Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Saigusa, Daisuke; Matsukawa, Naomi; Hishinuma, Eiji; Koshiba, S.

doi:10.1016/j.dmpk.2020.11.008

Cited by 41 publications

(30 citation statements)

References 190 publications

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“…For serum samples, we investigated the metabolome using both LC-MS and NMR to obtain a broader coverage of the metabolome. Although LC-MS has a higher sensitivity and coverage (130 identified metabolites), the reproducibility and robustness of NMR present great clinical applicablitity [ 43 ] since the 38 identified metabolites could be quantified (μM concentration).…”

Section: Discussionmentioning

confidence: 99%

Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

et al. 2021

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Background Alzheimer's Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD. Methods Blood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000× g , 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals. Results While no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine ( p = 0.001, fold change, FC = 0.8), histidine ( p = 0.001, FC = 0.9), allopurinol riboside ( p = 0.002, FC = 0.2), inosine ( p = 0.002, FC = 0.3), 4-pyridoxic acid ( p = 0.006, FC = 1.6), and guanosine ( p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls. Conclusion Using a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.

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Section: Discussionmentioning

confidence: 99%

Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

et al. 2021

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“…Targeted metabolomic analysis was performed using the Biocrates MxP ® Quant 500 kit (Biocrates Life Science AG, Innsbruck, Austria) with an ultra-performance liquid chromatograph (ACQUITY UPLC H-Class, Waters Corporation, Milford, MA, USA) connected to a triple quadrupole mass spectrometer (MS; Xevo TQ-S, Waters Corporation) as previously described [ 44 ]. The blank solution (10 μL), calibration standard solutions, quality control solutions, and plasma samples were added to the predetermined wells of a 96-well plate.…”

Section: Methodsmentioning

confidence: 99%

Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer

Hishinuma

Shimada

Matsukawa

et al. 2021

Toxins

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Epithelial ovarian cancer (EOC) is a fatal gynecologic cancer, and its poor prognosis is mainly due to delayed diagnosis. Therefore, biomarker identification and prognosis prediction are crucial in EOC. Altered cell metabolism is a characteristic feature of cancers, and metabolomics reflects an individual’s current phenotype. In particular, plasma metabolome analyses can be useful for biomarker identification. In this study, we analyzed 624 metabolites, including uremic toxins (UTx) in plasma derived from 80 patients with EOC using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Compared with the healthy control, we detected 77 significantly increased metabolites and 114 significantly decreased metabolites in EOC patients. Especially, decreased concentrations of lysophosphatidylcholines and phosphatidylcholines and increased concentrations of triglycerides were observed, indicating a metabolic profile characteristic of EOC patients. After calculating the parameters of each metabolic index, we found that higher ratios of kynurenine to tryptophan correlates with worse prognosis in EOC patients. Kynurenine, one of the UTx, can affect the prognosis of EOC. Our results demonstrated that plasma metabolome analysis is useful not only for the diagnosis of EOC, but also for predicting prognosis with the variation of UTx and evaluating response to chemotherapy.

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“…To determine metabolite levels in maternal urine samples, GC–MS/MS analysis was performed using a previously described method 49 , 54 . The urine metabolites were extracted by a robotic system (Microlab STARlet Robot System, Hamilton, Reno, NV, USA).…”

Section: Methodsmentioning

confidence: 99%

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

Yamauchi

Ochi

Matsukawa

et al. 2021

Sci Rep

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The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

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Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Cited by 41 publications

References 190 publications

Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

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