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Chen Z, Yin H, Long Y, Zhu H, Xiong R, Duan X, Liu H, Li J. Metabonomics analysis of decidual tissue in patients with recurrent spontaneous abortion. J Reprod Immunol 2024; 167:104398. [PMID: 39608278 DOI: 10.1016/j.jri.2024.104398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2024] [Revised: 11/01/2024] [Accepted: 11/18/2024] [Indexed: 11/30/2024]
Abstract
OBJECTIVE This study aimed to delineate the metabolic differences and identify enriched pathways in the decidual tissue of patients with recurrent spontaneous abortion (RSA) compared to normal pregnant women. METHODS A cohort of 25 RSA patients and 25 normal pregnant women was recruited for the study. Non-targeted metabolomic analysis of decidual tissue was conducted using high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to identify differential metabolites. Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to elucidate associated metabolic pathways. Quantitative polymerase chain reaction (qPCR) was utilized to assess the expression levels of key proteins related to these pathways, including acyl-CoA synthetase long-chain family member 4 (ACSL4), glutathione peroxidase 4 (GPX4), and indoleamine 2,3-dioxygenase (IDO). RESULTS A total of 54 metabolites were identified with significant differences between the decidual tissues of RSA patients and normal controls, corresponding to 29 significantly enriched metabolic pathways (P<0.05). The expression of ACSL4 was markedly upregulated, while the expression of GPX4 and IDO were significantly downregulated in RSA patients (P<0.05). CONCLUSIONS This study elucidates substantial metabolic disruptions in the decidual tissue of RSA patients, identifying 54 differential metabolites and 29 enriched pathways. The altered expression of ACSL4, GPX4, and IDO underscores their potential involvement in the pathogenesis of RSA. These findings provide critical insights into the metabolic mechanisms underlying RSA and suggest promising targets for diagnostic and therapeutic interventions.
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Affiliation(s)
- Zhihui Chen
- School of Life Sciences, Central South University, Changsha, Hunan, China; Changsha Hospital for Maternal and Child Health Care, Changsha, Hunan, China
| | - Huifang Yin
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Youmei Long
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Huiling Zhu
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Renmei Xiong
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Xin Duan
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Hongyu Liu
- School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Jiada Li
- School of Life Sciences, Central South University, Changsha, Hunan, China.
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Turkoglu O, Citil A, Katar C, Mert I, Quinn RA, Bahado-Singh RO, Graham SF. Untargeted Metabolomic Biomarker Discovery for the Detection of Ectopic Pregnancy. Int J Mol Sci 2024; 25:10333. [PMID: 39408663 PMCID: PMC11476625 DOI: 10.3390/ijms251910333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Revised: 09/23/2024] [Accepted: 09/25/2024] [Indexed: 10/20/2024] Open
Abstract
Ectopic pregnancy (EP) is the leading cause of maternal morbidity and mortality in the first trimester. Using an untargeted metabolomic approach, we sought to identify putative plasma biomarkers using tandem liquid chromatography-mass spectrometry for the detection of tubal EP. This case-control study included the prospective recruitment of 50 tubal EP cases and 50 early intrauterine pregnancy controls. To avoid over-fitting, logistic regression models were developed in a randomly selected discovery group (30 cases vs. 30 controls) and validated in the test group (20 cases vs. 20 controls). In total, 585 mass spectral features were detected, of which 221 molecular features were significantly altered in EP plasma (p < 0.05). Molecular networking and metabolite identification was employed using the Global Natural Products Social Molecular Networking (GNPS) database, which identified 97 metabolites at a high confidence level. Top significant metabolites include subclasses of sphingolipids, carnitines, glycerophosphocholines, and tryptophan metabolism. The top regression model, consisting of D-erythro-sphingosine and oleoyl-carnitine, was validated in a test group and achieved an area under receiving operating curve (AUC) (95% CI) = 0.962 (0.910-1) with a sensitivity of 100% and specificity of 95.9%. Metabolite alterations indicate alterations related to inflammation and abnormal placentation in EP. The validation of these metabolite biomarkers in the future could potentially result in improved early diagnosis.
