Calcium-deficiency assessment and biomarker identification by an integrated urinary metabonomics analysis
- Maoqing Wang†1,
- Xue Yang†1,
- Fan Wang2,
- Ran Li3,
- Hua Ning1,
- Lixin Na1,
- Yifan Huang1,
- Yue Song1,
- Liyan Liu1,
- Hongzhi Pan1,
- Qiuju Zhang4,
- Lijun Fan4,
- Ying Li1Email author and
- Changhao Sun1Email author
© Wang et al; licensee BioMed Central Ltd. 2013
Received: 14 August 2012
Accepted: 28 March 2013
Published: 28 March 2013
Calcium deficiency is a global public-health problem. Although the initial stage of calcium deficiency can lead to metabolic alterations or potential pathological changes, calcium deficiency is difficult to diagnose accurately. Moreover, the details of the molecular mechanism of calcium deficiency remain somewhat elusive. To accurately assess and provide appropriate nutritional intervention, we carried out a global analysis of metabolic alterations in response to calcium deficiency.
The metabolic alterations associated with calcium deficiency were first investigated in a rat model, using urinary metabonomics based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis. Correlations between dietary calcium intake and the biomarkers identified from the rat model were further analyzed to confirm the potential application of these biomarkers in humans.
Urinary metabolic-profiling analysis could preliminarily distinguish between calcium-deficient and non-deficient rats after a 2-week low-calcium diet. We established an integrated metabonomics strategy for identifying reliable biomarkers of calcium deficiency using a time-course analysis of discriminating metabolites in a low-calcium diet experiment, repeating the low-calcium diet experiment and performing a calcium-supplement experiment. In total, 27 biomarkers were identified, including glycine, oxoglutaric acid, pyrophosphoric acid, sebacic acid, pseudouridine, indoxyl sulfate, taurine, and phenylacetylglycine. The integrated urinary metabonomics analysis, which combined biomarkers with regular trends of change (types A, B, and C), could accurately assess calcium-deficient rats at different stages and clarify the dynamic pathophysiological changes and molecular mechanism of calcium deficiency in detail. Significant correlations between calcium intake and two biomarkers, pseudouridine (Pearson correlation, r = 0.53, P = 0.0001) and citrate (Pearson correlation, r = -0.43, P = 0.001), were further confirmed in 70 women.
To our knowledge, this is the first report of reliable biomarkers of calcium deficiency, which were identified using an integrated strategy. The identified biomarkers give new insights into the pathophysiological changes and molecular mechanisms of calcium deficiency. The correlations between calcium intake and two of the biomarkers provide a rationale or potential for further assessment and elucidation of the metabolic responses of calcium deficiency in humans.
Keywordsbiomarkers calcium deficiency metabonomics UPLC/Q-TOF MS/MS urine
Calcium deficiency is a global public-health problem, especially in developing countries [1, 2]. In China, the Nutrition and Health Status Survey in 2002 indicated that less than 10% of Chinese citizens had an adequate dietary intake of calcium; the average intake was 391 mg/day, which is only 41% of the recommended intake [3–5]. Chronic untreated calcium deficiency can cause many severe consequences, including osteomalacia (osteopenia) , rickets [7–9], and osteoporosis [2, 10, 11], and has received worldwide attention [2, 5]. However, the diagnosis of calcium deficiency, particularly in the initial stages, is easily missed because this state is asymptomatic and does not lead to obvious pathological changes. However, even in the initial stage, metabolic alterations or potential dysfunctions have already occurred.
A number of methods are currently used to assess calcium nutrition status, including epidemiological survey, calcium-balance study [12–14], serum biochemical analysis [15, 16], and radiological examination ; however, none of these is suitable for large-scale screening of calcium deficiency in a population. A calcium metabolic-balance study based on the dual-trace stable isotope technique is the classic method, and is considered the most effective method of assessing calcium nutrition status; however, this method is labor-intensive, lengthy, and costly . Serum biochemistry testing is an unpleasant and invasive examination, especially for children, and its sensitivity and specificity are not always satisfactory [15, 16]. Radiological changes or bone changes reflect only severe calcium deficiency, at which point there has already been serious damage to the body. Therefore, radiological examination, in general, cannot provide a diagnosis of calcium deficiency during the early stages, unless there is pathological injury to bone . Moreover, a detailed understanding of the molecular mechanism of calcium deficiency has remained somewhat elusive. For the aforementioned reasons, a sensitive and non-invasive tool is needed to assess calcium nutrition status accurately and to identify biomarkers that can elucidate the mechanisms underlying calcium deficiency.
Metabonomics, which involves 'the quantitative measurement of the global, dynamic multi-parametric metabolic response of living systems to pathophysiological stimuli or perturbations of whatever source'  is an emerging science. Metabonomics has shown considerable potential in many applications, including identification of biomarkers, diagnosis, and prediction of disease, and elucidation of the dynamic responses of organisms to disease or environmental changes [20–26]. Owing to its high chromatographic resolution, high sensitivity, and rapid separation, ultra-performance liquid chromatography (UPLC) coupled with mass spectrometry (MS) has been used widely in metabonomics to investigate subtle metabolite alterations in complex mixtures [27–30]. Complex three-dimensional datasets (for example, retention time, mass:charge ratio, and intensity) with thousands of detectable ions can be obtained when biological specimens are analyzed using UPLC-MS. To reduce the complexity of MS spectra, simplify the data interpretation, and screen the potential biomarkers, investigators have used multivariate statistical methods such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) for metabonomics studies .
As a rule, urine is the best biological fluid to use for studying nutrient intake or identifying biomarkers. Urine is essentially the body's liquid waste repository, and any endogenous or exogenous metabolites that are not needed or are present in excess may be found in the urine . Therefore, more compounds can be found in urine than in any other biological matrix. In addition, compared with other biological specimens, urine collection is non-invasive and is not volume-limited. Further, urine can easily be sampled in a serial manner, which allows the temporal metabolic changes to be studied . Thus, urine is a key biological matrix in metabolic-profiling studies, and is considered a potential source of biomarkers. Urinary metabonomics have proven to be of great value in studies of drug toxicology [20, 34], nutrition metabolism [35, 36], and diagnosis of several diseases, including colorectal cancer [28, 37], bladder cancer , and prostate cancer .
