Showing information for HMDB0000904 ('citrulline', 'Cit')

Metabolite information
HMDB ID HMDB0000904
Synonyms
2-amino-5-Uredovalerate
2-amino-5-Uredovaleric acid
2-amino-5-Ureidovalerate
2-amino-5-Ureidovaleric acid
CIR
Cit
Csf
Cucurbits
Cytoplasma
Cytrulline
D-Ureidonorvaline
DL-Citrulline
Dietary supplement
Digestion
Epileptic spasms
Essential mineral
Faecal
Faeces
Fauna
Fecal
Flora
Gammaureidonorvaline
Gourds
Gramineae
H-Cit-OH
Hyperammonemia iii
Kidneys
L-2-amino-5-Ureidovalerate
L-2-amino-5-Ureidovaleric acid
L-2-amino-5-ureido-Valerate
L-2-amino-5-ureido-Valeric acid
L-Citrulline
L-Cytrulline
L-N5-Carbamoyl-ornithine
L[+]-2-amino-5-Ureidovalerate
L[+]-2-amino-5-Ureidovaleric acid
L[+]-Citrulline
Legume
Myelin
N-Carbamylornithine
N5-Carbamoyl-L-ornithine
N5-Carbamoylornithine
N5-Carbamylornithine
N5-[Aminocarbonyl]-L-ornithine
N5-[Aminocarbonyl]-ornithine
N5-[Aminocarbonyl]ornithine
ND-Carbamylornithine
N[5]-[Aminocarbonyl]-DL-ornithine
N[5]-[Aminocarbonyl]-L-ornithine
N[]-Carbamylornithine
N[delta]-Carbamylornithine
N[δ]-carbamylornithine
Nags deficiency
Ndelta-carbamy-ornithine
Ndelta-carbamylornithine
Neuron
Ngamma-carbamylornithine
Nutraceutical
Papilionoideae
Prostate gland
Sitrulline
Soy
Soya
Soya bean
Soybean
Stool
Thrombocyte
Trace mineral
Ureidonorvaline
Ureidovalerate
Ureidovaleric acid
[2S]-2-amino-5-[carbamoylamino]Pentanoate
[2S]-2-amino-5-[carbamoylamino]Pentanoic acid
[S]-2-amino-5-Ureidopentanoate
[S]-2-amino-5-Ureidopentanoic acid
[S]-2-amino-5-[Aminocarbonyl]aminopentanoate
[S]-2-amino-5-[Aminocarbonyl]aminopentanoic acid
a-amino-D-Ureidovalerate
a-amino-D-Ureidovaleric acid
a-amino-delta-Ureidovalerate
a-amino-delta-Ureidovaleric acid
a-amino-δ-ureidovalerate
a-amino-δ-ureidovaleric acid
alpha-amino-delta-Ureidovalerate
alpha-amino-delta-Ureidovaleric acid
alpha-amino-gamma-Ureidovalerate
alpha-amino-gamma-Ureidovaleric acid
amino-Ureidovalerate
amino-Ureidovaleric acid
delta-Ureidonorvaline
α-amino-δ-ureidovalerate
α-amino-δ-ureidovaleric acid
δ-ureidonorvaline
Chemical formula C6H13N3O3
IUPAC name
(2S)-2-amino-5-(carbamoylamino)pentanoic acid
CAS registry number 372-75-8
Monisotopic molecular weight 175.095691297

Chemical taxonomy
Super class Organic acids and derivatives
Class Carboxylic acids and derivatives
Sub class Amino acids, peptides, and analogues

Biological properties
Pahtways
Arginine and Proline Metabolism
Arginine: Glycine Amidinotransferase Deficiency [AGAT Deficiency]
Argininemia
Argininosuccinic Aciduria
Aspartate Metabolism
Canavan Disease
Carbamoyl Phosphate Synthetase Deficiency
Citrullinemia Type I
Creatine deficiency, guanidinoacetate methyltransferase deficiency
Guanidinoacetate Methyltransferase Deficiency [GAMT Deficiency]
Hyperornithinemia with gyrate atrophy [HOGA]
Hyperornithinemia-hyperammonemia-homocitrullinuria [HHH-syndrome]
Hyperprolinemia Type I
Hyperprolinemia Type II
Hypoacetylaspartia
L-arginine:glycine amidinotransferase deficiency
Ornithine Aminotransferase Deficiency [OAT Deficiency]
Ornithine Transcarbamylase Deficiency [OTC Deficiency]
Prolidase Deficiency [PD]
Prolinemia Type II
Urea Cycle
Author-emphasized biomarker in the paper(s)

Lung cancer metabolomics studies that identify HMDB0000904 ('citrulline', 'Cit')

