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. 2017 Jan 16:7:40543.
doi: 10.1038/srep40543.

Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model

Deying Chen  1 , Xiaoling Su  1 , Nan Wang  2 , Yunong Li  2 , Hua Yin  3 , Liang Li  1   2 , Lanjuan Li  1
Affiliations

Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model

Deying Chen et al. Sci Rep. .

Abstract

We report a chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) method generally applicable for tracking metabolomic changes from samples collected in an animal model for studying disease development and treatment. A rat model of surgically induced osteoarthritis (OA) was used as an example to illustrate the workflow and technical performance. Experimental duplicate analyses of 234 plasma samples were carried out using dansylation labeling LC-MS targeting the amine/phenol submetabolome. These samples composed of 39 groups (6 rats per group) were collected at multiple time points with sham operation, OA control group, and OA rats with treatment, separately, using glucosamine/Celecoxib and three traditional Chinese medicines (Epimedii folium, Chuanxiong Rhizoma and Bushen-Huoxue). In total, 3893 metabolites could be detected and 2923 of them were consistently detected in more than 50% of the runs. This high-coverage submetabolome dataset could be used to track OA progression and treatment. Many differentiating metabolites were found and 11 metabolites including 2-aminoadipic acid, saccharopine and GABA were selected as potential biomarkers of OA progression and OA treatment. This study illustrates that CIL LC-MS is a very useful technique for monitoring incremental metabolomic changes with high coverage and accuracy for studying disease progression and treatment in animal models.

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Figures

Figure 1
Figure 1
(A) Rat model design for studying OA development and treatment and (B) number distribution of samples collected at different time points from different groups of rats.
Figure 2
Figure 2. Workflow of the differential chemical isotope labeling LC-MS method for rat plasma metabolomics for monitoring OA development and OA treatment.
Figure 3
Figure 3
(A) Average total-concentration of labeled metabolites for different groups of OA rats. (B) Representative ion chromatogram obtained from a labeled plasma sample. (C) Typical mass spectrum displaying a pair of protonated molecules from a differentially labeled metabolite (m/z 339.1023 and m/z 341.1087). (D) Plot of the number of peak pairs detected as a function of percentage of common pairs.
Figure 4
Figure 4
Images of rat joint tissues at week 6 stained by Hematoxylin and Eosin (A–C) and by Safranin O Staining (D–F) where A and D were from the normal control group, B and E were from the sham operation group, and C and F were from the OA group. Images of rat joint tissues at week 14 after drug treatment stained by Hematoxylin and Eosin (G–J) and Safranin O Staining (K–N) where G and K were from Drug A treatment group by Epimedii folium, H and L were from Drug B treatment group by Chuanxiong Rhizoma, I and M were from Drug C treatment group by Bushen-Huoxue, i.e., the combination of Epimedii folium and Chuanxiong Rhizoma, and J and N were from Drug D treatment group by the combination of glucosamine and Celebrex.
Figure 5
Figure 5
(A) PCA plots and (B) OPLS-DA plots of three groups (normal, sham and OA model) over a period of 6 weeks.
Figure 6
Figure 6
OPLS-DA plots of six groups (sham at week 14, OA at week 14, and individual treatment from week 8 to week 14): (A) treatment with Drug A, (B) treatment with Drug B, (C) treatment with Drug C and (D) treatment with Drug D. OPLS-DA plots of five groups (sham at week 14 and individual treatment from week 8 to week 14): (E) treatment with Drug A, (F) treatment with Drug B, (G) treatment with Drug C and (H) treatment with Drug D.
Figure 7
Figure 7
Peak pair intensity ratio changes of three potential biomarkers as a function of time from week 0 to week 14: OA group, sham group, OA treatment groups of A to D. *Denotes a significant change from the previous time point with p < 0.05.
Figure 8
Figure 8. Lysine degradation pathways with metabolic changes determined from the metabolomics study of OA rat models.
The metabolites in yellow boxes showed significant changes over the time course (consistently increasing in metabolite concentration), those in green boxes showed random changes that did not follow a pattern of consistently up or down, and those in blue were not detected using dansylation LC-MS.
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