In the current study, we carried out a urinary metabonomics study on calcium-deficient rats, using UPLC and quadrupole time-of-flight tandem mass spectrometry (Q-TOF MS/MS) coupled with multivariate statistics to identify reliable biomarkers and to explore the metabolic alterations associated with the initiation and progression of calcium deficiency. We verified the reproducibility and reliability of the calcium-deficient model and biomarkers using a repeated low-calcium study, and we investigated the effect of calcium supplementation on the urinary metabolic profiling of calcium deficiency. We elucidated the implicated biological mechanisms and the biochemical pathways of the identified biomarkers, and analyzed the possible molecular mechanisms underlying the calcium deficiency. Furthermore, correlations between dietary calcium intakes and the identified biomarkers from rat models were investigated for their potential application to humans.
The study was approved by the Harbin Medical University Institutional Animal Care Committee and performed in accordance with the Harbin Medical University guidelines for the care and use of laboratory animals.
Chemicals and reagents
The products used in this study were high-performance liquid chromatography (HPLC)-grade acetonitrile and methanol (Honeywell Burdick & Jackson, Muskegon, MI, USA), HPLC-grade formic acid (Beijing Reagent Company, Beijing, China), and leucine-enkephalin (Sigma-Aldrich, St Louis, MO, USA). Ultrapure water was prepared in our laboratory (Ultra Clear System; Siemens Water Technologies, Nuremberg, Germany). Biomarker standards of the highest grade available were obtained from commercial sources.
Male Wistar rats aged 4 weeks (70 ± 10 g body weight; Shanghai Laboratory Animal Co. Ltd., Shanghai, China) were housed in individual metabolism cages at 21 ± 2°C and 50 ± 5% relative humidity with free access to food and distilled water for an adaptation period of 7 days. Food intake and body weight were recorded weekly.
The rats were divided randomly into two groups and fed on either a diet with either normal (AIN-93G Growth Purified Diet containing 0.50% (w/w) calcium) or low (AIN-93G Growth Purified Diet containing 0.15% (w/w) calcium) levels of calcium for 12 weeks (see Additional file 1). The low-calcium group (LCG) was the calcium-deficiency model (n = 24) and the normal-calcium group (NCG was the control (n = 24). Once a week, 24-h urine samples were collected in the metabolism cages for the metabonomics study. All urine samples were separated by centrifugation at 3,000 rpm (835 g) for 10 minutes, and the supernatant was stored at -80°C. At weeks 2, 4, 6, 8, 10 and 12, 24-h urine and feces samples were taken over 3 consecutive days from eight rats for the calcium metabolic-balance study. After the volumes of urine and the weights of feces were measured, they were stored at -80°C until measurement of calcium. At the end of weeks 4, 8, and 12, eight rats were selected randomly from each group, fasted for 12 hours, anesthetized by intraperitoneal injection of sodium pentobarbital (40 mg/kg body weight), and then killed by bloodletting. Blood samples were taken from the abdominal aorta, and immediately separated by centrifugation at 3,000 rpm (835 g) for 15 minutes. The sera were prepared for biochemical analysis. The left femur of each rat was isolated by dissection, and freed from the muscle and connective tissue.
Serum and urine analysis
Levels of serum calcium, phosphorus and alkaline phosphatase (AP) were measured with commercial kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) using an automatic biochemical analyzer (AUTOLAB PM 4000; AMS Corporation, Rome, Italy). ELISA was used to assay serum parathyroid hormone (PTH; Alpco 31-IPTMS-E01 Mouse/Rat Intact PTH EIA (96 wells); ALPCO Diagnostics, Salem, NH, USA; inter-assay/intra-assay percentage coefficient of variation (% CV) < 6% and < 8%, respectively; detection limit 1.0 pg/ml), rat fibroblast growth factor 23 (FGF23, catalog number E90746Ra; % CV < 5% and< 8%, respectively; detection limit 6.1 pg/ml) and 1,25(OH)2D3 (catalog number: E90467Ge; %CV < 5% and < 7%, respectively; detection limit 4.1 pg/ml) (both Uscn Life Science Inc., Wuhan, China).
Calcium-balance study determination
The detailed experiment procedure has been described previously . The calcium contents of urine, feces, and diet were measured with an atomic absorption spectrophotometer (AA-7010; East & West Analytical Instruments, Inc. Beijing, China).
Bone-mineral density measurement
The bone-mineral density (BMD) of the rat femurs was measured using dual energy X-ray absorptiometry (Norland XR-36 DEXA System; Cooper Surgical, Trumball, CT, USA) in the Second Affiliated Hospital of Harbin Medical University (Nangang District, China).
Experiment II was designed to verify the reproducibility and reliability of the calcium-deficient model and the identified biomarkers from experiment I. Thirty-six rats were divided randomly into two groups, NCG (n = 12) and LCG (n = 24), as before, and the diets described for experiment I were supplied to the two groups for 8 weeks. At the end of week 8, the LCG rats were divided randomly into two sub-groups: the maintained low-calcium group (MLCG; n = 12) and the calcium-supplementation group (CSG; n = 12). The CSG rats were fed a different diet, with a calcium content of 1.5% (w/w) (see Additional file 1), while the diets supplied for the NCG and MLCG rats were unchanged. All rats were fed for another 4 weeks. The 24-h urine samples were collected as described for experiment I during weeks 1-12.
Global metabolic-profiling analysis of rat urine by UPLC/Q-TOF MS/MS
Urine samples were allowed to thaw at room temperature, diluted 1:1 (v/v) with water, mixed by vortex for 1 minute, and then separated by centrifugation at 12,000 rpm (13362 g) for 10 minutes. The supernatants were then transferred to autosampler vials.
Chromatographic separation was performed on a 1.8 μm T3 column (ACQUITY (HSS); Waters Corp., Milford, MA, USA; 2.1 mm × 100 mm) equipped with a UPLC system (ACQUITY UPLC; Waters Corp., USA). The temperatures of the column and autosampler were maintained at 35°C and 4°C, respectively. A sample (2 μl) of the urine supernatant obtained by centrifugation was injected onto the column at a flow rate of 0.35 ml/min. The mobile phase consisted of water containing 0.1% formic acid (solution A) and acetonitrile (solution B). The elution gradient was as follows: 2% B for 0.5 minutes; 2 to 20% B over 0.5 to 6.0 minutes; 20 to 35% B over 6.0 to 7.0 minutes; 35 to 70% B over 7.0 to 9.0 minutes; 70 to 98% B over 9.0 to 10.5 minutes; 98% B for 2.0 minutes;, then return to 2% B for 6.0 minutes. Once the initial settings were established, the column was equilibrated for 2.0 minutes. Acetonitrile was run every fifth sample as a blank solution, and the urine samples in the two analysis batches (experiment I and II) were injected alternately as five NCG and five LCG samples.
