Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen  A number of techniques have been developed that exploit combinations of protein features in training data and can predict protein-protein interactions when applied to novel proteins. Our study is motivated by a study by Jansen et al. [ provides more details about the quantification of these variables. Their Bayesian method predicts protein-protein interactions genome-wide by probabilistic integration of genomic features that are weakly associated with interactions . The model was used for two separate predictions of probabilistic interactomes (PI), one of which (PIE) is built on four high-throughput experimental interaction data sets, and the other (PIP) on the mRNA expression, Gene Ontology, MIPS functional and coessentiality data. Within the PIP sub-network, different genomic features are assumed to be independent in prior. In addition, this method involved discretizing the raw data into groups and representing the two mRNA expression profiles (cell cycle and Rosetta compendium data) by their first principal component for computational convenience.Proteins transmit regulatory signals throughout the cell, catalyze large numbers of chemical reactions, and are crucial for the stability of numerous cellular structures. Interactions among proteins are key for cell functioning and identifying such interactions is crucial for deciphering the fundamental molecular mechanisms of the cell. As relevant genomic information is exponentially increasing both in quantity and complexity, n et al. , who pron et al.  complexen et al.  as gold n et al.  and the n et al. , biologin et al.  and the n et al. , and hign et al. -10. The Our current study focuses on assessing the contributions of different types of genomic data towards predicting protein-protein interactions. This may help us to understand which genomic features have the closest biological relationship with protein-protein interactions and hence to construct a better prediction model. As prediction rules involving less relevant information may have lower prediction accuracy, our analysis can give us insights into how to construct more parsimonious models with comparable or better prediction accuracy. A potential disadvantage of the Bayesian network approach may be that the data discretization can obscure information contained in the raw genomic data. Thus, in addition to assessing the information content of the data sources, we also propose alternative non-Bayesian models that fully utilize the data without discretization. These methods, such as logistic regression and random forests, do not require prior knowledge, and we can evaluate the importance of the different genomic features in the context of these methods.. In doing so, we find that some of the features have stronger influence on the overall prediction. While this might be true for the larger problem as well, there are a number of caveats that one has to keep in mind, such as that the features that are present in this subset might not be the strongest in the whole set of 18 million protein pairs.To accurately and quantitatively assess the information contributions of different genomic features, we construct in essence a simplified problem that has some but not all of the elements of the original study. Here, we only look at a subset of the data from  comprisiHere, we construct models for predicting protein-protein interactions that, given the gold standards, are basically dichotomous classifiers. Multiple logistic regression  is one cThe logistic model has the advantage that it provides an estimated probability that a pair of proteins interact, and is readily available in standard statistical packages. In this paper, the logistic regression analysis was generated using PROC LOGISTIC in SAS/STAT software, Version 9 of the SAS System. Moreover, we can evaluate the importance of different genomic features by variable selections. Among many available schemes, we chose stepwise variable selection that is widely used in standard packages. Stepwise selection is a greedy search algorithm that selects variables with the best marginal prediction power given the current model. To quantify the importance of the predictor variables to the model fitting, we can use the deviance measure1 - log L0),-2 on all the features for random forest, logistic regression and the Bayesian network method. Figure Information assessment of different genomic data may help us understand their relationship with protein-protein interactions, and form a guideline for future model development.We saw that the MIPS and Gene Ontology functional similarities were the two most important information sources under both the logistic regression and random forests methods. Histograms of the MIPS and GO functional similarity data Figures  and 4 shErr) defined as follows.In the following paragraphs, we show quantitatively that the MIPS and Gene Ontology functional similarities are the dominating information contributors for predicting protein-protein interactions, while other genomic features have negligible benefit and can not provide credible predictions by themselves. We examine the performance of random forests using three different genomic feature sets: (i) all genomic features included, (ii) MIPS and Gene Ontology functional similarities only, and (iii) genomic features other than the MIPS and Gene Ontology functional similarities. The random forest performance is evaluated with the classification error  and f2(X) for the gold standard positives and negatives, respectively. Denote a classifier by C(X). Then the classification error can be written asI(A) is an indicator function equal to 1 when A is true and 0 otherwise. A minimal classification error Errmin can be computed by minimizing (1) across the space of X. It is easy to see thatwhere C(X) = I(f1(X) >f2(X)). With this formula, we can estimate the optimal (minimum) classification error based on any estimates of f1(X) and f2(X). In our study, f1(X) and f2(X) are estimated by their empirical density functions.is achieved at Table We also estimated the classification errors using the other genomic features within the random forest framework. Table Moreover, we compared the ROC curves Figure  of the rIn the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed in . Random We considered alternative non-Bayesian methods such as logistic regression and random forest for predicting protein-protein interactions. These existing methods do not require prior information needed for the Bayesian approach, and can fully utilize the raw data without discretization. The logistic model performs similarly as the Bayesian method in terms of classifications and, like the Bayesian method, produces estimated probabilities that two proteins interact. As a dichotomous classifier, the random forest method outperforms the other methods considered and efficiently uses the information, although it is computationally more expensive. In particular, its importance measure provides a more objective assessment of different genomic features on predicting protein-protein interactions than simply considering contributions to model fitting. These findings are motivation to look for other, more sensible data resources and superior models.We found that the data discretizations used in the Bayesian methods decreased classification performance. We note here that the genomic features datasets investigated here themselves are highly processed versions of the datasets they were derived from and that there may be better ways to take the original data into account.Another caveat is that the predictions might be just defining groups of proteins that have the same genomic properties as the protein complexes in the MIPS data. This does not necessarily mean that they really represent protein complexes. Rather, they may represent groups of proteins that have the same properties as protein complexes.In this analysis we have looked at the relative weights of various features in predicting protein-protein interactions based on the previous study in . We lookX1, X2, ..., Xn. Let Y = 1 when two proteins interact, i.e., they are in the same complex, and Y = 0 when not. The logistic model is of the formDenote the gold standards by random variable Y and the other genomic features by X consists of X1, X2, ..., Xn and their interaction terms.where the random vector The stepwise selection procedure starts from a null model. At each step, it adds a variable with the most significant score statistics among those not in the model, then sequentially removes the variable with the least score statistic among those in the model whose score statistics are not significant. The process terminates if no further variable can be added to the model or if the variable just entered into the model is the only variable removed in the subsequent elimination. Here, the score statistic measures the significance of the effect of a variable.sensitivity and specificity. Sensitivity is calculated by dividing the number of true positives (TP) through the number of all positives, which equals the sum of the true positives and the false negatives (FN); specificity is calculated by dividing the number of true negatives (TN) through the number of all negatives, which equals the sum of the true negatives and the false positives (FP).Receiving operator characteristic (ROC) curve  is a graSensitivity = TP/(TP + FN), Specificity = TN/(TN + FP).specificity on the X axis and sensitivity on the Y axis. A good classifier has its ROC curve climbing rapidly towards upper left hand corner of the graph. This can also be quantified by measuring the area under the curve. The closer the area is to 1.0, the better the classifier is; and the closer the area is to 0.5, the worse the classifier is.The ROC curve plot shows 1 - NL and BW conducted the major part of the data analysis, and created all the tables and figures, under the supervision of HZ. RJ and MG provided the data sets for the analysis and contributed to the discussion on the comparisons of different methods. All authors read and approved the final manuscript.The complete-information subset in ZIP file.Click here for file


