Animal Models of Inflammatory Bowel Disease: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Rita Xu and Version 1 by Lina Knudsen.

In the development of inflammatory bowel disease (IBD), the gut microbiota has been established as a key factor. Recently, metabolomics has become important for understanding the functional relevance of gut microbial changes in disease. Animal models for IBD enable the study of factors involved in disease development. However, results from animal studies may not represent the human situation. The aim of this study was to investigate whether results from metabolomics studies on animal models for IBD were similar to those from studies on IBD patients. Medline and Embase were searched for relevant studies up to May 2017. The Covidence systematic review software was used for study screening, and quality assessment was conducted for all included studies. Data showed a convergence of ~17% for metabolites differentiated between IBD and controls in human and animal studies with amino acids being the most differentiated metabolite subclass. The acute dextran sodium sulfate model appeared as a good model for analysis of systemic metabolites in IBD, but analytical platform, age, and biological sample type did not show clear correlations with any significant metabolites. In conclusion, this systematic review highlights the variation in metabolomics results, and emphasizes the importance of expanding the applied detection methods to ensure greater coverage and convergence between the various different patient phenotypes and animal models of inflammatory bowel disease.

  • inflammatory bowel disease
  • metabolomics
  • animal models
  • systematic review

Study Characteristics

Fifty-eight studies met our search criteria and were included in this review (Figure 1), of which 32 were human studies, 25 were animal model studies, and one study presented data from both humans and an animal model. The human studies were categorized according to disease (CD, UC, IBD) and age, while the animal model studies were categorized according to model type and age of the animals (Table 1). If animals in a study were grouped spanning more than one age group, the study was characterized according to the older age group. Descriptive characteristics for all studies were extracted, with different tables for the human and animal studies, respectively (Supplementary Tables S1 and S2).

Fifty-eight studies met our search criteria and were included in this review (Figure 1), of which 32 were human studies, 25 were animal model studies, and one study presented data from both humans and an animal model. The human studies were categorized according to disease (CD, UC, IBD) and age, while the animal model studies were categorized according to model type and age of the animals (Table 1). If animals in a study were grouped spanning more than one age group, the study was characterized according to the older age group. Descriptive characteristics for all studies were extracted, with different tables for the human and animal studies, respectively (Supplementary Tables S1 and S2).

Ijms 21 03856 g001 550

 

Figure 1. Flowchart of the study screening process for original studies in metabolomics for inflammatory bowel disease (IBD) patients and IBD animal models.
 

Flowchart of the study screening process for original studies in metabolomics for inflammatory bowel disease (IBD) patients and IBD animal models.

Table 1. Age categories for mouse studies (a) and human studies (b) in the systematic review on metabolomics in inflammatory bowel disease (IBD) patients and IBD animal models.

Age categories for mouse studies (a) and human studies (b) in the systematic review on metabolomics in inflammatory bowel disease (IBD) patients and IBD animal models.

Mouse Studies Human Studies
Phase of Life
Table 3. Number of differentiated metabolites detected across study types included in the systematic review on metabolomics in inflammatory bowel disease (IBD) patients and IBD animal models.

Number of differentiated metabolites detected across study types included in the systematic review on metabolomics in inflammatory bowel disease (IBD) patients and IBD animal models.

Number of Different Metabolites Detected Ijms 21 03856 i001 Ijms 21 03856 i002 Animal Studies Ijms 21 03856 i003
Animal StudiesAge in Weeks Phase of Life Age (Years)
Table 2. Quality assessment of studies included in the systematic review on metabolomics in inflammatory bowel disease (IBD) patients and IBD animal models.

Quality assessment of studies included in the systematic review on metabolomics in inflammatory bowel disease (IBD) patients and IBD animal models.

Level of Quality % of Criteria Fulfilled Animal Studies Human Studies All Studies
Human Studies
Metabolite *Both
Disease Activity Sample Type Age Group Platform References Species Sample Type Age (Weeks) Platform
Table 5. Metabolites significantly decreased in inflammatory bowel disease (IBD) vs healthy controls in both humans and animals in the systematic review.

Metabolites significantly decreased in inflammatory bowel disease (IBD) vs healthy controls in both humans and animals in the systematic review.

  Human Studies Animal Studies
Model References
Infant 0–3 Infant 0–1
3-Hydroxybutyric UC, IBD
Juvenile >3–8 Very early onset and young >1 and <18
40–70% 69% 79% 75% Adult >8–24 Adult 18–60
Old >24 Old 60+

Quality Assessment

Two sets of quality criteria were used to assess the quality of the human and animal studies, respectively (Supplementary Tables S3 and S4). Each study was assigned as being of “good”, “medium”, or “poor” quality, based on the amount of quality criteria fulfilled, as presented in Table 2. The majority of studies (75%) were of medium quality, while only 9% of all studies were considered good.
 

Two sets of quality criteria were used to assess the quality of the human and animal studies, respectively (Supplementary Tables S3 and S4). Each study was assigned as being of “good”, “medium”, or “poor” quality, based on the amount of quality criteria fulfilled, as presented in Table 2. The majority of studies (75%) were of medium quality, while only 9% of all studies were considered good.

Metabolite * Disease Activity Sample Type Age Group Platform References Species Sample Type Age (Weeks) Platform Model References
Good ≥70% 12% 6% 9%
Poor
<40% 19% 15% 17%

Metabolites Differentiated in Inflammatory Bowel Disease (IBD) Cases Versus Healthy Controls in Both Humans and Animal Models

A total of 200 different metabolites were reported as being increased in IBD across all included human studies, while 218 were decreased (Table 3). The numbers were higher for the animal studies with a total of 280 different metabolites reported as being increased in IBD, while 253 were decreased. Some metabolites were reported as both increased and decreased in each study type, but the majority was exclusively reported as increased or decreased. Results for human and animal model studies, respectively, are presented in separate tables for metabolites that are increased and decreased in each type of study (Supplementary Tables S5–S8).
 

A total of 200 different metabolites were reported as being increased in IBD across all included human studies, while 218 were decreased (Table 3). The numbers were higher for the animal studies with a total of 280 different metabolites reported as being increased in IBD, while 253 were decreased. Some metabolites were reported as both increased and decreased in each study type, but the majority was exclusively reported as increased or decreased. Results for human and animal model studies, respectively, are presented in separate tables for metabolites that are increased and decreased in each type of study Supplementary Tables S5-S8.

Increased 280 200 AC48 48/280 = 17% Serum A, O 1H NMR [14,15][1][2] Mouse Serum >3–8 1H NMR DSS (A) [16][3]
4-Cresol sulfate CD All Urine Y, A, O
Medium
1H NMR Decreased 253 218 41 41/253 = 16%
[48][40] Mouse Urine >8–24 1H NMR DSS (A) [13][10] acid             Mouse
Acetic acid CD ACSerum >8–24 8 >3–8 15
BALB/c 2 IL10-/- (C) 6 GC-MS 6 Urine 8 >8–24 19
C57Bl6/N 1 TNBS (A) 3     Serum 7 >24 2
Winnie 1 TNFΔARE/WT (C) 1     Feces 4 Not reported 2
ICR 1 T-synthase 1     Liver 4    
CD1 1 deficiency (C)   Cecum 1    
129/SvEv
Table 7. Overview of the variation in key experimental elements in human studies and the number of studies containing the different versions of each element in the systematic review on metabolomics in inflammatory bowel disease (IBD) patients and IBD animal models.

Overview of the variation in key experimental elements in human studies and the number of studies containing the different versions of each element in the systematic review on metabolomics in inflammatory bowel disease (IBD) patients and IBD animal models.

IBD/IBD Subtype Analytical Platform Biological Sample Type Age Group (Years)
CD 27 NMR * 13 Feces 9 0–1 0
GC-MS DSS (A) Serum A, O 1H NMR [18][
UC5] 24 GC-MS ** 11Mouse Serum >3–8 1 Urine[17][4 Exclusively increased 215 135 27  
]
9 >1 and <18 6 4-Hydroxyphenyl- CD AC Urine A, O 1H NMR [18] Exclusively decreased 190 153 20  
To assess the similarities in metabolomics findings between study types, metabolites increased or decreased in IBD in both human and animal studies were identified and are presented in Table 4; Table 5. Forty-eight metabolites were found to be increased in both types of studies, while 41 metabolites were decreased. This corresponds to 17% of metabolites found increased and 16% of metabolites found decreased in IBD in animal studies also being reported as increased and decreased, respectively, in human IBD studies. Of this subgroup of metabolites, 21 were reported as both increased and decreased, respectively, in IBD including several amino acids, and this overlap can largely be explained by the variation in study details. This leaves 27 metabolites exclusively increased, and 20 metabolites exclusively decreased in IBD in both human and animal studies (in bold in Table 4 and Table 5).

To assess the similarities in metabolomics findings between study types, metabolites increased or decreased in IBD in both human and animal studies were identified and are presented in Table 4; Table 5. Forty-eight metabolites were found to be increased in both types of studies, while 41 metabolites were decreased. This corresponds to 17% of metabolites found increased and 16% of metabolites found decreased in IBD in animal studies also being reported as increased and decreased, respectively, in human IBD studies. Of this subgroup of metabolites, 21 were reported as both increased and decreased, respectively, in IBD including several amino acids, and this overlap can largely be explained by the variation in study details. This leaves 27 metabolites exclusively increased, and 20 metabolites exclusively decreased in IBD in both human and animal studies (in bold in Table 4 and Table 5).

 
Table 4. Metabolites significantly increased in inflammatory bowel disease (IBD) vs healthy controls in both humans and animals in the systematic review.

Metabolites significantly increased in inflammatory bowel disease (IBD) vs healthy controls in both humans and animals in the systematic review.