Mass spectrometry conditions
Q-TOF MS/MS was performed with a mass spectrometer (Micromass Q-TOF mass spectrometer; Waters Corp., Manchester, UK) using an electrospray ionization (ESI) interface with the ESI source operated in the negative ion mode (ESI-). The analytical parameters were as follows: capillary voltage, 2800 V; sample cone voltage, 35 V; collision energy, 6 eV; source temperature, 100°C; desolvation gas (nitrogen) flow, 650 L/h; desolvation temperature, 320°C; cone gas (nitrogen) flow, 50 l/h; collision gas, argon; MCP detector voltage, 2200 V. The Q-TOF mass acquisition rate was set at 0.4 seconds with an interscan delay of 0.1 second. The scan mass range was from 50 to 1000 m/z. The Q-TOF MS/MS data were collected in centroid mode, using the lock spray to ensure accuracy and reproducibility. A concentration of 200 pg/ml leucine-enkephalin was used as lock mass (m/z 554.2615) in ESI-. The lock spray frequency was set at 10 seconds, and the lock mass data were averaged over 10 scans for correction. The MS/MS spectra of potential biomarkers were obtained by UPLC-MS/MS.
The raw data were imported into MarkerLynx software (which is incorporated in the MassLynx software; version 4.1; SCN 714; Waters Corp., USA). The MarkerLynx ApexTrack peak integration was used for peak detection and alignment. The ApexTrack peak parameters were set as follows: peak width at 5% height, 1 second, and peak-to-peak baseline noise (calculated automatically). Collection parameters were set as follows: mass window, 0.05 Da; retention time window, 0.2 minutes; minimum intensity, 80; noise elimination level, 6.0; deisotope data, Yes. Data were only used for the period 0.4 to 10.5 minutes; that is, up to the point at which the column-washing phase of the analysis started. After being recognized and aligned, the intensity of each ion was normalized to the summed total ion intensity of each chromatogram, to take into account the variation in urine concentration and volume. The data-reduction process was handled in accordance with the '80% rule' . The three-dimensional data including peak number (RT-m/z pair), sample name, and normalized peak areas were exported to the EZinfo software (version 184.108.40.206; June 5 2008; Waters Corp., USA) for multivariate statistical analysis. The data were mean-centered and Pareto-scaled before multivariate statistical analysis. PCA, an unsupervised multivariate statistical approach, can reduce the dimensionalities of complex datasets and provide an overview of all observations in data tables, such as groupings, trends, and outliers of the different groups of samples [30, 31]. The PLS method was used for the analysis of time changes . After an initial overview of the UPLC/Q-TOF-MS data, the supervised partial least-squares discriminant analysis (PLS-DA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) were applied to visualize the maximal difference associated with calcium deficiency for each LCG versus NCG at all time points. A default seven-fold (Leave-1/7th Samples-Out) cross-validation procedure and testing with 100 random permutations were performed to avoid the over-fitting of supervised PLS-DA models, using SIMCA-P software (version 11.5; Umetrics AB, Umeå, Sweden).
Identification of biomarkers and metabolic pathway analysis
The probable empirical formulas of the biomarkers were first derived based on accurate mass measurement (mass error of <20 ppm) and by considering the relative intensities of the isotope peaks through the high-resolution MS spectra. The MassFragment™ application manager (MassLynx v4.1, Waters Corp., USA) was used to facilitate the MS/MS fragment ion-analysis process by way of chemically intelligent peak-matching algorithms. The identification of potential biomarkers was achieved by comparison with free online databases, such as ChemSpider (http://www.chemspider.com), the Human Metabolome Database (HMDB) (http://www.hmdb.ca), Metlin (http://metlin.scripps.edu/) and Pubchem (http://pubchem.ncbi.nlm.nih.gov/), using exact mass and MS/MS spectra. Finally, the biomarkers were further confirmed by standard compounds based on both retention times and MS/MS spectra.
The implicated pathways of biomarkers were interpreted using databases, including HMDB and KEGG.
Because the ultimate goal of this non-invasive technique is to screen for calcium deficiency in humans, we further confirmed the potential application of three of the biomarkers identified from the rat models in calcium-deficient human subjects. Post-menopausal women have been reported to be the core of the population at risk for calcium deficiency, thus, to investigate the correlations between calcium intake and the identified biomarkers of calcium deficiency, we chose post-menopausal women as the subjects in this study.
The project was approved by the Ethics Committee of Harbin Medical University, and informed consent was obtained from all subjects before entry into the study.
In total, 70 participants who had lived in Harbin for more than 5 years were randomly recruited (from Hongqi Community Health Service Center, Harbin City, China). All the participants were women aged 50 to 64 years (mean ± SD 56.55 ± 3.8 years), had natural menopause for up to 1 year or were post-menopausal.
The exclusion criteria included subjects with: any conditions affecting calcium absorption or metabolism, such as hyperparathyroidism, inherited bone disease, ovarian surgery, gastrointestinal resection, thyroid resection, malabsorption, or diseases of the digestive tract; (2) other diseases, such as coronary heart disease, stroke, diabetes, cancer, thyroid or parathyroid disease, and chronic disease of the liver or kidney; or current treatment with hormone-based drugs or other medications such as estrogen.
The average calcium intake per day was derived from 3 days of weighed dietary records for each woman, and 24-h urine samples were collected from all subjects. The normalized peak areas of the biomarkers were detected for quantitative analysis by UPLC/Q-TOF MS/MS.
Serum biochemical indicators and BMDs of the LCG and NCG rats were expressed as the mean ± SD. Differences between the two groups were analyzed by the independent t-test and Mann-Whitney U-test. Correlations between dietary calcium intake and the biomarkers in human subjects were examined by linear regression analysis. Log-transformed values were used for the skewed distributions of the biomarkers. A two-tailed value of P < 0.05 was considered significant. All analyses including the error bars of three unique metabolites were performed using SPSS software (version 16.0; SPSS Inc., Chicago, IL, USA).
Serum biochemical indicators and bone-mineral density
Serum biological indicators and bone-mineral density between rats on the normal-calcium diet and rats on thelow-calcium diet.
