Transcriptomics

Dataset Information

6

Transcription profiling by array of normal human bronchial epithelial cells treated with tumor necrosis factor-alpha


ABSTRACT: High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.
Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFa, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFa-induced perturbation for each network model when compared against NF-kB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.
The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.

REANALYSED by: E-MTAB-1027

INSTRUMENT(S): Affymetrix GeneChip Scanner 3000 7G

ORGANISM(S): Homo sapiens  

DISEASE(S): Normal

SUBMITTER: Sewer Alain   Mathis Carole   Sam Ansari  

PROVIDER: E-MTAB-1027 | ArrayExpress | 2012-04-15

REPOSITORIES: ArrayExpress

Dataset's files

Source:
Action DRS
E-MTAB-1027.idf.txt Idf
E-MTAB-1027.idf.txt_original Idf
E-MTAB-1027.raw.1.zip Raw
E-MTAB-1027.raw.2.zip Raw
E-MTAB-1027.sdrf.txt Txt
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Publications

Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks.

Martin Florian F   Thomson Ty M TM   Sewer Alain A   Drubin David A DA   Mathis Carole C   Weisensee Dirk D   Pratt Dexter D   Hoeng Julia J   Peitsch Manuel C MC  

BMC systems biology 20120531


<h4>Background</h4>High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interprete  ...[more]

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