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 – – –
Mu et al. 2019 – serum diagnosis NSCLC I, II, III, IV 30 0, 30 60.4 ± 9.7 non-smoker healthy 30 0, 30 54.7 ± 14.3 non-smoker
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
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 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 –
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 adenocarcinoma I, II, III 33 24, 9 62.11 ± 9.73 – tumor vs. adjacent normal tissue 33 24, 9 62.11 ± 9.73 –
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 –
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
Roś-Mazurczyk et al. 2017 – serum diagnosis adenocarcinoma, squamous cell carcinoma I, II, III 31 17, 14 52-72 – healthy 92 52, 40 52-73 –
Yue et al. 2018 – plasma diagnosis SCLC – 20 – – – healthy 20 – – –
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 –
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
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 –
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 –
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
Mu et al. 2019 GC – – – –
Miyamoto et al. 2015 GC EI – TOF MS/MS
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
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
Mazzone et al. 2016 LC ESI positive linear ion-trap MS/MS
Miyamoto et al. 2015 GC EI – TOF MS/MS
Roś-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Yue et al. 2018 LC ESI positive, negative QTRAP MS/MS
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
Fahrmann et al. 2015 GC EI – TOF –
Fahrmann et al. 2015 GC EI – TOF –
Wikoff et al. 2015b GC EI – TOF –
Reference Data processing software Database search
Ni et al. 2019 – HMDB, KEGG, SMPDB
Ni et al. 2016 – –
Mu et al. 2019 – –
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Moreno et al. 2018 – KEGG, HMDB
Maeda et al. 2010 Xcalibur –
Moreno et al. 2018 – KEGG, HMDB
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Roś-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Yue et al. 2018 Analyst, MultiQuant –
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Klupczynska et al. 2016a – –
Ni et al. 2019 – HMDB, KEGG, SMPDB
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
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 23.41 40.8 – <0.001 – –
Ni et al. 2016 one‐way ANOVA 23.41 ± 10.00 μmol/L 42.14 ± 12.77 μmol/L – <0.0001 – –
Mu et al. 2019 PCA, PLS-DA, Mann-Whitney U test – – 0.684 < 0.001 < 0.001 1.457
Miyamoto et al. 2015 Analysis of Covariance 6277.66666666667 5999.2 1.04641730008446 0.758606079008397 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 892 ± 388 999 ± 671 0.89 0.38 0.672 –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 0.893693885914484 0.330100647939372 0.368420023854012 –
Maeda et al. 2010 Mann-Whitney U-test, PCA 34.4 ± 10.6 μM 33.2±8.3 μM – 0.2 – –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 0.865745931011288 0.160796444401297 0.216818340267796 –
Mazzone et al. 2016 two- sample independent t test 1.016207± 0.4586533 1.104633± 0.439792 0.919949883807563 0.1167183 0.209727511 –
Miyamoto et al. 2015 Analysis of Covariance 5684.18181818182 6578.27272727273 0.864084244275231 0.115016549334893 – –
Roś-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 0.6485 ± 0.50829 0.78608 ± 0.4787 0.824979645837574 0.034879 0.2424 –
Yue et al. 2018 OPLS-DA, student’s t-test 735.70±238.92 ng/mL 394.84±95.35 ng/mL 2.39495740923786 0.031 – 1.01
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 733 ± 340 860 ± 278 0.851 0.02 0.157 –
Klupczynska et al. 2016a t-test, Welch’s t-test or the Mann-Whitney U test, one-way ANOVA 26.13±10.23 ?M 28.1±8.25 ?M 0.93 0.0192 – –
Ni et al. 2019 Mann-Whitney U test, Student's t-test, Welch's F test 32 37.12 – 0.01 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 359 ± 191 461 ± 169 0.78 0.003 0.037 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 471 ± 183 632 ± 280 0.75 0 0.011 –
Wikoff et al. 2015b OPLS-DA – – 1.5 – 0.004 –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Ni et al. 2019 ROC analysis – 0.103 – – –
Ni et al. 2016 – – – – – –
Mu et al. 2019 – – – – – –
Miyamoto et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Moreno et al. 2018 – – – – – –
Maeda et al. 2010 ROC curve – combination of 21 amino acid: 0.812 – – –
Moreno et al. 2018 – – – – – –
Mazzone et al. 2016 – – – – – –
Miyamoto et al. 2015 – – – – – –
Roś-Mazurczyk et al. 2017 ROC curve – – – – –
Yue et al. 2018 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Klupczynska et al. 2016a ROC curve analysis (Monte-Carlo cross validation), discriminant function analysis – 0.611 β-alanine+histidine+citrulline+asparagine+phenylalanine+aspartic acid=82.2 β-alanine+histidine+citrulline+asparagine+phenylalanine+aspartic acid=69.8 –
Ni et al. 2019 ROC analysis – 0.277 – – –
Fahrmann et al. 2015 random forest – maltose+maltotriose+cystine+3-Phosphoglycerate+citrulline=0.699 (0.583, 0.815) maltose+maltotriose+cystine+3-Phosphoglycerate+citrulline+pyrophosphate+tryptophan+adenosine-5-Phosphate=0.670 (0.552, 0.789) 55.8 maltose+maltotriose+cystine+3-Phosphoglycerate+citrulline=65.1 76.7 maltose+maltotriose+cystine+3-Phosphoglycerate+citrulline=81.4 66.3
Fahrmann et al. 2015 random forest – maltose+maltotriose+cystine+3-Phosphoglycerate+citrulline=0.880 (0.805, 0.954) maltose+maltotriose+cystine+3-Phosphoglycerate+citrulline+pyrophosphate+tryptophan+adenosine-5-Phosphate=0.883 (0.812, 0.955) – – 67.5 maltose+maltotriose+cystine+3-Phosphoglycerate+citrulline=79.5
Wikoff et al. 2015b – – – – – –