2.53 ± 0.26
2.55 ± 0.19
2.56 ± 0.28
2.55 ± 0.23
2.51 ± 0.17
2.31 ± 0.28*
9.38 ± 1.94
9.29 ± 2.35
9.17 ± 2.11
9.29 ± 2.03
8.85 ± 1.60
8.67 ± 2.03
85.8 ± 21.0
82.4 ± 14.2
80.1 ± 16.3
96.6 ± 17.9
111.7 ± 20.3*
117.8 ± 17.9*
45.6 ± 6.08
42.3 ± 2.22
31.2 ± 1.75
59.4 ± 6.87*
53.7 ± 1.95*
42.8 ± 2.13*
1,25, OH2D3, pg/ml
70.7 ± 14.9
71.6 ± 18.1
72.2 ± 19.9
87.2 ± 23.1*
120.2 ± 26.4*
121.3 ± 19.1*
211 ± 9.28
234 ± 14.44*
0.258 ± 0.02
0.285 ± 0.02
0.298 ± 0.02
0.254 ± 0.03
0.277 ± 0.01
0.262 ± 0.03*
Quality assessment of the metabonomics platform
To verify the reproducibility and reliability of the data, a representative pooled quality control (QC) sample was prepared by mixing equal volumes of the urine samples from five LCG rats and five NCG rats; this was analyzed as every 15th sample throughout the analytical run.
An initial overview of the quality of the analytical run was obtained by PCA of the sample dataset that included the QC injections. The QC results were clustered tightly in the middle of the scores plot (see Additional file 3; red stars). The relative standard deviations (RSD; %) of the retention time and peak area in intra-batch/inter-batch assays ranged from 0 to 0.73 and 0.8 to 4, respectively, in the intra-batch assay, and from 0.1 to 2.7 and 1.5 to 6.2, respectively in the inter-batch assay (see Additional file 3). The results showed that the stability of the metabonomics platform was excellent throughout the run, and was sufficient to ensure the data quality for further global metabonomics analysis.
Urinary metabolic-profiling analysis of experiment I
To identify the difference in metabolites between the NCG and LCG rats, OPLS-DA models were constructed for all time points. Discriminating variables were selected primarily according to their variable importance in projection values (VIP > 1.5) and S-plot by OPLS-DA. The discriminating variables obtained from the OPLS-DA models were further validated by the Wilcoxon test (P<0.05). Although hundreds of discriminating variables were selected as potential markers at each time point, only a small percentage were shared across different time points. It was expected that the real biomarkers should change in accordance with the progression of calcium deficiency, therefore, only the biomarkers with regular trends of change were considered as potential biomarkers in this study.
Urinary metabolic-profiling analysis of experiment II (repeated low-calcium diet experiment)
The analytical method of urinary metabolite profiling used in experiment I was used in the repeated low-calcium diet experiment (urine samples, n = 288). Similar results were obtained for classification trends by PCA, PLS-DA, batch PLS scores plot, and PCA trajectory analysis (see Additional file 6), which indicated that the urinary metabolic profiling of calcium-deficient rats had a high degree of repeatability. These similar results further confirmed that the cluster between the two groups was robust and valid, and that the urinary metabolite profiling of calcium-deficient rats was significantly altered by the second week of the low-calcium diet. Most of the biomarkers from experiment I were identified, and these had the same changing trends as seen in the earlier experiment. Only nine biomarkers were not seen in the repeated low-calcium diet experiment, and these were excluded. Accordingly, the list of markers now comprised 29 potential biomarkers (see Additional file 7).
Urinary metabolic-profiling analysis of experiment II (calcium-supplement experiment)
Significant changes in several serum biological indicators and a decreasing trend for BMD were found in the LCG rats after week 8 (Table 1). Moreover, based on the metabolic trajectory analysis by PCA (Figure 5), it seemed that calcium deficiency would progress from the middle stage to the late stage if the LCG rats were kept on a low-calcium diet after week 8. Therefore, to investigate the effect of calcium supplementation on the urinary metabolic profiling of calcium-deficient rats, half of the LCG rats (CSG) were supplied with the 1.5% high-calcium diet after week 8, while the other half (MLCG) remained on the low-calcium diet.
Identifications and changing trends for the biomarkers of calcium deficiency.
Mass error, ppm
Ion fragment of citrateb
Cholanoic acid c
L-Glutamic acid, N-[[[(1S)-1-carboxy-2-mercapto-ethyl]amino]carbonyl]- d
4-Oxo-2-pentenoic acid d
6-(2-hydroxycyclopentyl) hexanoic acid d
Ethyl isobutyl ketone d
Cyclic adenosine monophosphateb
Identification of biomarkers
In this study, 27 metabolites were selected as calcium deficiency biomarkers. Eleven biomarkers were confirmed by comparing their retention times and MS/MS fragmentation patterns with those of the standards (Table 2). Detailed identification experiments using Q-TOF MS/MS and standards to elucidate the elemental composition of each biomarker (for example, cAMP) were performed (see Additional file 8). Ten further biomarkers were identified by searching several free online databases. Hence, 21 biomarkers were identified (Table 2). Six more biomarkers were determined by exact mass, isotopic distribution, and mass spectra fragmentation patterns, using MassFragment software (for detailed data, see Additional file 9).
Correlation between dietary calcium intake and each metabolite in human
Calcium deficiency is reversible when diagnosed at an early stage. However, without proper intervention, calcium deficiency can develop to an irreversible stage, such as rickets. Several methods, including epidemiological survey, calcium-balance study, serum biochemical investigations, and radiological examination, are currently used for the assessment of calcium nutrition status. However, none of these methods is suitable for large-scale population screening or for the clinical diagnosis of calcium deficiency at an early stage. Therefore, a non-invasive method and sufficiently sensitive and specific biomarkers are urgently needed for assessing the presence and progression of calcium deficiency. Urinary metabonomics based on UPLC/Q-TOF MS/MS has been of great value in the discovery of biomarkers and the elucidation of the pathogenesis of various diseases [37–39]. To date, there has been no report of urinary metabonomics focused on calcium deficiency. Therefore, this unbiased global urinary metabonomics study based on UPLC/Q-TOF MS/MS coupled with multivariate statistical analysis is the first to identify potential biomarkers and unravel the molecular mechanisms of calcium deficiency.
