Metabolomic characterization of congenital microtia: a possible analysis for early diagnosis

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Abstract

Background

Although metabolic abnormalities have been deemed one of the essential risk factors for growth and development, the relationship between metabolic abnormalities and microtia is still unclear. In this study, we aimed to establish a cell model of microtia and the changes of serum metabolites in patients with microtia.

Methods

After constructing a cell model of microtia with low expression of BMP5, we performed integrative metabolomics analysis. For the altered metabolites, the content of glycerophosphocholine (PC), triacylglycerol (TG), and choline in the serum of 28 patients (15 patients with microtia and 13 controls) with microtia was verified by enzyme-linked immunosorbent assay (ELISA).

Results

Detailed metabolomic evaluation showed distinct clusters of metabolites between BMP5-low expressing cells and normal control (NC) cells. The cell model of microtia had significantly higher levels of TG, PC, glycerophosphoethanolamine (PE), sphingomyelin, sulfatide, glycerophosphoglycerol, diacylglycerol, and glycosphingolipid. The main abnormal metabolites were mainly concentrated in the glycerophospholipid metabolism pathway, and PC and choline were closely related. In the serum of patients with microtia, the contents of PC, TG, and choline were significantly increased.

Conclusions

The individual serum samples confirmed the different metabolites between patients with microtia and controls. In particular, we showed that a newly developed metabolic biomarker panel has a high sensitivity and specificity for separating patients with microtia from controls.

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Most cited references 63

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MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

Zhiqiang Pang, Jasmine Chong, Guangyan Zhou … (2021)

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC–MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca . Graphical Abstract From raw data to statistical and functional insights using MetaboAnalyst 5.0.

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Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Bo Shen, Xiao Yi, Yaoting Sun … (2020)

Summary Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using ten independent patients, seven of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 new COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

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Untargeted Metabolomics Strategies—Challenges and Emerging Directions

Stacy D Sherrod, John A. McLean, Alexandra C. Schrimpe-Rutledge … (2016)

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes, and as such the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating, workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS based untargeted metabolomics studies – specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.

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Author and article information

Journal

Journal ID (nlm-ta): Ann Transl Med

Journal ID (iso-abbrev): Ann Transl Med

Journal ID (publisher-id): ATM

Title: Annals of Translational Medicine

Publisher: AME Publishing Company

ISSN (Print): 2305-5839

ISSN (Electronic): 2305-5847

Publication date (Print and electronic): December 2022

Publication date (Print): December 2022

Volume: 10

Issue: 24

Electronic Location Identifier: 1330

Affiliations

[1 ]deptDepartment of Otolaryngology, Sun Yat-sen Memorial Hospital , Sun Yat-sen University , Guangzhou, China;

[2 ]deptDepartment of Otolaryngology, Longgang ENT Hospital & Shenzhen Key Laboratory of E.N.T , Institute of ENT Shenzhen , Shenzhen, China;

[3 ]Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University , deptGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation , Guangzhou, China;

[4 ]deptZhongshan School of Medicine , Sun Yat-sen University , Guangzhou, China;

[5 ]deptThe Fifth Clinical Institute , Zunyi Medical University , Zhuhai, China

Author notes

Contributions: (I) Conception and design: SJ Chen, HS Zhang, YQ Zheng, HY Zhao; (II) Administrative support: YQ Zheng, SJ Chen, HY Zhao; (III) Provision of study materials or patients: SJ Chen, HS Zhang; (IV) Collection and assembly of data: SJ Chen, HS Zhang, XP Huang, WH Li, Y Liu, C Fan, FY Liu; (V) Data analysis and interpretation: SJ Chen, HS Zhang, XP Huang, WH Li, C Fan; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

[#]

These authors contributed equally to this work.

Correspondence to: Yi-Qing Zheng. Department of Otolaryngology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 107 Yuanjiang West Road, Guangzhou 510120, China. Email: zhengyiq@ 123456mail.sysu.edu.cn ; Hui-Ying Zhao. Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China. Email: zhaohy8@ 123456mail.sysu.edu.cn .

Article

Publisher ID: atm-10-24-1330

DOI: 10.21037/atm-22-5614

PMC ID: 9843322

SO-VID: 18ef114c-efa2-40a1-b878-ba30f2dae7e4

License:

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

Metabolomic characterization of congenital microtia: a possible analysis for early diagnosis

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Abstract

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Core Readings in Statistical Mediation Analysis

Most cited references 63

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

Untargeted Metabolomics Strategies—Challenges and Emerging Directions

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