Literature DB >> 16503971

Transcriptomic response to differentiation induction.

G W Patton1, R Stephens, I A Sidorov, X Xiao, R A Lempicki, D S Dimitrov, R H Shoemaker, G Tudor.   

Abstract

BACKGROUND: Microarrays used for gene expression studies yield large amounts of data. The processing of such data typically leads to lists of differentially-regulated genes. A common terminal data analysis step is to map pathways of potentially interrelated genes.
METHODS: We applied a transcriptomics analysis tool to elucidate the underlying pathways of leukocyte maturation at the genomic level in an established cellular model of leukemia by examining time-course data in two subclones of U-937 cells. Leukemias such as Acute Promyelocytic Leukemia (APL) are characterized by a block in the hematopoietic stem cell maturation program at a point when expansion of clones which should be destined to mature into terminally-differentiated effector cells get locked into endless proliferation with few cells reaching maturation. Treatment with retinoic acid, depending on the precise genomic abnormality, often releases the responsible promyelocytes from this blockade but clinically can yield adverse sequellae in terms of potentially lethal side effects, referred to as retinoic acid syndrome.
RESULTS: Briefly, the list of genes for temporal patterns of expression was pasted into the ABCC GRID Promoter TFSite Comparison Page website tool and the outputs for each pattern were examined for possible coordinated regulation by shared regelems (regulatory elements). We found it informative to use this novel web tool for identifying, on a genomic scale, genes regulated by drug treatment.
CONCLUSION: Improvement is needed in understanding the nature of the mutations responsible for controlling the maturation process and how these genes regulate downstream effects if there is to be better targeting of chemical interventions. Expanded implementation of the techniques and results reported here may better direct future efforts to improve treatment for diseases not restricted to APL.

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 16503971      PMCID: PMC1395336          DOI: 10.1186/1471-2105-7-81

Source DB:  PubMed          Journal:  BMC Bioinformatics        ISSN: 1471-2105            Impact factor:   3.169


  21 in total

1.  Object-oriented transcription factors database (ooTFD).

Authors:  D Ghosh
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Switch from Myc/Max to Mad1/Max binding and decrease in histone acetylation at the telomerase reverse transcriptase promoter during differentiation of HL60 cells.

Authors:  D Xu; N Popov; M Hou; Q Wang; M Björkholm; A Gruber; A R Menkel; M Henriksson
Journal:  Proc Natl Acad Sci U S A       Date:  2001-03-27       Impact factor: 11.205

3.  DAVID: Database for Annotation, Visualization, and Integrated Discovery.

Authors:  Glynn Dennis; Brad T Sherman; Douglas A Hosack; Jun Yang; Wei Gao; H Clifford Lane; Richard A Lempicki
Journal:  Genome Biol       Date:  2003-04-03       Impact factor: 13.583

4.  The Molecular Biology Database Collection: 2004 update.

Authors:  Michael Y Galperin
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

5.  Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro.

Authors:  Erik C Gunther; David J Stone; Robert W Gerwien; Patricia Bento; Melvyn P Heyes
Journal:  Proc Natl Acad Sci U S A       Date:  2003-07-17       Impact factor: 11.205

6.  Identification and characterization of rapidly dividing U937 clones with differential telomerase activity and gene expression profiles: role of c-Myc/Mad1 and Id/Ets proteins.

Authors:  X Xiao; S K Phogat; I A Sidorov; J Yang; I Horikawa; D Prieto; J Adelesberger; R Lempicki; J C Barrett; D S Dimitrov
Journal:  Leukemia       Date:  2002-09       Impact factor: 11.528

7.  Systems analysis of transcriptome and proteome in retinoic acid/arsenic trioxide-induced cell differentiation/apoptosis of promyelocytic leukemia.

Authors:  Pei-Zheng Zheng; Kan-Kan Wang; Qun-Ye Zhang; Qiu-Hua Huang; Yan-Zhi Du; Qing-Hua Zhang; Da-Kai Xiao; Shu-Hong Shen; Sandrine Imbeaud; Eric Eveno; Chun-Jun Zhao; Yu-Long Chen; Hui-Yong Fan; Samuel Waxman; Charles Auffray; Gang Jin; Sai-Juan Chen; Zhu Chen; Ji Zhang
Journal:  Proc Natl Acad Sci U S A       Date:  2005-05-13       Impact factor: 11.205

8.  Cancer treatment by telomerase inhibitors: predictions by a kinetic model.

Authors:  Igor A Sidorov; Ken S Hirsch; Calvin B Harley; Dimiter S Dimitrov
Journal:  Math Biosci       Date:  2003-02       Impact factor: 2.144

9.  Comparative analysis of genes regulated by PML/RAR alpha and PLZF/RAR alpha in response to retinoic acid using oligonucleotide arrays.

Authors:  Dorothy J Park; Peter T Vuong; Sven de Vos; Dan Douer; H Phillip Koeffler
Journal:  Blood       Date:  2003-07-31       Impact factor: 22.113

10.  Microarray results: how accurate are they?

Authors:  Ravi Kothapalli; Sean J Yoder; Shrikant Mane; Thomas P Loughran
Journal:  BMC Bioinformatics       Date:  2002-08-23       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.