As shown by our experiments, there were no significant changes in the serum levels of phosphate, calcium, or AP before week 9, but there were changed in PTH and 1,25(OH)VD3 levels at this point. Although PTH secretion was exquisitely sensitive to very small changes in serum calcium concentration, PTH is not a specific biomarker of calcium deficiency. Numerous experiments have shown that a rise in PTH can be caused by other factors, such as primary hyperparathyroidism and secondary hyperparathyroidism [41, 42]. Although the calcium balance changed significantly during the study, this method took at least 3 days to perform. In addition, the serum calcium level was significantly decreased until week 12, indicating the body's strong ability to maintain calcium homeostasis. Similarly, significant decreases in BMD were not found until week 12. These results suggest that both serum calcium and bone radiological change can only reflect long-term or severe calcium deficiency. In contrast to the serum indicators, urinary metabolic alterations between the NCG and LCG rats were detected at week 2 using global urinary metabolic profiling. This observation was also confirmed by a calcium-balance study. Moreover, after calcium supplementation for 1 week, the altered calcium metabolism had been largely restored. These results show that urinary metabolic profiling has high sensitivity to subtle metabolic perturbation. We were able to preliminarily distinguish the LCG from the NCG rats by urinary metabolic-profiling analysis.
To gain insight into the metabolic mechanism of calcium deficiency and to provide an accurate assessment of calcium nutrition status, reliable biomarkers should be identified. However, in metabonomics studies, a major challenge to date has been how to distinguish reliable biomarkers that are closely associated with the initiation and progress of disease from those that are altered but are unrelated to the disease. The ideal strategy for discovering reliable biomarkers should include a time-course analysis for discriminating metabolites in a experiment on rats with low-calcium diet, and subsequent validation of the candidate biomarkers by a repeated experiment on rats with low-calcium diet along with a nutrition-supplement experiment. Therefore, we developed an integrated screening method consisting of the following steps. First, the discriminating variables with regular and reasonable changing trends (types A, B and C; Figure 6), were selected as potential biomarkers by time-course analysis. Second, the repeatability of biomarkers is crucial for their identification and biological interpretation in metabonomics studies. Thus, to verify the repeatability of biomarkers, the same urinary metabolic profiling and time-course analysis of discriminating variables were applied in a repeated low-calcium experiment. Third, a calcium-supplement experiment based on the low-calcium experiment was used to confirm the above biomarkers.
As shown by our study results, 27 variables were chosen as reliable biomarkers of calcium deficiency. Biomarkers with the type A changing trend (for example, glycine, sebacic acid) were present throughout the progression of calcium deficiency. Biomarkers with the type B changing trend were defined as the specific biomarkers that were present in the early (for example, uric acid, weeks 1-5), early and middle (for example, indoxyl sulfate, weeks 1-8) stages of calcium deficiency. By contrast, biomarkers with the type C changing trend were defined as the specific biomarkers that were present in the middle (for example, cholanoic acid, weeks 5-12), middle and late (for example, pseudouridine, weeks 8-12) stages. Hence, we concluded that if significantly increased levels of glycine (type A) and decreased levels of uric acid (type B) were both detected, but biomarkers with the type C changing trend were not detected, this would suggest that the body was in the early stages of calcium deficiency. If significantly increased levels of glycine (type A) and indoxyl sulfate (type B), and decreased levels of ions (cholanoic acid; type C) were all detected in urine, this would indicate that the body was in the middle stage of calcium deficiency. Finally, if significantly increased levels of glycine (type A) and decreased levels of uridine (type C) were detected, this would suggest severe calcium deficiency. These results proved that the biomarkers identified by urinary metabolomics were sufficient to assess the level of calcium deficiency at different stages in the male rat, which cannot be achieved by serum biochemical analysis. After further validation of several biomarkers (oxoglutaric acid, phenylacetylglycine, pyrophosphoric acid, citric acid, sebacic acid, pseudouridine, indoxyl sulfate, and taurine) that occur frequently in humans, these biomarkers could be used alone or in combination as a non-invasive method with greater sensitivity and specificity for the diagnosis of calcium deficiency.
A high concentration of PTH can promote the transformation of preosteoclasts and stromal cells into osteoclasts. Highly active osteoclasts dissolve bone minerals by secreting acid onto the isolated surface, and simultaneously break down the bone matrix by secreting collagenase and hydrolase. Increased levels of glycylproline, the end product of collagen metabolism, were found when bone collagen was disrupted. Glycylproline can be further digested by prolidase to glycine and proline, and we found decreased glycylproline and increased glycine levels in urine collected from the LCG rats. Glycine is involved in the synthesis of collagen. Phenylacetylglycine is a glycine conjugate of phenylacetic acid. Decreased glycylproline and increased glycine and phenylacetylglycine levels in urine suggested disruption of bone collagen. The results of our study suggest that glycylproline, glycine, and phenylacetylglycine could be involved in the metabolic pathway of the degradation and biosynthesis of bone collagen, and that the low-calcium diet could have induced disruption of bone collagen. Ascorbic acid is necessary for the maintenance of connective tissue and bone, and it is an electron donor for enzymes involved in collagen hydroxylation [45, 46]. D-Glucurono-6,3-lactone participates in the synthesis of ascorbate. The significant increase of D-glucurono-6,3-lactone levels in LCG urine indicates that the synthesis of vitamin C was reduced because of the lack of D-glucurono-6,3-lactone, and a decrease in ascorbic acid levels in the LCG urine was also seen. The decreased ascorbic acid and increased D-glucurono-6, 3-lactone levels could be an indication or a possible consequence, of impaired connective tissue and bone.
PTH induced the demineralization and inhibition of citrate excretion , and reduced citrate excretion was found in the LCG urine. Citrate has an important role in bone metabolism and bone diseases [48–50], and it can be considered a biomarker of calcium deficiency. Citric acid (citrate) can be formed in the Krebs cycle. Oxoglutaric acid and acetoacetic acid are the important biological compounds and are key intermediates in the Krebs cycle, therefore, the decreased oxoglutaric acid, acetoacetic acid, and citrate levels found in LCG urine suggest that the Krebs cycle could be impaired by calcium deficiency.