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Affiliation(s)
- Onur Turkoglu
- Department of Obstetrics and Gynecology, Maternal Fetal Medicine Division, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ayse Citil
- Department of Obstetrics and Gynecology, Zekai Tahir Burak Women’s Health Education and Research Hospital, Ankara 06230, Turkey
| | - Ceren Katar
- Department of Obstetrics and Gynecology, Zekai Tahir Burak Women’s Health Education and Research Hospital, Ankara 06230, Turkey
| | - Ismail Mert
- Department of Obstetrics and Gynecology, Division of Gynecological Oncology, Advocate Health, Chicago, IL 60642, USA
| | - Robert A. Quinn
- Department of Biochemistry, Michigan State University, Lansing, MI 48824, USA
| | - Ray O. Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Corewell Health, William Beaumont University Hospital, Royal Oak, MI 48073, USA
| | - Stewart F. Graham
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Corewell Health, William Beaumont University Hospital, Royal Oak, MI 48073, USA
- Metabolomics Department, Corewell Health Research Institute, William Beaumont University Hospital, Royal Oak, MI 48073, USA
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Hill CJ, Phelan MM, Dutton PJ, Busuulwa P, Maclean A, Davison AS, Drury JA, Tempest N, Horne AW, Gutiérrez EC, Hapangama DK. Diagnostic utility of clinicodemographic, biochemical and metabolite variables to identify viable pregnancies in a symptomatic cohort during early gestation. Sci Rep 2024; 14:11172. [PMID: 38750192 PMCID: PMC11096363 DOI: 10.1038/s41598-024-61690-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 05/08/2024] [Indexed: 05/18/2024] Open
Abstract
A significant number of pregnancies are lost in the first trimester and 1-2% are ectopic pregnancies (EPs). Early pregnancy loss in general can cause significant morbidity with bleeding or infection, while EPs are the leading cause of maternal mortality in the first trimester. Symptoms of pregnancy loss and EP are very similar (including pain and bleeding); however, these symptoms are also common in live normally sited pregnancies (LNSP). To date, no biomarkers have been identified to differentiate LNSP from pregnancies that will not progress beyond early gestation (non-viable or EPs), defined together as combined adverse outcomes (CAO). In this study, we present a novel machine learning pipeline to create prediction models that identify a composite biomarker to differentiate LNSP from CAO in symptomatic women. This prospective cohort study included 370 participants. A single blood sample was prospectively collected from participants on first emergency presentation prior to final clinical diagnosis of pregnancy outcome: LNSP, miscarriage, pregnancy of unknown location (PUL) or tubal EP (tEP). Miscarriage, PUL and tEP were grouped together into a CAO group. Human chorionic gonadotrophin β (β-hCG) and progesterone concentrations were measured in plasma. Serum samples were subjected to untargeted metabolomic profiling. The cohort was randomly split into train and validation data sets, with the train data set subjected to variable selection. Nine metabolite signals were identified as key discriminators of LNSP versus CAO. Random forest models were constructed using stable metabolite signals alone, or in combination with plasma hormone concentrations and demographic data. When comparing LNSP with CAO, a model with stable metabolite signals only demonstrated a modest predictive accuracy (0.68), which was comparable to a model of β-hCG and progesterone (0.71). The best model for LNSP prediction comprised stable metabolite signals and hormone concentrations (accuracy = 0.79). In conclusion, serum metabolite levels and biochemical markers from a single blood sample possess modest predictive utility in differentiating LNSP from CAO pregnancies upon first presentation, which is improved by variable selection and combination using machine learning. A diagnostic test to confirm LNSP and thus exclude pregnancies affecting maternal morbidity and potentially life-threatening outcomes would be invaluable in emergency situations.
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Affiliation(s)
- Christopher J Hill
- Department of Women's and Children's Health, Centre for Women's Health Research, Institute of Life Course and Medical Sciences, University of Liverpool, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
| | - Marie M Phelan
- High Field NMR Facility, Liverpool Shared Research Facilities, University of Liverpool, Liverpool, L69 7TX, UK
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Philip J Dutton
- Department of Women's and Children's Health, Centre for Women's Health Research, Institute of Life Course and Medical Sciences, University of Liverpool, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
- Liverpool Women's Hospital NHS Foundation Trust, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
| | - Paula Busuulwa
- Department of Women's and Children's Health, Centre for Women's Health Research, Institute of Life Course and Medical Sciences, University of Liverpool, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
- Liverpool Women's Hospital NHS Foundation Trust, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
| | - Alison Maclean
- Department of Women's and Children's Health, Centre for Women's Health Research, Institute of Life Course and Medical Sciences, University of Liverpool, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
| | - Andrew S Davison
- Department of Women's and Children's Health, Centre for Women's Health Research, Institute of Life Course and Medical Sciences, University of Liverpool, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
- Department of Clinical Biochemistry and Metabolic Medicine, Liverpool Clinical Laboratories, Liverpool University Hospitals NHS Foundation Trust, Liverpool, L7 8SP, UK
| | - Josephine A Drury
- Department of Women's and Children's Health, Centre for Women's Health Research, Institute of Life Course and Medical Sciences, University of Liverpool, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
| | - Nicola Tempest
- Department of Women's and Children's Health, Centre for Women's Health Research, Institute of Life Course and Medical Sciences, University of Liverpool, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
- Liverpool Women's Hospital NHS Foundation Trust, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK
| | - Andrew W Horne
- Centre for Reproductive Health, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, EH16 4UU, UK
| | - Eva Caamaño Gutiérrez
- High Field NMR Facility, Liverpool Shared Research Facilities, University of Liverpool, Liverpool, L69 7TX, UK
- Computational Biology Facility, Liverpool Shared Research Facilities, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Dharani K Hapangama
- Department of Women's and Children's Health, Centre for Women's Health Research, Institute of Life Course and Medical Sciences, University of Liverpool, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK.
- Liverpool Women's Hospital NHS Foundation Trust, Member of Liverpool Health Partners, Liverpool, L8 7SS, UK.
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Abbasi Ranjbar Z, Sharami SH, Fakor F, Milani F, Kabodmehri R, Haghparast Z, Dalil Heirati SF. Lactate plasma level as a potential biomarker in early diagnosis of ectopic pregnancy: A case-control survey. Health Sci Rep 2023; 6:e1705. [PMID: 38028671 PMCID: PMC10654378 DOI: 10.1002/hsr2.1705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/25/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction A novel metabolomics survey proposed lactic acid as a diagnostic biomarker to detect ectopic pregnancy (EP). Here we investigate the plasma level of lactate for early diagnosis of EP as a potential biomarker. Methods In a case-control study, the reproductive aged women with definite tubal EP (6-10 weeks' gestation), referred to our department during 2021-2022, considered as case group, and women with normal singleton pregnancy in the same gestational age as control group. After informed concept, demographic data (maternal and gestational age and parity) recorded and 5 mL venous blood samples were taken to detect the lactate plasma level. The data analyzed using SPSS software ver22. Results Finally, 95 participations (50 in case and 45 in control group) enrolled. The clinical results showed that the most of case group were aged more than 35 years old with had higher parity and body mass index, but, no statistically significant difference showed up. On the other hand, although the lactate level was slightly higher in women with EP, but, the plasma lactate level did not statistically differ between the two study groups. Also, the logistic regression showed no relationship between the demographic variables and the lactate plasma level. Conclusion It seems that the plasma level of lactate cannot be a diagnostic biomarker for EP.
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Affiliation(s)
- Zahra Abbasi Ranjbar
- Reproductive Health Research CenterGuilan University of Medical SciencesRashtIran
| | - Seyedeh Hajar Sharami
- Department of Obstetrics & Gynecology, Reproductive Health Research Center, Al‐Zahra Hospital, School of MedicineGuilan University of Medical SciencesRashtIran
| | - Fereshteh Fakor
- Department of Obstetrics & Gynecology, Reproductive Health Research Center, Al‐Zahra Hospital, School of MedicineGuilan University of Medical SciencesRashtIran
| | - Forozan Milani
- Department of Obstetrics & Gynecology, Reproductive Health Research Center, Al‐Zahra Hospital, School of MedicineGuilan University of Medical SciencesRashtIran
| | - Roya Kabodmehri
- Department of Obstetrics & Gynecology, Reproductive Health Research Center, Al‐Zahra Hospital, School of MedicineGuilan University of Medical SciencesRashtIran
| | - Zahra Haghparast
- Department of Obstetrics & Gynecology, Reproductive Health Research Center, Al‐Zahra Hospital, School of MedicineGuilan University of Medical SciencesRashtIran
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Bahado-Singh R, Vlachos KT, Aydas B, Gordevicius J, Radhakrishna U, Vishweswaraiah S. Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection. Front Oncol 2022; 12:790645. [PMID: 35600397 PMCID: PMC9114890 DOI: 10.3389/fonc.2022.790645] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 04/04/2022] [Indexed: 12/12/2022] Open
Abstract
Background Lung cancer (LC) is a leading cause of cancer-deaths globally. Its lethality is due in large part to the paucity of accurate screening markers. Precision Medicine includes the use of omics technology and novel analytic approaches for biomarker development. We combined Artificial Intelligence (AI) and DNA methylation analysis of circulating cell-free tumor DNA (ctDNA), to identify putative biomarkers for and to elucidate the pathogenesis of LC. Methods Illumina Infinium MethylationEPIC BeadChip array analysis was used to measure cytosine (CpG) methylation changes across the genome in LC. Six different AI platforms including support vector machine (SVM) and Deep Learning (DL) were used to identify CpG biomarkers and for LC detection. Training set and validation sets were generated, and 10-fold cross validation performed. Gene enrichment analysis using g:profiler and GREAT enrichment was used to elucidate the LC pathogenesis. Results Using a stringent GWAS significance threshold, p-value <5x10-8, we identified 4389 CpGs (cytosine methylation loci) in coding genes and 1812 CpGs in non-protein coding DNA regions that were differentially methylated in LC. SVM and three other AI platforms achieved an AUC=1.00; 95% CI (0.90-1.00) for LC detection. DL achieved an AUC=1.00; 95% CI (0.95-1.00) and 100% sensitivity and specificity. High diagnostic accuracies were achieved with only intragenic or only intergenic CpG loci. Gene enrichment analysis found dysregulation of molecular pathways involved in the development of small cell and non-small cell LC. Conclusion Using AI and DNA methylation analysis of ctDNA, high LC detection rates were achieved. Further, many of the genes that were epigenetically altered are known to be involved in the biology of neoplasms in general and lung cancer in particular.
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Affiliation(s)
- Ray Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
| | - Kyriacos T Vlachos
- Department of Biomedical Sciences, Wayne State School of Medicine, Basic Medical Sciences, Detroit, MI, United States
| | - Buket Aydas
- Department of Healthcare Analytics, Meridian Health Plans, Detroit, MI, United States
| | | | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, United States
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Beaumont Research Institute, Royal Oak, MI, United States
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Aries ML, Cloninger MJ. NMR Hydrophilic Metabolomic Analysis of Bacterial Resistance Pathways Using Multivalent Antimicrobials with Challenged and Unchallenged Wild Type and Mutated Gram-Positive Bacteria. Int J Mol Sci 2021; 22:ijms222413606. [PMID: 34948402 PMCID: PMC8715671 DOI: 10.3390/ijms222413606] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 11/19/2022] Open
Abstract
Multivalent membrane disruptors are a relatively new antimicrobial scaffold that are difficult for bacteria to develop resistance to and can act on both Gram-positive and Gram-negative bacteria. Proton Nuclear Magnetic Resonance (1H NMR) metabolomics is an important method for studying resistance development in bacteria, since this is both a quantitative and qualitative method to study and identify phenotypes by changes in metabolic pathways. In this project, the metabolic differences between wild type Bacillus cereus (B. cereus) samples and B. cereus that was mutated through 33 growth cycles in a nonlethal dose of a multivalent antimicrobial agent were identified. For additional comparison, samples for analysis of the wild type and mutated strains of B. cereus were prepared in both challenged and unchallenged conditions. A C16-DABCO (1,4-diazabicyclo-2,2,2-octane) and mannose functionalized poly(amidoamine) dendrimer (DABCOMD) were used as the multivalent quaternary ammonium antimicrobial for this hydrophilic metabolic analysis. Overall, the study reported here indicates that B. cereus likely change their peptidoglycan layer to protect themselves from the highly positively charged DABCOMD. This membrane fortification most likely leads to the slow growth curve of the mutated, and especially the challenged mutant samples. The association of these sample types with metabolites associated with energy expenditure is attributed to the increased energy required for the membrane fortifications to occur as well as to the decreased diffusion of nutrients across the mutated membrane.
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Huang X, Wang Z, Su B, He X, Liu B, Kang B. A computational strategy for metabolic network construction based on the overlapping ratio: Study of patients' metabolic responses to different dialysis patterns. Comput Biol Chem 2021; 93:107539. [PMID: 34246891 DOI: 10.1016/j.compbiolchem.2021.107539] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/25/2021] [Accepted: 07/01/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Uremia is a worldwide epidemic disease and poses a serious threat to human health. Both maintenance hemodialysis (HD) and maintenance high flux hemodialysis (HFD) are common treatments for uremia and are generally used in clinical applications. In-depth exploration of patients' metabolic responses to different dialysis patterns can facilitate the understanding of pathological alterations associated with uremia and the effects of different dialysis methods on uremia, which may be used for future personalized therapy. However, due to variations of multiple factors (i.e., genetic, epigenetic and environment) in the process of disease treatments, identification of the similarities and differences in plasma metabolite changes in uremic patients in response to HD and HFD remains challenging. METHODS In this study, a computational strategy for metabolic network construction based on the overlapping ratio (MNC-OR) was proposed for disease treatment effect research. In MNC-OR, the overlapping ratio was introduced to measure metabolic reactions and to construct metabolic networks for analysis of different treatment options. Then, MNC-OR was employed to analyze HD-pattern-dependent changes in plasma metabolites to explore the pathological alterations associated with uremia and the effectiveness of different dialysis patterns (i.e., HD and HFD) on uremia. Based on the networks constructed by MNC-OR, two network analysis techniques, namely, similarity analysis and difference analysis of network topology, were used to find the similarity and differences in metabolic signals in patients under treatment with either HD or HFD, which can facilitate the understanding of pathological alterations associated with uremia and provide the guidance for personalized dialysis therapy. RESULTS Similarity analysis of network topology suggested that abnormal energy metabolism, gut metabolism and pyrimidine metabolism might occur in uremic patients, and maintenance of both HFD and HD therapies have beneficial effects on uremia. Then, difference analysis of network topology was employed to extract the crucial information related to HD-pattern-dependent changes in plasma metabolites. Experimental results indicated that the amino acid metabolism was closer to the normal status in HFD-treated patients; however, in HD-treated patients, the ability of antioxidation showed greater reduction, and the protein O-GlcNAcylation level was higher. Our findings demonstrate the potential of MNC-OR for explaining the metabolic similarities and differences of patients in response to different dialysis methods, thereby contributing to the guidance of personalized dialysis therapy.
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Affiliation(s)
- Xin Huang
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning, China.
| | - Zeyu Wang
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning, China
| | - Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China
| | - Xinyu He
- School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning, China
| | - Bing Liu
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning, China
| | - Baolin Kang
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning, China
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Differential metabolic network construction for personalized medicine: Study of type 2 diabetes mellitus patients' response to gliclazide-modified-release-treated. J Biomed Inform 2021; 118:103796. [PMID: 33932596 DOI: 10.1016/j.jbi.2021.103796] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/26/2021] [Accepted: 04/26/2021] [Indexed: 11/21/2022]
Abstract
Individual variation in genetic and environmental factors can cause the differences in metabolic phenotypes, which may have an effect on drug responses of patients. Deep exploration of patients' responses to therapeutic agents is a crucial and urgent event in the personalized treatment study. Using machine learning methods for the discovery of suitability evaluation biomarkers can provide deep insight into the mechanism of disease therapy and facilitate the development of personalized medicine. To find important metabolic network signals for the prediction of patients' drug responses, a novel method referred to as differential metabolic network construction (DMNC) was proposed. In DMNC, concentration changes in metabolite ratios between different pathological states are measured to construct differential metabolic networks, which can be used to advance clinical decision-making. In this study, DMNC was applied to characterize type 2 diabetes mellitus (T2DM) patients' responses against gliclazide modified-release (MR) therapy. Two T2DM metabolomics datasets from different batches of subjects treated by gliclazide MR were analyzed in depth. A network biomarker was defined to assess the patients' suitability for gliclazide MR. It can be effective in the prediction of significant responders from nonsignificant responders, achieving area under the curve values of 0.893 and 1.000 for the discovery and validation sets, respectively. Compared with the metabolites selected by the other methods, the network biomarker selected by DMNC was more stable and precise to reflect the metabolic responses in patients to gliclazide MR therapy, thereby contributing for the personalized medicine of T2DM patients. The better performance of DMNC validated its potential for the identification of network biomarkers to characterize the responses against therapeutic treatments and provide valuable information for personalized medicine.
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Bliziotis NG, Engelke UFH, Aspers RLEG, Engel J, Deinum J, Timmers HJLM, Wevers RA, Kluijtmans LAJ. A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics. Metabolomics 2020; 16:64. [PMID: 32358672 PMCID: PMC7196944 DOI: 10.1007/s11306-020-01686-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 04/23/2020] [Indexed: 12/15/2022]
Abstract
INTRODUCTION When analyzing the human plasma metabolome with Nuclear Magnetic Resonance (NMR) spectroscopy, the Carr-Purcell-Meiboom-Gill (CPMG) experiment is commonly employed for large studies. However, this process can lead to compromised statistical analyses due to residual macromolecule signals. In addition, the utilization of Trimethylsilylpropanoic acid (TSP) as an internal standard often leads to quantification issues, and binning, as a spectral summarization step, can result in features not clearly assignable to metabolites. OBJECTIVES Our aim was to establish a new complete protocol for large plasma cohorts collected with the purpose of describing the comparative metabolic profile of groups of samples. METHODS We compared the conventional CPMG approach to a novel procedure that involves diffusion NMR, using the Longitudinal Eddy-Current Delay (LED) experiment, maleic acid (MA) as the quantification reference and peak picking for spectral reduction. This comparison was carried out using the ultrafiltration method as a gold standard in a simple sample classification experiment, with Partial Least Squares-Discriminant Analysis (PLS-DA) and the resulting metabolic signatures for multivariate data analysis. In addition, the quantification capabilities of the method were evaluated. RESULTS We found that the LED method applied was able to detect more metabolites than CPMG and suppress macromolecule signals more efficiently. The complete protocol was able to yield PLS-DA models with enhanced classification accuracy as well as a more reliable set of important features than the conventional CPMG approach. Assessment of the quantitative capabilities of the method resulted in good linearity, recovery and agreement with an established amino acid assay for the majority of the metabolites tested. Regarding repeatability, ~ 85% of all peaks had an adequately low coefficient of variation (< 30%) in replicate samples. CONCLUSION Overall, our comparison yielded a high-throughput untargeted plasma NMR protocol for optimized data acquisition and processing that is expected to be a valuable contribution in the field of metabolic biomarker discovery.
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Affiliation(s)
- Nikolaos G. Bliziotis
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Udo F. H. Engelke
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Ruud L. E. G. Aspers
- Institute for Molecules and Materials, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands
| | - Jasper Engel
- Institute for Molecules and Materials, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands
- Present Address: Biometris, Wageningen UR, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Jaap Deinum
- Department of Internal Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Henri J. L. M. Timmers
- Department of Internal Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Ron A. Wevers
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Leo A. J. Kluijtmans
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
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