Metabolite information |
|
| HMDB ID | HMDB0000696 |
| Synonyms |
2-amino-4-Methylthiobutanoate2-amino-4-Methylthiobutanoic acid2-amino-4-[methylthio]Butyrate2-amino-4-[methylthio]Butyric acidAcimethinAnaemiaCoffeeCoffee beanCsfCucurbitsCymethionCystathionine beta synthase deficiencyCytoplasmaDietary supplementDigestionEpileptic spasmsEssential mineralExtracellular regionFaecalFaecesFaunaFecalFloraGourdsGramineaeH-Met-HH-Met-OHHfKidneysL-2-amino-4-Methylthiobutyric acidL-2-amino-4-[methylthio]Butyric acidL-Isomer methionineL-MethioninL-MethioninumL-[-]-MethionineL-a-amino-g-MethylmercaptobutyrateL-a-amino-g-Methylmercaptobutyric acidL-a-amino-g-MethylthiobutyrateL-a-amino-g-Methylthiobutyric acidL-alpha-amino-gamma-MethylmercaptobutyrateL-alpha-amino-gamma-Methylmercaptobutyric acidL-alpha-amino-gamma-MethylthiobutyrateL-alpha-amino-gamma-Methylthiobutyric acidL-gamma-methylthio-alpha-Aminobutyric acidL-α-amino-γ-methylmercaptobutyrateL-α-amino-γ-methylmercaptobutyric acidL[-]-amino-alpha-amino-alpha-Aminobutyric acidL[-]-amino-gamma-Methylthiobutyric acidLegumeLeukaemiaLiquimethMMETHIONINEMepronMetMethilaninMethionine, L isomerMethionine, L-isomerMethioninumMetioninaMthfr deficiencyNutraceuticalPapilionoideaePedamethPoly-L-methioninePolymethionineProstate glandS-MethionineS-Methyl-L-homocysteineSoySoyaSoya beanSoybeanStoolToxin warTrace mineral[2S]-2-amino-4-[Methylsulfanyl]butanoate[2S]-2-amino-4-[Methylsulfanyl]butanoic acid[2S]-2-amino-4-[Methylsulphanyl]butanoate[2S]-2-amino-4-[Methylsulphanyl]butanoic acid[L]-Methionine[S]-2-amino-4-[methylthio]-Butanoate[S]-2-amino-4-[methylthio]-Butanoic acid[S]-2-amino-4-[methylthio]Butanoate[S]-2-amino-4-[methylthio]Butanoic acid[S]-2-amino-4-[methylthio]Butyrate[S]-2-amino-4-[methylthio]Butyric acid[S]-Methionine[S]-[+]-Methioninea-amino-g-Methylmercaptobutyratea-amino-g-Methylmercaptobutyric acidalpha-amino-alpha-Aminobutyric acidalpha-amino-gamma-Methylmercaptobutyratealpha-amino-gamma-Methylmercaptobutyric acidg-methylthio-a-Aminobutyrateg-methylthio-a-Aminobutyric acidgamma-methylthio-alpha-Aminobutyrategamma-methylthio-alpha-Aminobutyric acidneo-Methidin |
| Chemical formula | C5H11NO2S |
| IUPAC name | (2S)-2-amino-4-(methylsulfanyl)butanoic acid |
| CAS registry number | 63-68-3 |
| Monisotopic molecular weight | 149.051049291 |
Chemical taxonomy |
|
| Super class | Organic acids and derivatives |
| Class | Carboxylic acids and derivatives |
| Sub class | Amino acids, peptides, and analogues |
Biological properties |
|
| Pahtways |
3-Phosphoglycerate dehydrogenase deficiencyAmikacin Action PathwayArbekacin Action PathwayAzithromycin Action PathwayBetaine MetabolismChloramphenicol Action PathwayClarithromycin Action PathwayClindamycin Action PathwayClomocycline Action PathwayCystathionine Beta-Synthase DeficiencyDemeclocycline Action PathwayDihydropyrimidine Dehydrogenase Deficiency [DHPD]Dimethylglycine Dehydrogenase DeficiencyDimethylglycine Dehydrogenase DeficiencyDoxycycline Action PathwayErythromycin Action PathwayGentamicin Action PathwayGlycine N-methyltransferase DeficiencyGlycine and Serine MetabolismHomocystinuria-megaloblastic anemia due to defect in cobalamin metabolism, cblG complementation typeHyperglycinemia, non-ketoticHypermethioninemiaJosamycin Action PathwayKanamycin Action PathwayLincomycin Action PathwayLymecycline Action PathwayMethacycline Action PathwayMethionine Adenosyltransferase DeficiencyMethionine MetabolismMethylenetetrahydrofolate Reductase Deficiency [MTHFRD]Minocycline Action PathwayNeomycin Action PathwayNetilmicin Action PathwayNon Ketotic HyperglycinemiaOxytetracycline Action PathwayParomomycin Action PathwayRolitetracycline Action PathwayRoxithromycin Action PathwayS-Adenosylhomocysteine [SAH] Hydrolase DeficiencySarcosine Oncometabolite PathwaySarcosinemiaSpectinomycin Action PathwaySpermidine and Spermine BiosynthesisStreptomycin Action PathwayTelithromycin Action PathwayTetracycline Action PathwayTigecycline Action PathwayTobramycin Action PathwayTranscription/TranslationTroleandomycin Action Pathway |
| Author-emphasized biomarker in the paper(s) |
|
| Reference | Country | Specimen | Marker function | Participants (Case) | Participants (Control) | |||||||||
| Cancer type | Stage | Number | Gender (M,F) | Age mean (range) (M/F) | Smoking status | Type | Number | Gender (M,F) | Age mean (range) (M/F) | Smoking status | ||||
| Ni et al. 2019 | – | serum | diagnosis | lung cancer | – | 40 | 26, 14 | 66.7 (49-83) | – | healthy | 100 | 65, 35 | 64.1 (41-90) | – |
| Ni et al. 2016 | – | serum | diagnosis | lung cancer | – | 40 | 14, 26 | 67 | – | healthy | 100 | – | – | – |
| Fahrmann et al. 2015 | – | serum | diagnosis | adenocarcinoma | I, II, III, IV | 49 | 17, 32 | 65.9 ± 9.87 | – | healthy | 31 | 11, 20 | 64.1 ± 8.97 | – |
| Hori et al. 2011 | – | serum | diagnosis | adenocarcinoma, squamous cell carcinoma, SCLC | III, IV | 22 | – | – | – | healthy | 29 | 23, 6 | median: 64 (34-78) | smoker, non-smoker, unknown |
| Fahrmann et al. 2015 | – | plasma | diagnosis | adenocarcinoma | I, II, III, IV | 52 | 17, 35 | 65.9 ± 9.66 | – | healthy | 31 | 11, 20 | 64.1 ± 8.97 | – |
| Fahrmann et al. 2015 | – | serum | diagnosis | adenocarcinoma | I, II, III, IV | 43 | 21, 22 | 67.3 ± 10.10 | – | healthy | 43 | 21, 22 | 65.9 ± 8.05 | – |
| Miyamoto et al. 2015 | – | blood | diagnosis | adenocarcinoma | unknown (mostly late stage) | 18 | 10, 8 | 67 (50-85) / 62 (53-72) | former, current | healthy | 20 | 8, 12 | 64 (49-80) / 66 (58-82) | former, current |
| Miyamoto et al. 2015 | – | blood | diagnosis | NSCLC, SCLC, mesothelioma, secondary metastasis to lung | I, II, III, IV | 11 | 4, 7 | 67 (61-73) / 67 (47-76) | smoker, non-smoker | healthy | 11 | 5, 6 | 69 (61-83) / 54 (44-61) | unknown |
| Hori et al. 2011 | – | serum | diagnosis | adenocarcinoma, squamous cell carcinoma, SCLC | I, II | 11 | – | – | – | healthy | 29 | 23, 6 | median: 64 (34-78) | smoker, non-smoker, unknown |
| Hori et al. 2011 | – | serum | diagnosis | adenocarcinoma, squamous cell carcinoma, SCLC | I, II, III, IV | 33 | 26, 7 | median: 65 (55-81) | smoker, non-smoker, unknown | healthy | 29 | 23, 6 | median: 64 (34-78) | smoker, non-smoker, unknown |
| Fahrmann et al. 2015 | – | plasma | diagnosis | adenocarcinoma | I, II, III, IV | 43 | 21, 22 | 67.3 ± 10.10 | – | healthy | 43 | 21, 22 | 65.9 ± 8.05 | – |
| Klupczynska et al. 2016a | – | serum | diagnosis | adenocarcinoma, squamous cell carcinoma | I, II, III | 90 | 58, 32 | 64 (48-86) | current, non-smoker, unknown | healthy | 63 | 41, 22 | 62 (43-78) | smoker, non-smoker, unknown |
| Ni et al. 2019 | – | serum | diagnosis | NSCLC, SCLC | II, III, IV | 17 | 13, 4 | 66.3 (53-77) | former, current, non-smoker | healthy | 30 | 23, 7 | 62.8 (34-85) | former, current, non-smoker |
| Maeda et al. 2010 | – | plasma | – | NSCLC | I, II, III, IV | 141 | 93, 48 | 62.7 ± 9.2 | former, current, non-smoker | healthy | 423 | 279, 144 | 61.1 ± 8.7 | former, current, non-smoker |
| Hori et al. 2011 | – | tissue | diagnosis | adenocarcinoma, squamous cell carcinoma, SCLC | – | 7 | 6, 1 | median: 61 (53-82) | smoker, non-smoker | tumor vs. adjacent normal tissue | 7 | 6, 1 | median: 61 (53-82) | smoker, non-smoker |
| Klupczynska et al. 2017 | – | serum | diagnosis | adenocarcinoma, squamous cell carcinoma | I, II | 50 | 28, 22 | 65 (53-86) | – | healthy | 25 | 14, 11 | 64 (50-78) | – |
| Klupczynska et al. 2017 | – | serum | diagnosis | adenocarcinoma, squamous cell carcinoma | I, II | 50 | 28, 22 | 65 (53-86) | – | healthy | 25 | 14, 11 | 64 (50-78) | – |
| Klupczynska et al. 2017 | – | serum | diagnosis | adenocarcinoma, squamous cell carcinoma | I, II | 50 | 28, 22 | 65 (53-86) | – | healthy | 25 | 14, 11 | 64 (50-78) | – |
| Mazzone et al. 2016 | – | serum | – | adenocarcinoma, squamous cell carcinoma | I, II, III | 94 | 55.3%, 44.7% | 68.7 | – | at-risk controls | 190 | 50.5%, 49.5% | 66.2 | – |
| Moreno et al. 2018 | – | tissue | therapy, diagnosis | adenocarcinoma | I, II, III | 33 | 24, 9 | 62.11 ± 9.73 | – | tumor vs. adjacent normal tissue | 33 | 24, 9 | 62.11 ± 9.73 | – |
| Moreno et al. 2018 | – | tissue | therapy, diagnosis | squamous cell carcinoma | I, II, III | 35 | 35, 0 | 68.71 ± 7.46 | – | tumor vs. adjacent normal tissue | 35 | 35, 0 | 68.71 ± 7.46 | – |
| Wikoff et al. 2015b | – | tissue | diagnosis | adenocarcinoma | I | 39 | 15, 24 | 72.33 ± 8.78 | smoker, non-smoker | tumor vs. adjacent normal tissue | 39 | 15, 24 | 72.33 ± 8.78 | smoker, non-smoker |
| Hao et al. 2016 | – | serum | diagnosis | lung cancer | I, II, III, IV | 25 | 15, 10 | 64 (42–77) | smoker, non-smoker | before vs. after treatment (radiation treatment) | – | – | – | smoker, non-smoker |
| Reference | Chromatography | Ion source | Positive/Negative mode | Mass analyzer | Identification level |
| Ni et al. 2019 | LC | ESI | positive | triple quadrupole | MS/MS |
| Ni et al. 2016 | LC | ESI | positive | Triple quadrupole | MS/MS |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Hori et al. 2011 | GC | – | – | – | – |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Miyamoto et al. 2015 | GC | EI | – | TOF | MS/MS |
| Miyamoto et al. 2015 | GC | EI | – | TOF | MS/MS |
| Hori et al. 2011 | GC | – | – | – | – |
| Hori et al. 2011 | GC | – | – | – | – |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Klupczynska et al. 2016a | LC | – | – | QTRAP | MS/MS |
| Ni et al. 2019 | LC | ESI | positive | triple quadrupole | MS/MS |
| Maeda et al. 2010 | LC | ESI | positive | quadrupole | – |
| Hori et al. 2011 | GC | – | – | – | – |
| Klupczynska et al. 2017 | LC | ESI | positive | Quadrupole- Orbitrap | MS/MS |
| Klupczynska et al. 2017 | LC | ESI | positive | Quadrupole- Orbitrap | MS/MS |
| Klupczynska et al. 2017 | LC | ESI | positive | Quadrupole- Orbitrap | MS/MS |
| Mazzone et al. 2016 | LC | ESI | positive | linear ion-trap | MS/MS |
| Moreno et al. 2018 | LC, GC | ESI, EI | positive, negative | LC: linear ion‐trap, GC: single‐quadrupole | LC: MS/MS |
| Moreno et al. 2018 | LC, GC | ESI, EI | positive, negative | LC: linear ion‐trap, GC: single‐quadrupole | LC: MS/MS |
| Wikoff et al. 2015b | GC | EI | – | TOF | – |
| Hao et al. 2016 | – | – | – | NMR | – |
| Reference | Data processing software | Database search |
| Ni et al. 2019 | – | HMDB, KEGG, SMPDB |
| Ni et al. 2016 | – | – |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Hori et al. 2011 | Shimadzu GCMSsolution software | commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08) |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Miyamoto et al. 2015 | ChromaTOF software (Leco) | UC Davis Metabolomics BinBase database |
| Miyamoto et al. 2015 | ChromaTOF software (Leco) | UC Davis Metabolomics BinBase database |
| Hori et al. 2011 | Shimadzu GCMSsolution software | commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08) |
| Hori et al. 2011 | Shimadzu GCMSsolution software | commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08) |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Klupczynska et al. 2016a | – | – |
| Ni et al. 2019 | – | HMDB, KEGG, SMPDB |
| Maeda et al. 2010 | Xcalibur | – |
| Hori et al. 2011 | Shimadzu GCMSsolution software | commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08) |
| Klupczynska et al. 2017 | MZmine 2.19 software | In-house library |
| Klupczynska et al. 2017 | MZmine 2.19 software | In-house library |
| Klupczynska et al. 2017 | MZmine 2.19 software | In-house library |
| Mazzone et al. 2016 | Metabolon LIMS system | Metabolon LIMS system |
| Moreno et al. 2018 | – | KEGG, HMDB |
| Moreno et al. 2018 | – | KEGG, HMDB |
| Wikoff et al. 2015b | BinBase | NIST11, BinBase |
| Hao et al. 2016 | Chenomx NMR Suite 7.1, Metabolite Detector | – |
| Reference | Difference method | Mean concentration (case) | Mean concentration (control) | Fold change (case/control) | P-value | FDR | VIP |
| Ni et al. 2019 | Mann-Whitney U test, Student's t-test, Welch's F test | 21.02 | 32.55 | – | <0.001 | – | – |
| Ni et al. 2016 | one‐way ANOVA | 21.02 ± 6.66 μmol/L | 33.09 ± 6.38 μmol/L | – | <0.0001 | – | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 2525 ± 828 | 2551 ± 786 | 0.99 | 0.923 | 0.972 | – |
| Hori et al. 2011 | student’s t-test, PLS-DA | – | – | 1.13 | 0.677 | – | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 2119 ± 776 | 2249 ± 687 | 0.94 | 0.494 | 0.788 | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 2083 ± 1003 | 1888 ± 614 | 1.103 | 0.44 | 0.749 | – |
| Miyamoto et al. 2015 | Analysis of Covariance | 14725.4444444444 | 16198.9 | 0.909039777049333 | 0.321285741767073 | – | – |
| Miyamoto et al. 2015 | Analysis of Covariance | 10998.6363636364 | 20003.0909090909 | 0.549846841851714 | 0.28589228319165 | – | – |
| Hori et al. 2011 | student’s t-test, PLS-DA | – | – | 1.24 | 0.212 | – | – |
| Hori et al. 2011 | student’s t-test, PLS-DA | – | – | 1.17 | 0.209 | – | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 976 ± 445 | 1093 ± 461 | 0.89 | 0.183 | 0.441 | – |
| Klupczynska et al. 2016a | t-test, Welch’s t-test or the Mann-Whitney U test, one-way ANOVA | 21.52±5.7 ?M | 22.74±4.98 ?M | 0.95 | 0.1535 | – | – |
| Ni et al. 2019 | Mann-Whitney U test, Student's t-test, Welch's F test | 27.22 | 30.24 | – | 0.027 | – | – |
| Maeda et al. 2010 | Mann-Whitney U-test, PCA | 29.4 ± 6.1 μM | 28±5.2 μM | – | 0.013 | – | – |
| Hori et al. 2011 | student’s t-test, PLS-DA | – | – | 2.05 | 0.013 | – | – |
| Klupczynska et al. 2017 | t-test | – | – | 0.84 | 0.00427 | 0.02859 | – |
| Klupczynska et al. 2017 | t-test | – | – | 0.83 | 0.00121 | 0.01002 | – |
| Klupczynska et al. 2017 | t-test | – | – | 0.82 | 0.00079 | 0.00794 | – |
| Mazzone et al. 2016 | two- sample independent t test | 0.9703404± 0.2671491 | 1.0918958± 0.2642577 | 0.88867490835664 | 0.0003276 | 0.015006832 | – |
| Moreno et al. 2018 | paired two‐sample t‐test, PLS-DA | – | – | 1.28920375852924 | 0.000326458709370355 | 0.000930619307880426 | – |
| Moreno et al. 2018 | paired two‐sample t‐test, PLS-DA | – | – | 1.41771974093054 | 0.00000632362359262674 | 0.0000141748641205509 | – |
| Wikoff et al. 2015b | OPLS-DA | – | – | 1.1 | – | 0.405 | – |
| Hao et al. 2016 | OPLS-DA, CV-ANOVA | – | – | – | – | – | – |
| Reference | Classification method | Cutoff value | AUROC 95%CI | Sensitivity (%) | Specificity (%) | Accuracy (%) |
| Ni et al. 2019 | ROC analysis | – | 0.076 | – | – | – |
| Ni et al. 2016 | – | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Hori et al. 2011 | – | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Miyamoto et al. 2015 | – | – | – | – | – | – |
| Miyamoto et al. 2015 | – | – | – | – | – | – |
| Hori et al. 2011 | – | – | – | – | – | – |
| Hori et al. 2011 | – | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Klupczynska et al. 2016a | ROC curve analysis (Monte-Carlo cross validation), discriminant function analysis | – | 0.568 | – | – | – |
| Ni et al. 2019 | ROC analysis | – | 0.316 | – | – | – |
| Maeda et al. 2010 | ROC curve | – | combination of 21 amino acid: 0.812 | – | – | – |
| Hori et al. 2011 | – | – | – | – | – | – |
| Klupczynska et al. 2017 | ROC curve analysis (Monte-Carlo cross validation) | – | – | – | – | – |
| Klupczynska et al. 2017 | ROC curve analysis (Monte-Carlo cross validation) | – | 0.685 (0.541–0.795) | 0.6 | 0.68 | – |
| Klupczynska et al. 2017 | ROC curve analysis (Monte-Carlo cross validation) | – | – | – | – | – |
| Mazzone et al. 2016 | – | – | – | – | – | – |
| Moreno et al. 2018 | – | – | – | – | – | – |
| Moreno et al. 2018 | – | – | – | – | – | – |
| Wikoff et al. 2015b | – | – | – | – | – | – |
| Hao et al. 2016 | – | – | – | – | – | – |