Recognizing the forest for the trees: testing temporal patterns of cladogenesis using a null model of stochastic diversification

Computer simulations are developed and employed to examine the expected temporal distributions of nodes under a null model of stochastic lineage bifurcation and extinction. These Markovian models of phylogenetic process were constructed so as to permit direct comparisons against empirical phylogenetic trees generated from molecular or other information available solely from extant species. For replicate simulated phylads with n extant species, cumulative distribution functions (cdf's) of branching times were calculated, and compared (using the Kolmogorov-Smirnov test statistic D) to those from three published empirical trees. Molecular phylogenies for columbine plants and avian cranes showed statistically significant departures from the null expectations, in directions indicating recent and ancient species' radiations, respectively, whereas a molecular phylogeny for the Drosophila virilis species group showed no apparent historical clustering of branching events. Effects of outgroup choice and phylogenetic frame of reference were investigated for the columbines and found to have a predictable influence on the types of conclusions to be drawn from such analyses. To enable other investigators to statistically test for nonrandomness in temporal cladogenetic pattern in empirical trees generated from data on extant species, we present tables of mean cdf's and associated probabilities under the null model for expected branching times in phylads of varying size. The approaches developed in this report complement and extend those of other recent methods for employing null models to assess the statistical significance of pattern in evolutionary trees.      

Haptoglobin and CCR2 receptor expression in ovarian cancer cells that were exposed to ascitic fluid: Exploring a new role of haptoglobin in the tumoral microenvironment

Haptoglobin (Hp) is an acute-phase protein that is produced by the liver to capture the iron that is present in the blood circulation, thus avoiding its accumulation in the blood. Moreover, Hp has been detected in a wide variety of tissues, in which it performs various functions. In addition, this protein is considered a potential biomarker in many diseases, such as cancer, including ovarian carcinoma; however, its participation in the cancerous processes has not yet been determined. The objective of this work was to demonstrate the expression of Hp and its receptor CCR2 in the ovarian cancer cells and its possible involvement in the process of cell migration through changes in the rearrangement of the actin cytoskeleton using western blot and wound-healing assays and confirming by confocal microscopy. Ovarian cancer cells express both Hp and its receptor CCR2 but only after exposure to ascitic fluid, inducing moderated cell migration. However, when the cells are exposed to exogenous Hp, the expression of CCR2 is induced together with drastic changes in the actin cytoskeleton rearrangement. At the same time, Hp induced cell migration in a much more efficient manner than did ascitic fluid. These effects were blocked when the CCR2 synthetic antagonist RS102895 was used to pretreat the cells. These results suggest that Hp-induced changes in the cell morphology, actin cytoskeleton structure, and migration ability of tumor cells, is possibly “preparing” these cells for the potential induction of the metastatic phenotype.     

Regulation of cellular adhesion molecule expression in murine oocytes, peri-implant ation and post-implantation embryos

Expression of the adhesion molecules, ICAM-1, VCAM-1, NCAM, CD44, CD49d (VLA-4, a chain), and CDlla (LFA-1, a chain) on mouse oocytes, and pre- and peri-implantation stage embryos was examined by quantitative indirect immunofluorescence microscopy. ICAM-1 was most strongly expressed at the oocyte stage, gradually declining almost to undetectable levels by the expanded blastocyst stage. NCAM, also expressed maximally on the oocyte, declined to undetectable levels beyond the morula stage. On the other hand, CD44 declined from highest expression at the oocyte stage to show a second maximum at the compacted 8-cell/morula. This molecule exhibited high expression around contact areas between trophecto-derm and zona pellucida during blastocyst hatching. CD49d was highly expressed in the oocyte, remained significantly expressed throughout and after blastocyst hatching was expressed on the polar trophecto-derm. Like CD44, CD49d declined to undetectable levels at the blastocyst outgrowth stage. Expression of both     

Gene-Trait Similarity Regression for Multimarker-Based Association Analysis

Summary .  We propose a similarity-based regression method to detect associations between traits and multimarker genotypes. The model regresses similarity in traits for pairs of "unrelated" individuals on their haplotype similarities, and detects the significance by a score test for which the limiting distribution is derived. The proposed method allows for covariates, uses phase-independent similarity measures to bypass the needs to impute phase information, and is applicable to traits of general types (e.g., quantitative and qualitative traits). We also show that the gene-trait similarity regression is closely connected with random effects haplotype analysis, although commonly they are considered as separate modeling tools. This connection unites the classic haplotype sharing methods with the variance-component approaches, which enables direct derivation of analytical properties of the sharing statistics even when the similarity regression model becomes analytically challenging.     

Latent-Model Robustness in Joint Models for a Primary Endpoint and a Longitudinal Process

Summary .  Joint modeling of a primary response and a longitudinal process via shared random effects is widely used in many areas of application. Likelihood-based inference on joint models requires model specification of the random effects. Inappropriate model specification of random effects can compromise inference. We present methods to diagnose random effect model misspecification of the type that leads to biased inference on joint models. The methods are illustrated via application to simulated data, and by application to data from a study of bone mineral density in perimenopausal women and data from an HIV clinical trial.