Retroviruses have several distinct advantages over other vectors, especially when permanent gene transfer is the preferred outcome. The most important advantage that retroviral vectors offer is their ability to transform their single stranded RNA genome into a double stranded DNA molecule that stably integrates into the target cell genome. This means that retroviral vectors can be used to permanently modify the host cell nuclear genome. Recently, retroviral vector-mediated gene transfer, as well as the broader gene therapy field, has been re-invigorated with the development of a new class of retroviral vectors which are derived from lentiviruses. These have the unique ability amongst retroviruses of being able to infect non-cycling cells. Vectors derived from lentiviruses have provided a quantum leap in technology and seemingly offer the means to achieve significant levels of gene transfer The ability of retroviruses to integrate into the host cell chromosome also raises the possibility of insertional mutagenesis and oncogene activation. Both these phenomena are well known in the interactions of certain types of wild-type retroviruses with their hosts. However, until recently they had not been observed in replication defective retroviral vector-mediated gene transfer, either in animal models or in clinical trials. This has meant the potential disadvantages of retroviral mediated gene therapy have, until recently, been seen as largely, if not entirely, hypothetical. The recent clinical trial of γc mediated gene therapy for X-linked severe combined immunodeficiency (X-SCID) has proven the potential of retroviral mediated gene transfer for the treatment of inherited metabolic disease. However, it has also illustrated the potential dangers involved, with 2 out of 10 patients developing T cell leukemia as a consequence of the treatment. A considered review of retroviral induced pathogenesis suggests these events were qualitatively, if not quantitatively, predictable. In addition, it is clear that the probability of such events can be greatly reduced by relatively simple vector modifications, such as the use of self-inactivating vectors and vectors derived from non-oncogenic retroviruses. However, these approaches remain to be fully developed and validated. This review also suggests that, in all likelihood, there are no other major retroviral pathogenetic mechanisms that are of general relevance to replication defective retroviral vectors. These are important conclusions as they suggest that, by careful design and engineering of retroviral vectors, we can continue to use this gene transfer technology with confidence. Retroviruses are viruses that are found throughout the animal kingdom, including in chickens, mice, cats, sheep, goats, cattle, primates, fish and humans. The first retro viruses were identified as cell free oncogenic factors in chickens. Subsequently, many of the oncogenic retroviruses have been shown to be replication defective forms that have substituted a part of their normal viral gene complement with an oncogene sequence . Replicagag gene. In addition, the virion contains two identical copies of a genomic RNA molecule , a tRNA primer for reverse transcription as well as small amounts of the products of the viral pol gene. The virion may also include a range of other host cell derived proteins although it is unclear whether these represent a random assortment of proteins that are coincidently incorporated into the virion or whether they play some role in the viral life cycle. Both possibilities are probably true, certainly HIV-1 is known to incorporate into its virion a number of host cell proteins that play a vital role in its life cycle [The retroviral virion is a spherical particle of about 80–100 nm in diameter. It is enclosed by a lipid bilayer derived from the host cell plasma membrane into which one of the retroviral gene products, the envelope protein, is inserted. The virion has considerable internal structure that is mainly comprised of the products of the viral fe cycle ,10.gag, pol and env, the complex retroviruses encode a number of other proteins that are involved in regulating viral replication or the host cells response to the virus. For example, HIV-1 has six gene sequences in addition to the minimal retroviral complement of gag, pol and env. Two of these, tat and rev, encode proteins that regulate expression of the viral genome, while the other four, vpu, vif, vpr and nef, encode proteins that play multiple roles in enhancing viral replication.While the simple retroviruses have only three genes, It is the unique nature of the retroviral life cycle, combined with the simplicity and advantageous arrangement of the retroviral genome, which has made retroviruses so attractive as vectors for gene therapy ,12. The cis  and trans (protein coding sequences) functions  vectors. With these vectors the resulting provirus contains no active retroviral derived transcriptional promoter or enhancer elements [The genetic structure of the virus and the existence of the proviral form make it easy to manipulate retroviruses to make replication defective vectors for transfer of heterologous gene sequences. The proviral form, being DNA, can be readily isolated in standard plasmid cloning vectors and so made amenable to molecular manipulation. The genetic structure of the virus is such that the viral ons Fig.  are largems Fig. . As the elements ,14.transduction, to distinguish it from the process of infection with replication competent viruses. It is theoretically possible that with most, if not all, recombinant vector systems, that recombination of the constituent parts of the system with each other, or with cellular sequences, can regenerate a replication competent retrovirus (RCR) [The use of a replication-defective retroviral vector to transfer gene sequences into target cells has been termed us (RCR) ,16. HoweRetroviruses have historically been most intensively studied in animals that are either the subject of scientific experimentation , or are of commercial significance , where they cause a number of commercially significant diseases. Indeed, the first retroviruses to be described were the oncogenic retroviruses Avian leukosis virus (ALV), and Rous sarcoma virus (RSV), which are both found in chickens. A large number of oncogenic retroviruses have now been described. These tend to cause malignant disease in a very high proportion of infected hosts. In addition, the complex retroviruses human T-cell leukemia virus (HTLV) and bovine leukemia virus (BLV) can cause leukemia in their hosts, although they do so in only a small percentage of infected individuals.The lentiviruses are also overtly pathogenic and have been shown to be the causative agent of several slow progressive diseases in animals including arthritis and encephalitis in goats, leukemia in cattle, anaemia in horses, and immunodeficiency in cats, cattle, primates and humans. The AIDS epidemic means that the lentivirus HIV-1 is now the most intensively studied retrovirus ever-incredibly, given the relative genetic simplicity of the retroviruses, there appears to be much still to learn about many aspects of HIV-1. There are also a number of viruses that cause central nervous system (CNS) pathology. For some of these, such as HIV and HTLV, CNS disease is a secondary pathology, while others are more specific in their effects. Similarly, while ALV and RSV are best known as oncogenic viruses, they are also associated with wasting syndromes.The archetypal retroviral pathogen is the oncogenic retrovirus. Some of these are replication defective retroviruses that carry and express an oncogene sequence-indeed it was these retroviruses that largely allowed the concept of oncogenes to be first defined. These viruses induce cancers with relatively short latency periods. In addition, there are a large number of non-defective retroviruses that are oncogenic. These generally induce cancers after longer latency periods. HTLV and BLV and related viruses form a separate class of complex retroviruses that cause leukemia in a small percentage of infected individuals after very long latency periods. Retroviruses have also been associated with sarcomas in fish but these viruses have not been studied in great detail.These have been described in a number of species, but have been most extensively studied in the laboratory mouse. These are replication defective, simple retroviruses in which part of the normal viral genome has been replaced with a cDNA copy of a cellular oncogene. The viral oncogene sequence often contains mutations that make the protein it encodes act in a dominant manner. The capture of a cellular oncogene by a retrovirus is an extremely rare event, the major significance of these viruses in scientific terms is that they led to the discovery of cellular oncogenes. These viruses depend on the presence of a replication competent helper virus in order to replicate and they induce cancers with relatively short latency periods. The existence of a latency period suggests that oncogene expression is, in itself, not enough to cause malignant disease, but that additional genetic events are required. The majority of the cancers caused by these retroviruses are found in the haematopoietic system although sarcomas are also common. They are also able to transform the phenotype of cells grown in culture, principally by causing cells to lose their contact inhibition. The type of malignant event caused by any one virus is determined by the nature of the oncogene expressed by the virus and by the nature of the enhancer sequences present in the long terminal repeat which control the tissue specific expression of the oncogene.Replication defective vectors obviously also have the same potential to capture oncogenes. However, the mechanism of oncogene capture by retroviruses, and its extreme rarity, means it is probably not of major relevance when considering the risk factors associated with the use of retroviral vectors for gene therapy.via a number of different mechanisms. In contrast to the oncogene carrying retroviruses, these viruses are associated with much longer latency periods. This is a reflection of the relatively low probability that proviral insertion will result in activation of an oncogene, in combination with the requirement for other genetic changes before a cancer eventuates. Although proviral integration can also result in gene inactivation, inactivation of tumour suppressor genes does not appear to be a mechanism associated with any known instances of retroviral induced malignancy.Non-defective, replication competent retroviruses are also associated with malignant diseases. These viruses do not carry oncogene sequences. Although first discovered in the chicken they have been most extensively studied in the laboratory mouse. These viruses induce cancer by activating cellular oncogenes The principal routes of oncogene activation are transcriptional promotion from one of the viral LTRs, and activation of endogenous cellular promoters by the strong transcriptional enhancer elements present in the viral LTRs. In the former case the provirus must obviously integrate in the sense orientation and upstream of the relevant coding sequence. Transcription can be from either LTR , and mayNot all non-defective simple retroviruses are overtly oncogenic and the oncogenic, non-defective simple retroviruses show a spectrum of tissue specificity and oncogenic potential. Analysis of the oncogenic potential of different retroviruses has clearly shown that the major determinant of both the overall oncogenic potential of the virus, and the cell specificity of the type of cancer that results, is the viral long terminal repeat -27. Moreenv gene sequences. For example, the SU protein (p55) of the polycythemic strain of Friend virus binds to, and activates, the erythropoietin receptor resulting in massive erythroid proliferation and splenomegaly [sag gene of Murine Mammary Tumour Viruses (MMTV) induces an immune response by interacting with the T-cell receptor [via the antigen binding site itself, a large proportion of the T-cell population (up to 10%) is stimulated. This, in turn, stimulates B-cells, the initial cellular target for infecting MMTV, allowing enhanced viral replication and the subsequent infection of mammary epithelial cells, the eventual site of tumour formation. Although Sag is a major determinant of the oncogenic potential of MMTV it should be noted that in the final analysis malignancy is due to oncogene activation.In contrast to oncogene activation, the oncogenic potential of some retroviruses maps to the nomegaly . Howeverreceptor . This dogag, pol and env genes common to all retroviruses, have two genes that encode regulatory proteins. HTLV causes adult T-cell leukemia, often after a very long latency period . Only a small percentage of infected individuals (about 1% for HTLV) develop cancer. Although the mechanism of disease induction is unclear it is certainly related to the clonal proliferation of infected cells in vivo. Although viral gene expression does not appear to be necessary for maintenance of the disease, evidence suggests that one of the regulatory proteins, Tax, is important in inducing the initial T cell proliferation.How HTLV  and BLV Given the recent development of vectors from lentiviruses, including HIV, it is worth noting that despite intense scientific scrutiny, examples of insertional mutagenesis or gene activation resulting from infection with these viruses have not been documented. However, in the case of HIV-1 the limited lifespan of most infected cells means that this observation must be interpreted with caution.via the provision of promoter or enhancer sequences, but especially the latter, will be the major risk factor for disease induction. In addition, selection of the retroviral envelope used for vector pseudotyping could also potentially play a role as could inadvertent transfer and expression of other retroviral proteins, at least for vectors developed from particular retroviruses, such as Friend virus.In terms of replication defective retroviral vectors, the study of oncogenic retroviruses suggests that oncogene activation, via infection of the epithelial cells of the blood-brain barrier. After these become infected they release virus directly into the CNS where it infects microglial cells, resulting in a spongiform encephalopathy. The SU (env) protein has been shown to be a major determinant of the neuropathogenesis of Cas-Br-E MLV [Several retroviruses cause CNS disease. Some of these, such as the murine retroviruses Cas-Br-E MLV  and FMCFBr-E MLV  and otheBr-E MLV , analogoHTLV causes CNS disease in only a small percentage (about 1%) of infected individuals after a latent period that can be as short as two, or as long as thirty years . The devin vitro and a number of the cytokines induced by HIV infection of monocytes and macrophages also have the capacity to damage neural tissue, either directly or indirectly [Most individuals infected with HIV have virus within the CNS and the route of infection is thought to be transmigration of infected macrophages across the blood-brain barrier. As well as allowing opportunistic infections within the CNS there is a specific condition, AIDS dementia complex (ADC), which is a direct result of HIV infection of the CNS . Within directly .All of the retroviruses that cause CNS disease would appear to do so as a consequence of their active replication. In the case of HIV there is direct evidence for this-treatment of patients with antiretrovirals can significantly decrease the severity of CNS disease . Howevergag gene product, Pr60 Gag, which is not processed normally [The AIDS epidemic has brought a substantial focus to bear on the retroviruses that cause immunodeficiencies in general, and the subset of these that are lentiviruses in particular. Simple retroviruses that cause immune deficiencies in mice , cats 4 and primnormally , is requnormally .via expression of an altered SU (Env) protein. This protein is incapable of causing resistance to superinfection [In cats the simple retroviruses that induce immunodeficiency do so nfection ; as a confection  and immuThe lentiviruses that have been associated with immune deficiency are FIV, SIV and HIV. All appear to share a common pathogenetic mechanism where virus infection of, and replication in, T-cells directly causes cell death, T-cell depletion and immunodeficiency . Cell deAgain, the pathogenetic mechanisms involved with these retroviruses do not have major relevance to replication defective retroviral vectors. However, the pathogenetic mechanisms involved in the murine and feline immunodeficiencies caused by simple retroviruses do reiterate the point that expression of certain retroviral gene products can induce serious pathogenetic states and that this fact may have some relevance to vector design.Apart from the lentiviruses mentioned above that result in immunodeficiency, there are a number of other lentiviral-associated diseases including those caused by caprine arthritis encephalitis virus (CAEV) , equine For all these viruses pathology appears to be intimately linked to viral replication. Therefore, the pathological mechanisms involved are not of direct relevance to replication defective retroviral vectors.Retroviral infection has also been shown to be the cause of wasting and osteopetrosis in birds  and anaeper se did not appear to have an overt pathogenetic affect, rather a T-cell lymphoma eventuated [From the known mechanisms of retroviral pathogenesis discussed above the most obvious pathogenic potential of retroviral vectors is (i) the production of a replication competent virus, and (ii) insertional mutagenesis, specifically oncogene activation. Clearly, the production of replication competent virus not only creates the potential of pathogenetic disease, but will also greatly increase the probability of insertional mutagenesis. In fact, in the one instance where a vector contaminated with a replication competent virus was administered to animals viral replication entuated , presumagag, env and other retroviral genes also has the potential of inducing a pathogenetic state this would appear to depend on the specific retroviral gene sequence in question and to not be of general significance. Even so, minimizing the inadvertent transfer of retroviral gene sequences should clearly be an objective when developing retroviral vectors, not only because of this issue but also because it will have a bearing on the likelihood of replication competent virus being produced and of an endogenous retrovirus being activated.While the inadvertent transfer of In addition, even though oncogene capture by retroviruses is an extremely rare event, the very significant pathogenic potential of the viruses that result means that it should also be taken into consideration during the development of retroviral vector systems.For various reasons, not least of which has been the problem of achieving positive experimental outcomes, only the issue of reducing the probability of replication competent virus arising has been systematically addressed during the development of retroviral vector technology. Indeed, great care has been taken in the development of retroviral vector systems to minimise the chance of producing replication competent retroviruses ,64. Howetrans function plasmids), either with themselves or with endogenous viral sequences in the cell lines used for virus production [cis sequences present in the vector; (iv) the development of SIN vectors; (v) the minimisation of homology between the separate plasmids that make up the system; and (vi) the use of cell lines that do not contain endogenous retroviral sequences with homology to the vector system [via any of these mechanisms appears to be remote. While this doesn't negate the need for appropriate quality control procedures, especially as there is still the remote probability of inadvertent activation of an endogenous retrovirus from the cell line used for virus production, it means