Generation of Immortal Cell Lines from the Adult Pituitary: Role of cAMP on Differentiation of SOX2-Expressing Progenitor Cells to Mature Gonadotropes

The pituitary is a complex endocrine tissue composed of a number of unique cell types distinguished by the expression and secretion of specific hormones, which in turn control critical components of overall physiology. The basic function of these cells is understood; however, the molecular events involved in their hormonal regulation are not yet fully defined. While previously established cell lines have provided much insight into these regulatory mechanisms, the availability of representative cell lines from each cell lineage is limited, and currently none are derived from adult pituitary. We have therefore used retroviral transfer of SV40 T-antigen to mass immortalize primary pituitary cell culture from an adult mouse. We have generated 19 mixed cell cultures that contain cells from pituitary cell lineages, as determined by RT-PCR analysis and immunocytochemistry for specific hormones. Some lines expressed markers associated with multipotent adult progenitor cells or transit-amplifying cells, including SOX2, nestin, S100, and SOX9. The progenitor lines were exposed to an adenylate cyclase activator, forskolin, over 7 days and were induced to differentiate to a more mature gonadotrope cell, expressing significant levels of α-subunit, LHβ, and FSHβ mRNAs. Additionally, clonal populations of differentiated gonadotropes were exposed to 30 nM gonadotropin-releasing hormone and responded appropriately with a significant increase in α-subunit and LHβ transcription. Further, exposure of the lines to a pulse paradigm of GnRH, in combination with 17β-estradiol and dexamethasone, significantly increased GnRH receptor mRNA levels. This array of adult-derived pituitary cell models will be valuable for both studies of progenitor cell characteristics and modulation, and the molecular analysis of individual pituitary cell lineages

Two-pion Bose-Einstein correlations in central Pb-Pb collisions at sNN\sqrt{s_{\rm NN}} = 2.76 TeV

The first measurement of two-pion Bose-Einstein correlations in central Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 2.76$ TeV at the Large Hadron Collider is presented. We observe a growing trend with energy now not only for the longitudinal and the outward but also for the sideward pion source radius. The pion homogeneity volume and the decoupling time are significantly larger than those measured at RHIC.Comment: 17 pages, 5 captioned figures, 1 table, authors from page 12, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/388

Suppression of charged particle production at large transverse momentum in central Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm NN}} = 2.76 TeV

Inclusive transverse momentum spectra of primary charged particles in Pb-Pb collisions at $\sqrt{s_{_{\rm NN}}}$ = 2.76 TeV have been measured by the ALICE Collaboration at the LHC. The data are presented for central and peripheral collisions, corresponding to 0-5% and 70-80% of the hadronic Pb-Pb cross section. The measured charged particle spectra in $|\eta|<0.8$ and $0.3 < p_T < 20$ GeV/$c$ are compared to the expectation in pp collisions at the same $\sqrt{s_{\rm NN}}$, scaled by the number of underlying nucleon-nucleon collisions. The comparison is expressed in terms of the nuclear modification factor $R_{\rm AA}$. The result indicates only weak medium effects ($R_{\rm AA} \approx$ 0.7) in peripheral collisions. In central collisions, $R_{\rm AA}$ reaches a minimum of about 0.14 at $p_{\rm T}=6$-7GeV/$c$ and increases significantly at larger $p_{\rm T}$. The measured suppression of high-$p_{\rm T}$ particles is stronger than that observed at lower collision energies, indicating that a very dense medium is formed in central Pb-Pb collisions at the LHC.Comment: 15 pages, 5 captioned figures, 3 tables, authors from page 10, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/98

Elliptic flow of charged particles in Pb-Pb collisions at 2.76 TeV

We report the first measurement of charged particle elliptic flow in Pb-Pb collisions at 2.76 TeV with the ALICE detector at the CERN Large Hadron Collider. The measurement is performed in the central pseudorapidity region (|$\eta$|<0.8) and transverse momentum range 0.2< $p_{\rm T}$< 5.0 GeV/$c$. The elliptic flow signal v$_2$, measured using the 4-particle correlation method, averaged over transverse momentum and pseudorapidity is 0.087 $\pm$ 0.002 (stat) $\pm$ 0.004 (syst) in the 40-50% centrality class. The differential elliptic flow v$_2(p_{\rm T})$ reaches a maximum of 0.2 near $p_{\rm T}$ = 3 GeV/$c$. Compared to RHIC Au-Au collisions at 200 GeV, the elliptic flow increases by about 30%. Some hydrodynamic model predictions which include viscous corrections are in agreement with the observed increase.Comment: 10 pages, 4 captioned figures, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/389

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice