Induction in myeloid leukemic cells of genes that are expressed in different normal tissues

Using DNA microarray and cluster analysis of expressed genes in a cloned line (M1-t-p53) of myeloid leukemic cells, we have analyzed the expression of genes that are preferentially expressed in different normal tissues. Clustering of 547 highly expressed genes in these leukemic cells showed 38 genes preferentially expressed in normal hematopoietic tissues and 122 other genes preferentially expressed in different normal non-hematopoietic tissues including neuronal tissues, muscle, liver and testis. We have also analyzed the genes whose expression in the leukemic cells changed after activation of wild-type p53 and treatment with the cytokine interleukin 6 (IL-6) or the calcium mobilizer thapsigargin (TG). Out of 620 such genes in the leukemic cells that were differentially expressed in normal tissues, clustering showed 80 genes that were preferentially expressed in hematopoietic tissues and 132 genes in different normal non-hematopietic tissues that also included neuronal tissues, muscle, liver and testis. Activation of p53 and treatment with IL-6 or TG induced different changes in the genes preferentially expressed in these normal tissues. These myeloid leukemic cells thus express genes that are expressed in normal non-hematopoietic tissues, and various treatments can reprogram these cells to induce other such non-hematopoietic genes. The results indicate that these leukemic cells share with normal hematopoietic stem cells the plasticity of differentiation to different cell types. It is suggested that this reprogramming to induce in malignant cells genes that are expressed in different normal tissues may be of clinical value in therapy

A Minimum-Labeling Approach for Reconstructing Protein Networks across Multiple Conditions

The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. Here we propose a novel formulation for network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection in humans over time as well as to analyze a pair of ER export related screens in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes.Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013

Bridging topological and functional information in protein interaction networks by short loops profiling

Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.This research was supported by the Biotechnology and Biological Sciences Research Council (BB/H018409/1 to AP, ACCC and FF, and BB/J016284/1 to NSBT) and by the Leukaemia & Lymphoma Research (to NSBT and FF). SSC is funded by a Leukaemia & Lymphoma Research Gordon Piller PhD Studentship

Anti-apoptotic function of Xbp1 as an IL-3 signaling molecule in hematopoietic cells

Cytokine signaling is critical for proliferation, survival and differentiation of hematopoietic cell, and interleukin-3 (IL-3) is required for maintenance of many hematopoietic cell lines, such as BaF3. We have isolated apoptosis-resistant clones of BaF3 using retroviral insertional mutagenesis and the Xbp1 locus was identified as a retroviral integration site. Expression and splicing of the Xbp1 transcript was conserved in the resistant clone but was promptly disappeared on IL-3 withdrawal in parental BaF3. IL-3 stimulation of BaF3 cells enhanced Xbp1 promoter activity and induced phosphorylation of the endoplasmic reticulum stress sensor protein IRE1, resulting in the increase in Xbp1S that activates unfolded protein response. When downstream signaling from IL-3 was blocked by LY294002 and/or dn-Stat5, Xbp1 expression was downregulated and IRE1 phosphorylation was suppressed. Inhibition of IL-3 signaling as well as knockdown of Xbp1-induced apoptosis in BaF3 cells. In contrast, constitutive expression of Xbp1S protected BaF3 from apoptosis during IL-3 depletion. However, cell cycle arrest at the G1 stage was observed in BaF3 and myeloid differentiation was induced in IL-3-dependent 32Dcl3 cells. Expression of apoptosis-, cell cycle- and differentiation-related genes was modulated by Xbp1S expression. These results indicate that the proper transcriptional and splicing regulation of Xbp1 by IL-3 signaling is important in homeostasis of hematopoietic cells

KEYNOTE-022 part 3: A randomized, double-blind, phase 2 study of pembrolizumab, dabrafenib, and trametinib in BRAF-mutant melanoma

Background In the KEYNOTE-022 study, pembrolizumab with dabrafenib and trametinib (triplet) improved progression-free survival (PFS) versus placebo with dabrafenib and trametinib (doublet) without reaching statistical significance. Mature results on PFS, duration of response (DOR), and overall survival (OS) are reported. Methods The double-blind, phase 2 part of KEYNOTE-022 enrolled patients with previously untreated BRAF V600E/K-mutated advanced melanoma from 22 sites in seven countries. Patients were randomly assigned 1:1 to intravenous pembrolizumab (200 mg every 3 weeks) or placebo plus dabrafenib (150 mg orally two times per day) and trametinib (2 mg orally one time a day). Primary endpoint was PFS. Secondary endpoints were objective response rate, DOR, and OS. Efficacy was assessed in the intention-to-treat population, and safety was assessed in all patients who received at least one dose of study drug. This analysis was not specified in the protocol. Results Between November 30, 2015 and April 24, 2017, 120 patients were randomly assigned to triplet (n=60) or doublet (n=60) therapy. With 36.6 months of follow-up, median PFS was 16.9 months (95% CI 11.3 to 27.9) with triplet and 10.7 months (95% CI 7.2 to 16.8) with doublet (HR 0.53; 95% CI 0.34 to 0.83). With triplet and doublet, respectively, PFS at 24 months was 41.0% (95% CI 27.4% to 54.2%) and 16.3% (95% CI 8.1% to 27.1%); median DOR was 25.1 months (95% CI 14.1 to not reached) and 12.1 months (95% CI 6.0 to 15.7), respectively. Median OS was not reached with triplet and was 26.3 months with doublet (HR 0.64; 95% CI 0.38 to 1.06). With triplet and doublet, respectively, OS at 24 months was 63.0% (95% CI 49.4% to 73.9%) and 51.7% (95% CI 38.4% to 63.4%). Grade 3-5 treatment-related adverse events (TRAEs) occurred in 35 patients (58%, including one death) receiving triplet and 15 patients (25%) receiving doublet. Conclusion In BRAF V600E/K-mutant advanced melanoma, pembrolizumab plus dabrafenib and trametinib substantially improved PFS, DOR, and OS with a higher incidence of TRAEs. Interpretation of these results is limited by the post hoc nature of the analysis

CRISPR-Cas9 screens in human cells and primary neurons identify modifiers of C9ORF72 dipeptide-repeat-protein toxicity.

Hexanucleotide-repeat expansions in the C9ORF72 gene are the most common cause of amyotrophic lateral sclerosis and frontotemporal dementia (c9ALS/FTD). The nucleotide-repeat expansions are translated into dipeptide-repeat (DPR) proteins, which are aggregation prone and may contribute to neurodegeneration. We used the CRISPR-Cas9 system to perform genome-wide gene-knockout screens for suppressors and enhancers of C9ORF72 DPR toxicity in human cells. We validated hits by performing secondary CRISPR-Cas9 screens in primary mouse neurons. We uncovered potent modifiers of DPR toxicity whose gene products function in nucleocytoplasmic transport, the endoplasmic reticulum (ER), proteasome, RNA-processing pathways, and chromatin modification. One modifier, TMX2, modulated the ER-stress signature elicited by C9ORF72 DPRs in neurons and improved survival of human induced motor neurons from patients with C9ORF72 ALS. Together, our results demonstrate the promise of CRISPR-Cas9 screens in defining mechanisms of neurodegenerative diseases

The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees

Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma

Dispersion of the second-order nonlinear susceptibility in ZnTe, ZnSe, and ZnS

We have measured the absolute values of the second-harmonic generation (SHG) coefficient |d| for the zinc-blende II-VI semiconductors ZnTe, ZnSe, and ZnS at room temperature. The investigated spectral region of the fundamental radiation λF ranges from 520 to 1321 nm using various pulsed laser sources. In the transparent region of the II-VI semiconductors, the SHG coefficient exceeds the values of birefringent materials as ammonium dihydrogen phosphate (ADP) and potassium dihydrogen phosphate (KDP) by one or two orders of magnitudes. Above the E0 band gap a strong dispersion of |d| is observed, showing a maximum for a second-harmonic frequency close to the E1 gap. The experimental results are compared to calculated values using a simple three-band model including spin-orbit splitting. Substantial agreement is found to the experimentally observed dispersion of the second-order nonlinear susceptibility

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

<p>Abstract</p> <p>Background</p> <p>In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.</p> <p>Results</p> <p>The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.</p> <p>Conclusions</p> <p>The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms.</p