that the major safety issue faced by those wishing to use retroviral vectors is that of insertional mutagenesis and oncogene activation.The generation of replication competent virus has, from the very beginning, been seen as the major safety issue for retroviral vectors and this has led to a prolonged effort to develop means of minimising the probability of it arising. There are two principal ways in which replication competent virus can be produced. The first of these is through recombination of the constituent parts of the vector system .The minimization of the inadvertent transfer of retroviral genes to target cells is clearly a worthwhile objective as some of these genes have direct pathogenic potential and they may also influence the probability of endogenous retroviral sequences in the target cell being activated. Generally, the principles applied to the design of vector systems in order to minimize the probability of RCR being produced will also minimize the probability of inadvertent retroviral gene transfer. However, as the production of RCR requires multiple recombination events more effort should be made to analyse the rate of transfer and expression of individual retroviral gene sequences by vector systems. It is clear that the rate of individual gene transfer is much higher than the rate of RCR generation and can occur at a significant frequency even in highly evolved systems where RCR cannot be detected . This suThe mechanism of oncogene capture appears to be dependent on the generation of a chimeric retroviral-oncogene transcript . This suggests that the risk of oncogene capture will be related to the efficiency of termination/polyadenylation of the proviral transcript and that this should be considered and assessed in the process of vector development, especially as retroviral polyadenylation sequences are often relatively inefficient, perhaps reflecting the necessity for the polyadenylation signal to be inactive in the context of the 5' LTR. However, in transient virus production systems, where the transfected vector plasmid presumably remains either entirely, or almost entirely extrachromosomal, this mechanism would appear to preclude the probability of oncogene capture. In the case of stable producer cell lines there is clearly an argument for categorizing the integration site of the vector sequence and discarding any clones where this is in a known or suspected oncogene.The adverse events that have been observed in animal experiments and clinical trials reinforce the conclusions discussed above, that replication competent virus  and inseIn terms of the vector technology used on the two occasions where oncogene activation has been observed the following comments can be made:via splicing. MoMLV is a strongly oncogenic, non-defective virus that causes B-and T-cell lymphomas and leukemias in mice. As with other non-defective oncogenic retroviruses the primary determinant of its pathological properties is the long terminal repeat enhancer. MoMLV has been shown to induce oncogenesis via activation of any one of a number of different cellular genes .1) The vector is derived from MoMLV and uses the LTR sequence to drive the transgene 2) The vector LTR is used to control transcription of the transgene. In the case of the X-SCID trial there is a strong selective pressure for gene corrected cells and accordingly there will clearly be an equally strong selection for transduced T-cell clones in which the LTR is active.3) The PHSC is notoriously difficult to transduce with oncogenic retroviral vectors and the protocol used was designed to enhance transduction by using multiple cytokines to stimulate division of PHSC. This is likely to induce many genes involved in regulating cell growth. As retroviruses preferentially integrate into active gene sequences, this would increase the number of growth regulating genes accessible as targets for provirus integration and hence promiscuous, unregulated activation. Specifically, LMO2, the oncogene activated in the X-SCID trial, is normally expressed in primitive haematopoietic cells (the target for gene transfer) but not in mature cells (80). Therefore, it will be accessible for proviral integration during the transduction process and its continuing expression in maturing T cells generated from gene corrected precursors is biologically inappropriate.The problems that occurred in this X-SCID trial, their broader relevance and possible answers, have all been reviewed from a number of aspects ,73,77. HGiven what is known about retroviral mediated insertional mutagenesis it is surprising that more attention has not been paid to the technology used in many of the retroviral mediated gene therapy animal studies and human trials. With hindsight, it seems that the technologies used were selected on the basis of efficacy, not safety, that is achieving adequate gene expression took preference over consideration and assessment of insertional mutagenesis. However, given the technical difficulties involved in developing a workable protocol this is not surprising, and it is a pre-occupation that was, and is, shared by all gene therapy researchers.Possible technological approaches that would appear to provide answers to these issues include:1) The use of self-inactivating (SIN) vectors would make a major difference in that the provirus would lack all U3 enhancer sequences, negating the ability of the LTR to activate cellular genes. The vector should also not contain active splice signals. However, given the ability of SIN vectors to be repaired at a significant rate during virus production (see below) careful selection of the retrovirus used to build the vector backbone is also important if this risk is to be minimised. Clearly the construction of vectors from non-oncogenic retroviruses and the development of more effective (i.e. less prone to LTR repair) SIN vectors is warranted. If SIN vectors are to be used the transgene must be expressed from an internal promoter which must also be presumed to have the potential for oncogene activation. Therefore it would be preferable to use a promoter without highly active enhancer elements. In addition, the wisdom of incorporating matrix/scaffold attachment regions into vectors to increase expression may also be contraindicated as these sequences have long-range enhancer like properties (81). If high levels of gene product are required, consideration should be given to other means to enhance transgene expression, such as codon-optimisation of coding sequences.trans-activate adjacent promoters. The different integration specificities of lentiviral  and oncogenic (promoter adjacent in active gene sequences) retroviruses and vectors [2) Vectors should be developed from non-oncogenic retroviruses. The recent development of vectors from HIV-1 and other lentiviruses for unrelated reasons (predominantly their ability to transduce non-cycling cells) means that this has already happened. The Tat dependence of the HIV-1 LTR may also provide an extra measure of safety as long as Tat is not transferred along with the vector. However, the enhancing properties of the HIV-1 LTR in the presence and absence of Tat needs to be carefully defined in order to test the assumption that the HIV-1 LTR lacks the ability to  vectors  also giv3) The incorporation of strong transcription termination/polyadenylation signals and gene isolator sequences (83) may provide another means to reduce the possibility of adjacent genes being activated. These sequences should also reduce the probability of oncogene capture in virus producer cells. However, the incorporation of insulator sequences appears to lead to a significant loss of vector titre (84).4) When the transgene plays a role in regulating cell growth, extra consideration should be given to using the relevant control signals from the gene in question to regulate expression of the transgene.5) Although the PHSC is theoretically a very attractive target for gene transfer it is extremely difficult to transduce with retroviral vectors derived from oncogenic viruses such as MoMLV. Although efficient transduction of human PHSC can now be achieved this requires exposure to multiple cytokines over a relatively long culture period. The potential of new retroviral vectors derived from lentiviruses (85) and spumaviruses (86) to transduce PHSC with shorter exposure to less cytokines needs to be fully explored.+ cells were infused into these patients. Therefore, it would appear the patient is exposed to a much greater number of transduced cells than is theoretically necessary to produce the desired result. In other words, the process of generating gene corrected T cell clones by transduction of CD34+ cells is very inefficient.6) In general the limitations of vectors should be taken into account when designing gene therapy protocols. For example, in the case of X-SCID, it may be just as efficacious to target a more committed T-cell precursor that can be transduced more easily, and without biological manipulation using multiple cytokines. Alternatively, if the PHSC is to be targeted as highly enriched a PHSC population as possible should be used in order to expose the patient to the minimum number of transduction events compatible with the desired outcome. In the two X-SCID patients who developed T cell leukemia, molecular analysis of samples collected before the appearance of malignant disease showed the presence of >50 γc transduced T cell clones. Approximately 14 to 20 million transduced CD34both LTRs of the provirus. As the transcriptional enhancer elements in the 3' LTR are redundant in the context of a retroviral vector construct they can theoretically be deleted without affecting vector performance. After transduction of the target cell both LTRs are deleted and are transcriptionally silent. Although this requires that an internal promoter is used to control expression of the transgene, and makes it more difficult to generate high titre stable packaging cell lines, the advantages of the approach are obvious. However, SIN vectors have not been widely used in the case