Dataset Information

Integration of multilevel OMICs data based on the identification of regulatory modules from protein-protein interaction networks

ABSTRACT: Complex scientific experiments provide researchers with a wealth of data and knowledge from heterogeneous sources. Analyzed in its entirety, OMICs data provide a deep insight into the overall biological processes of organisms. However, the integration of data from different cellular levels (e.g., transcriptomics and proteomics) is challenging. Analyzing lists of differentially abundant molecules from different cellular levels often results in a small overlap, which can be accounted to, e.g., different regulatory mechanisms, different temporal scales as well as inherent properties of the measurement method. Thus, there is a need for approaches that allow efficient integration of OMICs data from different cellular levels. In this study, we make use of transcriptome, proteome and secretome data from the human pathogenic fungus Aspergillus fumigatus challenged with the antifungal drug caspofungin. Caspofungin targets the fungal cell wall leading to a compensatory stress response. We analyze the experimental data based on two different approaches. First, we apply a simple approach based on the comparison of differentially regulated genes and proteins with subsequent pathway analysis. Second, we compare the cellular levels based on the identification of regulatory or functional modules by two module-detecting algorithms from protein-protein interaction networks in conjunction with transcriptomic and proteomic data. Our results show that both approaches associate the fungal caspofungin response with biological pathways like cell wall biosynthesis, fatty acid metabolism as well as carbohydrate metabolism. Compared to results of the simple approach, the use of regulatory modules shows a notably higher agreement between the different cellular levels. The additional structural information of the networks provided by the module-based approach allows for topological analysis as well as the analysis of the temporal evolution of cellular response at a molecular level. However, we also found that quality of the module-based results depends on the comprehensiveness of the underlying protein-protein interaction network itself. Thus, while our results highlight the benefits and potential provided by a module-based analysis of OMICs data from different cellular levels, future studies will have to focus on the expansion of organism specific protein-protein interaction networks.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Neosartorya Fumigata (aspergillus Fumigatus)

TISSUE(S): Hyphal Cell

SUBMITTER: Thomas Krüger

LAB HEAD: Olaf Kniemeyer

PROVIDER: PXD008153 | Pride | 2018-10-25

REPOSITORIES: Pride

ACCESS DATA

Publications

Module-detection approaches for the integration of multilevel omics data highlight the comprehensive response of Aspergillus fumigatus to caspofungin.

Conrad T T Kniemeyer O O Henkel S G SG Krüger T T Mattern D J DJ Valiante V V Guthke R R Jacobsen I D ID Brakhage A A AA Vlaic S S Linde J J

BMC systems biology 20181020 1

<h4>Background</h4>Omics data provide deep insights into overall biological processes of organisms. However, integration of data from different molecular levels such as transcriptomics and proteomics, still remains challenging. Analyzing lists of differentially abundant molecules from diverse molecular levels often results in a small overlap mainly due to different regulatory mechanisms, temporal scales, and/or inherent properties of measurement methods. Module-detecting algorithms identifying s ...[more]

PMID: 30342519

Similar Datasets

Project description:Wiskott-Aldrich syndrome (WAS) predisposes patients to leukemia and lymphoma. WAS is caused by mutations in the protein WASP which impair its interaction with the WIPF1 protein. Here, we aim to identify a module of WIPF1-coexpressed genes and to assess its use as a prognostic signature for colorectal cancer, glioma, and breast cancer patients. Two public colorectal cancer microarray data sets were used for discovery and validation of the WIPF1 co-expression module. Based on expression of the WIPF1 signature, we classified more than 400 additional tumors with microarray data from our own experiments or from publicly available data sets according to their WIPF1 signature expression. This allowed us to separate patient populations for colorectal cancers, breast cancers, and gliomas for which clinical characteristics like survival times and times to relapse were analyzed. Groups of colorectal cancer, breast cancer, and glioma patients with low expression of the WIPF1 co-expression module generally had a favorable prognosis. In addition, the majority of WIPF1 signature genes are individually correlated with disease outcome in different studies. Literature gene network analysis revealed that among WIPF1 co-expressed genes known direct transcriptional targets of c-myc, ESR1 and p53 are enriched. The mean expression profile of WIPF1 signature genes is correlated with the profile of a proliferation signature. The WIPF1 signature is the first microarray-based prognostic expression signature primarily developed for colorectal cancer that is instrumental in other tumor types: low expression of the WIPF1 module is associated with better prognosis. We used microarrays for the validation of a WIPF1 co-expression module which was developed on two publically available datasets. Keywords: disease state analysis For the generation of our own microarray data set, 62 CRC patients undergoing elective standard oncological resection at the Department of General, Vascular and Thoracic Surgery, Campus Benjamin Franklin, Charité, were prospectively recruited.

Dataset Information

Integration of multilevel OMICs data based on the identification of regulatory modules from protein-protein interaction networks

Publications

Module-detection approaches for the integration of multilevel omics data highlight the comprehensive response of Aspergillus fumigatus to caspofungin.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure