Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Inference and validation of predictive gene networks from biomedical literature and gene expression data

ABSTRACT: Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples. Knock-down of eight genes were performed on colorectal cancer cell lines to identify the genes whose expression was significantly affected. These genes were subsequently used to validate the quality of our causal gene interaction network.

ORGANISM(S): Homo sapiens

SUBMITTER: Benjamin Haibe-Kains

PROVIDER: E-GEOD-53091 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:The low phytic acid trait in soybeans can be conferred by loss-of-function mutations in genes encoding myo-inositol phosphate synthase and two epistatically interacting genes encoding multidrug-resistance protein ABC transporters. However, perturbations in phytic acid biosynthesis are associated with poor seed vigor. Since the benefits of the low phytic acid trait, in terms of end-use quality and sustainability, far outweigh the negatives associated with poor seed performance, a fuller understanding of the molecular basis behind the negatives will assist crop breeders and engineers in producing variates with low phytic acid and better germination rate. The gene regulatory network for developing low and normal phytic acid soybean seeds was previously constructed, with genes modulating a variety of processes pertinent to phytic acid metabolism and seed viability being identified. In this study, a comparative time series analysis of low and normal phytic acid soybeans was carried out to investigate the transcriptional regulatory elements governing the transitional dynamics from dry seed to germinated seed. Gene regulatory networks were reverse engineered from time series transcriptomic data of three distinct genotypic subsets composed of low phytic acid soybean lines and their normal phytic acid sibling lines. Using a robust unsupervised network inference scheme, putative regulatory interactions were inferred for each subset of genotypes. These interactions were further validated by published regulatory interactions found in Arabidopsis thaliana and motif sequence analysis. Results indicate that low phytic acid seeds have increased sensitivity to stress, which could be due to changes in phytic acid levels, disrupted inositol phosphate signaling, disrupted phosphate ion homeostasis, and altered myo-inositol metabolism. Putative regulatory interactions were identified for the latter two processes. Changes in abscisic acid signaling candidate transcription factors putatively regulating genes in this process were identified as well. Analysis of the gene regulatory networks reveal altered regulation in processes that may be affecting the germination of low phytic acid soybean seeds. Therefore, this work contributes to the ongoing effort to elucidate molecular mechanisms underlying altered seed viability, germination and field emergence of low phytic acid crops, understanding of which is necessary in order to mitigate these problems.

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Inference and validation of predictive gene networks from biomedical literature and gene expression data

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