of oncogenic retroviral vectors, principally because viral titres were low, because of high rates of repair of the SIN deletion [With hindsight SIN vectors ,87,88 nodeletion ,89 and bdeletion . Subsequdeletion  but thisIn contrast, SIN vectors have been widely adopted in the lentiviral vector field ,65,92,93The most important determinant of the safety of retroviral vectors remains ensuring they are free of replication competent retrovirus of any sort. Clearly, the technologies available for the production of vector virions would appear able to preclude the production of replication competent virus by recombination of the constituent parts of the vector system (i.e. vector and helper plasmids) with a very high degree of certainty. However, production of replication competent virus from the cell lines used for virus production remains a theoretical possibility and more work needs to be done on generic assays for replication competent retroviruses.gag, env (SU) and other retroviral gene sequences would appear to of relevance and needs to be specifically addressed in the development of vector systems.Apart from the issue of replication competent virus, analysis of the pathologies associated with retroviruses, and the results of the X-SCID trial, demonstrate that careful attention must be paid to the ability of sequences in retroviral vectors to activate transcription of genes adjacent to proviral integration sites. Although the use of SIN vectors will greatly reduce the risk of such events, given the predilection of current SIN vectors to be repaired during virus production these vectors need to be further developed, especially for vectors derived from strongly oncogenic viruses. In addition, inadvertent transfer to, and expression in, transduced cells of As both oncogenic (MoMLV derived) and lentiviral (HIV-1 derived) vectors have been shown to preferentially integrate into transcribed sequences it would appear logical that the likelihood of proviral integration near cellular genes involved in the positive regulation of cell growth would be increased in actively growing cell populations. This suggests that the use of transduction protocols that target non-cycling cells, or cells that are subjected to the minimum of stimulatory signals as is compatible with efficient gene transfer, would be greatly advantageous in terms of minimising the risk of malignant events after the stimulatory signals are removed.With hindsight, the observation of malignant events induced by replication defective MoMLV retroviral vectors is not surprising although the frequency of these events in the X-SCID gene therapy trail certainly was. The concern is that these events will now cause a significant backlash against the use of all retroviral vectors while the real message is that we need to make better use of the knowledge we now have in terms of designing vectors and gene therapy protocols. Clearly, the known oncogenic potential of MoMLV and its relationship to viral sequences has, for one reason or another, been largely ignored to date. Indeed, most of the retroviral vectors used in trials to date are based on MoMLV and contain an intact 5' LTR. While the historic reasons for this are obvious we now need to evaluate and adopt more appropriate technologies as rapidly as possible.et al [There are several obvious conclusions to be drawn from the X-SCID trial and the results of Li et al . The firet al ,86,95-97Regulatory authorities also have a role to play. Clinical trials are based on extensive preclinical experimentation and animal trials that take many years to complete. Clearly, the particular vector system that has been used to develop a protocol may no longer be the best to use ten years later when clinical trials become a reality. The question is, then, how can the regulation of clinical trials be made flexible enough to allow the introduction of new and improved vector technology late in the process?In conclusion, retroviral mediated gene transfer remains an extremely attractive option for gene therapy when the stable and permanent genetic modification of the target cell is optimal. However, we must take greater care, and utilise more resources, for the pro-active, rather than reactive, refinement and testing of the basic technology that is used for gene therapy and for the adoption of improved vector systems if adverse events are to be minimised.


The objective of this study was to demonstrate the use of the Heckman two-step method to assess and correct for bias due to missing health related quality of life (HRQL) surveys in a clinical study of acute coronary syndrome (ACS) patients.We analyzed data from 2,733 veterans with a confirmed diagnosis of acute coronary syndromes (ACS), including either acute myocardial infarction or unstable angina. HRQL outcomes were assessed by the Short-Form 36 (SF-36) health status survey which was mailed to all patients who were alive 7 months following ACS discharge. We created multivariable models of 7-month post-ACS physical and mental health status using data only from the 1,660 survey respondents. Then, using the Heckman method, we modeled survey non-response and incorporated this into our initial models to assess and correct for potential bias. We used logistic and ordinary least squares regression to estimate the multivariable selection models.We found that our model of 7-month mental health status was biased due to survey non-response, while the model for physical health status was not. A history of alcohol or substance abuse was no longer significantly associated with mental health status after controlling for bias due to non-response. Furthermore, the magnitude of the parameter estimates for several of the other predictor variables in the MCS model changed after accounting for bias due to survey non-response.Recognition and correction of bias due to survey non-response changed the factors that we concluded were associated with HRQL seven months following hospital admission for ACS as well as the magnitude of some associations. We conclude that the Heckman two-step method may be a valuable tool in the assessment and correction of selection bias in clinical studies of HRQL. The potential impact of missing survey responses is often ignored in health-related quality of life (HRQL) studies ,2. MissiNewer statistical techniques have been developed to assess and correct for bias resulting from missing HRQL surveys ,3. One tThe objective of this study was to demonstrate the use of the Heckman two-step method to assess and correct for bias due to missing HRQL surveys. To accomplish this goal, we evaluated HRQL outcomes in a cohort of patients with acute coronary syndromes (ACS).We analyzed data from the VA Access To Cardiology study, which was a multi-center prospective cohort study of 2,733 veterans with a confirmed diagnosis of acute coronary syndromes (ACS), including either acute myocardial infarction or unstable angina . BaselinThe outcome variables were the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores from the 7-month SF-36 health status survey. The PCS and MCS scores reflect a patient's overall physical and mental health status, respectively ,11. The Baseline characteristics of the patients who did and did not complete an SF-36 were compared using analysis of variance for continuous variables and chi-square for categorical variables Table . Then, aThere are two steps in the Heckman method. The first step is the development of a selection equation (i.e. a model of factors associated with survey non-response). This step includes derivation of a variable from the selection equation called the Inverse Mills Ratio (IMR). The second step of the Heckman method is the insertion of the IMR variable into the initial regression models  from a given study in order to assess for, and attempt to control for, selection bias.The first step in the Heckman method is to create the selection model, which estimates whether or not the quality of life survey was completed. The Heckman selection equation is usually estimated using a probit estimator ,21. The Once the Heckman selection equation is estimated, the residuals (error term) from this equation are used to form a new variable called the Inverse Mills Ratio (IMR). The formula to create the IMR variable depends on the distributional assumption in the outcome equation. In most HRQL applications, the quality of life score is the outcome of interest and is usually estimated using multivariable linear regression. In this case, the distributional assumption of the error term is the standard normal distribution, so that the ratio of the standard normal probability density function (pdf) and cumulative density function (cdf) applied to the residuals for each individual in the data set is created. The ratio of pdf/cdf is the IMR.Each individual in the study sample receives an individual value of the IMR based on the residual observed for that individual in the selection equation. In this study, the value of the IMR for each individual represented the predicted probability that they completed the 7-month SF-36 survey. It is important to note that the IMR is a function not only of observed or measured variables that are included in the selection equation, but also of unobserved or unmeasured variables. These are captured through the error term or residual in the selection equation, and included through the non-linear function used to estimate the IMR. As a result, adding the IMR into the outcome equation introduces a term that attempts to capture both observed and unobserved variables that affect selection, or non-response.We estimated the Heckman model using the maximum likelihood estimation method in Stata version 8.0. In this approach, the outcome and selection models are estimated jointly, which can result in slightly different selection models for different outcomes, in this case the PCS and MCS scores from the SF-36. However, for clarity of presentation of the Heckman process, we present only one table of selection equation results (the probit estimation of the probability of returning the