Metabolite information |
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| HMDB ID | HMDB0000177 |
| Synonyms |
3-[1H-Imidazol-4-yl]-L-alanineBeautification productCoffeeCoffee beanCsfCucurbitsCytoplasmaDengue hemorrhagic feverDhfDietary supplementDigestionEpileptic spasmsEssential mineralExtracellular regionFaecalFaecesFaunaFecalFloraGlyoxaline-5-alanineGourdsGramineaeHHISTIDINEHisHistidine, L isomerHistidine, L-isomerL-HistidinL-Isomer histidineL-[-]-HistidineLegumeLeukaemiaNutraceuticalPapilionoideaePcpPersonal hygieneProstate glandSemi-essential amino acidSoySoyaSoya beanSoybeanStoolToiletriesToiletryTrace mineral[S]-1H-Imidazole-4-alanine[S]-2-amino-3-[4-Imidazolyl]propionsaeure[S]-4-[2-amino-2-Carboxyethyl]imidazole[S]-Histidine[S]-a-amino-1H-Imidazole-4-propanoate[S]-a-amino-1H-Imidazole-4-propanoic acid[S]-a-amino-1H-Imidazole-4-propionate[S]-a-amino-1H-Imidazole-4-propionic acid[S]-alpha-amino-1H-Imidazole-4-propanoate[S]-alpha-amino-1H-Imidazole-4-propanoic acid[S]-alpha-amino-1H-Imidazole-4-propionate[S]-alpha-amino-1H-Imidazole-4-propionic acid[S]-α-amino-1H-imidazole-4-propanoate[S]-α-amino-1H-imidazole-4-propanoic acid[S]-α-amino-1H-imidazole-4-propionate[S]-α-amino-1H-imidazole-4-propionic acid[S]1H-Imidazole-4-alanineamino-1H-Imidazole-4-propanoateamino-1H-Imidazole-4-propanoic acidamino-4-Imidazoleproprionateamino-4-Imidazoleproprionic acid |
| Chemical formula | C6H9N3O2 |
| IUPAC name | (2S)-2-amino-3-(1H-imidazol-5-yl)propanoic acid |
| CAS registry number | 71-00-1 |
| Monisotopic molecular weight | 155.069476547 |
Chemical taxonomy |
|
| Super class | Organic acids and derivatives |
| Class | Carboxylic acids and derivatives |
| Sub class | Amino acids, peptides, and analogues |
Biological properties |
|
| Pahtways |
Amikacin Action PathwayAmmonia RecyclingArbekacin Action PathwayAzithromycin Action PathwayBeta-Alanine MetabolismCarnosinuria, carnosinemiaChloramphenicol Action PathwayClarithromycin Action PathwayClindamycin Action PathwayClomocycline Action PathwayDemeclocycline Action PathwayDoxycycline Action PathwayErythromycin Action PathwayGABA-Transaminase DeficiencyGentamicin Action PathwayHistidine MetabolismHistidinemiaJosamycin Action PathwayKanamycin Action PathwayLincomycin Action PathwayLymecycline Action PathwayMethacycline Action PathwayMethylhistidine MetabolismMinocycline Action PathwayNeomycin Action PathwayNetilmicin Action PathwayOxytetracycline Action PathwayParomomycin Action PathwayRolitetracycline Action PathwayRoxithromycin Action PathwaySpectinomycin Action PathwayStreptomycin Action PathwayTelithromycin Action PathwayTetracycline Action PathwayTigecycline Action PathwayTobramycin Action PathwayTranscription/TranslationTroleandomycin Action PathwayUreidopropionase Deficiency |
| 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 | – | – | – |
| 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 |
| 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 | – |
| 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 | – |
| 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 | – |
| 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 | – |
| 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 |
| 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 |
| 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 |
| 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 | – |
| 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 |
| 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 | – |
| 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) | – |
| 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 | – |
| Yue et al. 2018 | – | plasma | diagnosis | SCLC | – | 20 | – | – | – | healthy | 20 | – | – | – |
| 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 | – |
| 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 |
| 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 |
| Klupczynska et al. 2016a | LC | – | – | QTRAP | MS/MS |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Moreno et al. 2018 | LC, GC | ESI, EI | positive, negative | LC: linear ion‐trap, GC: single‐quadrupole | LC: MS/MS |
| 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 |
| Maeda et al. 2010 | LC | ESI | positive | quadrupole | – |
| Moreno et al. 2018 | LC, GC | ESI, EI | positive, negative | LC: linear ion‐trap, GC: single‐quadrupole | LC: MS/MS |
| Ni et al. 2019 | LC | ESI | positive | triple quadrupole | MS/MS |
| Fahrmann et al. 2015 | GC | EI | – | TOF | – |
| Klupczynska et al. 2017 | LC | ESI | positive | Quadrupole- Orbitrap | MS/MS |
| Moreno et al. 2018 | LC, GC | ESI, EI | positive, negative | LC: linear ion‐trap, GC: single‐quadrupole | LC: MS/MS |
| Yue et al. 2018 | LC | ESI | positive, negative | QTRAP | MS/MS |
| Mazzone et al. 2016 | LC | ESI | negative | linear ion-trap | MS/MS |
| Wikoff et al. 2015b | GC | EI | – | TOF | – |
| Reference | Data processing software | Database search |
| Ni et al. 2019 | – | HMDB, KEGG, SMPDB |
| Ni et al. 2016 | – | – |
| Klupczynska et al. 2016a | – | – |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Moreno et al. 2018 | – | KEGG, HMDB |
| 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 |
| Maeda et al. 2010 | Xcalibur | – |
| Moreno et al. 2018 | – | KEGG, HMDB |
| Ni et al. 2019 | – | HMDB, KEGG, SMPDB |
| Fahrmann et al. 2015 | – | UC Davis Metabolomics BinBase database |
| Klupczynska et al. 2017 | MZmine 2.19 software | In-house library |
| Moreno et al. 2018 | – | KEGG, HMDB |
| Yue et al. 2018 | Analyst, MultiQuant | – |
| Mazzone et al. 2016 | Metabolon LIMS system | Metabolon LIMS system |
| Wikoff et al. 2015b | BinBase | NIST11, BinBase |
| 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 | 122.48 | 98 | – | <0.001 | – | – |
| Ni et al. 2016 | one‐way ANOVA | 122.48 ± 34.12 μmol/L | 96.47 ± 20.59 μmol/L | – | <0.0001 | – | – |
| Klupczynska et al. 2016a | t-test, Welch’s t-test or the Mann-Whitney U test, one-way ANOVA | 67.28±19.93 ?M | 66.45±14.02 ?M | 1.01 | 0.9616 | – | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 6119 ± 3068 | 5707 ± 2562 | 1.072 | 0.753 | 0.932 | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 9588 ± 3206 | 9696 ± 3829 | 0.99 | 0.721 | 0.869 | – |
| Moreno et al. 2018 | paired two‐sample t‐test, PLS-DA | – | – | 0.971973255902642 | 0.594204710694153 | 0.644088562949958 | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 2955 ± 1404 | 3510 ± 1602 | 0.84 | 0.053 | 0.202 | – |
| Miyamoto et al. 2015 | Analysis of Covariance | 31053.4545454545 | 36840.1818181818 | 0.842923487693773 | 0.0468013410799662 | – | – |
| Miyamoto et al. 2015 | Analysis of Covariance | 30526.0555555556 | 37025.45 | 0.824461432759239 | 0.0357542588407981 | – | – |
| Maeda et al. 2010 | Mann-Whitney U-test, PCA | 77.3 ± 15 μM | 80.8±10.7 μM | – | 0.01 | – | – |
| Moreno et al. 2018 | paired two‐sample t‐test, PLS-DA | – | – | 1.14648430479461 | 0.00524410747544295 | 0.0076087232098245 | – |
| Ni et al. 2019 | Mann-Whitney U test, Student's t-test, Welch's F test | 84.06 | 104.19 | – | 0.003 | – | – |
| Fahrmann et al. 2015 | regress (by the covariates: age, gender and smoking history [packs per year]), permutation test | 5227 ± 3688 | 7350 ± 3212 | 0.71 | 0.002 | 0.072 | – |
| Klupczynska et al. 2017 | t-test | – | – | 0.9 | 0.00127 | 0.01002 | – |
| Moreno et al. 2018 | paired two‐sample t‐test, PLS-DA | – | – | 0.491156198262389 | 0.000168031030753274 | 0.00029930527352927 | – |
| Yue et al. 2018 | OPLS-DA, student’s t-test | 24879.00±3290.40 ng/mL | 15432.00±2151.27 ng/mL | 3.63007662126864 | 0.00000072 | – | 1.66 |
| Mazzone et al. 2016 | two- sample independent t test | 0.9254521± 0.1700337 | 1.0373216± 0.170973 | 0.892155431835219 | 0.000000387 | 0.015006832 | – |
| Wikoff et al. 2015b | OPLS-DA | – | – | 1.6 | – | 0.012 | – |
| Reference | Classification method | Cutoff value | AUROC 95%CI | Sensitivity (%) | Specificity (%) | Accuracy (%) |
| Ni et al. 2019 | ROC analysis | – | 0.754 | – | – | – |
| Ni et al. 2016 | – | – | – | – | – | – |
| Klupczynska et al. 2016a | ROC curve analysis (Monte-Carlo cross validation), discriminant function analysis | – | 0.502 | alanine+histidine+ornithine+isoleucine+tryptophan+valine=84.4 alanine+histidine+ornithine+glutamine+lysine+serine=82.2 β-alanine+histidine+citrulline+asparagine+phenylalanine+aspartic acid=82.2 | alanine+histidine+ornithine+isoleucine+tryptophan+valine=52.4 alanine+histidine+ornithine+glutamine+lysine+serine=58.7 β-alanine+histidine+citrulline+asparagine+phenylalanine+aspartic acid=69.8 | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Moreno et al. 2018 | – | – | – | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Miyamoto et al. 2015 | – | – | – | – | – | – |
| Miyamoto et al. 2015 | – | – | – | – | – | – |
| Maeda et al. 2010 | ROC curve | – | combination of 21 amino acid: 0.812 | – | – | – |
| Moreno et al. 2018 | – | – | – | – | – | – |
| Ni et al. 2019 | ROC analysis | – | 0.242 | – | – | – |
| Fahrmann et al. 2015 | random forest | – | – | – | – | – |
| Klupczynska et al. 2017 | ROC curve analysis (Monte-Carlo cross validation) | – | 0.687 (0.549–0.813) | 0.56 | 0.68 | – |
| Moreno et al. 2018 | – | – | – | – | – | – |
| Yue et al. 2018 | – | – | – | – | – | – |
| Mazzone et al. 2016 | – | – | – | – | – | – |
| Wikoff et al. 2015b | – | – | – | – | – | – |