LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Yang, Qingxia; Li, Bo; Wang, Panpan; Xie, Jicheng; Feng, Yuhao; Liu, Ziqiang; Zhu, Feng

doi:10.1093/bib/bbac455

Cited by 31 publications

(13 citation statements)

References 94 publications

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“…Calculation of the CI values for each pathway highlighted two pathways with CI < 1, the glycine, serine, and threonine metabolism pathway and glycerophospholipid metabolism pathway (Table ), indicating that OXA and VC have synergistic effects on these two pathways. We also analyzed the HCC data set using two other conventional pathway enrichment methods and employing meta-analysis tools. − The comparison results of these analyses are detailed in Material S1, Figure S1, Figure S2, and Table S1.…”

Section: Resultsmentioning

confidence: 99%

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions

et al. 2023

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Drug combinations are commonly used to treat various diseases to achieve synergistic therapeutic effects or to alleviate drug resistance. Nevertheless, some drug combinations might lead to adverse effects, and thus, it is crucial to explore the mechanisms of drug interactions before clinical treatment. Generally, drug interactions have been studied using nonclinical pharmacokinetics, toxicology, and pharmacology. Here, we propose a complementary strategy based on metabolomics, which we call interaction metabolite set enrichment analysis, or iMSEA, to decipher drug interactions. First, a digraph-based heterogeneous network model was constructed to model the biological metabolic network based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Second, treatment-specific influences on all detected metabolites were calculated and propagated across the whole network model. Third, pathway activity was defined and enriched to quantify the influence of each treatment on the predefined functional metabolite sets, i.e., metabolic pathways. Finally, drug interactions were identified by comparing the pathway activity enriched by the drug combination treatments and the single drug treatments. A data set consisting of hepatocellular carcinoma (HCC) cells that were treated with oxaliplatin (OXA) and/or vitamin C (VC) was used to illustrate the effectiveness of the iMSEA strategy for evaluation of drug interactions. Performance evaluation using synthetic noise data was also performed to evaluate sensitivities and parameter settings for the iMSEA strategy. The iMSEA strategy highlighted synergistic effects of combined OXA and VC treatments including the alterations in the glycerophospholipid metabolism pathway and glycine, serine, and threonine metabolism pathway. This work provides an alternative method to reveal the mechanisms of drug combinations from the viewpoint of metabolomics.

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Section: Resultsmentioning

confidence: 99%

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions

et al. 2023

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“…In large-scale metabolomics analysis, it is imperative to preprocess and integrate raw data from multiple LC-MS runs in order to ensure the accuracy of follow-up statistical comparisons and qualitative analysis. , Accordingly, peak alignment represents a crucial step toward enabling a meaningful comparison of LC-MS-based data across multiple samples …”

Section: Resultsmentioning

confidence: 99%

“…Metabolomics has emerged as a promising tool for elucidating the molecular phenotypes of organisms, providing comprehensive insights into the metabolic processes occurring within biological systems, and shedding light on regulatory mechanisms. − Liquid chromatography-mass spectrometry (LC-MS) has become the preferred platform for metabolomics research owing to its high sensitivity, selectivity, and coverage. − However, the complexity of biological matrices and the potential variability in analytical workflows often cause LC-MS data to exhibit drifts, shifts, and distortions that may impact the comparability and interpretation of metabolomics results. − Therefore, peak alignment is a fundamental and indispensable step in the LC-MS-based metabolomics workflows, responsible for integrating data on corresponding metabolites between different batches of LC-MS analysis and reducing the impact of such variations. ,− …”

Section: Introductionmentioning

confidence: 99%

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics

Hao,

Chen,

Wang

et al. 2023

Anal. Chem.

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Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of Nacyl glycine (C2−C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2−C23) as calibrants able to respond proficiently in both mass spectrometric positive-and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

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“…Although the gene pairs were significantly associated with survival in multiple independent cohorts and several of the genes have been reported to be functional in gliomas, further experiments are needed to study the function and mechanism of these genes. Besides, GPGPS is a powerful strategy in understanding and identifying candidate causative molecules underlying various diseases, not limited to coding genes, but also non-coding RNAs, proteins, microbes and metabolites ( Li et al , 2020 ; Liu et al , 2020 , 2021 ; Song et al , 2021 ; Yang et al , 2022 ).…”

Section: Discussionmentioning

confidence: 99%

GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion

Cheng

Zheng

et al. 2023

Bioinformatics

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Motivation Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear. Results We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high AUCs to predict 1-, 3-, and 5- year OS (0.92, 0.88, and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, C-incex = 0.74). In conclusion, the ensemble prognostic signature with ten gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilicate prognostic studies. Availability Codes are available at https://github.com/Kimxbzheng/GPGPS.git Supplementary information Supplementary data are available at Bioinformatics online.

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LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Cited by 31 publications

References 94 publications

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions

iMSEA: A Novel Metabolite Set Enrichment Analysis Strategy to Decipher Drug Interactions

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics

GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion

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