Xanthosine and uric acid are involved in purine metabolism. The increased xanthosine and decreased uric acid levels suggest that purine metabolism was possibly related to calcium metabolism. N-acetyl-l-tyrosine and L-glutamic acid are amino acids. The alterations of these two metabolites in our study suggest that calcium deficiency could lead to the abnormal metabolism of amino acid. Sebacic acid belongs to the dicarboxylic acid, which is involved in the metabolism of lipids and carbohydrates. Previous reports have documented that the medium-chain dicarboxylic acids (such as sebacic acid) are naturally occurring substances formed by cytochrome P450-mediated ω-oxidation of fatty acids in the cytosol of cells . The decreased excretion of sebacic acid in rat urine suggests that calcium deficiency could impair ω-oxidation of medium-chain monocarboxylic acids. Taurine is a facilitator in the transport of calcium ions [52, 53] and influences bone metabolism . The decreased taurine levels in the LCG urine indicate impairment of bone metabolism. In earlier studies, it was found that indoxyl sulfate could impair osteoblast function and induce abnormal bone turnover [55, 56]. Our results suggest that osteoblast function and bone turnover of the LCG rats could be impaired by increased indoxyl sulfate.
An animal urinary metabonomics study can provide continuous time-dependent data to allow assessment of the dynamic metabolic alterations and reveal the time-dependent pathophysiological changes. For instance, significantly increased levels of pyrophosphate, cAMP, glycine, indoxyl sulfate, and xanthosine, and decreased levels of citrate, oxoglutaric acid, acetoacetic acid, sebacic acid, and ascorbic acid were seen in the LCG rats in week 2, indicating that a 2-week low-calcium diet could result in metabolic changes in calcium and phosphorus levels, and in mild disruption of bone, bone collagen and bone turnover, and osteoblasts. This result is consistent with the results of the calcium-balance study, which could further explain the reason for the decrease in calcium excretion in urine and feces and the increase in calcium absorptivity. The peak area of pyrophosphate was decreased and that of ascorbic acid was increased persistently during weeks 1 to 12 (Figure 6A1, 6A2), indicating that body calcium homeostasis was gradually disrupted as the calcium deficiency progressed. Moreover, pseudouridine, naphthoxyacetic acid, cholanoic acid, and monomethyl fumarate in the LCG rats appeared to be changed significantly after week 6. At this stage, there was significantly perturbed expression of phosphate, amino acids, organic acids, fatty acids and purine metabolism, and disruption of bone, bone collagen, osteoblasts, and the Krebs cycle in the LCG rats as measured by the urinary metabonomics study. These results suggest that the normal patterns of bone resorption and remodeling in the LCG rats was impaired, thus abnormal bone resorption and remodeling had led to disorders of bone metabolism and decreased BMD (Figure 9B). Most of the identified biomarkers supported the molecular mechanisms and pathophysiological changes associated with calcium deficiency that have been reported previously. In the current study, we found, for the first time to our knowledge, that purine metabolism and the Krebs cycle are possibly related to calcium deficiency. It is possible that new metabolic pathways and unknown mechanisms are involved in the progression of calcium deficiency. Further research focused on these biomarkers is needed.
Because they allow greater control of conditions to reduce extrinsic variability, animal models have advantages over human studies when investigating biomarkers and elucidating subtle metabolite alterations. Thus, we firstly identified the calcium-deficient biomarkers from animal models, before verifying the potential application of three biomarkers in humans. Significant correlations were found between dietary calcium intakes and two of the biomarkers (pseudouridine and citrate). Because we did not control for confounding factors, such as gender, age, health state, disease, and the serum levels of 25(OH)VD3 and PTH, the true correlations might have been concealed to some extent.
There were some limitations to our studies. We used only male rats in the animal study, thus, the gender may have been a confounder in the metabolomics experiments. Moreover, the human study in post-menopausal women regarding the correlations between calcium intake and two biomarkers provides only a potential application of these two biomarkers in humans. Large, well-designed studies (including male and female subjects) that control for confounding factors are needed to verify in humans the potential application of these two biomarkers from our animal models.
We have identified for the first time reliable biomarkers of calcium deficiency in male rats, by using a time-course analysis of discriminating metabolites in a low-calcium diet experiment, then repeating the low-calcium diet experiment and performing a calcium-supplement experiment. The identified biomarkers give new insights into the pathophysiological changes and molecular mechanisms of calcium deficiency. The correlations between calcium intake and two of the biomarkers provide the potential for further assessment and elucidation of the metabolic responses of calcium deficiency in humans. After further verification of these two biomarkers in large population, we anticipate that these two biomarkers might be useful for population screening and clinical diagnosis of calcium deficiency in the future.
baseline peak intensity
orthogonal projection to latent structures discriminate analysis
principal component analysis
partial least-squares discriminant analysis
- UPLC/Q-TOF MS/MS:
Ultra-performance liquid chromatography and quadrupole time-of-flight tandem mass spectrometry.
This work was funded by a grant from the National Nature Foundation (81102113), National Natural Science Fund of China key project (81130049), National High Technology Research and Development Program of China (863 program) (2010AA023002) and the National 12th Five-Year Scientific and Technical Support Program of China (2012BAI02B02).
- Miller GD, Jarvis JK, McBean LD: The importance of meeting calcium needs with foods. J Am Coll Nutr. 2001, 20: 168S-185S.PubMedView Article
- Ma J, Johns R, Stafford R: Americans are not meeting current calcium recommendations. Am J Clin Nutr. 2007, 85: 1361-1366.PubMed
- Ge Keyou CS: Dietary intake of micronutrition of chinese inhabitants. Acta Nutrim enta Siniea. 1999, 21: 1-7.
- Hui Y, Changhao S, Ying L: The investigation analysis of dietary calcium intake and calcium nutritional status of Harbin resident. J Hyg Res. 2007, 36: 105-107.
- Yuna H, Fengying Z, Zhihong W, Yisong H, Wentao Y, Xiaoguang Y: Status of dietary calcium intake of Chinese residents. J Hyg Res. 2007, 36: 600-602.
