and to find out how important resistin is as a link between obesity and diabetes, and how resistin promotes diabetes.http://www.niddk.nih.gov/health/nutrit/pubs/unders.htmUnited States National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK) information on obesity: http://diabetes.niddk.nih.gov/NIDDK information on diabetes: http://www.idf.org/International Diabetes Federation: