A systematic approach to therapeutic target selection in oesophago-gastric cancer

Objective The success of personalised therapy depends on identification and inhibition of the oncogene(s) on which that tumour is dependent. We aimed to determine whether a receptor tyrosine kinase (RTK) array could be used to select the most effective therapeutic strategies in molecularly heterogeneous oesophago-gastric adenocarcinomas. Design Gene expression profiling from oesophago-gastric tumours (n=75) and preinvasive stages (n=57) identified the active signalling pathways, which was confirmed using immunohistochemistry (n=434). RTK arrays on a cell line panel (n=14) determined therapeutic targets for in vitro cytotoxic testing. Feasibility of this personalised approach was tested in tumour samples (n=46). Results MAPK was the most frequently activated pathway (32/75 samples (42.7%)) with progressive enrichment in preinvasive disease stages (p<0.05) and ERK phosphorylation in 148/434 (34.3%) independent samples. Cell lines displayed a range of RTK activation profiles. When no RTKs were activated, tyrosine kinase inhibitors (TKIs) and a Mek inhibitor were not useful (MKN1). In lines with a dominant phosphorylated RTK (OE19, MKN45 and KATOIII), selection of this TKI or Mek in nM concentrations induced cytotoxicity and inhibited Erk and Akt phosphorylation. In cells lines with complex activation profiles (HSC39 and OE33), a combination of TKIs or Mek inhibition (in nM concentrations) was necessary for cytotoxicity and inhibition of Erk and Akt phosphorylation. Human tumours demonstrated diverse activation profiles and 65% of cases had two or more active RTKs. Conclusions The MAPK pathway is commonly activated in oesophago-gastric cancer following activation of a variety of RTKs. Molecular phenotyping can inform a rational choice of targeted therapy

Composition and distribution of the peracarid crustacean fauna along a latitudinal transect off Victoria Land (Ross Sea, Antarctica) with special emphasis on the Cumacea

The following study was the first to describe composition and structure of the peracarid fauna systematically along a latitudinal transect off Victoria Land (Ross Sea, Antarctica). During the 19th Antarctic expedition of the Italian research vessel “Italica” in February 2004, macrobenthic samples were collected by means of a Rauschert dredge with a mesh size of 500 m at depths between 85 and 515 m. The composition of peracarid crustaceans, especially Cumacea was investigated. Peracarida contributed 63% to the total abundance of the fauna. The peracarid samples were dominated by amphipods (66%), whereas cumaceans were represented with 7%. Previously, only 13 cumacean species were known, now the number of species recorded from the Ross Sea increased to 34. Thus, the cumacean fauna of the Ross Sea, which was regarded as the poorest in terms of species richness, has to be considered as equivalent to that of other high Antarctic areas. Most important cumacean families concerning abundance and species richness were Leuconidae, Nannastacidae, and Diastylidae. Cumacean diversity was lowest at the northernmost area (Cape Adare). At the area off Coulman Island, which is characterized by muddy sediment, diversity was highest. Diversity and species number were higher at the deeper stations and abundance increased with latitude. A review of the bathymetric distribution of the Cumacea from the Ross Sea reveals that most species distribute across the Antarctic continental shelf and slope. So far, only few deep-sea records justify the assumption of a shallow-water–deep-sea relationship in some species of Ross Sea Cumacea, which is discussed from an evolutionary point of view

The Epstein-Barr Virus G-Protein-Coupled Receptor Contributes to Immune Evasion by Targeting MHC Class I Molecules for Degradation

Epstein-Barr virus (EBV) is a human herpesvirus that persists as a largely subclinical infection in the vast majority of adults worldwide. Recent evidence indicates that an important component of the persistence strategy involves active interference with the MHC class I antigen processing pathway during the lytic replication cycle. We have now identified a novel role for the lytic cycle gene, BILF1, which encodes a glycoprotein with the properties of a constitutive signaling G-protein-coupled receptor (GPCR). BILF1 reduced the levels of MHC class I at the cell surface and inhibited CD8+ T cell recognition of endogenous target antigens. The underlying mechanism involves physical association of BILF1 with MHC class I molecules, an increased turnover from the cell surface, and enhanced degradation via lysosomal proteases. The BILF1 protein of the closely related CeHV15 c1-herpesvirus of the Rhesus Old World primate (80% amino acid sequence identity) downregulated surface MHC class I similarly to EBV BILF1. Amongst the human herpesviruses, the GPCR encoded by the ORF74 of the KSHV c2-herpesvirus is most closely related to EBV BILF1 (15% amino acid sequence identity) but did not affect levels of surface MHC class I. An engineered mutant of BILF1 that was unable to activate G protein signaling pathways retained the ability to downregulate MHC class I, indicating that the immune-modulating and GPCR-signaling properties are two distinct functions of BILF1. These findings extend our understanding of the normal biology of an important human pathogen. The discovery of a third EBV lytic cycle gene that cooperates to interfere with MHC class I antigen processing underscores the importance of the need for EBV to be able to evade CD8+ T cell responses during the lytic replication cycle, at a time when such a large number of potential viral targets are expressed

Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining

Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC–MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling

Computational Integration of Homolog and Pathway Gene Module Expression Reveals General Stemness Signatures

The stemness hypothesis states that all stem cells use common mechanisms to regulate self-renewal and multi-lineage potential. However, gene expression meta-analyses at the single gene level have failed to identify a significant number of genes selectively expressed by a broad range of stem cell types. We hypothesized that stemness may be regulated by modules of homologs. While the expression of any single gene within a module may vary from one stem cell type to the next, it is possible that the expression of the module as a whole is required so that the expression of different, yet functionally-synonymous, homologs is needed in different stem cells. Thus, we developed a computational method to test for stem cell-specific gene expression patterns from a comprehensive collection of 49 murine datasets covering 12 different stem cell types. We identified 40 individual genes and 224 stemness modules with reproducible and specific up-regulation across multiple stem cell types. The stemness modules included families regulating chromatin remodeling, DNA repair, and Wnt signaling. Strikingly, the majority of modules represent evolutionarily related homologs. Moreover, a score based on the discovered modules could accurately distinguish stem cell-like populations from other cell types in both normal and cancer tissues. This scoring system revealed that both mouse and human metastatic populations exhibit higher stemness indices than non-metastatic populations, providing further evidence for a stem cell-driven component underlying the transformation to metastatic disease

Defects in the Outer Limiting Membrane Are Associated with Rosette Development in the Nrl−/− Retina

The neural retinal leucine zipper (Nrl) knockout mouse is a widely used model to study cone photoreceptor development, physiology, and molecular biology in the absence of rods. In the Nrl−/− retina, rods are converted into functional cone-like cells. The Nrl−/− retina is characterized by large undulations of the outer nuclear layer (ONL) commonly known as rosettes. Here we explore the mechanism of rosette development in the Nrl−/− retina. We report that rosettes first appear at postnatal day (P)8, and that the structure of nascent rosettes is morphologically distinct from what is seen in the adult retina. The lumen of these nascent rosettes contains a population of aberrant cells protruding into the subretinal space that induce infolding of the ONL. Morphologically adult rosettes do not contain any cell bodies and are first detected at P15. The cells found in nascent rosettes are photoreceptors in origin but lack inner and outer segments. We show that the adherens junctions between photoreceptors and Müller glia which comprise the retinal outer limiting membrane (OLM) are not uniformly formed in the Nrl−/− retina and thus allow protrusion of a population of developing photoreceptors into the subretinal space where their maturation becomes delayed. These data suggest that the rosettes of the Nrl−/− retina arise due to defects in the OLM and delayed maturation of a subset of photoreceptors, and that rods may play an important role in the proper formation of the OLM

Surprised at All the Entropy: Hippocampal, Caudate and Midbrain Contributions to Learning from Prediction Errors