SF36), assuming that the patient survived to the 7-month survey point.The second step of the Heckman method is to include the IMR as a separate predictor variable in the initial regression models. In this study, the IMR variable derived from our Heckman selection model was inserted into the initial PCS and MCS models. Once this variable is inserted, two factors can be evaluated to help determine whether there is significant bias from missing responses in the initial models. First, one can examine the significance of the IMR variable itself. If significant, it suggests there was significant bias in the initial model. However, one potential limitation of the Heckman method is that if the Heckman selection model is not well-specified, and the variables in the selection model do not predict response/non-response well, the IMR may be weaker than expected and the Heckman method may have limited power to detect bias. Therefore, a second factor to examine following the addition of the IMR variable into the initial outcome models is whether or not there have been significant changes in any of the parameter estimates of the other predictor variables in the model. While somewhat arbitrary, changes in parameter estimates of >10% may indicate that these estimates were biased due to missing surveys. Where possible, one should apply clinical judgment about whether changes in parameter estimates are 'biologically important' .With these factors taken together, the insertion of the IMR variable into the initial risk models allows the assessment of whether or not there was bias in the initial models, and suggests which initial predictors may have been most associated with this bias. Furthermore, by including the effect of unmeasured as well as measured variables from the selection equation, bias due to selection is controlled.Compared to patients that completed the 7-month SF-36 survey, patients who did not respond to the survey were older, more likely to be current smokers and more likely to have a history of alcohol or substance abuse, dementia, stroke, or depression Table . FurtherThe initial multivariable PCS and MCS models not accounting for potential selection bias from missing HRQL surveys are presented in Tables Variables significantly associated with better 7-month physical health status included ST-segment elevation MI on electrocardiogram and coronary revascularization during the index ACS hospital admission. Variables significantly associated with worse 7-month physical health status included older age, history of prior CABG surgery, chronic heart failure, arthritis, COPD, stroke, depression, diabetes, and elevated serum creatinine during index ACS admission.Variables significantly associated with better 7-month mental health status included older age and ST-segment elevation MI on electrocardiogram. Variables significantly associated with worse 7-month mental health status included a history of prior MI, alcohol and/or substance abuse, COPD, and arthritis.The Heckman selection model (modeling response to the SF-36) is presented in Table The final multivariable models for PCS and MCS (after addition of the IMR variables from the Heckman selection model) are presented in Tables By contrast, when the IMR variable was inserted into the initial MCS model, we found evidence of selection bias. In this case, the parameter estimate for history of alcohol or substance abuse changed from significant to insignificant with the introduction of the IMR variable. Therefore, it appears that a history of alcohol or substance abuse was associated with lower likelihood of responding to the survey, but not directly associated with mental health status. In addition, a number of parameter estimates changed quantitatively, with larger changes than those observed in the PCS findings. Finally, the coefficient on the IMR variable itself was significant, and was negatively associated with MCS, implying that unobserved variables in the selection equation appear to be associated with worse MCS scores.The goal of this study was to demonstrate the use of the Heckman two-step method to assess and correct for bias due to missing HRQL surveys in a clinical study of acute coronary syndrome patients. We created initial multivariable models of 7-month post-ACS physical and mental health status using data only from survey respondents. Then, using the Heckman method, we modeled survey non-response, derived an Inverse Mills Ratio variable for each patient that captured the likelihood of survey response, and incorporated this variable into our initial models to assess and correct for potential bias from survey non-response.We found that our initial model of 7-month physical health status was not biased due to survey non-response. In contrast, our initial model of 7-month mental health status was biased. After controlling for bias due to non-response, a history of alcohol or substance abuse was no longer associated with mental health status. Furthermore, the magnitude of the parameter estimates for several of the other predictor variables in the MCS model changed after accounting for bias due to survey non-response.Given these results, biased parameter estimates of the association between these variables and mental health status would have been reported if we had used the standard approach to evaluating the predictors of HRQL outcomes in this population. Furthermore, we might have concluded that alcohol/substance abuse was significantly associated with mental health status outcomes following ACS, and may therefore be an important target for interventions to improve post-ACS HRQL . While alcohol/substance abuse may be important for other reasons, it would have been incorrect to conclude that it was associated with HRQL in our study population. Rather, it was a marker for survey non-response. This analysis demonstrates the utility of the Heckman method in its application for assessing and correcting survey response bias in clinical studies of HRQL.Health-related quality of life data are usually not missing at random, and failure to account for missing HRQL assessments can bias estimates of associations and may lead to inappropriate conclusions about the determinants of HRQL outcomes -3. OftenThere are other statistical techniques, or approaches, to assess and correct for bias resulting from missing HRQL surveys, including index function models, propensity scores, instrumental variables, and multiple imputation methods -4,23. ThPropensity score approaches can be analogous to the Heckman method in that a multivariable model of survey non-response is developed and the probability of survey non-response is used to stratify the study population and/or the propensity score is used as an independent variable in the primary HRQL models. In other words, propensity scores are similar to the Heckman method in that the predicted probability of non-response is used as the basis for assessing the impact of missing data and controlling for it . Unlike Instrumental variable approaches are also used to address similar questions to those addressed by the Heckman method. In the full instrumental variable approach, a single exogenous variable  is used to stratify the full sample, removing the effect of the correlated error terms that lead to biased estimates . An instFinally, multiple imputation methods can be employed to address missing HRQL data . In contThe Heckman method has several limitations. First, the selection equation must have at least one variable that is associated with survey response but not the outcome of the study (i.e. HRQL). In some clinical applications, the inability to identify such a variable may make it difficult to use the full Heckman method to control bias. However, it is still possible to use the first step of estimating a selection equation to assess the degree to which selection bias may affect the parameter estimates in an outcome equation. If there are variables that are significant in both the selection equation and the outcomes equation, it is likely that there is bias due to selection effects in the outcome equation. Acknowledging this and commenting on the likely magnitude of effect may provide helpful guidance to clinicians and other researchers. Another limitation of the Heckman method is that this technique depends heavily on the quality of the data available for the selection equation. If the amount of variance explained is relatively low, then there is a possibility that selection bias in the outcomes equation may not be detected. In other words, the Heckman method can be under-powered for the detection of bias in some cases.Finally, the Heckman method is very sensitive to how the model is specified; in other words, omitting variables that are associated with either non-response or with the outcome of interest  can lead to inaccurate findings and biased estimates of the parameters in the final models. Careful attention to specifying the models, and ensuring that model specification follows what is known in the literature to be associated with the outcomes of interest is essential .This study demonstrated the use of the Heckman two-step method to assess and control for bias from missing HRQL surveys in a clinical study. We found that our mental health status model was significantly biased due to missing HRQL assessments. Recognition and correction of this bias changed the parameter estimates of association and the factors that we concluded were associated with HRQL seven months following hospital admission for an acute coronary syndrome. We conclude that the Heckman two-step method may be a valuable tool in the assessment and correction of selection bias in clinical studies of HRQL.AES conceived of the study, participated in design and coordination, conducted analyses, and drafted the manuscript; MEP conducted analyses and contributed to the manuscript; DJM and JAS reviewed and contributed to the manuscript; JSR participated in the design and coordination of the study and participated in the drafting and revision of the manuscript. All authors read and approved the final manuscript.


Proteins may be the workhorse of the cell, but when a cell can synthesize one protein in a matter of minutes, chances are some will become obsolete. Though many proteins put in years of productive service, others quickly outlive their usefulness and can even damage the cell. Proteins that help form bone and muscle, for example, function for years while regulators of mitosis and cell proliferation might finish their jobs in seconds. Such short-timers are soon tagged as superfluous by a chain of small proteins called ubiquitin, which marks the proteins for degradation in an enzyme called the proteasome. Once in the proteasome, these proteins are broken down and can then be recycled for more productive ventures.A massive structure by cellular standards, the proteasome consists of multiple subunits, including a cylindrical core particle called 20S, which catalyzes degradation, and regulatory complexes called 19S caps, which form lid and base structures at both ends of the core. While the structure and biomechanics of the 20S core have been well characterized, much less is known about the functional mechanics of the regulatory complexes. The lid--base complex recognizes only ubiquitin-tagged proteins, which are then unfolded so they can enter the proteasome. But first ubiquitin chains must be detached from the protein, a task performed by an enzyme in the proteasome called Rpn11 isopeptidase. How the lid–base complex removes the ubiquitin tag, unfolds the protein, and shuttles it into the proteasome's core is not clear. Now Raymond Deshaies and colleagues present the structure of a homolog of the 19S lid's isopeptidase enzymatic center and provide new insights into these questions.The proteasome Rpn11 subunit contains a key region called the JAMM motif, which Deshaies' lab has shown previously is required for the proteasome to remove ubiquitin tags. For the work discussed in this paper, the researchers set out to understand how the proteasome strips off ubiquitin tags from proteins about to be destroyed by determining the three-dimensional structure of the JAMM motif.Archaeoglobus fulgidus. After determining the structure of the JAMM protein , the researchers discovered that AfJAMM looks nothing like the well-known deubiquitinating enzymes. But the arrangement of a set of amino acids that binds a zinc ion and forms the proposed active site of AfJAMM does resemble that found in a well-known protein-degrading metalloprotease called thermolysin, even though in other respects AfJAMM and thermolysin have very different features. The researchers mutated amino acid residues in another JAMM protein called Csn5  and found that the residues are indeed important for Csn5 function. These results suggest that JAMM does indeed represent a novel family of metalloproteases.The researchers tested many genes to look for a JAMM-containing protein that would crystallize properly and found one in the heat-loving prokaryote As for the wider function of JAMM proteins, the researchers speculate that these proteins are likely to be involved in a variety of important regulatory systems since they appear in life forms that lack ubiquitin and ubiquitin-like proteins. The crystal structure reported in this paper will provide a valuable tool for investigations into the underlying structural and functional mechanisms of these enzymes. And it may have important therapeutic implications. Proteasome inhibitors are promising anticancer therapies—fighting cancer by blocking machinery required by rapidly dividing cells. In the hopes of developing more targeted therapies, scientists are trying to fine-tune their control of the ubiquitin system and the proteasome. Inhibiting the JAMM domain of enzymes like Csn5, which remove ubiquitin-like tags from proteins upstream of the proteasome, for example, might just do the trick.


Degradation in the mechanical properties of recycled polymer materials has been recently appearing as a big issue in polymer science. The molecular mechanism of the degradation is considered in part due to residual memories of flow experienced during molding processes, and therefore the mechanical recycling through remolding involving melting and recrystallization has been attempted in recent years. In the present paper, the molecular processes of melting and recrystallization are investigated by the molecular dynamics simulation for polyethylene with special interest in the melt memory effects. We also studied the mechanical properties of the recrystalized materials that have undergone different recrystallization processes aiming to discover better recycling strategies. A successive step-by-step approach is adopted to study the loss of the crystal memory during retention in the melt, the effects of the melt memory on the mode of recrystallization, the relation between the recrystallization mode and the resulting higher-order structure, and the mechanical properties controlled by the higher-order structures. It is shown that the melt memory clearly remains in various order parameters that persist over time scales corresponding to the Rouse time, the remaining melt memory markedly affects the crystallization mode leading to distinct crystalline morphologies, and the distinct morphologies obtained give different mechanical responses during large deformations. Lower mechanical properties in recycled polymer materials are due to various external conditions, such as temperature, stress, ultraviolet rays, etc., during their usage before recycling, where degradation in molecular and/or higher order level, as well as the residual foreign substances, are considered the origins. Understanding the mechanism of the deterioration in physical properties due to these factors has recently become a large issue in polymer science .Elucidating a wide variety of physical degradation processes at the molecular level and clarifying their basic principles are research subjects that form the basis of developing new methods of controlling the physical property of recycled materials. Mechanical recycling is accomplished through a remolding process that involves the melting of the used plastics and recrystallization into recycled ones .Regarding the melting of the plastics, the history of the initial molding process is considered to remain even after melting, and this residual history of the so-called melt memory has a significant influence on the recrystallization . HoweverMany experimental studies have long been reported regarding the melt memory effects on crystallization . Some “sHowever physical entity of the memories are considered local and transient, and there are great difficulties in directly identifying their molecular images due to the limited spatial and temporal resolutions. On the other hand, some previous studies suggested the importance of molecular entanglements  instead What is the molecular mechanism of retaining the melt memory of the previous history? How does it affect the material’s recrystallization during the molding process? The molecular mechanism of the melt retaining the memory of previous history, and the way it greatly affects the material’s recrystallization during molding process are our major subjects of interest. Our present goal is to have a unified understanding of recrystallization and higher-order structure formation and to elucidate the origin of the reduced mechanical properties in recycled materials. As described before, various factors can cause degradation of the plastic materials also including environmental influences causing chemical degradation during their usage.However, our present work only focuses on the physical processes of higher order structure formation during recrystallization and the resulting alteration of the mechanical properties. For this purpose, our powerful research tool is computer modeling, which has been rapidly developed in recent years . It willWe here investigate polyethylene, the simplest polymer, as our starting material for the molecular dynamics (MD) investigations of the mechanical recycling of crystalline polymers. Although many experimental investigations have already reported on the PE recycling, there is no systematic approach, as far as the authors are aware, based on the molecular level simulations of recrystallization and deformation.In this paper, we study PE, as in our previous papers ,12, usina, b, and c), which is replicated by the periodic boundary condition (PBC). Most of the MD simulations are done using the program LAMMPS  [We placed PE molecules within a rectangular parallelepiped MD cell