Glioblastoma Subclasses Can Be Defined by Activity among Signal Transduction Pathways and Associated Genomic Alterations

Glioblastoma multiforme (GBM) is an umbrella designation that includes a heterogeneous group of primary brain tumors. Several classification strategies of GBM have been reported, some by clinical course and others by resemblance to cell types either in the adult or during development. From a practical and therapeutic standpoint, classifying GBMs by signal transduction pathway activation and by mutation in pathway member genes may be particularly valuable for the development of targeted therapies.We performed targeted proteomic analysis of 27 surgical glioma samples to identify patterns of coordinate activation among glioma-relevant signal transduction pathways, then compared these results with integrated analysis of genomic and expression data of 243 GBM samples from The Cancer Genome Atlas (TCGA). In the pattern of signaling, three subclasses of GBM emerge which appear to be associated with predominance of EGFR activation, PDGFR activation, or loss of the RAS regulator NF1. The EGFR signaling class has prominent Notch pathway activation measured by elevated expression of Notch ligands, cleaved Notch receptor, and downstream target Hes1. The PDGF class showed high levels of PDGFB ligand and phosphorylation of PDGFRbeta and NFKB. NF1-loss was associated with lower overall MAPK and PI3K activation and relative overexpression of the mesenchymal marker YKL40. These three signaling classes appear to correspond with distinct transcriptomal subclasses of primary GBM samples from TCGA for which copy number aberration and mutation of EGFR, PDGFRA, and NF1 are signature events.Proteomic analysis of GBM samples revealed three patterns of expression and activation of proteins in glioma-relevant signaling pathways. These three classes are comprised of roughly equal numbers showing either EGFR activation associated with amplification and mutation of the receptor, PDGF-pathway activation that is primarily ligand-driven, or loss of NF1 expression. The associated signaling activities correlating with these sentinel alterations provide insight into glioma biology and therapeutic strategies

Redundant Mechanisms Prevent Mitotic Entry Following Replication Arrest in the Absence of Cdc25 Hyper-Phosphorylation in Fission Yeast

Following replication arrest the Cdc25 phosphatase is phosphorylated and inhibited by Cds1. It has previously been reported that expressing Cdc25 where 9 putative amino-terminal Cds1 phosphorylation sites have been substituted to alanine results in bypass of the DNA replication checkpoint. However, these results were acquired by expression of the phosphorylation mutant using a multicopy expression vector in a genetic background where the DNA replication checkpoint is intact. In order to clarify these results we constructed a Cdc25(9A)-GFP native promoter integrant and examined its effect on the replication checkpoint at endogenous expression levels. In this strain the replication checkpoint operates normally, conditional on the presence of the Mik1 kinase. In response to replication arrest the Cdc25(9A)-GFP protein is degraded, suggesting the presence of a backup mechanism to eliminate the phosphatase when it cannot be inhibited through phosphorylation

A Drosophila Model for EGFR-Ras and PI3K-Dependent Human Glioma

Gliomas, the most common malignant tumors of the nervous system, frequently harbor mutations that activate the epidermal growth factor receptor (EGFR) and phosphatidylinositol-3 kinase (PI3K) signaling pathways. To investigate the genetic basis of this disease, we developed a glioma model in Drosophila. We found that constitutive coactivation of EGFR-Ras and PI3K pathways in Drosophila glia and glial precursors gives rise to neoplastic, invasive glial cells that create transplantable tumor-like growths, mimicking human glioma. Our model represents a robust organotypic and cell-type-specific Drosophila cancer model in which malignant cells are created by mutations in signature genes and pathways thought to be driving forces in a homologous human cancer. Genetic analyses demonstrated that EGFR and PI3K initiate malignant neoplastic transformation via a combinatorial genetic network composed primarily of other pathways commonly mutated or activated in human glioma, including the Tor, Myc, G1 Cyclins-Cdks, and Rb-E2F pathways. This network acts synergistically to coordinately stimulate cell cycle entry and progression, protein translation, and inappropriate cellular growth and migration. In particular, we found that the fly orthologs of CyclinE, Cdc25, and Myc are key rate-limiting genes required for glial neoplasia. Moreover, orthologs of Sin1, Rictor, and Cdk4 are genes required only for abnormal neoplastic glial proliferation but not for glial development. These and other genes within this network may represent important therapeutic targets in human glioma