  Human Studies
H NMR
DSS (A) [16][7]
[5] Mouse
CD All Urine A 1H NMR [43][33Colon
IBD 1] LC-MS ***>8–24 GC-MS DSS (A) Mouse Plasma >8–24 1H NMR 5DSS (A)[17][4]
[13 Colon][10] 4 18–60 21 acetic acid CD, UC All Urine Y
CD1H NMR [19][ Unknown6]
    SIFT-MS           
Feces Y, A, O 31H NMR [25][12]           Breath 4  60+ 13 Acetoacetatic acid IBD AC Serum A, O 1H NMR [15][2] Mouse Serum >3–8 1H NMR DSS (A) [16]NMR
 
UC AC Serum A, O 1H NMR [18][5][5]
            IBD IA Urine A, O 1      Spleen 2    
129/SvEv Rag2-/-
    ESI-MS 1 Serum 3 Not reported 1 H NMR [15][2]            
    FT-ICR-MS 1 Plasma 2     Acetylaspartic acid UC All, AC, IA Serum Y, A, O GC-MS [20][7] Mouse Colon (distal), cecum 0–3 UPLC/Tof-MS T-syn deficiency
 [21  ][8 Proton MRS]
1 Ileum 1     Acetylcarnitine CD, UC AC Urine A, O 11H NMR H. hepaticus (C) 1  [50][26[18][5] Mouse Colon >8–24 LC-qTOF-MS ]  DSS (C)  [  22    ]  [9]
  Ileum 1     Acylcarnitine
        PBMC Macrophages 1CD All
Acetylcarnitine UC ACUrine Y 1H NMR [19 129(B6)-Il10tm1Cgn/J][6] Mouse Ileum (distal) Serum>8–24 A, O 1H NMR [18][5] Mouse Spleen >8–24LC-MS LC-qTOF-MSTNFΔARE/WT DSS (C)[23][10]
[ 122 Winnie] 1[29  ] Alanine CD All Serum Y, A, O GC-MS [24][
Acetylglutamic acid11] CD IA Feces Unknown UPLC-tof-MS [Mouse Colon >8–24 GC-MS DSS (A) 1 (spontaneous) (C)  30][17][6]
[4] Mouse Serum     Small intestine 1     CD Unknown Feces Y, A, O
  1H NMR [25][12] Mouse Plasma >3–24 1H NMR IL10-/- [ Rat 3 Adoptive 1    26 Red blood cells 1]  [13]
  CD, UC AC Feces A, O 1H NMR [
Sprague-Dawley 24 Transfer (C)][14]            
      Masseter 1     Arachidonic acid CD (ICD) IA Feces Y, A, O FT-ICR-MS [27][15] Mouse Ileum (distal) >8–24 LC-MS TNFΔARE/WT [
Fischer 344 1        23][16]
Longissimus dorsi 1                 Mouse Colon (distal), cecum 0–3
PigletUPLC/Tof-MS T-syn deficiency 1    [21][14]
  Arginine CD AC Plasma, serum A, O 1H NMR [18][5] Mouse Liver >8–24 LC-qTOF-MS DSS (C) [22][15]
UC AC Urine A, O 1H NMR [18][5] Mouse Plasma >3–24 1H NMR IL10-/- [26][17]
Butanal CD All Breath A, O SIFT-MS [28][16] Mouse Feces >8–24 GC-MS Winnie [29][18]
Carnitine CD, UC AC Urine A, O 1H NMR [18][5] Mouse Colon >8–24 LC-qTOF-MS DSS (C) [22][15]
Cholic acid CD IA Feces Y, Unknown UPLC/ToFMS [30][4] Rat Plasma ? UPLC-ESI-QTOF-MS TNBS [31][19]
Creatine CD AC Plasma A, O 1H NMR [18][5] Mouse Serum >3–8 1H NMR DSS (A) [16][5]
          UC AC Plasma, serum A, O 1H NMR [18][5] Mouse Plasma >3–8 1H NMR IL10-/- [26][17]
Dimethylamine IBD IA Serum A, O 1H NMR [15][2] Rat Urine ? UPLC-MS/MS TNBS [32][20]
Ethylmalonic acid UC All, AC, IA Serum Y, A, O GC-MS [20][7] Mouse Colon >8–24 GC-MS DSS (A) [17][6]
Fructose UC IA Serum Y, A, O GC-MS [20][7] Mouse Feces >8–24 GC-MS Winnie [29][21]
Fumaric acid CD, UC All Serum Y, A, O GC-MS [24][11] Mouse Urine >8–24 1H NMR DSS (A) [13][22]
            Mouse Plasma >3–8 Feces A, O 1H NMR [4][1H NMR 14]IL10-/- [26][17]
            Glucose UC AC Serum A, O 1H NMR [14,18][1][5] Mouse Urine >8–24 GC-MS IL10-/- [33][23]
UC
CD Unknown Feces Y, A, O 1H NMR [25][12]             All Feces A, O 1H NMR [34][24]            
Carnitine CD, UC All Urine Y 1H NMR [19][6] Mouse Serum >3–8 1H NMR DSS (A) [16][12] UC All, AC, IA Serum Y, A, O GC-MS [20][7]            
Citric acid CD, UC AC Serum A, O 1H NMR [18][5] Mouse Serum >3–8 1H NMR DSS (A) [16][12] UC IA Colon Unknown
CD, UCProton MRS All Urine A 1[35][17]   H NMR [43][33] Mouse        Plasma 
>8–24 UPLC-MS DSS (A) [41][22] CD, UC AC Colon Unknown Proton MRS [35][17]            
UC AC Urine A, O 1H NMR [18][5] Mouse Serum >8–24 GC-MS DSS (A) [17][4] IBD AC Colon A 1H NMR [36][25]    
UC  All Rectum Y, A, O GC-MS [24][     
11] Mouse Urine >8–24 NMR IL10-/- [44][28] Glutamic acid UC Unknown Feces Y, A, O 1
UCH NMR AC, IA, All Serum Y, A, O GC-MS [20][7[25][12] Mouse Colon ]>8–24 GC-MS DSS (A) [ Mouse17 Serum >8 UPLC-ESI-TOF-MS][ H. hepaticus6]
[45][30] UC All, AC, IA Serum Y, A, O GC-MS [20][
IBD AC, IA7 Urine] A, O 1H NMR [15  ][  2    ]   
            Glycerol UC AC Serum Y, A, O GC-MS
IBD All Urine A, O NMR [50][20][7] Mouse Plasma [26]>8–24  1H NMR DSS (A)      [13][26]
    CD AC Plasma A, O 1H NMR [18][5] Mouse Feces >8–24 GC-MS Winnie [29][21]
 
Creatine IBD AC Serum A, O 1H NMR [15][2] Mouse Plasma >8–24 1H NMR IL10-/- [26][21] Glycine CD AC Serum A, O 1H NMR [18][5] Mouse Colon >8–24 GC-MS DSS (A) [37][27]
IBD All Urine A, O NMR [50][26]             CD AC, IA Feces A, O 1H NMR [4][14] Mouse Feces >8–24 1H NMR Adoptive [38][28]
CD, UC
Dimethylglycine CD All Urine A 1H NMR [43][33] Mouse Plasma 0–3, >8–24 1H NMR IL10-/- [26][21] All Urine Y 1H NMR [19][6
Fumaric acid]         transfer  
UC All Rectum Y, A, O CD, UC All Serum Y, A, O GC-MS [24][11]            
IBD AC Serum A, O 1H NMR [15][2]            
Hydroxybenzoic acid UC All, AC Serum Y, A, O GC-MS [20][7] Mouse Colon, serum >8–24 GC-MS DSS (A) [17][6]
Inositol CD AC Feces A GC-MS [18][5] Mouse Feces >8–24 GC-MS Winnie [29][21]
Isoleucine CD AC Serum A 1H NMR [39][29] Mouse Colon, serum >8–24 GC-MS DSS (A) [17][6]
CD Unknown Feces Y, A, O 1H NMR [25][12] Mouse Plasma >8–24 1H NMR IL10-/- [26][17]
CD, UC AC Feces A, O 1H NMR [4][14] Mouse Feces >8–24 1H NMR Adoptive [38][10]
CD, UC AC Serum, plasma A, O 1H NMR [18][5]         transfer  
IBD AC Serum A, O 1H NMR [15][2]            
Kynurenine UC All, AC, IA Serum Y, A, O GC-MS [20][7] Mouse Plasma >8–24 LC-MS IL10-/- [40][30]
            Mouse Plasma >8–24 UPLC-MS DSS (A) [41][31]
Lactic acid CD AC Plasma, urine A, O 1H NMR [18][5] Mouse Colon >8–24 NMR (1H, 1C, 1P) DSS (A) [42][32]
UC AC Urine A, O 1H NMR [18][5] ]
>24 UPLC-ESI-TOF-MS
 
 
 
