Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Network Perturbation Amplitude on NHBE cells

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.

INSTRUMENT(S): Affymetrix GeneChip Scanner 3000 7G

ORGANISM(S): Rattus norvegicus

SUBMITTER: Sam Ansari

PROVIDER: E-MTAB-1311 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models.

Martin Florian F Sewer Alain A Talikka Marja M Xiang Yang Y Hoeng Julia J Peitsch Manuel C MC

BMC bioinformatics 20140711

<h4>Background</h4>High-throughput measurement technologies such as microarrays provide complex datasets reflecting mechanisms perturbed in an experiment, typically a treatment vs. control design. Analysis of these information rich data can be guided based on a priori knowledge, such as networks or set of related proteins or genes. Among those, cause-and-effect network models are becoming increasingly popular and more than eighty such models, describing processes involved in cell proliferation, ...[more]

PMID: 25015298

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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.

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.

Project description:Analysis of wildtype C. elegans (N2) and pcca-1(ok2282) and metr-1(ok521) mutants fed Comamonas DA1877 Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network comprised of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several TFs that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network comprised of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several TFs that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. 3 biological replicates for each condition, wild type is reference sample

Dataset Information

Network Perturbation Amplitude on NHBE cells

Publications

Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models.

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