- Marie PJ, Pettifor JM, Ross FP, Glorieux FH: Histological osteomalacia due to dietary calcium deficiency in children. N Engl J Med. 1982, 307: 584-588. 10.1056/NEJM198209023071003.PubMedView Article
- Kooh SW, Fraser D, Reilly BJ, Hamilton JR, Gall DG, Bell L: Rickets due to calcium deficiency. N Engl J Med. 1977, 297: 1264-1266. 10.1056/NEJM197712082972307.PubMedView Article
- Pettifor JM: Nutritional rickets: deficiency of vitamin D, calcium, or both?. Am J Clin Nutr. 2004, 80: 1725S.PubMed
- Thacher TD, Fischer PR, Pettifor JM: Rickets: vitamin D and calcium deficiency. J Bone Miner Res. 2007, 22: 638-638. 10.1359/jbmr.070118.PubMedView Article
- Hunt CD, Johnson LAK: Calcium requirements: new estimations for men and women by cross-sectional statistical analyses of calcium balance data from metabolic studies. Am J Clin Nutr. 2007, 86: 1054-1063.PubMed
- Hasling C, Charles P, Jensen FTJ, Mosekilde L: Calcium metabolism in postmenopausal osteoporosis: the influence of dietary calcium and net absorbed calcium. J Bone Miner Res. 1990, 5: 939-946.PubMedView Article
- Graff M, Thacher TD, Fischer PR, Stadler D, Pam SD, Pettifor JM, Isichei CO, Abrams SA: Calcium absorption in Nigerian children with rickets. Am J Clin Nutr. 2004, 80: 1415-1421.PubMed
- LeBlanc A, Evans H, Johnson P, Jhingran S: Changes in total body calcium balance with exercise in the rat. J Appl Physiol. 1983, 55: 201-204.PubMed
- Kotchen T, Ott C, Whitescarver S, Resnick L, Gertner J, Blehschmidt N: Calcium and calcium regulating hormones in the" prehypertensive" Dahl salt sensitive rat (calcium and salt sensitive hypertension). Am J Hypertens. 1989, 2: 747-10.1093/ajh/2.10.747.PubMedView Article
- Iba K, Takada J, Yamashita T: The serum level of bone-specific alkaline phosphatase activity is associated with aortic calcification in osteoporosis patients. J Bone Miner Metab. 2004, 22: 594-596. 10.1007/s00774-004-0528-9.PubMedView Article
- Seibel MJ: Biochemical markers of bone turnover part I: biochemistry and variability. The Clin biochem Rev/Austr Associa Clin Biochem. 2005, 26: 97.
- Strand MA, Perry J, Jin M, Tracer DP, Fischer PR, Zhang P, Xi W, Li S: Diagnosis of rickets and reassessment of prevalence among rural children in northern China. Pediatr Int. 2007, 49: 202-209. 10.1111/j.1442-200X.2007.02343.x.PubMedView Article
- Abrams SA: Calcium absorption in infants and small children: methods of determination and recent findings. Nutrients. 2010, 2: 474-480. 10.3390/nu2040474.PubMedPubMed CentralView Article
- Nicholson J, Lindon J, Holmes E: Metabonomics: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999, 29: 1181-1189. 10.1080/004982599238047.PubMedView Article
- Li Y, Liu S, Wang C, Li K, Shan YJ, Wang XJ, Sun CH: Novel biomarkers of 3-chloro-1, 2-propanediol exposure by ultra performance liquid chromatography/mass spectrometry based metabonomic analysis of rat urine. Chem Res Toxicol. 2010, 23: 1012-1017. 10.1021/tx900400p.PubMedView Article
- Manna SK, Patterson AD, Yang Q, Krausz KW, Li H, Idle JR, Fornace AJ, Gonzalez FJ: Identification of noninvasive biomarkers for alcohol-induced liver disease using urinary metabolomics and the Ppara-null mouse. J Proteome Res. 2010, 9: 4176-4188. 10.1021/pr100452b.PubMedPubMed CentralView Article
- Wang DC, Sun CH, Liu LY, Sun XH, Jin XW, Song WL, Liu XQ, Wan XL: Serum fatty acid profiles using GC-MS and multivariate statistical analysis: potential biomarkers of Alzheimer's disease. Neurobiology of Aging. 2010, 33: 1057-1066.PubMedView Article
- Mäkinen VP, Soininen P, Forsblom C, Parkkonen M, Ingman P, Kaski K, Groop PH, Ala-Korpela M: 1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death. Mol Sys Biol. 2008, 4: 167.
- Nevedomskaya E, Meissner A, Goraler S, de Waard M, Ridwan Y, Zondag G, van der Pluijm I, Deelder AM, Mayboroda OA: Metabolic profiling of accelerated aging ERCC1d/- mice. J Proteome Res. 2010, 9: 3680-3687. 10.1021/pr100210k.PubMedView Article
- Ellis JK, Athersuch TJ, Thomas LDK, Teichert F, Pérez-Trujillo M, Svendsen C, Spurgeon DJ, Singh R, Järup L, Bundy JG: Metabolic profiling detects early effects of environmental and lifestyle exposure to cadmium in a human population. BMC Med. 2012, 10: 61-10.1186/1741-7015-10-61.PubMedPubMed CentralView Article
- Shen G, Chen Y, Sun J, Zhang R, Zhang Y, He J, Tian Y, Song Y, Chen X, Abliz Z: Time-Course changes in potential biomarkers detected using a metabonomic approach in Walker 256 tumor-bearing rats. J Proteome Res. 2011, 10: 1953-1961. 10.1021/pr101198q.PubMedView Article
- Jov M, Serrano JCE, Ortega N, Ayala V, Angls N, Reguant J, Morell JR, Romero MP, Motilva MJ, Prat J: Multicompartmental LC-Q-ToF-based metabonomics as an exploratory tool to identify novel pathways affected by polyphenol rich diets in mice. J Proteome Res. 2011, 10: 3501-3512. 10.1021/pr200132s.View Article
- Cheng Y, Xie G, Chen T, Qiu Y, Zou X, Zheng M, Tan B, Feng B, Dong T, He P, Zhao L, Zhao A, Xu LX, Zhang Y, Jia W: Distinct urinary metabolic profile of human colorectal cancer. J Proteome Res. 2011, 11: 1354-1363.PubMedView Article
- Qiu Y, Cai G, Su M, Chen T, Zheng X, Xu Y, Ni Y, Zhao A, Xu LX, Cai S, Jia W: Serum metabolite profiling of human colorectal cancer using GC-TOFMS and UPLC-QTOFMS. J Proteome Res. 2009, 8: 4844-4850. 10.1021/pr9004162.PubMedView Article
- Wang X, Yang B, Zhang A, Sun H, Yan G: Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach. J Proteomics. 2011, 4: 1411-1427.
