Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

Huan, Tao; Palermo, Amelia; Ivanišević, Julijana; Rinehart, Duane; Edler, David; Phommavongsay, Thiery; Benton, H. Paul; Guijas, Carlos; Domingo-Almenara, Xavier; Warth, Benedikt; Siuzdak, Gary

doi:10.1021/acs.analchem.8b00875

Cited by 26 publications

(28 citation statements)

References 39 publications

(62 reference statements)

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“…The eicosanoid precursor arachidonic acid is associated with proinflammatory responses ( Dennis and Norris, 2015 ). Detection of significantly changed lipid profiles and introduction of lipidomic datasets into pathway analyses will enable the prediction of dysregulation in certain metabolic pathways and elucidate the underlying molecular mechanisms of IBD ( Huan et al ., 2018 ). Pathway analyses are recommended in future lipidomic studies on IBD to identify the metabolic changes associated with it.…”

Section: Analysis and Presentation Of Extracted Datamentioning

confidence: 99%

Systematic Review of Recent Lipidomics Approaches Toward Inflammatory Bowel Disease

Lee

Yoon

et al. 2021

Biomol Ther (Seoul)

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Researchers have endeavored to identify the etiology of inflammatory bowel diseases, including Crohn's disease and ulcerative colitis. Though the pathogenesis of inflammatory bowel diseases remains unknown, dysregulation of the immune system in the host gastrointestinal tract is believed to be the major causative factor. Omics is a powerful methodological tool that can reveal biochemical information stored in clinical samples. Lipidomics is a subset of omics that explores the lipid classes associated with inflammation. One objective of the present systematic review was to facilitate the identification of biochemical targets for use in future lipidomic studies on inflammatory bowel diseases. The use of high-resolution mass spectrometry to observe alterations in global lipidomics might help elucidate the immunoregulatory mechanisms involved in inflammatory bowel diseases and discover novel biomarkers for them. Assessment of the characteristics of previous clinical trials on inflammatory bowel diseases could help researchers design and establish patient selection and analytical method criteria for future studies on these conditions. In this study, we curated literature exclusively from four databases and extracted lipidomics-related data from literature, considering criteria. This paper suggests that the lipidomics approach toward research in inflammatory bowel diseases can clarify their pathogenesis and identify clinically valuable biomarkers to predict and monitor their progression.

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Section: Analysis and Presentation Of Extracted Datamentioning

confidence: 99%

Systematic Review of Recent Lipidomics Approaches Toward Inflammatory Bowel Disease

Lee

Yoon

et al. 2021

Biomol Ther (Seoul)

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“…In addition, an autonomous multimodal pathway analysis was performed using both the positive and negative mode-acquired and processed data set as previously described. 56 Using this integrated approach, metabolic pathways involving carbohydrate/sugar metabolism, amino acids, nucleic acids, fatty acids, and nicotine were identified ( Tables S3 and S4 ). The nicotine degradation ( p -value = 0.0002) and d -glucuronate degradation ( p -value = 0.0002) pathways were significantly and differentially regulated between the two groups with reduced amounts of the putative metabolites detected in AA compared to whites.…”

Section: Resultsmentioning

confidence: 99%

Metabolomics Profiles of Smokers from Two Ethnic Groups with Differing Lung Cancer Risk

Dator

Villalta

Thomson

et al. 2020

Chem. Res. Toxicol.

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African American (AA) smokers are at a higher risk of developing lung cancer compared to whites. The variations in the metabolism of nicotine and tobacco-derived carcinogens in these groups were reported previously with the levels of nicotine metabolites and carcinogen-derived metabolites measured using targeted approaches. While useful, these targeted strategies are not able to detect global metabolic changes for use in predicting the detrimental effects of tobacco use and ultimately lung cancer susceptibility among smokers. To address this limitation, we have performed global untargeted metabolomics profiling in urine of AA and white smokers to characterize the pattern of metabolites, identify differentially regulated pathways, and correlate these profiles with the observed variations in lung cancer risk between these two populations. Urine samples from AA ( n = 30) and white ( n = 30) smokers were used for metabolomics analysis acquired in both positive and negative electrospray ionization modes. LC-MS data were uploaded onto the cloud-based XCMS online ( ) platform for retention time correction, alignment, feature detection, annotation, statistical analysis, data visualization, and automated systems biology pathway analysis. The latter identified global differences in the metabolic pathways in the two groups including the metabolism of carbohydrates, amino acids, nucleotides, fatty acids, and nicotine. Significant differences in the nicotine degradation pathway (cotinine glucuronidation) in the two groups were observed and confirmed using a targeted LC-MS/MS approach. These results are consistent with previous studies demonstrating AA smokers with lower glucuronidation capacity compared to whites. Furthermore, the d -glucuronate degradation pathway was found to be significantly different between the two populations, with lower amounts of the putative metabolites detected in AA compared to whites. We hypothesize that the differential regulation of the d -glucuronate degradation pathway is a consequence of the variations in the glucuronidation capacity observed in the two groups. Other pathways including the metabolism of amino acids, nucleic acids, and fatty acids were also identified, however, the biological relevance and implications of these differences across ethnic groups need further investigation. Overall, the applied metabolomics approach revealed global differences in the metabolic networks and endogenous metabolites in AA and whites, which could be used and validated as a new potential panel of biomarkers that could be used to predict lung cancer susceptibility among smokers in population-based studies.

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“…A pipeline developed by Huna et al. [53] first extracts relevant metabolic pathways based on metabolomic data and then overlay proteomic and transcriptomic data on these subnetworks. Finally, the HotNet [54] algorithm generalises approaches based on graph theory [45] to find altered subnetwork across multiple biological scales and integrate different types of omic data together.…”

Section: Multiscale Networkmentioning

confidence: 99%

Footprint-based functional analysis of multiomic data

Dugourd

Sáez-Rodríguez

2019

Current Opinion in Systems Biology

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Omic technologies allow us to generate extensive data, including transcriptomic, proteomic, phosphoproteomic and metabolomic. These data can be used to study signal transduction, gene regulation and metabolism. In this review, we summarise resources and methods to analysis these types of data. We focus on methods developed to recover functional insights using footprints. Footprints are signatures defined by the effect of molecules or processes of interest. They integrate information from multiple measurements whose abundances are under the influence of a common regulator. For example, transcripts controlled by a transcription factor or peptides phosphorylated by a kinase. Footprints can also be generalised across multiple types of omic data. Thus, we also present methods to integrate multiple types of omic data and features (such as the ones derived from footprints) together. We highlight some examples of studies that leverage such approaches to discover new biological mechanisms.

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Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

Cited by 26 publications

References 39 publications

Systematic Review of Recent Lipidomics Approaches Toward Inflammatory Bowel Disease

Systematic Review of Recent Lipidomics Approaches Toward Inflammatory Bowel Disease

Metabolomics Profiles of Smokers from Two Ethnic Groups with Differing Lung Cancer Risk

Footprint-based functional analysis of multiomic data

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