having three sides of lengths (mulator)  with themulator)  as a pre. We can here define a local order parameter σ . We here regard the crystallizing system as a network composed of the crystallites, ties, folds, and cilia. In order to describe the network structure, we define basic network components as given in our previous work . Since tThis crystal criterion may be slightly weaker and resulting crystallinity ous work .We also studied the possible effects of chain entanglement in the melt on recrystallization; we also calculated the entanglements remaining in the solid state, such as the so-called trapped entanglements between folds. Several methods have been proposed for counting the number of entanglements, and we here use the Z1 code proposed and made available by Kröger .Z-axis direction of stretching , and the inter-crystalline regions are filled with folds or loops (yellow), ties or bridges (red), and cilia or tails (green).First, we aimed to clarify the origin of the melt memory by directly observing the structure of the melt, which was generated by constant heating of the stacked lamellae. This starting crystalline system was melted by a constant heating (10 K/ns), and the melting process was investigated. In order to quantitatively evaluate the melting process, we used the crystallinity In Z-axis direction during retention in the melt suggesting that the initial stretched molecules in the melt are gradually relaxing and shortening in the Z-axis direction.An essential question in the melt memory effect is how long the melt memory remains. The time period that the melt memory remains will largely depend on the holding temperature Z-axis rapidly shrink and relax to an almost equilibrated random coil state in around 10 ns.e chains d; the moIn addition to these The local orientation order der flow  as will Extensive research has long been done on the polymer entanglements in the melt especially focusing on rheological problems, where the number of entanglements Z per chain and the segment lengths between the adjacent entanglements are discussed. We investigated the number of entanglements Z in the melt and their possible roles in the process crystallization by using the Z1-code proposed by Kröger . Figure The tendency for the number Z to slightly increase with The ample memory is confirmed to remain in the melt. Then, the way it affects recrystallization is the next important question. I and the degree of chain elongation in the melt ter  P2z .We then considered the later stages of the crystal growth. t as n, called the Avrami exponent, represents the spatial form of the growing crystals and the birth rate of the nucleus. n is about 1 when the memory remains strong  gradually lose their original preference and begin to show considerable chain tilting from the Z-axis.The snapshots given in the lowest row of Z-axis elongation for the two representative textures (upper) and their mechanical properties (lower); the two textures (a) and (b) correspond to the state A and the state B defined in Z-axis  is represented as the ratio of the length Z-axis orientation reflecting the strong Z-orientation remaining in the melt.Such large changes in the crystalline texture are expected to give distinct mechanical response to the elongation.  state A a, the crZ-axis, which corresponds to the considerable loss of the melt memory. Our preliminary studies of the stress–strain relations for the well-developed crystalline textures shows that the highly oriented crystalline structure recrystallized under strong memory gives clear cavities in the amorphous regions  of 0.05/ns; Z-axis) direction. We also notice the formation of fibrils extending along the Z-axis on both sides of the cavity.Let us first consider the uniaxial deformation along the crystal chain axis as shown in Such a structure, consisting of cavities and neighboring fibrils, resembles that of the craze observed in the brittle fracture of amorphous polymers. When a cavity starts growing from one place, the macroscopic deformation is mainly due to the cavity with the deformation in other amorphous regions being small. The stress–strain curve in this case gives only small elongation before a sudden brittle fracture by the elongation of about 5% b. One ofWithin an amorphous layer, the folds that are rooted on the opposite lamellar surfaces generally do not form the so-called trapped entanglements; this is readily noticed from This finding may appear somewhat strange and against previous simulation reports . HoweverZ-axis accomplish large-scale reorientation of about 90° toward the X-axis direction [In the transverse drawing perpendicular to the chain axis b, the crirection . Figure X-axis, which inevitably leads to the thinning of the lamellae along the Z-axis. The crystallites with largely tilted chains give rise to facile shear deformation by significant crystalline chain slips; the macroscopic deformation is nearly constant volume process without cavitation. The chain reorientation and crystal reorganization, which is often called “crystal breaking” or “mechanical melting”, is particularly conspicuous until 30 ns, after which many fibrils grow along the X-axis.In the early stage of deformation, we first notice the large reorientation of the crystalline chains toward the We here investigate the molecular processes of the transverse elongation by using various physical parameters: the level of stress, the volume of the MD cell, the crystallinity, and the degree of chain reorientations both in the crystalline and in the amorphous regions . In the This is then followed by an elongation under nearly constant stress level up to around 30 ns (εH~1.5) a, duringεH~1.5) b; also nεH~1.5) c. This sX-axis and that along the Z-axis nearly replace giving complete (90 deg.) conversion of the crystal orientations.The degree of chain reorientation during this stage is clearly seen in X-axis direction ; the mode of crystal growth expressed in terms of the Virial exponent was shown to vary from about 1.0 to 4.0 during the process of losing melt memory; with the loss of memory the mode of crystal growth changes from the one-dimensional along the draw direction to the three dimensional one.Z-axis orientation, thus, giving more isotropic chain orientation in the crystals. These findings were also consistent with our previous MD simulation of crystallization from the melts under various tensions along the Z-direction [At the same time, the crystalline texture in terms of the chain orientation in the crystalline regions responded systematically to the melt memory, where the chain orientation in the crystalline regions gradually lost their initial irection . Higher We also found that the different crystalline textures obtained gave quite distinct mechanical responses to the elongation. The highly ordered stacked lamellae grown from the melt with higher melt memory resulted in the larger initial modulus but followed by the brittle facture with pronounced emergence of voids (cavities) in the amorphous regions. While the crystals of less aligned chains showed much larger plastic deformation under nearly constant volume condition at much lower stress levels, which was considered due to much easier chain slip in the crystalline regions.The crystalline texture and the way the crystalline chains align in each crystal domain were considered to dominate the mode of macroscopic deformation. We investigated the two extreme cases of the crystalline textures. We found that the elongation parallel to the crystalline chains (transverse to the long axis of the lamellae) was the brittle fracture with pronounced void formation, while the transverse elongation  was the remarkable plastic deformation and large scale chain-reorientation resulting in the well-developed fibril texture along the new stretched direction.The quantitative comparison of the transverse plastic deformation with experimental mechanical data showed unexpectedly good correspondence. In the real spherulitic texture of PE, the crystalline lamellae highly oriented to the draw direction, as with P in We expect the overall average behavior of the real spherulite to be well-reconstructed from the two extreme cases of the textures. Indeed the stress–strain curve produced by the present MD simulation a compareThe remaining melt memory was found to give marked influences on recrystallization leading to the distinct crystalline morphologies. In addition, the distinct morphologies observed were found to cause quite different mechanical responses during their large deformation.