Network Perturbation Amplitude on NHBE cells - Replication
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Affymetrix GeneChip Scanner 3000 7G
ORGANISM(S): Homo sapiens
SUBMITTER: Sam Ansari
PROVIDER: E-MTAB-1312 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Modern M-^QomicsM-^R technologies, where changes in the expression of thousands of molecular species can be investigated in even a simple experiment, provide contemporary biomedical researchers with an unprecedented level of molecular granularity. An outstanding challenge is transformation of this experimental detail and complexity into meaningful biological insight. This is especially true for drug development and toxicological risk assessment, where molecular profiling data is increasingly being used to investigate the biological effects of a variety of exposures, from drugs to environmental factors, on human systems. Recently, we proposed a systems-based strategy for objectively predicting the mechanistic impact of biologically active substances from transcriptomic data, and report here on an initial implementation of this strategy. We applied a set of biological network models and novel scoring algorithms to investigate transcriptomic data from a range of experimental systems. Importantly, this approach enabled continuity between investigation at the molecular, pathway, and systems levels. When applied to targeted perturbations on in vitro systems, the method was able to identify and provide relative quantitation for the precise mechanisms that were impacted. In normal human bronchial epithelial (NHBE) cells, the method correctly indicated the temporal activation of the cell cycle following removal of a small molecule cyclin-dependent kinase inhibitor. In rat nasal epithelium exposed to formaldehyde, the method identified known mechanistic effects caused by exposure, including increased cell proliferation and necrosis. Importantly, many of the effects indicated by the methodology were supported by experimental endpoint data, providing further objective validation of the general approach. We propose that various fields of human disease research, from drug development to consumer product testing, could benefit from using this or similar approaches to evaluate the biological impact of exposures.
2013-04-01 | E-MTAB-1272 | biostudies-arrayexpress
Project description:Modern 'omic' technologies, where changes in the expression of thousands of molecular species can be investigated in even a simple experiment, provide contemporary biomedical researchers with an unprecedented level of molecular granularity. An outstanding challenge is transformation of this experimental detail and complexity into meaningful biological insight. This is especially true for drug development and toxicological risk assessment, where molecular profiling data is increasingly being used to investigate the biological effects of a variety of exposures, from drugs to environmental factors, on human systems. Recently, we proposed a systems-based strategy for objectively predicting the mechanistic impact of biologically active substances from transcriptomic data, and report here on an initial implementation of this strategy. We applied a set of biological network models and novel scoring algorithms to investigate transcriptomic data from a range of experimental systems. Importantly, this approach enabled continuity between investigation at the molecular, pathway, and systems levels. For both in vitro systems with simple exposures and in vivo systems with complex exposures, the method was able to identify and provide relative quantitation for the precise mechanisms that were impacted. For example, when applied to data from mice exposed to cigarette smoke, the methodology correctly identified known mechanistic effects of the exposure, including increased pulmonary inflammation, necrosis, and proliferation. Importantly, many of the effects indicated by the methodology were supported by experimental endpoint data, providing further objective validation of the general approach. We propose that various fields of human disease research, from drug development to consumer product testing, could benefit from using this or similar approaches to evaluate the biological impact of exposures.
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Project description: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.<br>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.<br>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.
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Project description: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.
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