- Trygg J, Holmes E, Lundstedt T: Chemometrics in metabonomics. J Proteome Res. 2007, 6: 469-479. 10.1021/pr060594q.PubMedView Article
- Wishart DS: Metabolomics: applications to food science and nutrition research. Trends Food Sci & Tech. 2008, 19: 482-493. 10.1016/j.tifs.2008.03.003.View Article
- Want EJ, Wilson ID, Gika H, Theodoridis G, Plumb RS, Shockcor J, Holmes E, Nicholson JK: Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc. 2010, 5: 1005-1018. 10.1038/nprot.2010.50.PubMedView Article
- Plumb RS, Stumpf CL, Gorenstein MV, Castro-Perez JM, Dear GJ, Anthony M, Sweatman BC, Connor SC, Haselden JN: Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development. Rapid Commun Mass Spectrom. 2002, 16: 1991-1996. 10.1002/rcm.813.PubMedView Article
- Guy PA, Tavazzi I, Bruce SJ, Ramadan Z, Kochhar S: Global metabolic profiling analysis on human urine by UPLC-TOFMS: Issues and method validation in nutritional metabolomics. J Chromatogr B. 2008, 871: 253-260. 10.1016/j.jchromb.2008.04.034.View Article
- Heinzmann SS, Brown IJ, Chan Q, Bictash M, Dumas ME, Kochhar S, Stamler J, Holmes E, Elliott P, Nicholson JK: Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption. Am J Clin Nutr. 2010, 92: 436-443. 10.3945/ajcn.2010.29672.PubMedPubMed CentralView Article
- Qiu Y, Cai G, Su M, Chen T, Liu Y, Xu Y, Ni Y, Zhao A, Cai S, Xu LX: Urinary metabonomic study on colorectal cancer. J Proteome Res. 2010, 9: 1627-1634. 10.1021/pr901081y.PubMedView Article
- Pasikanti KK, Esuvaranathan K, Ho PC, Mahendran R, Kamaraj R, Wu QH, Chiong E, Chan ECY: Noninvasive urinary metabonomic diagnosis of human bladder cancer. J Proteome Res. 2010, 9: 2988-2995. 10.1021/pr901173v.PubMedView Article
- Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y: Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009, 457: 910-915. 10.1038/nature07762.PubMedPubMed CentralView Article
- Bijlsma S, Bobeldijk I, Verheij ER, Ramaker R, Kochhar S, Macdonald IA, Van Ommen B, Smilde AK: Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem. 2006, 78: 567-574. 10.1021/ac051495j.PubMedView Article
- Bilezikian JP, Potts JT, Fuleihan GEH, Kleerekoper M, Neer R, Peacock M, Rastad J, Silverberg SJ, Udelsman R, Wells SA: Summary statement from a workshop on asymptomatic primary hyperparathyroidism: a perspective for the 21st century. J Clin Endocrinol Metab. 2002, 87: 5353-5361. 10.1210/jc.2002-021370.PubMedView Article
- Slatopolsky E, Brown A, Dusso A: Pathogenesis of secondary hyperparathyroidism. Kidney Int. 1999, 56: S14-S19.View Article
- Gertner J: Disorders of calcium and phosphorus homeostasis. Pediatr Clin North Am. 1990, 37: 1441.PubMed
- Shimada T, Urakawa I, Yamazaki Y, Hasegawa H, Hino R, Yoneya T, Takeuchi Y, Fujita T, Fukumoto S, Yamashita T: FGF-23 transgenic mice demonstrate hypophosphatemic rickets with reduced expression of sodium phosphate cotransporter type IIa. Biochem Biophys Res Commun. 2004, 314: 409-414. 10.1016/j.bbrc.2003.12.102.PubMedView Article
- Franceschi RT, Iyer BS, Cui Y: Effects of ascorbic acid on collagen matrix formation and osteoblast differentiation in murine MC3T3-E1 cells. J Bone Miner Res. 1994, 9: 843-854.PubMedView Article
- Franceschi RT, Young J: Regulation of alkaline phosphatase by 1,25-dihydroxyvitamin D3 and ascorbic acid in bone-derived cells. J Bone Miner Res. 1990, 5: 1157-1167.PubMedView Article
- Luben RA, Cohn DV: Effects of parathormone and calcitonin on citrate and hyaluronate metabolism in cultured bone. Endocrinology. 1976, 98: 413-419. 10.1210/endo-98-2-413.PubMedView Article
- Raisz LG, Kream BE: Regulation of bone formation. N Engl J Med. 1983, 309 (1): 29-35. 10.1056/NEJM198307073090107.PubMedView Article
- Borensztein P, Patron P, Bichara M, Gardin J, Paillard M: Effects of citrate on urinary calcium excretion. Minel and Electrol Metab. 1989, 15: 353.
- Caudarella R, Vescini F, Buffa A, Stefoni S: Citrate and mineral metabolism: kidney stones and bone disease. Frontiers in bioscience: a journal and virtual library. 2003, 8: s1084-10.2741/1119.View Article
- Kølvraa S, Gregersen N: In vitro studies on the oxidation of medium-chain dicarboxylic acids in rat liver. Biochimica et Biophysica Acta (BBA)-Lipids and Lipid Metabolism. 1986, 876: 515-525. 10.1016/0005-2760(86)90039-1.View Article
- Yuan LQ, Lu Y, Luo XH, Xie H, Wu XP, Liao EY: Taurine promotes connective tissue growth factor (CTGF) expression in osteoblasts through the ERK signal pathway. Amino Acids. 2007, 32: 425-430. 10.1007/s00726-006-0380-4.PubMedView Article
- Yuan LQ, Xie H, Luo XH, Wu XP, Zhou HD, Lu Y, Liao EY: Taurine transporter is expressed in osteoblasts. Amino Acids. 2006, 31: 157-163. 10.1007/s00726-005-0313-7.PubMedView Article
- Park S, Kim H, Kim SJ: Stimulation of ERK2 by taurine with enhanced alkaline phosphatase activity and collagen synthesis in osteoblast-like UMR-106 cells. Biochem Pharmacol. 2001, 62: 1107-1111. 10.1016/S0006-2952(01)00741-9.PubMedView Article
- Muteliefu G, Enomoto A, Jiang P, Takahashi M, Niwa T: Indoxyl sulphate induces oxidative stress and the expression of osteoblast-specific proteins in vascular smooth muscle cells. Nephrol Dialy Transpl. 2009, 24: 2051-2058. 10.1093/ndt/gfn757.View Article
- Nii-Kono T, Iwasaki Y, Uchida M, Fujieda A, Hosokawa A, Motojima M, Yamato H, Kurokawa K, Fukagawa M: Indoxyl sulfate induces skeletal resistance to parathyroid hormone in cultured osteoblastic cells. Kidney Int. 2007, 71: 738-743. 10.1038/sj.ki.5002097.PubMedView Article
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1741-7015/11/86/prepub
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