Influential concepts in neuroscientific research cast the brain a predictive machine that revises its predictions when they are violated by sensory input. This relates to the predictive coding account of perception, but also to learning. Learning from prediction errors has been suggested for take place in the hippocampal memory system as well as in the basal ganglia. The present fMRI study used an action-observation paradigm to investigate the contributions of the hippocampus, caudate nucleus and midbrain dopaminergic system to different types of learning: learning in the absence of prediction errors, learning from prediction errors, and responding to the accumulation of prediction errors in unpredictable stimulus configurations. We conducted analyses of the regions of interests' BOLD response towards these different types of learning, implementing a bootstrapping procedure to correct for false positives. We found both, caudate nucleus and the hippocampus to be activated by perceptual prediction errors. The hippocampal responses seemed to relate to the associative mismatch between a stored representation and current sensory input. Moreover, its response was significantly influenced by the average information, or Shannon entropy of the stimulus material. In accordance with earlier results, the habenula was activated by perceptual prediction errors. Lastly, we found that the substantia nigra was activated by the novelty of sensory input. In sum, we established that the midbrain dopaminergic system, the hippocampus, and the caudate nucleus were to different degrees significantly involved in the three different types of learning: acquisition of new information, learning from prediction errors and responding to unpredictable stimulus developments. We relate learning from perceptual prediction errors to the concept of predictive coding and related information theoretic accounts

Genome-Wide Functional Profiling Identifies Genes and Processes Important for Zinc-Limited Growth of Saccharomyces cerevisiae

Zinc is an essential nutrient because it is a required cofactor for many enzymes and transcription factors. To discover genes and processes in yeast that are required for growth when zinc is limiting, we used genome-wide functional profiling. Mixed pools of ∼4,600 deletion mutants were inoculated into zinc-replete and zinc-limiting media. These cells were grown for several generations, and the prevalence of each mutant in the pool was then determined by microarray analysis. As a result, we identified more than 400 different genes required for optimal growth under zinc-limiting conditions. Among these were several targets of the Zap1 zinc-responsive transcription factor. Their importance is consistent with their up-regulation by Zap1 in low zinc. We also identified genes that implicate Zap1-independent processes as important. These include endoplasmic reticulum function, oxidative stress resistance, vesicular trafficking, peroxisome biogenesis, and chromatin modification. Our studies also indicated the critical role of macroautophagy in low zinc growth. Finally, as a result of our analysis, we discovered a previously unknown role for the ICE2 gene in maintaining ER zinc homeostasis. Thus, functional profiling has provided many new insights into genes and processes that are needed for cells to thrive under the stress of zinc deficiency

A Computational Model of the Ionic Currents, Ca2+ Dynamics and Action Potentials Underlying Contraction of Isolated Uterine Smooth Muscle

Uterine contractions during labor are discretely regulated by rhythmic action potentials (AP) of varying duration and form that serve to determine calcium-dependent force production. We have employed a computational biology approach to develop a fuller understanding of the complexity of excitation-contraction (E-C) coupling of uterine smooth muscle cells (USMC). Our overall aim is to establish a mathematical platform of sufficient biophysical detail to quantitatively describe known uterine E-C coupling parameters and thereby inform future empirical investigations of physiological and pathophysiological mechanisms governing normal and dysfunctional labors. From published and unpublished data we construct mathematical models for fourteen ionic currents of USMCs: currents (L- and T-type), current, an hyperpolarization-activated current, three voltage-gated currents, two -activated current, -activated current, non-specific cation current, - exchanger, - pump and background current. The magnitudes and kinetics of each current system in a spindle shaped single cell with a specified surface area∶volume ratio is described by differential equations, in terms of maximal conductances, electrochemical gradient, voltage-dependent activation/inactivation gating variables and temporal changes in intracellular computed from known fluxes. These quantifications are validated by the reconstruction of the individual experimental ionic currents obtained under voltage-clamp. Phasic contraction is modeled in relation to the time constant of changing . This integrated model is validated by its reconstruction of the different USMC AP configurations (spikes, plateau and bursts of spikes), the change from bursting to plateau type AP produced by estradiol and of simultaneous experimental recordings of spontaneous AP, and phasic force. In summary, our advanced mathematical model provides a powerful tool to investigate the physiological ionic mechanisms underlying the genesis of uterine electrical E-C coupling of labor and parturition. This will furnish the evolution of descriptive and predictive quantitative models of myometrial electrogenesis at the whole cell and tissue levels