 
UC AC Feces A, O GC-MS [49][41]            
UC All Feces A GC-MS [6][36]            
IBD All Urine A, O H. hepaticus [45][30]
Aconitic acid CD, UC All Urine Y 1H NMR [19][6] Mouse Urine >3–24 GC-MS IL10-/- [33,40][39][20]
UC AC Serum Y, A, O GC-MS [20][7]            
IBD All Urine A, O NMR [50][26]            
Acylcarnitine UC All Urine Y 1H NMR [19][6] Mouse Ileum (distal) >3–24 LC-MS TNFΔARE/WT [23][16] Mouse Plasma >3–24 1H NMR IL10-/- [26][17]
Lysine CD AC Plasma A, O 1H NMR [18][5] Mouse Colon, plasma, liver >8–24 1H NMR DSS (A) [13][26]
UC AC Serum, plasma A, O 1H NMR [18][5] Mouse Plasma >3–8 1H NMR IL10-/- [26][17]
Alanine CD All Urine A 1H NMR [43][33] Mouse Serum >3–8 1H NMR DSS (A) [16][7]
UC All Rectum Y, A, O GC-MS [24][11] Mouse Urine >8–24 1H NMR Adoptive transfer [38][16]
CD, UC AC Colonic mucosa Unknown Proton MRS [35][17]            
IBD IA Urine A, O 1H NMR [15][2]            
IBD AC Colonic mucosa A 1H NMR [36][25]            
Aspartic acid CD IA Feces A, O 1H NMR [4][14] Mouse Feces >3–8 1H NMR DSS (A) [51][22]
UC AC, IA, All Serum Y, A, O GC-MS [20][7]            
Betaine CD, UC AC Plasma, urine A, O 1H NMR [18][5] Mouse Serum >3–8 1H NMR DSS (A) [16][12]
            Mouse Colon >8–24 NMR (1H, 1C, 1P) DSS (A) [42][23]
Butanoic acid CD, UC AC Feces A, O GC-MS [49][41] Mouse Urine >8–24 1H NMR DSS (A) [13][16]
CD AC Feces A GC-MS [52][37] Rat Urine, Feces ? UPLC-MS/MS TNBS [32] GC-MS [24][11] Mouse Serum >8–24 GC-MS DSS (A) [17][4]
UC AC, IA, all Serum Y, A, O GC-MS [20][7] Mouse Liver >8–24 1H NMR DSS (A) [13][16]
            Mouse Serum >3–8 1H NMR DSS (A) [16][12]
            Mouse Urine >8–24 NMR IL10-/- [44][28]
            Mouse Plasma 0–3 1H NMR IL10-/- [26][21]
Glucose CD AC Plasma A, O 1H NMR [18][5] Mouse Serum >3–8 1H NMR DSS (A) [16][12]
            Mouse Plasma, liver >8–24 1H NMR DSS (A) [13][16]
            Mouse Serum >8–24 GC-MS DSS (A) [37][13]
            Mouse Urine >3–24 GC-MS IL10-/- [33,40][6][26]
            Mouse Plasma >8–24 1H NMR IL10-/- [26][21]
Glutamic acid CD, UC AC Colonic mucosa Unknown Proton MRS [35][17] Mouse Feces >3–8 1H NMR DSS (A) [ UC AC Feces A, O 1H NMR [4][14]            
[26] UC All Urine Y 1H NMR [19][6]            
UC All, AC, IACD, UC
CD AC 51][3]
CD IA Feces A, O 1H NMR [4][14]            
UC IA, All Serum Y, A, O GC-MS [20][7]            
UC All Rectum Y, A, O GC-MS [24][11]            
IBD AC Colonic mucosa A 1H NMR [36][25]            
Glutamine CD AC Plasma, urine A, O 1H NMR [18][5] Mouse Feces >3–8 1H NMR DSS (A) [51][3] Serum Y, A, O GC-MS [20][7]          
CD All Serum Y, A, O GC-MS [24][11 
] Mouse Serum >3–8 1H NMR DSS (A) [16][12] IBD AC Serum A, O
CD, UC AC Colonic mucosa1H NMR [15][ Unknown2] Proton MRS [35][17    ] Mouse Colon, serum       
>8–24 GC-MS DSS (A) [17][4] Leucine CD Unknown Feces Y, A, O 1H NMR [25][12] Mouse Colon, serum >8–24
UCGC-MS All Serum, rectum Y, A, O GC-MS [24][DSS (A) 11][17][6]
Mouse Liver >8–24 1H NMR DSS (A) [13][16] CD AC, IA Feces A, O 1H NMR [4][14]          
UC 
AC, IA, All Serum Y, A, O GC-MS [20][7] Mouse Plasma >8–24 1H NMR IL10-/- [26][21] UC AC Feces A, O 1H NMR [4][14]            
UC AC Serum A, O GC-MS [17][4] Mouse Feces >8–24 1H NMR Adoptive [38][33] IBD AC Serum A, O 1H NMR
IBD AC Colonic mucosa A 1H NMR[15][2]           [ 
36][9]         transfer   Linoleic acid CD (ICD) IA Feces Y, A, O FT-ICR-MS [27][15] Mouse Colon (distal), cecum >3–8 UPLC/ToFMS T-syn deficiency
Glycero-

phosphocholine		[21] CD, UC[ AC Colonic mucosa Unknown Proton MRS [35][21]14 Mouse Colon >8–24 1H NMR DSS (A) [13][16]
UC IA Colonic mucosa Unknown Proton MRS [35][21]            
IBD AC Colonic mucosa A 1H NMR [36][9]             AC Feces A, O
Glycine UC1 AllH NMR Rectum[4 Y, A, O][14] Mouse GC-MS [Feces 24>8–24 1H NMR Adoptive ][4][38] Mouse[10]
Serum >3–8 1H NMR DSS (A) [16][12] CD, UC Unknown Feces Y, A, O
IBD IA1 UrineH NMR A[25 1H NMR][12] [15][  2    ]  transfer Mouse Serum >8–24 GC-MS 
DSS (A) [17][4] Maleic acid UC All, AC, IA Serum
     Y, A, O GC-MS      [20][7] Mouse Colon >8–24 GC-MS DSS (A) [17][ Mouse6]
Feces >8–24 GC-MS Winnie [29][21] Malic acid CD All Serum Y, A, O GC-MS [24][11] Mouse Colon, serum >8–24 GC-MS DSS (A) [17,37]
Hippuric acid CD[ IA6] Urine[9]
A, O 1H NMR [53][42] Mouse Urine >8–24 1H NMR DSS (A) [13][16] Mannose CD, UC AC Serum, plasma A, O 1H NMR [18][5] Mouse Serum >3–8 1H NMR
CD, UCDSS (A) AC Urine A, O 1H NMR [18][5[16][5]
] Mouse Serum >24 UPLC-ESI-TOF-MS H. hepaticus [45][30] Methionine CD
CD, UC AllAll Serum Y, A, O GC-MS [24][11] Mouse UrineColon A 1H NMR [>8–24 43GC-MS DSS (A) ][33][17][6]
            UC AC Serum A, O 1H NMR [18][5] Mouse Plasma >3–8 1H NMR IL10-/- [26][17]
CD, UC All Urine Y, A, O 1H NMR [48][40]                         Mouse Feces
CD, UC All Urine>8–24 GC-MS Winnie Y[ 1H NMR [19][6]  29      ][  21]
  Oleic acid CD (ICD) IA Feces Y, A, O FT-ICR-MS [27][15] Mouse Feces >8–24 GC-MS Winnie
IBD AC, IA[ Urine A, O 1H NMR [15][2]29][21]
            Phenylacetylglycine UC All Urine A 1H NMR [43][33] Mouse Urine >8–24 NMR
IBD All Urine A, O NMR [50][26]IL10-/-      [44][34]
           
Histidine CD, UC All      Serum Y, A, OMouse GC-MS [24][11Serum >24 ] Mouse SerumUPLC-ESI-TOF-MS H. hepaticus [ >3–8 1H NMR45][35]
DSS (A) [16][12]      
UC AC, IA, All    Serum Y, A, O GC-MS  [20Rat ][7Urine ]? UPLC-MS/MS, UPLC-ESI-QTOF-MS TNBS [31,32][36  ]  [    26  ]
  Phenylalanine CD AC Feces A, O 1H NMR [4][14] Mouse Plasma >8–24 1H NMR DSS (A) [13][10]
IBD AC Serum A, O 1H NMR [15][2]             UC AC Serum A, O 1H NMR [14][1] Mouse
IBD All UrineColon, serum >8–24 GC-MS A, O NMRDSS (A) [50][26][          17][11]
  IBD AC Serum A, O 1H NMR [
Hypoxanthine CD15] AC[ Urine2 A, O] Mouse Plasma >3–24 1H NMR 1H NMRIL10-/- [26 [18][5]] Mouse[21]
Spleen >8–24 1H NMR DSS (A) [13][16]             Mouse Feces >8–24 GC-MS Winnie [29][37]
Inositol CD, UC AC Colonic mucosa Unknown Proton MRS [35][21] Mouse Colon >8–24 GC-MS DSS (A) [37][13]             Mouse Feces >8–24 1H NMR Adoptive transfer [38][16]
Proline
UC IA Colonic mucosa Unknown Proton MRS [35][21]             CD AC Serum A, O 1
UCH NMR AC, IA, All Serum Y, A, O GC-MS [20][7[18][5] Mouse Colon ]>8–24 GC-MS DSS (A) [  17      ][  11]
  CD All Serum Y, A, O GC-MS
IBD AC[ Colonic mucosa24][11]     A 1  H NMR [  36   
][9]             Prostaglandin E2 CD Unknown Urine A LC-MS [46][21] Rat Colon >8–24 LC-MS DSS (C) [47][38]
Isocitric acid UC All Rectum Y, A, O GC-MS [24][11] Mouse Serum >8–24 GC-MS DSS (A) [17][4] Pyruvic acid UC AC Serum, urine A, O 1H NMR [18][5] Mouse
UCPlasma >3–24 1H NMR AC, IA, AllIL10-/- Serum Y, A, O GC-MS [20][7] Mouse Urine >3–24 GC-MS IL10-/- [33,40][6][26][26][21]
           
Isoleucine CD, UC ACMouse Feces >8–24 GC-MS Colonic mucosaWinnie [29][ Unknown37]
Proton MRS [35][21] Mouse Feces >8–24 GC-MS Winnie [29][21] Succinic acid CD All Serum Y, A, O GC-MS [24]
UC[11] Mouse Urine >3–24 GC-MS IL10-/- [40 AC, IA, All][20 Serum]
Y, A, O GC-MS [20][7]                         Mouse Colon >8–24 GC-MS DSS (A) [17][11
UC]
All Rectum Y, A, O GC-MS [24][11]                         Mouse Plasma >3–8 1H NMR IL10-/-
Lactic acid CD AC[26][21]
Colonic mucosa Unknown Proton MRS [35][21] Mouse Serum >3–8 1H NMR DSS (A) [16][             Rat Urine ? UPLC-MS/MS TNBS [32][26]
12]
UC AC, IA Colonic mucosa Unknown Proton MRS [35][21]             Taurocholic acid CD (ICD) IA
IBD AC Colonic mucosaFeces Y, A, O FT-ICR-MS A NMR [36][9][27][15] Mouse    Colon (distal), cecum >3–8 UPLC/ToFMS T-syn deficiency [21]  [25]
      Threonine CD, UC AC Urine
Leucine CD, UCA, O 1H NMR [18][5] Mouse Colon, serum >8–24 GC-MS DSS (A) AC Colonic mucosa[ Unknown Proton MRS [35][21]17 Mouse Plasma >8–24 1H NMR][ IL10-/-11]
[26][21] Tryptophan UC AC Urine A, O 1H NMR [18][5] Mouse Feces >8–24 1H NMR Adoptive transfer [38][16]
UC All Urine Y 1H NMR [19][6] Mouse Serum >3–8 1H NMR DSS (A) [16][7]
           
