Multi-Omic Metabolic Enrichment NeTwork Analysis (MOMENTA) Reveals Altered Metabolite-Protein Physical Association Networks
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Andrew Emili
PROVIDER: MSV000084811 | MassIVE | Thu Jan 16 06:38:00 GMT 2020
REPOSITORIES: MassIVE
Similar Datasets
Project description:Raw data and processed files for MOMENTA publication.
2020-01-16 | MSV000084811 | GNPS
Project description:Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.
| S-EPMC8761777 | biostudies-literature
Project description:BackgroundTwo important challenges in the analysis of molecular biology information are data (multi-omic information) integration and the detection of patterns across large scale molecular networks and sequences. They are are actually coupled beause the integration of omic information may provide better means to detect multi-omic patterns that could reveal multi-scale or emerging properties at the phenotype levels.ResultsHere we address the problem of integrating various types of molecular information (a large collection of gene expression and sequence data, codon usage and protein abundances) to analyse the E.coli metabolic response to treatments at the whole network level. Our algorithm, MORA (Multi-omic relations adjacency) is able to detect patterns which may represent metabolic network motifs at pathway and supra pathway levels which could hint at some functional role. We provide a description and insights on the algorithm by testing it on a large database of responses to antibiotics. Along with the algorithm MORA, a novel model for the analysis of oscillating multi-omics has been proposed. Interestingly, the resulting analysis suggests that some motifs reveal recurring oscillating or position variation patterns on multi-omics metabolic networks. Our framework, implemented in R, provides effective and friendly means to design intervention scenarios on real data. By analysing how multi-omics data build up multi-scale phenotypes, the software allows to compare and test metabolic models, design new pathways or redesign existing metabolic pathways and validate in silico metabolic models using nearby species.ConclusionsThe integration of multi-omic data reveals that E.coli multi-omic metabolic networks contain position dependent and recurring patterns which could provide clues of long range correlations in the bacterial genome.
| S-EPMC6069781 | biostudies-literature
Project description:To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.
| S-EPMC3882664 | biostudies-literature
Project description:Photodynamic therapy (PDT) is an established palliative treatment for perihilar cholangiocarcinoma that is clinically promising. However, tumors tend to regrow after PDT, which may result from the PDT-induced activation of survival pathways in sublethally afflicted tumor cells. In this study, tumor-comprising cells (i.e., vascular endothelial cells, macrophages, perihilar cholangiocarcinoma cells, and EGFR-overexpressing epidermoid cancer cells) were treated with the photosensitizer zinc phthalocyanine that was encapsulated in cationic liposomes (ZPCLs). The post-PDT survival pathways and metabolism were studied following sublethal (LC50) and supralethal (LC90) PDT. Sublethal PDT induced survival signaling in perihilar cholangiocarcinoma (SK-ChA-1) cells via mainly HIF-1-, NF-кB-, AP-1-, and heat shock factor (HSF)-mediated pathways. In contrast, supralethal PDT damage was associated with a dampened survival response. PDT-subjected SK-ChA-1 cells downregulated proteins associated with EGFR signaling, particularly at LC90. PDT also affected various components of glycolysis and the tricarboxylic acid cycle as well as metabolites involved in redox signaling. In conclusion, sublethal PDT activates multiple pathways in tumor-associated cell types that transcriptionally regulate cell survival, proliferation, energy metabolism, detoxification, inflammation/angiogenesis, and metastasis. Accordingly, tumor cells sublethally afflicted by PDT are a major therapeutic culprit. Our multi-omic analysis further unveiled multiple druggable targets for pharmacological co-intervention.
| S-EPMC5309296 | biostudies-literature
Project description:Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-free, automated pipeline enabling integration of categorical and numeric data spanning clinical and multi-omic profiles for unsupervised clustering to identify disease subsets. Using simulations and real-world data from The Cancer Genome Atlas, we demonstrate that MANAclust's feature selection algorithms are accurate and outperform competitors. We also apply MANAclust to a clinically and multi-omically phenotyped asthma cohort. MANAclust identifies clinically and molecularly distinct clusters, including heterogeneous groups of "healthy controls" and viral and allergy-driven subsets of asthmatic subjects. We also find that subjects with similar clinical presentations have disparate molecular profiles, highlighting the need for additional testing to uncover asthma endotypes. This work facilitates data-driven personalized medicine through integration of clinical parameters with multi-omics. MANAclust is freely available at https://bitbucket.org/scottyler892/manaclust/src/master/.
| S-EPMC8195153 | biostudies-literature
Project description:MotivationThe increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism.ResultsLeveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation.Availability and implementationThe MoNET package is freely available on https://github.com/JW-Yan/MONET.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC10060724 | biostudies-literature
Project description:Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.
| S-EPMC5937130 | biostudies-literature
Project description:The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods.
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Project description:Bacterial phenotypic traits and lifestyles in response to diverse environmental conditions depend on changes in the internal molecular environment. However, predicting bacterial adaptability is still difficult outside of laboratory controlled conditions. Many molecular levels can contribute to the adaptation to a changing environment: pathway structure, codon usage, metabolism. To measure adaptability to changing environmental conditions and over time, we develop a multi-omic model of Escherichia coli that accounts for metabolism, gene expression and codon usage at both transcription and translation levels. After the integration of multiple omics into the model, we propose a multiobjective optimization algorithm to find the allowable and optimal metabolic phenotypes through concurrent maximization or minimization of multiple metabolic markers. In the condition space, we propose Pareto hypervolume and spectral analysis as estimators of short term multi-omic (transcriptomic and metabolic) evolution, thus enabling comparative analysis of metabolic conditions. We therefore compare, evaluate and cluster different experimental conditions, models and bacterial strains according to their metabolic response in a multidimensional objective space, rather than in the original space of microarray data. We finally validate our methods on a phenomics dataset of growth conditions. Our framework, named METRADE, is freely available as a MATLAB toolbox.
| S-EPMC4611489 | biostudies-literature