Dephosphorylation of p-ERK1/2 in relation to tumor remission after HER-2 and Raf1 blocking therapy in a conditional mouse tumor model

Several studies have shown that HER-2/neu (erbB-2) blocking therapy strategies can cause tumor remission. However, the responsible molecular mechanisms are not yet known. Both ERK1/2 and Akt/PKB are critical for HER-2-mediated signal transduction. Therefore, we used a mouse tumor model that allows downregulation of HER-2 in tumor tissue by administration of anhydrotetracycline (ATc). Switching-off HER-2 caused a rapid tumor remission by more than 95% within 7 d of ATc administration compared to the volume before switching-off HER-2. Interestingly, HER-2 downregulation caused a dephosphorylation of p-ERK1/2 by more than 80% already before tumor remission occurred. Levels of total ERK protein were not influenced. In contrast, dephosphorylation of p-Akt occurred later, when the tumor was already in remission. These data suggest that in our HER-2 tumor model dephosphorylation of p-ERK1/2 may be more critical for tumor remission than dephosphorylation of p-Akt. To test this hypothesis we used a second mouse tumor model that allows ATc controlled expression of BXB-Raf1 because the latter constitutively signals to ERK1/2, but cannot activate Akt/PKB. As expected, downregulation of BXB-Raf1 in tumor tissue caused a strong dephosphorylation of p-ERK1/2, but did not decrease levels of p-Akt. Interestingly, tumor remission after switching-off BXB-Raf1 was similarly efficient as the effect of HER-2 downregulation, despite the lack of p-Akt dephosphorylation. In conclusion, two lines of evidence strongly suggest that dephosphorylation of p-ERK1/2 and not that of p-Akt is critical for the rapid tumor remission after downregulation of HER-2 or BXB-Raf1 in our tumor model: (i) dephosphorylation of p-ERK1/2 but not that of p-Akt precedes tumor remission after switching-off HER-2 and (ii) downregulation of BXB-Raf1 leads to a similarly efficient tumor remission as downregulation of HER-2, although no p-Akt dephosphorylation was observed after switching-off BXB-Raf1.

On the Behaviour of Marginal and Conditional Akaike Information Criteria in Linear Mixed Models

In linear mixed models, model selection frequently includes the selection of random effects. Two versions of the Akaike information criterion (AIC) have been used, based either on the marginal or on the conditional distribution. We show that the marginal AIC is no longer an asymptotically unbiased estimator of the Akaike information, and in fact favours smaller models without random effects. For the conditional AIC, we show that ignoring estimation uncertainty in the random effects covariance matrix, as is common practice, induces a bias that leads to the selection of any random effect not predicted to be exactly zero. We derive an analytic representation of a corrected version of the conditional AIC, which avoids the high computational cost and imprecision of available numerical approximations. An implementation in an R package is provided. All theoretical results are illustrated in simulation studies, and their impact in practice is investigated in an analysis of childhood malnutrition in Zambia.

REMOVING TECHNICAL VARIABILITY IN RNA-SEQ DATA USING CONDITIONAL QUANTILE NORMALIZATION

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade’s worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show RNA-seq data demonstrates unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find GC-content has a strong sample specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here we describe statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization (CQN) algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content, and quantile normalization to correct for global distortions.

Establishment of murine cell lines by constitutive and conditional immortalization

Mouse cell lines were immortalized by introduction of specific immortalizing genes. Embryonic and adult animals and an embryonal stem cell line were used as a source of primary cells. The immortalizing genes were either introduced by DNA transfection or by ecotropic retrovirus transduction. Fibroblasts were obtained by expression of SV40 virus large T antigen (TAg). The properties of the resulting fibroblast cell lines were reproducible, independent of the donor mouse strains employed and the cells showed no transformed properties in vitro and did not form tumors in vivo. Endothelial cell lines were generated by Polyoma virus middle T antigen expression in primary embryonal cells. These cell lines consistently expressed relevant endothelial cell surface markers. Since the expression of the immortalizing genes was expected to strongly influence the cellular characteristics fibroblastoid cells were reversibly immortalized by using a vector that allows conditional expression of the TAg. Under inducing conditions, these cells exhibited properties that were highly similar to the properties of constitutively immortalized cells. In the absence of TAg expression, cell proliferation stops. Cell growth is resumed when TAg expression is restored. Gene expression profiling indicates that TAg influences the expression levels of more than 1000 genes that are involved in diverse cellular processes. The data show that conditionally immortalized cell lines have several advantageous properties over constitutively immortalized cells.

Transgenic system for conditional induction and rescue of chronic myocardial hibernation provides insights into genomic programs of hibernation

A key energy-saving adaptation to chronic hypoxia that enables cardiomyocytes to withstand severe ischemic insults is hibernation, i.e., a reversible arrest of contractile function. Whereas hibernating cardiomyocytes represent the critical reserve of dysfunctional cells that can be potentially rescued, a lack of a suitable animal model has hampered insights on this medically important condition. We developed a transgenic mouse system for conditional induction of long-term hibernation and a system to rescue hibernating cardiomyocytes at will. Via myocardium-specific induction (and, in turn, deinduction) of a VEGF-sequestering soluble receptor, we show that VEGF is indispensable for adjusting the coronary vasculature to match increased oxygen consumption and exploit this finding to generate a hypoperfused heart. Importantly, ensuing ischemia is tunable to a level at which large cohorts of cardiomyocytes are driven to enter a hibernation mode, without cardiac cell death. Relieving the VEGF blockade even months later resulted in rapid revascularization and full recovery of contractile function. Furthermore, we show that left ventricular remodeling associated with hibernation is also fully reversible. The unique opportunity to uncouple hibernation from other ischemic heart phenotypes (e.g., infarction) was used to determine the genetic program of hibernation; uncovering hypoxia-inducible factor target genes associated with metabolic adjustments and induced expression of several cardioprotective genes. Autophagy, specifically self-digestion of mitochondria, was identified as a key prosurvival mechanism in hibernating cardiomyocytes. This system may lend itself for examining the potential utility of treatments to rescue dysfunctional cardiomyocytes and reverse maladaptive remodeling.