UC All Rectum Y, A, O GC-MS [24][11]            
UC AC Plasma A, O 1H NMR [18][5]             Mouse Liver >8–24 LC-qTOF-MS DSS (C) [22][29]
Lysine UC All Rectum Y, A, O GC-MS [24][11] Mouse Feces >3–8 1H NMR DSS (A) [51][22] Tyrosine CD AC Feces A, O 1H NMR [4][14] Mouse Colon >8–24 GC-MS DSS (A) [
UC All, IA17 Serum Y, A, O GC-MS [20]][11]
[7]             CD (ICD) IA Feces
IBD AllY, A, O FT-ICR-MS Urine A, O NMR[27][15] Mouse Plasma [50]>3–8 [26]1H NMR IL10-/-  [  26    ]  [21]    
Malic acid UC AC, IA, All Serum Y, A, OMouse Feces >8–24 GC-MS1H NMR [20Adoptive transfer ][7][38][16]
Mouse Serum >8–24 GC-MS DSS (A) [17][4] Uracil UC All, AC, IA Serum Y, A, O GC-MS
UC All Rectum Y, A, O GC-MS [24][11][20][7] Mouse Urine >3–24 GC-MS, NMR IL10-/- [33,40,44][  39  ]    [  20][28]
            Mouse Colon, serum >8–24 GC-MS DSS (A) [17][11]
  Urea UC All, AC, IA Serum Y, A, O GC-MS [20][7] Mouse Serum >8–24 GC-MS DSS (A) [37][15]
Methionine UC AC, IA, All Serum Y, A, O GC-MS [20][7] Mouse Serum >3–8 1H NMR DSS (A) [16][12]
UC All Rectum Y, A, O GC-MS [24][11] Mouse Plasma >8–24 1H NMR IL10-/- [26][21] Valine CD Unknown Feces Y, A, O 1H NMR [25][12]
MethylamineMouse CD, UC Unknown Feces Y, A, O 1H NMR [Plasma >8–24 1H NMR DSS (A) 25][13][10]
[12] Mouse Urine >8–24 1H NMR DSS (A) [13][16] CD AC, IA Feces A, O
IBD1H NMR All[4 Urine A, O] NMR[14] [50][26Mouse Colon, serum ]    >8–24 GC-MS DSS (A)    [17][11]
    UC AC Feces A, O 1H NMR [
Proline UC4 AC, All Serum] Y, A, O[14]     GC-MS       
[20][7] Mouse Serum >3–8 1H NMR DSS (A) [16][12] Xylose CD AC Urine A, O 1H NMR [18][5] Mouse Feces >8–24 GC-MS Winnie [29][37]
UC All Rectum Y, A, O GC-MS [24][11] Mouse Urine >8-24 1H NMR Adoptive transfer [38][33]   UC AC Serum Y, A, O GC-MS [20][7]            
Sebacic acid
UC
IA
Serum
Y, A, O
GC-MS
[
20
]
[
7
]
Mouse
Feces
>8–24
GC-MS
Winnie
[
29
]
[
21
]
Succinic acid
CD
AC
Plasma, urine
A, O
1
H NMR
[
18
]
[
5
]
Mouse
Serum
>8–24
GC-MS
DSS (A)
[
17
]
[
4
]
CD, UC
AC
Colonic mucosa
Unknown
Proton MRS
[
35
]
[
21
]
Mouse
Urine
>8–24
NMR
IL10
-/-
[
44
][28]
UC AC, IA, All Serum Y, A, O GC-MS [20][7] Mouse Urine >8–24 1H NMR Adoptive transfer [38][33]
UC AC Urine A, O 1H NMR [18][5]            
UC All Urine Y 1H NMR [19][6]            
UC All Rectum tissue Y, A, O GC-MS [24][11]            
IBD AC, IA Urine A, O 1H NMR [15][2]            
IBD All Urine A, O NMR [50][26]            
Taurine CD, UC All Urine Y 1H NMR [19][6] Mouse Colon, spleen >8–24 1H NMR DSS (A) [13][16][13]
CD AC Urine A, O 1H NMR [18][5]            
UC AC, IA, All Serum Y, A, O GC-MS [20][7]            
IBD AC, IA Urine A, O 1H NMR [15][2]            
IBD All Urine A, O NMR [50][26]            
Threonine UC IA, All Serum Y, A, O GC-MS [20][7] Mouse Feces >3–8 1H NMR DSS (A) [51][22]
UC All Rectum Y, A, O GC-MS [24][11]            
Triglyceride UC All Plasma A LC-MS/MS [54][43] Mouse Colon (proximal), ileum (distal) >8–24 1H NMR TNFΔARE/WT [23][10]
            Mouse Liver >8–24 1H NMR Adoptive transfer [38][33]
Trimethylamine CD, UC Unknown Feces Y, A, O 1H NMR [25][12] Mouse Plasma >8–24 1H NMR IL10-/- [26][21]
Tryptophan CD, UC All Serum Y, A, O GC-MS [24][11] Mouse Plasma >8–24 UPLC-MS DSS (A) [41][22]
UC AC, IA, All Serum Y, A, O GC-MS [20][7] Mouse Serum >8–24 GC-MS DSS (A) [17][4]
            Mouse Plasma >8–24 LC-MS IL10-/- [40][26]
Tyrosine CD AC Plasma A, O 1H NMR [18][5] Mouse Serum >3–8 1H NMR DSS (A) [16][12]
UC AC, IA, All Serum Y, A, O GC-MS [20][7] Mouse Serum >8–24 GC-MS DSS (A) [17][4]
UC AC Serum, plasma A, O 1H NMR [18][5] Mouse Plasma >8–24 UPLC-MS DSS (A) [41][22]
UC All Rectum Y, A, O GC-MS [24][11] Mouse Plasma >8–24 1H NMR IL10-/- [26][21]
            Mouse Feces >8–24 GC-MS Winnie [29][21]

Metabolites of Special Interest

Several tryptophan metabolites were found to be regulated in human studies, animal studies, or both. Kynurenine and quinolinic acid were increased in UC and CD patients, respectively (Supplementary Table S5). Kynurenine was also found to be increased in DSS (dextran sodium sulfate) and IL-10

Several tryptophan metabolites were found to be regulated in human studies, animal studies, or both. Kynurenine and quinolinic acid were increased in UC and CD patients, respectively (Supplementary Tables S5). Kynurenine was also found to be increased in DSS (dextran sodium sulfate) and IL-10

-/-

mouse models (Supplementary Table S7), while quinolinic acid was decreased in IL-10

-/-

mice along with kynurenic acid and 5-hydroxyindoleacetic acid (Supplementary Table S8). Additionally, 5-hydroxytryptophan and 3-hydroxykynurenine were also increased in DSS and IL-10

-/- mouse models, respectively (Supplementary Table S7). Conflicting observations were made for tryptophan itself, which was reported to be both increased and decreased in human studies as well as the DSS mouse model (see Table 4 and Table 5). SCFAs were reported to be regulated in numerous human IBD studies, although some results were conflicting. Formic acid and acetic acid were thus observed to be both increased and decreased in CD and UC patients, depending on the study (Supplementary Tables S5 and S6). However, propionic acid, butanoic acid, isobutyric acid, and pentanoic acid were all observed to be decreased in CD and UC patients (Supplementary Table S6). Interestingly, only animal studies using the acute DSS mouse model or the TNBS (2,4,6-trinitrobenzenesulfonic acid) rat model reported differentiated levels of SCFAs (Supplementary Tables S7 and S8). Acetic acid was decreased in the DSS model, while butanoic acid was decreased in the TNBS model (Supplementary Table S8). Dong et al. [13] also observed butanoic acid to be decreased, but only on the first day of DSS, after which it was increased throughout the experiment.

mouse models, respectively (Supplementary Table S7). Conflicting observations were made for tryptophan itself, which was reported to be both increased and decreased in human studies as well as the DSS mouse model (see Table 4 and Table 5). SCFAs were reported to be regulated in numerous human IBD studies, although some results were conflicting. Formic acid and acetic acid were thus observed to be both increased and decreased in CD and UC patients, depending on the study (Supplementary Tables S5 and S6). However, propionic acid, butanoic acid, isobutyric acid, and pentanoic acid were all observed to be decreased in CD and UC patients (Supplementary Table S6). Interestingly, only animal studies using the acute DSS mouse model or the TNBS (2,4,6-trinitrobenzenesulfonic acid) rat model reported differentiated levels of SCFAs (Supplementary Tables S7 and S8). Acetic acid was decreased in the DSS model, while butanoic acid was decreased in the TNBS model (Supplementary Table S8). Dong et al. [10] also observed butanoic acid to be decreased, but only on the first day of DSS, after which it was increased throughout the experiment.

Included Studies Are Characterized by Great Variation in the Key Experimental Elements

A metabolomics study consists of several different key experimental elements that can vary between studies. Here, these elements are the experimental subjects (disease subtype for the human studies and species, strain, and type of model for the animal studies), biological sample type, analysis methodology, and age of experimental subjects/study population. Large variations in these elements can make it difficult to compare results across the different studies and thereby difficult to draw any overall assumptions on the topic in question.

A metabolomics study consists of several different key experimental elements that can vary between studies. Here, these elements are the experimental subjects (disease subtype for the human studies and species, strain, and type of model for the animal studies), biological sample type, analysis methodology, and age of experimental subjects/study population. Large variations in these elements can make it difficult to compare results across the different studies and thereby difficult to draw any overall assumptions on the topic in question.

To clearly elucidate the large variation between the different studies included in this review, we tallied up the number of studies containing the different variants of each key experimental element in animal studies and human studies, respectively (see Table 6 and Table 7). Looking at Table 6 and Table 7, it becomes immediately clear that there could be a very high degree of variation between studies as a result of the different elements applied in the studies. For the animal studies (Table 6), three different species with a total of 11 different mouse and rat strains were used along with eight different IBD animal models, three main analytical platforms, 13 different sample types, and four different age groups across the 26 studies. The variation in study population and sample type was less for the human studies (Table 7), however seven different analytical platforms were applied, giving rise to a considerable heterogeneity across the human studies.
 

To clearly elucidate the large variation between the different studies included in this review, we tallied up the number of studies containing the different variants of each key experimental element in animal studies and human studies, respectively (see Table 6 and Table 7). Looking at Table 6 and Table 7, it becomes immediately clear that there could be a very high degree of variation between studies as a result of the different elements applied in the studies. For the animal studies (Table 6), three different species with a total of 11 different mouse and rat strains were used along with eight different IBD animal models, three main analytical platforms, 13 different sample types, and four different age groups across the 26 studies. The variation in study population and sample type was less for the human studies (Table 7), however seven different analytical platforms were applied, giving rise to a considerable heterogeneity across the human studies.

Table 6. Overview of the variation in key experimental elements in animal model studies and the number of studies containing the different versions of each element.

Overview of the variation in key experimental elements in animal model studies and the number of studies containing the different versions of each element.

Species & Strain * Model Analytical Platform Biological Sample Type Age Group (Weeks)
Mouse 22 DSS (A) 12 LC-MS ** 15 Colon 12 0–3 3
C57BL/6 14 DSS (C) 2 NMR *** 8 Plasma
A few studies did, however, share a high degree of similarity in experimental factors. Animal studies by Shiomi et al., Gu et al., and Wang et al. all used C57BL/6J mice from the same age group for a 3% DSS model as well as using gas chromatography-mass spectrometry (GC-MS) to detect metabolites in serum and colon samples (see Supplementary Table S2) [17,37,55], although it is worth noting that Gu et al. and Wang et al. belong to the same department at Kobe University, Japan. Equally, two studies by the same first author also shared a similar degree of similarity using an IL10

A few studies did, however, share a high degree of similarity in experimental factors. Animal studies by Shiomi et al., Gu et al., and Wang et al. all used C57BL/6J mice from the same age group for a 3% DSS model as well as using gas chromatography-mass spectrometry (GC-MS) to detect metabolites in serum and colon samples (see Supplementary Table S2) [4][29][44], although it is worth noting that Gu et al. and Wang et al. belong to the same department at Kobe University, Japan. Equally, two studies by the same first author also shared a similar degree of similarity using an IL10

-/- model [33,40]. For the human studies, two studies used proton nuclear magnetic resonance (

model [34][35]. For the human studies, two studies used proton nuclear magnetic resonance (

1H-NMR) to detect metabolites in serum samples from CD and UC patients of 18-60+ years of age [15,43], while two other studies detected metabolites in serum samples from CD and UC patients in the >1–60+ age groups using GC-MS [20,24]. The authors of the latter two studies are also from the same department and even co-authors the other study, again underlining the difficulties at present comparing studies from different research groups.

H-NMR) to detect metabolites in serum samples from CD and UC patients of 18-60+ years of age [2][5], while two other studies detected metabolites in serum samples from CD and UC patients in the >1–60+ age groups using GC-MS [8][38]. The authors of the latter two studies are also from the same department and even co-authors the other study, again underlining the difficulties at present comparing studies from different research groups.

Differentiation of Metabolites According to Key Experimental Elements

We found that in both human and animal studies, the vast majority of the metabolites were detected by more than one analytical platform (Supplementary Table S9). The study subjects in most of the human studies spanned all age groups from very early onset and young to old, making it difficult to differentiate metabolite detection between age groups in the human studies. However, most metabolites were generally detected in more than one age group in the animal studies, suggesting that age is not a deciding factor when it comes to the metabolome. Nevertheless the amino acid isoleucine stood out, as it was increased only in human subjects above 18 years of age and in mice of >8–24 weeks. One of the animal studies that reported increased levels of isoleucine also included animals of 1 week, but the amino acid was not significantly altered in this group [26].

We found that in both human and animal studies, the vast majority of the metabolites were detected by more than one analytical platform (Supplementary Table S9). The study subjects in most of the human studies spanned all age groups from very early onset and young to old, making it difficult to differentiate metabolite detection between age groups in the human studies. However, most metabolites were generally detected in more than one age group in the animal studies, suggesting that age is not a deciding factor when it comes to the metabolome. Nevertheless the amino acid isoleucine stood out, as it was increased only in human subjects above 18 years of age and in mice of >8–24 weeks. One of the animal studies that reported increased levels of isoleucine also included animals of 1 week, but the amino acid was not significantly altered in this group [37].

 

The subgroup of metabolites differentiated in both study types was sorted according to the biological sample types in which they were detected (Supplementary Table S9). This allowed us to examine any parallels between human and animal studies. Many metabolites were found in several different sample types in both humans and animals, but not necessarily the same. For example, alanine was increased in serum [38] and feces [14][15] from humans and in colon [4] and plasma [37] from mice, but it was decreased in urine [5] and colon [11][25] in humans and serum [12] and urine [33] in animals, illustrating the differences observed for many metabolites (Supplementary Table S9). The highest similarity to human studies was observed with the acute DSS mouse model (Supplementary Table S9). Since this model was used in almost half of the included animal studies, this finding is not surprising. However, only five of the acute DSS mouse model studies analyzed serum samples, but still 11 of the increased and 11 of the decreased metabolites were detected in serum samples from both humans and the DSS mouse model. A total of 34 and 29 different metabolites were reported as increased and decreased in IBD, respectively, in serum samples from the acute DSS mouse model. This means that 32% of the increased metabolites and 38% of the decreased metabolites in serum samples from the acute DSS mouse model were reported to be correspondingly differentiated in the human studies. Conversely, the acute DSS mouse model could account for 16% (22 out of 136 metabolites) of the overall metabolite changes observed in serum of IBD patients. This could suggest serum samples from the acute DSS mouse model as having good translational potential when analyzing systemic metabolites in IBD.

The subgroup of metabolites differentiated in both study types was sorted according to the biological sample types in which they were detected (Supplementary Table S9). This allowed us to examine any parallels between human and animal studies. Many metabolites were found in several different sample types in both humans and animals, but not necessarily the same. For example, alanine was increased in serum [24] and feces [4,25] from humans and in colon [17] and plasma [26] from mice, but it was decreased in urine [43] and colon [35,36] in humans and serum [16] and urine [38] in animals, illustrating the differences observed for many metabolites (Supplementary Table S9). The highest similarity to human studies was observed with the acute DSS mouse model (Supplementary Table S9). Since this model was used in almost half of the included animal studies, this finding is not surprising. However, only five of the acute DSS mouse model studies analyzed serum samples, but still 11 of the increased and 11 of the decreased metabolites were detected in serum samples from both humans and the DSS mouse model. A total of 34 and 29 different metabolites were reported as increased and decreased in IBD, respectively, in serum samples from the acute DSS mouse model. This means that 32% of the increased metabolites and 38% of the decreased metabolites in serum samples from the acute DSS mouse model were reported to be correspondingly differentiated in the human studies. Conversely, the acute DSS mouse model could account for 16% (22 out of 136 metabolites) of the overall metabolite changes observed in serum of IBD patients. This could suggest serum samples from the acute DSS mouse model as having good translational potential when analyzing systemic metabolites in IBD.

Correlation between Animal Models and IBD Subtypes

For all the metabolites significantly differing in both human and animal studies, it was investigated if some animal models were specifically good models for CD or UC when it comes to metabolomics (Supplementary Table S9). Most of the models had similarities with both CD and UC. For instance, regarding metabolites decreased in the IL10

For all the metabolites significantly differing in both human and animal studies, it was investigated if some animal models were specifically good models for CD or UC when it comes to metabolomics (Supplementary Table S9). Most of the models had similarities with both CD and UC. For instance, regarding metabolites decreased in the IL10

−/–

mouse model, glucose was also decreased in CD, while leucine was decreased in UC, and trimethylamine in both CD and UC. The TNF

ΔARE/WT model only had similarities with UC, but this could easily be due to the fact that only one study with this model was included. Overall, this indicates that the metabolomes of the animal models included in this review are not correlated specifically to CD or UC.

model only had similarities with UC, but this could easily be due to the fact that only one study with this model was included. Overall, this indicates that the metabolomes of the animal models included in this review are not correlated specifically to CD or UC.

Metabolite Classifications

All metabolites differentiated between IBD cases and controls in either humans or animals were sorted into metabolite subclasses according to the classification system used in The Human Metabolome Database (www.hmdb.ca) (Supplementary Tables S10 and S11). The most differentiated subclass was “amino acids, peptides, and analogues” in both human and animal studies, representing approximately 16% of all differentiated metabolites reported. “Fatty acids and conjugates” as well as “carbohydrates and carbohydrate conjugates” were also among the most differentiated in both human and animal study types. “Glycerophosphocholines” were also differentiated in both, but to a much larger extent in animal studies. In general, different kinds of lipids were reported more frequently as differentiated in IBD in animal studies compared to human studies. Metabolites from 142 different subclasses were reported as differentiated between IBD and controls overall. Of these, 47 were differentiated in both human and animal studies, while 48 and 47 differentiated subclasses were unique to human and animal studies, respectively. This shows a large gap between the type of metabolites that are investigated and detected in the two study types, as only a third of the total amount of differentiated subclasses are reported in both.

All metabolites differentiated between IBD cases and controls in either humans or animals were sorted into metabolite subclasses according to the classification system used in The Human Metabolome Database (www.hmdb.ca) (Supplementary Tables S10 and S11). The most differentiated subclass was “amino acids, peptides, and analogues” in both human and animal studies, representing approximately 16% of all differentiated metabolites reported. “Fatty acids and conjugates” as well as “carbohydrates and carbohydrate conjugates” were also among the most differentiated in both human and animal study types. “Glycerophosphocholines” were also differentiated in both, but to a much larger extent in animal studies. In general, different kinds of lipids were reported more frequently as differentiated in IBD in animal studies compared to human studies. Metabolites from 142 different subclasses were reported as differentiated between IBD and controls overall. Of these, 47 were differentiated in both human and animal studies, while 48 and 47 differentiated subclasses were unique to human and animal studies, respectively. This shows a large gap between the type of metabolites that are investigated and detected in the two study types, as only a third of the total amount of differentiated subclasses are reported in both.

 
When focusing on the metabolites differentiated in IBD in both human and animal studies, they represented a total of 25 subclasses overall. Metabolites from nine different subclasses were present among both the increased and decreased metabolites, while eight subclasses were exclusively increased and decreased, respectively.
 
 
 
  • Lin, H.M.; Edmunds, S.J.; Helsby, N.A.; Ferguson, L.R.; Rowan, D.D. Nontargeted urinary metabolite profiling of a mouse model of crohn’s disease. J. Proteome Res. 2009, 8, 2045–2057. [Google Scholar] [CrossRef] [PubMed]
  • Le Gall, G.; Noor, S.O.; Ridgway, K.; Scovell, L.; Jamieson, C.; Johnson, I.T.; Colquhoun, I.J.; Kemsley, E.K.; Narbad, A. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res. 2011, 10, 4208–4218. [Google Scholar] [CrossRef] [PubMed]
  • Balasubramanian, K.; Kumar, S.; Singh, R.R.; Sharma, U.; Ahuja, V.; Makharia, G.K.; Jagannathan, N.R. Metabolism of the colonic mucosa in patients with inflammatory bowel diseases: An in vitro proton magnetic resonance spectroscopy study. Magn. Reson. Imaging 2009, 27, 79–86. [Google Scholar] [CrossRef] [PubMed]
  • Sharma, U.; Singh, R.R.; Ahuja, V.; Makharia, G.K.; Jagannathan, N.R. Similarity in the metabolic profile in macroscopically involved and un-involved colonic mucosa in patients with inflammatory bowel disease: An in vitro proton (1H) MR spectroscopy study. Magn. Reson. Imaging 2010, 28, 1022–1029. [Google Scholar] [CrossRef]
  • Gu, X.; Song, Y.; Chai, Y.; Lu, F.; Gonzalez, F.J.; Fan, G.; Qi, Y. GC-MS metabolomics on PPARalpha-dependent exacerbation of colitis. Mol. Biosyst. 2015, 11, 1329–1337. [Google Scholar] [CrossRef]
  • Martin, F.P.J.; Lichti, P.; Bosco, N.; Brahmbhatt, V.; Oliveira, M.; Haller, D.; Benyacoub, J. Metabolic phenotyping of an adoptive transfer mouse model of experimental colitis and impact of dietary fish oil intake. J. Proteome Res. 2015, 14, 1911–1919. [Google Scholar] [CrossRef]
  • Fathi, F.; Majari-Kasmaee, L.; Mani-Varnosfaderani, A.; Kyani, A.; Rostami-Nejad, M.; Sohrabzadeh, K.; Naderi, N.; Zali, M.R.; Rezaei-Tavirani, M.; Tafazzoli, M.; et al. 1H NMR based metabolic profiling in Crohn’s disease by random forest methodology. Magn. Reson. Chem. MRC 2014, 52, 370–376. [Google Scholar] [CrossRef]
  • Lin, H.M.; Barnett, M.P.G.; Roy, N.C.; Joyce, N.I.; Zhu, S.; Armstrong, K.; Helsby, N.A.; Ferguson, L.R.; Rowan, D.D. Metabolomic analysis identifies inflammatory and noninflammatory metabolic effects of genetic modification in a mouse model of Crohn’s disease. J. Proteome Res. 2010, 9, 1965–1975. [Google Scholar] [CrossRef]
  • Qu, C.; Yuan, Z.W.; Yu, X.T.; Huang, Y.F.; Yang, G.H.; Chen, J.N.; Lai, X.P.; Su, Z.R.; Zeng, H.F.; Xie, Y.; et al. Patchouli alcohol ameliorates dextran sodium sulfate-induced experimental colitis and suppresses tryptophan catabolism. Pharmacol. Res. 2017, 121, 70–82. [Google Scholar] [CrossRef]
  • Kominsky, D.J.; Keely, S.; MacManus, C.F.; Glover, L.E.; Scully, M.; Collins, C.B.; Bowers, B.E.; Campbell, E.L.; Colgan, S.P. An endogenously anti-inflammatory role for methylation in mucosal inflammation identified through metabolite profiling. J. Immunol. 2011, 186, 6505–6514. [Google Scholar] [CrossRef] [PubMed]
  • Alonso, A.; Julia, A.; Vinaixa, M.; Domenech, E.; Fernandez-Nebro, A.; Canete, J.D.; Ferrandiz, C.; Tornero, J.; Gisbert, J.P.; Nos, P.; et al. Urine metabolome profiling of immune-mediated inflammatory diseases. BMC Med. 2016, 14, 133. [Google Scholar] [CrossRef] [PubMed]
  • Murdoch, T.B.; Fu, H.; MacFarlane, S.; Sydora, B.C.; Fedorak, R.N.; Slupsky, C.M. Urinary metabolic profiles of inflammatory bowel disease in interleukin-10 gene-deficient mice. Anal. Chem. 2008, 80, 5524–5531. [Google Scholar] [CrossRef] [PubMed]
  • Lu, K.; Knutson, C.G.; Wishnok, J.S.; Fox, J.G.; Tannenbaum, S.R. Serum metabolomics in a helicobacter hepaticus mouse model of inflammatory bowel disease reveal important changes in the microbiome, serum peptides, and intermediary metabolism. J. Proteome Res. 2012, 11, 4916–4926. [Google Scholar] [CrossRef] [PubMed]
  • Johnson, J.C.; Schmidt, C.R.; Shrubsole, M.J.; Billheimer, D.D.; Joshi, P.R.; Morrow, J.D.; Heslin, M.J.; Washington, M.K.; Ness, R.M.; Zheng, W.; et al. Urine PGE-M: A Metabolite of Prostaglandin E2 as a Potential Biomarker of Advanced Colorectal Neoplasia. Clin. Gastroenterol. Hepatol. 2006, 4, 1358–1365. [Google Scholar] [CrossRef]
  • Willenberg, I.; Ostermann, A.I.; Giovannini, S.; Kershaw, O.; Von Keutz, A.; Steinberg, P.; Schebb, N.H. Effect of acute and chronic DSS induced colitis on plasma eicosanoid and oxylipin levels in the rat. Prostaglandins Other Lipid Mediat. 2015, 120, 155–160. [Google Scholar] [CrossRef] [PubMed]
  • Williams, H.R.T.; Cox, I.J.; Walker, D.G.; North, B.V.; Patel, V.M.; Marshall, S.E.; Jewell, D.P.; Ghosh, S.; Thomas, H.J.W.; Teare, J.P.; et al. Characterization of inflammatory bowel disease with urinary metabolic profiling. Am. J. Gastroenterol. 2009, 104, 1435–1444. [Google Scholar] [CrossRef]
  • Ahmed, I.; Greenwood, R.; Costello, B.; Ratcliffe, N.; Probert, C.S. Investigation of faecal volatile organic metabolites as novel diagnostic biomarkers in inflammatory bowel disease. Aliment. Pharmacol. Ther. 2016, 43, 596–611. [Google Scholar] [CrossRef]
  • Stephens, N.S.; Siffledeen, J.; Su, X.; Murdoch, T.B.; Fedorak, R.N.; Slupsky, C.M. Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. J. Crohn’s Colitis 2013, 7, e42–e48. [Google Scholar] [CrossRef]
  • Hong, Y.S.; Ahn, Y.T.; Park, J.C.; Lee, J.H.; Lee, H.; Huh, C.S.; Kim, D.H.; Ryu, D.H.; Hwang, G.S. 1H NMR-based metabonomic assessment of probiotic effects in a colitis mouse model. Arch. Pharmacal Res. 2010, 33, 1091–1101. [Google Scholar] [CrossRef]
  • De Preter, V.; Joossens, M.; Ballet, V.; Shkedy, Z.; Rutgeerts, P.; Vermeire, S.; Verbeke Phd, K. Metabolic profiling of the impact of oligofructose-enriched inulin in Crohn’s disease patients: A double-blinded randomized controlled trial. Clin. Transl. Gastroenterol. 2013, 4, e30. [Google Scholar] [CrossRef] [PubMed]
  • Williams, H.R.; Cox, I.J.; Walker, D.G.; Cobbold, J.F.; Taylor-Robinson, S.D.; Marshall, S.E.; Orchard, T. Differences in gut microbial metabolism are responsible for reduced hippurate synthesis in Crohn’s disease. Gastroenterology 2010, 138, S579. [Google Scholar] [CrossRef]
  • Yau, Y.Y.; Leong, R.W.L.; Shin, S.; Bustamante, S.; Pickford, R.; Hejazi, L.; Campbell, B.; Wasinger, V.C. Bimodal plasma metabolomics strategy identifies novel inflammatory metabolites in inflammatory bowel diseases. Discov. Med. 2014, 18, 113–124. [Google Scholar] [PubMed]
  • Wang, R.; Gu, X.; Dai, W.; Ye, J.; Lu, F.; Chai, Y.; Fan, G.; Gonzalez, F.J.; Duan, G.; Qi, Y. A lipidomics investigation into the intervention of celastrol in experimental colitis. Mol. Biosyst. 2016, 12, 1436–1444. [Google Scholar] [CrossRef] [PubMed]
  • Kiesler, P.; Fuss, I.J.; Strober, W. Experimental Models of Inflammatory Bowel Diseases. Cell. Mol. Gastroenterol. Hepatol. 2015, 1, 154–170. [Google Scholar] [CrossRef] [PubMed]
  • Bamias, G.; Arseneau, K.O.; Cominelli, F. Mouse models of inflammatory bowel disease for investigating mucosal immunity in the intestine. Curr. Opin. Gastroenterol. 2017, 33, 411–416. [Google Scholar] [CrossRef]

When focusing on the metabolites differentiated in IBD in both human and animal studies, they represented a total of 25 subclasses overall. Metabolites from nine different subclasses were present among both the increased and decreased metabolites, while eight subclasses were exclusively increased and decreased, respectively.

References

  1. Ying Zhang; Lianjie Lin; Yanbin Xu; Yan Lin; Yu Jin; Changqing Zheng; 1H NMR-based spectroscopy detects metabolic alterations in serum of patients with early-stage ulcerative colitis. Biochemical and Biophysical Research Communications 2013, 433, 547-551, 10.1016/j.bbrc.2013.03.012.
  2. Tomasz Dawiskiba; Stanisław Deja; Agata Mulak; Adam Ząbek; Ewa Jawień; Dorota Pawełka; Mirosław Banasik; Agnieszka Mastalerz-Migas; Waldemar Balcerzak; Krzysztof Kaliszewski; et al.Jan SkóraPiotr BarćKrzysztof KortaKornel PormańczukPrzemyslaw SzyberAdam LitarskiPiotr Młynarz Serum and urine metabolomic fingerprinting in diagnostics of inflammatory bowel diseases. World Journal of Gastroenterology 2014, 20, 163-174, 10.3748/wjg.v20.i1.163.
  3. Natasha S. Stephens; Jesse Siffledeen; Xiaorong Su; Travis B. Murdoch; R. Fedorak; Carolyn M. Slupsky; Urinary NMR metabolomic profiles discriminate inflammatory bowel disease from healthy. Journal of Crohn's and Colitis 2013, 7, e42-e48, 10.1016/j.crohns.2012.04.019.
  4. Yuuki Shiomi; Shin Nishiumi; Makoto Ooi; Naoya Hatano; Masakazu Shinohara; Tomoo Yoshie; Yasuyuki Kondo; Keisuke Furumatsu; Hideyuki Shiomi; Hiromu Kutsumi; et al.Takeshi AzumaMasaru Yoshida GCMS-based metabolomic study in mice with colitis induced by dextran sulfate sodium. Inflammatory Bowel Diseases 2011, 17, 2261-2274, 10.1002/ibd.21616.
  5. Arnald Alonso; Antonio Julià; Maria Vinaixa; Eugeni Domènech; Antonio Fernández-Nebro; Juan D. Cañete; Carlos Ferrándiz; Jesús Tornero; Javier P. Gisbert; Pilar Nos; et al.Ana Gutiérrez CasbasLluís PuigIsidoro González-ÁlvaroJosé A. Pinto-TasendeRicardo BlancoMiguel A. RodríguezAntoni BeltranXavier CorreigSara MarsalIMID Consortiumfor the IMID ConsortiumEmilia Fernández Urine metabolome profiling of immune-mediated inflammatory diseases.. BMC Medicine 2016, 14, 133, 10.1186/s12916-016-0681-8.
  6. J. Chad Johnson; Carl R. Schmidt; Martha J Shrubsole; D. Dean Billheimer; Prashant R. Joshi; J D Morrow; Martin J. Heslin; M. Kay Washington; Reid M. Ness; Wei Zheng; et al.David A. SchwartzRobert J. CoffeyR. Daniel BeauchampNipun B Merchant Urine PGE-M: A Metabolite of Prostaglandin E2 as a Potential Biomarker of Advanced Colorectal Neoplasia. Clinical Gastroenterology and Hepatology 2006, 4, 1358-1365, 10.1016/j.cgh.2006.07.015.
  7. Gwénaëlle Le Gall; Samah O. Noor; Karyn Ridgway; Louise Scovell; Crawford Jamieson; Ian Johnson; Ian J. Colquhoun; E. Kate Kemsley; Arjan Narbad; Metabolomics of Fecal Extracts Detects Altered Metabolic Activity of Gut Microbiota in Ulcerative Colitis and Irritable Bowel Syndrome. Journal of Proteome Research 2011, 10, 4208-4218, 10.1021/pr2003598.
  8. Michitaka Kohashi; Shin Nishiumi; Makoto Ooi; Tomoo Yoshie; Atsuki Matsubara; Makoto Suzuki; Namiko Hoshi; Koji Kamikozuru; Yoko Yokoyama; Ken Fukunaga; et al.Shiro NakamuraTakeshi AzumaMasaru Yoshida A novel gas chromatography mass spectrometry-based serum diagnostic and assessment approach to ulcerative colitis. Journal of Crohn's and Colitis 2014, 8, 1010-1021, 10.1016/j.crohns.2014.01.024.
  9. Jonathan P. Jacobs; Lin Lin; Maryam Goudarzi; Paul Ruegger; Dermot P. B. McGovern; Albert J. Fornace; James Borneman; Lijun Xia; Jonathan Braun; Microbial, metabolomic, and immunologic dynamics in a relapsing genetic mouse model of colitis induced by T-synthase deficiency. Gut Microbes 2016, 8, 1-16, 10.1080/19490976.2016.1257469.
  10. Douglas J. Kominsky; Simon Keely; Christopher F. MacManus; Louise E. Glover; Melanie Scully; Colm B. Collins; Brittelle E. Bowers; Eric Campbell; Sean P. Colgan; An endogenously anti-inflammatory role for methylation in mucosal inflammation identified through metabolite profiling.. The Journal of Immunology 2011, 186, 6505-14, 10.4049/jimmunol.1002805.
  11. Krithika Balasubramanian; Sandeep Kumar; Rajeev R. Singh; Uma Sharma; Vineet Ahuja; Govind K. Makharia; Naranamangalam R. Jagannathan; Metabolism of the colonic mucosa in patients with inflammatory bowel diseases: an in vitro proton magnetic resonance spectroscopy study. Magnetic Resonance Imaging 2009, 27, 79-86, 10.1016/j.mri.2008.05.014.
  12. Rudolf Schicho; Alsu Nazyrova; Rustem Shaykhutdinov; Gavin Duggan; Hans J. Vogel; Martin Storr; Quantitative Metabolomic Profiling of Serum and Urine in DSS-Induced Ulcerative Colitis of Mice by1H NMR Spectroscopy. Journal of Proteome Research 2010, 9, 6265-6273, 10.1021/pr100547y.
  13. Lucy C. Hicks; Juzheng Huang; Sacheen Kumar; Sam T. Powles; Timothy R. Orchard; George B. Hanna; H.R.T. Williams; Analysis of Exhaled Breath Volatile Organic Compounds in Inflammatory Bowel Disease: A Pilot Study. Journal of Crohn's and Colitis 2015, 9, 731-737, 10.1093/ecco-jcc/jjv102.
  14. Julian R Marchesi; Elaine Holmes; Fatima Khan; Sunil Kochhar; Pauline D. Scanlan; Fergus Shanahan; Ian D Wilson; Yulan Wang; Rapid and Noninvasive Metabonomic Characterization of Inflammatory Bowel Disease. Journal of Proteome Research 2007, 6, 546-551, 10.1021/pr060470d.
  15. Jacob Tveiten Bjerrum; Yulan Wang; Fuhua Hao; Mehmet Coskun; Christian Ludwig; Ulrich Günther; Ole Haagen Nielsen; Metabonomics of human fecal extracts characterize ulcerative colitis, Crohn’s disease and healthy individuals. Metabolomics 2014, 11, 122-133, 10.1007/s11306-014-0677-3.
  16. Fangcong Dong; Lulu Zhang; Fuhua Hao; Huiru Tang; Yulan Wang; Systemic Responses of Mice to Dextran Sulfate Sodium-Induced Acute Ulcerative Colitis Using1H NMR Spectroscopy. Journal of Proteome Research 2013, 12, 2958-2966, 10.1021/pr4002383.
  17. H.R.T. Williams; I. Jane Cox; David G Walker; Bernard V North; Venisha M Patel; Sara E Marshall; Derek P Jewell; Subrata Ghosh; Huw J W Thomas; Julian Teare; et al.Simon JakobovitsSebastian ZekiKenneth I WelshSimon D Taylor-RobinsonTimothy R Orchard Characterization of Inflammatory Bowel Disease With Urinary Metabolic Profiling. American Journal of Gastroenterology 2009, 104, 1435-1444, 10.1038/ajg.2009.175.
  18. Travis B. Murdoch; Hao Fu; Sarah Macfarlane; Beate C. Sydora; R. Fedorak; Carolyn M. Slupsky; Urinary Metabolic Profiles of Inflammatory Bowel Disease in Interleukin-10 Gene-Deficient Mice. Analytical Chemistry 2008, 80, 5524-5531, 10.1021/ac8005236.
  19. Jing Liu; Hai-Tao Xiao; Hong-Sheng Wang; Huai-Xue Mu; Ling Zhao; Jun Du; Depo Yang; Dongmei Wang; Zhaoxiang Bian; Shu-Hai Lin; et al. Halofuginone reduces the inflammatory responses of DSS-induced colitis through metabolic reprogramming. Molecular BioSystems 2016, 12, 2296-2303, 10.1039/C6MB00154H.
  20. Kun Lu; Charles G. Knutson; John Wishnok; James G. Fox; Steven R. Tannenbaum; Serum Metabolomics in aHelicobacter hepaticusMouse Model of Inflammatory Bowel Disease Reveal Important Changes in the Microbiome, Serum Peptides, and Intermediary Metabolism. Journal of Proteome Research 2012, 11, 4916-4926, 10.1021/pr300429x.
  21. Fariba Fathi; Laleh Majari-Kasmaee; Ahmad Mani-Varnosfaderani; Anahita Kyani; Mohammad Rostami Nejad; Kaveh Sohrabzadeh; Nosratollah Naderi; Mohammad Reza Zali; Mostafa Rezaei Tavirani; Mohsen Tafazzoli; et al.Afsaneh Arefi Oskouie 1H NMR based metabolic profiling in Crohn's disease by random forest methodology. Magnetic Resonance in Chemistry 2014, 52, 370-376, 10.1002/mrc.4074.
  22. Ina Willenberg; Annika I. Ostermann; Samoa Giovannini; Olivia Kershaw; Anne Von Keutz; Pablo Steinberg; Nils Helge Schebb; Effect of acute and chronic DSS induced colitis on plasma eicosanoid and oxylipin levels in the rat. Prostaglandins & Other Lipid Mediators 2015, 120, 155-160, 10.1016/j.prostaglandins.2015.04.002.
  23. Jonathan P. Jacobs; Maryam Goudarzi; Namita Singh; Maomeng Tong; Ian H. McHardy; Paul Ruegger; Miro Asadourian; Bo-Hyun Moon; Allyson Ayson; James Borneman; et al.Dermot P.B. McGovernAlbert J. FornaceJonathan BraunMarla Dubinsky A Disease-Associated Microbial and Metabolomics State in Relatives of Pediatric Inflammatory Bowel Disease Patients. Cellular and Molecular Gastroenterology and Hepatology 2016, 2, 750-766, 10.1016/j.jcmgh.2016.06.004.
  24. Rudolf Schicho; Rustem Shaykhutdinov; Jennifer Ngo; Alsu Nazyrova; Christopher Schneider; Remo Panaccione; Gilaad G Kaplan; Hans J. Vogel; Martin Storr; Quantitative Metabolomic Profiling of Serum, Plasma, and Urine by1H NMR Spectroscopy Discriminates between Patients with Inflammatory Bowel Disease and Healthy Individuals. Journal of Proteome Research 2012, 11, 3344-3357, 10.1021/pr300139q.
  25. Uma Sharma; Rajiv R. Singh; Vineet Ahuja; Govind K. Makharia; Naranamangalam R. Jagannathan; Similarity in the metabolic profile in macroscopically involved and un-involved colonic mucosa in patients with inflammatory bowel disease: an in vitro proton (1H) MR spectroscopy study. Magnetic Resonance Imaging 2010, 28, 1022-1029, 10.1016/j.mri.2010.03.039.
  26. Jonathan Kaunitz; Machiels K; Joossens M; Sabino J; De Preter V; Arijs I; Eeckhaut V; Ballet V; Claes K; Van Immerseel F; et al.Verbeke KFerrante MVerhaegen JRutgeerts PVermeire S Faculty Opinions recommendation of A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature 2015, 63, 1275–1283, 10.3410/f.718105672.793509782.
  27. Baur, P.; Martin, F.P.; Gruber, L.; Bosco, N.; Brahmbhatt, V.; Collino, S.; Guy, P.; Montoliu, I.; Rozman, J.; Klingenspor, M.; et al.et al Metabolic phenotyping of the Crohn’s disease-like IBD etiopathology in the TNFDELTAARE/WT mouse model. J. Proteome Res. 2011, 10, 5523–5535.
  28. Zhang, X.; Choi, F.F.; Zhou, Y.; Leung, F.P.; Tan, S.; Lin, S.; Xu, H.; Jia, W.; Sung, J.J.; Cai, Z.; et al.et al Metabolite profiling of plasma and urine from rats with TNBS-induced acute colitis using UPLC-ESI-QTOF-MS-based metabonomics--a pilot study. FEBS J. 2012, 279, 2322–2338.
  29. Gu, X.; Song, Y.; Chai, Y.; Lu, F.; Gonzalez, F.J.; Fan, G.; Qi, Y; GC-MS metabolomics on PPARalpha-dependent exacerbation of colitis. Mol. Biosyst. 2015, 11, 1329–1337.
  30. Hou, W.; Zhong, D.; Zhang, P.; Li, Y.; Lin, M.; Liu, G.; Yao, M.; Liao, Q.; Xie, Z; A strategy for the targeted metabolomics analysis of 11 gut microbiota-host co-metabolites in rat serum, urine and feces by ultra high performance liquid chromatography-tandem mass spectrometry. J. Chromatogr. A 2016, 1429, 207–217.
  31. Chang Qu; Zhong-Wen Yuan; Xiu-Ting Yu; Yan-Feng Huang; Guang-Hua Yang; Jian-Nan Chen; Xiao-Ping Lai; Zi-Ren Su; Hui-Fang Zeng; Ying Xie; et al.Huang Song Patchouli alcohol ameliorates dextran sodium sulfate-induced experimental colitis and suppresses tryptophan catabolism. Pharmacological Research 2017, 121, 70-82, 10.1016/j.phrs.2017.04.017.
  32. Robinson, A.M.; Gondalia, S.V.; Karpe, A.V.; Eri, R.; Beale, D.J.; Morrison, P.D.; Palombo, E.A.; Nurgali, K; Fecal microbiota and metabolome in a mouse model of spontaneous chronic colitis: Relevance to human inflammatory bowel disease. Inflamm. Bowel Dis. 2016, 22, 2767–2787.
  33. Martin, F.P.J.; Lichti, P.; Bosco, N.; Brahmbhatt, V.; Oliveira, M.; Haller, D.; Benyacoub, J; Metabolic phenotyping of an adoptive transfer mouse model of experimental colitis and impact of dietary fish oil intake. J. Proteome Res. 2015, 14, 1911–1919.
  34. Lin, H.M.; Edmunds, S.J.; Helsby, N.A.; Ferguson, L.R.; Rowan, D.D; Nontargeted urinary metabolite profiling of a mouse model of crohn’s disease. J. Proteome Res. 2009, 8, 2045–2057.
  35. Hui-Ming Lin; Matthew P. G. Barnett; Nicole Roy; Nigel I. Joyce; Shuotun Zhu; Kelly Armstrong; Nuala Helsby; Lynnette R. Ferguson; Daryl Rowan; Metabolomic Analysis Identifies Inflammatory and Noninflammatory Metabolic Effects of Genetic Modification in a Mouse Model of Crohn’s Disease. Journal of Proteome Research 2010, 9, 1965-1975, 10.1021/pr901130s.
  36. Janet K. Jansson; Ben Willing; Marianna Lucio; Ages Fekete; Johan Dicksved; Jonas Halfvarson; Curt Tysk; Philippe Schmitt-Kopplin; Metabolomics Reveals Metabolic Biomarkers of Crohn's Disease. PLOS ONE 2009, 4, e6386, 10.1371/journal.pone.0006386.
  37. Francois-Pierre Martin; Serge Rezzi; David Philippe; Lionel Tornier; Anja Messlik; Gabriele Hölzlwimmer; Pia Baur; Leticia Quintanilla-Fend; Gunnar Loh; Michael Blaut; et al.Stéphanie BlumSunil KochharDirk Haller Metabolic Assessment of Gradual Development of Moderate Experimental Colitis in IL-10 Deficient Mice. Journal of Proteome Research 2009, 8, 2376-2387, 10.1021/pr801006e.
  38. Makoto Ooi; Shin Nishiumi; Tomoo Yoshie; Yuuki Shiomi; Michitaka Kohashi; Ken Fukunaga; Shiro Nakamura; Takayuki Matsumoto; Naoya Hatano; Masakazu Shinohara; et al.Yasuhiro IrinoTadaomi TakenawaTakeshi AzumaMasaru Yoshida GC/MS-based profiling of amino acids and TCA cycle-related molecules in ulcerative colitis. Inflammation Research 2011, 60, 831-840, 10.1007/s00011-011-0340-7.
  39. Vicky De Preter; Marie Joossens; Vera Ballet; Ziv Shkedy; Paul Rutgeerts; Séverine Vermeire; Kristin Verbeke; Metabolic Profiling of the Impact of Oligofructose-Enriched Inulin in Crohnʼs Disease Patients: A Double-Blinded Randomized Controlled Trial. Clinical and Translational Gastroenterology 2013, 4, e30-e30, 10.1038/ctg.2012.24.
  40. I. Ahmed; R. Greenwood; B. Costello; Norman Ratcliffe; C. S. Probert; Investigation of faecal volatile organic metabolites as novel diagnostic biomarkers in inflammatory bowel disease. Alimentary Pharmacology & Therapeutics 2016, 43, 596-611, 10.1111/apt.13522.
  41. Francois-Pierre Martin; Jessica Ezri; Ornella Cominetti; Laeticia Da Silva; Martin Kussmann; Jean-Philippe Godin; Andreas Nydegger; Urinary Metabolic Phenotyping Reveals Differences in the Metabolic Status of Healthy and Inflammatory Bowel Disease (IBD) Children in Relation to Growth and Disease Activity. International Journal of Molecular Sciences 2016, 17, 1310, 10.3390/ijms17081310.
  42. Williams, H.R.; Cox, I.J.; Walker, D.G.; Cobbold, J.F.; Taylor-Robinson, S.D.; Marshall, S.E.; Orchard, T; Differences in gut microbial metabolism are responsible for reduced hippurate synthesis in Crohn's disease. Gastroenterology 2010, 138, S579.
  43. Yunki Yau; Rupert W Leong; Sean Shin; Sonia Bustamante; Russell Pickford; Leila Hejazi; Beth Campbell; Valerie C. Wasinger; Bimodal plasma metabolomics strategy identifies novel inflammatory metabolites in inflammatory bowel diseases. Discovery medicine 2014, 18, 113–124.
  44. Renping Wang; Xueqin Gu; Weiquan Dai; Jun Ye; Feng Lu; Yifeng Chai; Guorong Fan; Frank J. Gonzalez; Geng-Li Duan; Yunpeng Qi; et al. A lipidomics investigation into the intervention of celastrol in experimental colitis. Molecular BioSystems 2016, 12, 1436-1444, 10.1039/c5mb00864f.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback