Multiple B-cell clones producing antibodies directed to the spacer and disintegrin/thrombospondin type-1 repeat 1 (TSP1) of ADAMTS13 in a patient with acquired thrombotic thrombocytopenic purpura

BACKGROUND: The cysteine-rich/spacer domains of ADAMTS13 contain a major binding site for antibodies in patients with acquired thrombotic thrombocytopenic purpura (TTP). OBJECTIVE: To study the heterogeneity of the antibody response towards these domains an immunoglobulin V-gene phage-display library was constructed to isolate monoclonal anti-ADAMTS13 antibodies from the immunoglobulin repertoire of a patient with acquired TTP. METHODS: Combined variable heavy chain (VH) and variable light chain (VL) segments, expressed as single-chain Fv fragments (scFv), were selected for binding to an ADAMTS13 fragment consisting of the disintegrin/thrombospondin type-1 repeat 1 (TSP1)/cysteine-rich/spacer domains. RESULTS: Seven different scFv antibody clones were identified that were assigned to four groups based on their homology to VH germline gene segments. Epitope-mapping revealed that scFv I-9 (VH1-69), I-26 (VH1-02), and I-41 (VH3-09) bind to an overlapping binding site in the ADAMTS13 spacer domain, whereas scFv I-16 (VH3-07) binds to the disintegrin/TSP1 domains. The affinity of scFv for the disintegrin/TSP1/cysteine-rich/spacer domain was determined by surface plasmon resonance analysis and the dissociation constants ranged from 3 to 254 nM. The scFv partially inhibited ADAMTS13 activity. However, full-length IgG prepared from the variable domains of scFv I-9 inhibited ADAMTS13 activity more profoundly. Plasma of six patients with acquired TTP competed for binding of scFv I-9 to ADAMTS13. CONCLUSION: Our data indicate that multiple B-cell clones producing antibodies directed against the spacer domain are present in the patient analyzed in this study. Our findings also suggest that antibodies with a similar epitope specificity as scFv I-9 are present in plasma of other patients with acquired TTP.

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.

A graph theoretic approach to testing associations between disparate sources of functional genomic data

The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology. We present a graph theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived "predictome" and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph theoretic framework to recast a surprising discrepancy presented in Giaever et al. (2002) between gene expression and knockout phenotype, using expression data from a different set of experiments.

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.