Signature Amino Acid Changes in Latent Membrane Protein 1 Distinguish Epstein–Barr Virus Strains

Sequence variations in the Epstein-Barr virus (EBV) latent membrane protein 1 gene have been described in numerous EBV-associated tumors with some of these variations, most notably a 30-base pair deletion in the cytoplasmic carboxyl-terminal domain, suggested as associated with an increase in tumorigenicity. In this study, EBV DNA sequence was determined from 92 tissue specimens or cell lines, including nasopharyngeal carcinoma, oral hairy leukoplakia, post-transplant lymphoma, post-transplant without pathology, mononucleosis, Burkitt's lymphoma, parotid tumor, and normal from distinct geographical regions. The amino- and carboxyl-terminal sequences and, in some cases, the full-length sequences of latent membrane protein 1 were determined. Characteristic sequence patterns distinguished strains, with the carboxyl-terminal sequence being the most informative in distinguishing among the strains. Phylogenetic relationships between strains were determined, as were signature amino acid changes that discriminate between them. A correlation between strain and disease or strain and geographic location was not detected. The sequence variation and signature sequences identified at least seven distinct strains, as well as hybrid strains that apparently result from recombination

Decreased Risk of Squamous Cell Carcinoma of the Head and Neck in Users of Nonsteroidal Anti-Inflammatory Drugs

We evaluated the chemopreventive effect of nonsteroidal anti-inflammatory drug (NSAID) use in head and neck squamous cell carcinomas (HNSCC) by conducting a case-control study based on the administration of a standardized questionnaire to 71 incident HNSCC cases and same number of healthy controls. NSAID use was associated with a 75% reduction in risk of developing HNSCC. A significant risk reduction was noted in association with frequency of NSAID use. Restricting the analysis to aspirin users revealed a significant 90% reduction in risk of developing HNSCC. This study provides evidence for a significant reduction in the risk of developing HNSCC in users of NSAIDs, and specifically aspirin users

Log-linear models for mutations in the HIV genome

We discuss a general application of categorical data analysis to mutations along the HIV genome. We consider a multidimensional table for several positions at the same time. Due to the complexity of the multidimensional table, we may collapse it by pooling some categories. However, the association between the remaining variables may not be the same as before collapsing. We discuss the collapsibility of tables and the change in the meaning of parameters after collapsing categories. We also address this problem with a log-linear model. We present a parameterization with the consensus output as the reference cell as is appropriate to explain genomic mutations in HIV. We also consider five null hypotheses and some classical methods to address them. We illustrate methods for six positions along the HIV genome, through consideration of all triples of positions

Analysis of human immunodeficiency virus type 1 nef gene sequences present in vivo.

The nef genes of the human immunodeficiency viruses type 1 and 2 (HIV-1 and HIV-2) and the related simian immunodeficiency viruses (SIVs) encode a protein (Nef) whose role in virus replication and cytopathicity remains uncertain. As an attempt to elucidate the function of nef, we characterized the nucleotide and corresponding protein sequences of naturally occurring nef genes obtained from several HIV-1-infected individuals. A consensus Nef sequence was derived and used to identify several features that were highly conserved among the Nef sequences. These features included a nearly invariant myristylation signal, regions of sequence polymorphism and variable duplication, a region with an acidic charge, a (Pxx)4 repeat sequence, and a potential protein kinase C phosphorylation site. Clustering of premature stop codons at position 124 was noted in 6 of the 54 Nef sequences. Further analysis revealed four stretches of residues that were highly conserved not only among the patient-derived HIV-1 Nef sequences, but also among the Nef sequences of HIV-2 and the SIVs, suggesting that Nef proteins expressed by these retroviruses are functionally equivalent. The "Nef-defining" sequences were used to evaluate the sequence alignments of known proteins reported to share sequence similarity with Nef sequences and to conduct additional computer-based searches for similar protein sequences. A gene encoding the consensus Nef sequence was also generated. This gene encodes a full-length Nef protein that should be a valuable tool in further studies of Nef function

Tumor Suppressor Function of Syk in Human MCF10A In Vitro and Normal Mouse Mammary Epithelium In Vivo

The normal function of Syk in epithelium of the developing or adult breast is not known, however, Syk suppresses tumor growth, invasion, and metastasis in breast cancer cells. Here, we demonstrate that in the mouse mammary gland, loss of one Syk allele profoundly increases proliferation and ductal branching and invasion of epithelial cells through the mammary fat pad during puberty. Mammary carcinomas develop by one year. Syk also suppresses proliferation and invasion in vitro. siRNA or shRNA knockdown of Syk in MCF10A breast epithelial cells dramatically increased proliferation, anchorage independent growth, cellular motility, and invasion, with formation of functional, extracellular matrix-degrading invadopodia. Morphological and gene microarray analysis following Syk knockdown revealed a loss of luminal and differentiated epithelial features with epithelial to mesenchymal transition and a gain in invadopodial cell surface markers CD44, CD49F, and MMP14. These results support the role of Syk in limiting proliferation and invasion of epithelial cells during normal morphogenesis, and emphasize the critical role of Syk as a tumor suppressor for breast cancer. The question of breast cancer risk following systemic anti-Syk therapy is raised since only partial loss of Syk was sufficient to induce mammary carcinomas

Estimation for the Prediction of Point Processes with Many Covariates

Estimation of the intensity of a point process is considered within a nonparametric framework. The intensity measure is unknown and depends on covariates, possibly many more than the observed number of jumps. Only a single trajectory of the counting process is observed. Interest lies in estimating the intensity conditional on the covariates. The impact of the covariates is modelled by an additive model where each component can be written as a linear combination of possibly unknown functions. The focus is on prediction as opposed to variable screening. Conditions are imposed on the coefficients of this linear combination in order to control the estimation error. The rates of convergence are optimal when the number of active covariates is large. As an application, the intensity of the buy and sell trades of the New Zealand dollar futures is estimated and a test for forecast evaluation is presented. A simulation is included to provide some finite sample intuition on the model and asymptotic properties

Detecting failure of climate predictions

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055

Analyzing 2D gel images using a two-component empirical bayes model

<p>Abstract</p> <p>Background</p> <p>Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and null densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the null density. Thus, there is no guarantee that the estimated null component will be no greater than the mixture density as it should be.</p> <p>Results</p> <p>We present an implementation of a two-component EB model for the analysis of 2D gel images. In contrast to the published estimation method, we propose to estimate the mixture and null densities simultaneously using a constrained estimation approach, which relies on an iteratively re-weighted least-squares algorithm. The assumption about the null density is naturally taken into account in the estimation of the mixture density. This strategy is illustrated using a set of 2D gel images from a factorial experiment. The proposed approach is validated using a set of simulated gels.</p> <p>Conclusions</p> <p>The two-component EB model is a very useful for large-scale hypothesis testing. In proteomic analysis, the theoretical null density is often not appropriate. We demonstrate how to implement a two-component EB model for analyzing a set of 2D gel images. We show that it is necessary to estimate the mixture density and empirical null component simultaneously. The proposed constrained estimation method always yields valid estimates and more stable results. The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.</p

Associations between cigarette smoking and mitochondrial DNA abnormalities in buccal cells

DNA alterations in mitochondria are believed to play a role in carcinogenesis and are found in smoking-related cancers. We sought to replicate earlier findings for the association of smoking with increased mitochondrial DNA (mtDNA) content in buccal cells and further hypothesized that there would be an increased number of somatic mtDNA mutations in smokers. Buccal cells and blood lymphocytes were studied from 42 healthy smokers and 30 non-smokers. Temporal temperature gradient electrophoresis screening and sequencing was used to identify mtDNA mutations. The relative mtDNA content was determined by real-time polymerase chain reaction. Assuming that mtDNA in lymphocytes represents the inherited sequence, it was found that 31% of smokers harbored at least one somatic mtDNA mutation in buccal cells with a total of 39 point mutations and 8 short deletions/insertions. In contrast, only 23% of non-smokers possessed mutations with a total of 10 point mutations and no insertions/deletions detected. mtDNA somatic mutation density was higher in smokers (0.68/10 000 bp per person) than in non-smokers (0.2/10 000 bp per person). There was a statistically significant difference in the pattern of homoplasmy and heteroplasmy mutation changes between smokers and non-smokers. Whereas non-smokers had the most mutations in D-loop region (70%), smokers had mutations in both messenger RNA encoding gene (36%) and D-loop region (49%). The mean ratio of buccal cells to lymphocytes of mtDNA content in smokers was increased (2.81) when compared with non-smokers (0.46). These results indicate that cigarette smoke exposure affects mtDNA in buccal cells of smokers. Additional studies are needed to determine if mitochondrial mutation assays provide new or complementary information for estimating cigarette smoke exposure at the cellular level or as a cancer risk biomarker

Forecasting distributions of inflation rates: the functional auto-regressive approach

In line with recent developments in the statistical analysis of functional data, we develop the semiparametric functional auto‐regressive modelling approach to the density forecasting analysis of national rates of inflation by using sectoral inflation rates in the UK over the period January 1997–September 2013. The pseudo‐out‐of‐sample forecasting evaluation and test results provide an overall support to superior performance of our proposed models over the aggregate auto‐regressive models and their statistical validity. The fan chart analysis and the probability event forecasting exercise provide further support for our approach in a qualitative sense, revealing that the modified functional auto‐regressive models can provide a complementary tool for generating the density forecast of inflation, and for analysing the performance of a central bank in achieving announced inflation targets. As inflation targeting monetary policies are usually set with recourse to the medium‐term forecasts, our proposed work may provide policy makers with an invaluably enriched information set