Importance of a C-Terminal Conserved Region of Chk1 for Checkpoint Function

BACKGROUND: The protein kinase Chk1 is an essential component of the DNA damage checkpoint pathway. Chk1 is phosphorylated and activated in the fission yeast Schizosaccharomyces pombe when cells are exposed to agents that damage DNA. Phosphorylation, kinase activation, and nuclear accumulation are events critical to the ability of Chk1 to induce a transient delay in cell cycle progression. The catalytic domain of Chk1 is well-conserved amongst all species, while there are only a few regions of homology within the C-terminus. A potential pseudosubstrate domain exists in the C-terminus of S. pombe Chk1, raising the possibility that the C-terminus acts to inhibit the catalytic domain through interaction of this domain with the substrate binding site. METHODOLOGY/PRINCIPAL FINDINGS: To evaluate this hypothesis, we characterized mutations in the pseudosubstrate region. Mutation of a conserved aspartic acid at position 469 to alanine or glycine compromises Chk1 function when the mutants are integrated as single copies, demonstrating that this domain of Chk1 is critical for function. Our data does not support, however, the hypothesis that the domain acts to inhibit Chk1 function as other mutations in the amino acids predicted to comprise the pseudosubstrate do not result in constitutive activation of the protein. When expressed in multi-copy, Chk1D469A remains non-functional. In contrast, multi-copy Chk1D469G confers cell survival and imposes a checkpoint delay in response to some, though not all forms of DNA damage. CONCLUSIONS/SIGNIFICANCE: Thus, we conclude that this C-terminal region of Chk1 is important for checkpoint function and predict that a limiting factor capable of associating with Chk1D469G, but not Chk1D469A, interacts with Chk1 to elicit checkpoint activation in response to a subset of DNA lesions

Consensus over Random Graph Processes: Network Borel-Cantelli Lemmas for Almost Sure Convergence

Distributed consensus computation over random graph processes is considered. The random graph process is defined as a sequence of random variables which take values from the set of all possible digraphs over the node set. At each time step, every node updates its state based on a Bernoulli trial, independent in time and among different nodes: either averaging among the neighbor set generated by the random graph, or sticking with its current state. Connectivity-independence and arc-independence are introduced to capture the fundamental influence of the random graphs on the consensus convergence. Necessary and/or sufficient conditions are presented on the success probabilities of the Bernoulli trials for the network to reach a global almost sure consensus, with some sharp threshold established revealing a consensus zero-one law. Convergence rates are established by lower and upper bounds of the $\epsilon$-computation time. We also generalize the concepts of connectivity/arc independence to their analogues from the $*$-mixing point of view, so that our results apply to a very wide class of graphical models, including the majority of random graph models in the literature, e.g., Erd\H{o}s-R\'{e}nyi, gossiping, and Markovian random graphs. We show that under $*$-mixing, our convergence analysis continues to hold and the corresponding almost sure consensus conditions are established. Finally, we further investigate almost sure finite-time convergence of random gossiping algorithms, and prove that the Bernoulli trials play a key role in ensuring finite-time convergence. These results add to the understanding of the interplay between random graphs, random computations, and convergence probability for distributed information processing.Comment: IEEE Transactions on Information Theory, In Pres

The Salivary Secretome of the Tsetse Fly Glossina pallidipes (Diptera: Glossinidae) Infected by Salivary Gland Hypertrophy Virus

Tsetse fly (Diptera; Glossinidae) transmits two devastating diseases to farmers (human African Trypanosomiasis; HAT) and their livestock (Animal African Trypanosomiasis; AAT) in 37 sub-Saharan African countries. During the rainy seasons, vast areas of fertile, arable land remain uncultivated as farmers flee their homes due to the presence of tsetse. Available drugs against trypanosomiasis are ineffective and difficult to administer. Control of the tsetse vector by Sterile Insect Technique (SIT) has been effective. This method involves repeated release of sterilized males into wild tsetse populations, which compete with wild type males for females. Upon mating, there is no offspring, leading to reduction in tsetse populations and thus relief from trypanosomiasis. The SIT method requires large-scale tsetse rearing to produce sterile males. However, tsetse colony productivity is hampered by infections with the salivary gland hypertrophy virus, which is transmitted via saliva as flies take blood meals during membrane feeding and often leads to colony collapse. Here, we investigated the salivary gland secretome proteins of virus-infected tsetse to broaden our understanding of virus infection, transmission and pathology. By this approach, we obtain insight in tsetse-hytrosavirus interactions and identified potential candidate proteins as targets for developing biotechnological strategies to control viral infections in tsetse colonies

Single-cell analysis tools for drug discovery and development

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed