MS based metabolomics

Food Research International 130 (2020) 108913

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Metabolic effect of AOS-iron in rats with iron deficiency anemia using LCMS/MS based metabolomics

T

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Hong Hea,1, Fengping Ana,b,1, Qun Huanga,b, Yuting Konga, Dan Hea, Lei Chena,b,c, , ⁎ Hongbo Songa,b, a

College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian, PR China Fujian Provincial Key Laboratory of Quality Science and Processing Technology in Special Starch, Fuzhou, Fujian, PR China c School of Chemistry and Food Engineering, Changsha University of Science and Technology, Changsha, Hunan, PR China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Iron deficiency anemia AOS-iron LC-MS/MS Metabolomics Serum Liver

Iron deficiency anemia (IDA) is a worldwide nutritional problem. The metabolic mechanism of IDA is still unclear. So, the underlying metabolic mechanism of iron supplementation has not been reported even if various iron supplements to treat IDA have been studied. The present study aimed to investigate the metabolic mechanisms of IDA and agar oligosaccharide-iron complex (AOS-iron) supplementation in IDA rats by assessing the changes of endogenous metabolites in serum and liver using LC-MS/MS metabolomics approach. Orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots showed significant separation of metabolites in serum and liver among the normal, anemia model and AOS-iron groups. Seventeen and eight metabolites were identified from serum and liver, respectively. Pathway enrichment analysis suggested that potential biomarkers were strongly involved in the biosynthesis of saturated and unsaturated fatty acids, sphingolipid metabolism, glycerophospholipid metabolism, linoleic acid metabolism, Fcγ receptor (FcγR)-mediated phagocytosis, pancreatic cancer metabolism, regulation of autophagy, gonadotropin releasing hormone (GnRH) signaling pathway, fatty acid metabolism, pantothenate and CoA biosynthesis, glutathione metabolism and primary bile acid biosynthesis. After supplementing 2 mg Fe/kg·bw AOS-iron for 4 weeks, the major metabolites in related pathways disrupted by IDA were restored to normal levels. Therefore, AOS-iron effectively treated IDA by regulating metabolic disorders.

1. Introduction As a trace element in the human body, iron is essential for various metabolic and life activities, such as oxygen transport, myelination, DNA synthesis, and neurotransmitter synthesis and metabolism (Niu et al., 2018; Padmanabhan, Brookes, & Iqbal, 2015). Iron deficiency causes a decrease in the activity of various iron-containing enzymes, which in turn induces a decrease in metabolic level of oxygen and in immunity, and various metabolic disorders (Kanjaksha, 2006; Soppi, 2018). Long-term iron deficiency can seriously deplete iron stores in the body, leading to iron deficiency anemia (IDA) (Clark, 2008). IDA is currently one of the world's four major nutritional deficiency diseases, especially in underdeveloped and developing countries. It is an important goal for future health to reduce IDA worldwide. Ferrous sulfate has been approved as an iron supplement by pharmacopoeias and food additive regulations in many countries. It has high

iron content and is inexpensive, but its bioavailability is low because of the influence of other ingredients in the food (Tolkien, Stecher, Mander, Pereira, & Powell, 2015). In recent years, some new iron supplements (such as polysaccharide-iron complexes, peptide-iron complexes, iron nanocomposite and ferrous fumarate) have been studied (Cui et al., 2017; Kianpour et al., 2017; Li, He, Shi, & Hou, 2018; Liang et al., 2018; Zhu, Yang, Fan, Wang, & Yu, 2017). The research mainly involves the preparation process, physicochemical properties, structural characteristics, antioxidant activity and iron supplementation effect. However, at present, the metabolic pathways for IDA and the metabolic mechanism of IDA treatment with iron supplements have not been reported. We previously prepared agar oligosaccharide-iron complex (AOS-iron) as a novel chelated iron supplement, which was soluble and stable at physiological pH. AOS-iron was absorbed in a molecular form, and the ferric iron in AOS-iron was quickly reduced to ferrous iron (He, An, Teng, Huang, & Song, 2019). AOS-iron were more effectively restored

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Corresponding authors at: College of Food Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian, PR China. E-mail addresses: [email protected] (Q. Huang), [email protected] (L. Chen), [email protected] (H. Song). 1 Authors contributed equally to this work. https://doi.org/10.1016/j.foodres.2019.108913 Received 27 May 2019; Received in revised form 2 December 2019; Accepted 15 December 2019 Available online 18 December 2019 0963-9969/ © 2019 Published by Elsevier Ltd.

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anemia model group and normal control group were intragastrically administered an equal volume of normal saline. The anemia model group and the AOS-iron group were always fed a low iron diet, while the control group was always fed the control diet.

the food intake, body weight, Hb, RBC, SI, SF and LI in IDA rats, compared to ferrous gluconate and ferrous sulfate (He et al., 2019). So, it is necessary to further study the mechanism of iron supplementation. Metabolomics, an important part of systems biology, is a new omics that has emerged following genomics, transcriptomics and proteomics, which primarily explore biological pathways involved in disease pathogenesis and determine the biological effects of treatment by measuring the content of endogenous metabolites (Turi, Romick-Rosendale, Ryckman, & Hartert, 2018; Zhang et al., 2015). Metabolomics has been successfully used in the diagnosis and monitoring of disease or drug treatment progress, such as hemolytic anemia (Liu, Liu, Gu, Qin, & Tian, 2016), type 2 diabetes (Merino et al., 2018; Zhou et al., 2018), Parkinson’s disease (Babu et al., 2018), and hypertension (Zheng et al., 2019). This implies that metabolomics is currently the most promising technique for diagnosing the pathophysiological changes in diseases development and treatment. Serum and liver are important organs for studying the iron metabolic pathway. Serum iron content reflects iron transport in the body (Bergsland et al., 2017). Liver is an important organ in metabolism and the main iron storage organ in the body, playing a central regulatory role in iron metabolism (Siimes & Dallman, 2010; Sikorska, Bernat, & Wróblewska, 2016). Therefore, the study of serum and liver metabolomics has great significance in the treatment of IDA. In the present study, an IDA rat model was established by feeding the animals a low iron diet, and the IDA rats were treated with AOSiron. The serum and liver metabolomics approach, based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with multivariate analyze (PCA and OPLS-DA), were applied to identify the potentially biomarkers for IDA and reveal the underlying mechanisms of AOS-iron treatment. The objectives of our study were to determine: (1) the metabolic pathways of IDA in rats, (2) whether AOSiron can restore the major metabolites in relevant pathways disrupted by IDA to normal levels, thus achieving the effect of iron supplementation.

2.3. Sample collection All rats survived throughout the iron supplementation experiment, anemia symptoms were observed in the anemia group, and all normal rats and AOS-iron rats grew normally. After 4 weeks of iron supplementation, all rats were fasted for 12 h. The rats were anesthetized with 10% chloral hydrate and blood was collected by cardiac puncture. The blood was transferred to blood collection tubes without additives and serum was obtained by ultracentrifugation at low temperature and was stored in a −80 °C freezer for metabolic analysis. All rats were dissected and liver tissues were completely removed. The livers were washed with 0.9% saline to remove residual blood, and stored at −80 °C for metabolic analysis. 2.4. Metabolomics analysis 2.4.1. Preparation of serum and liver samples Eight samples of serum and liver samples were selected randomly, respectively. Serum and liver metabolic samples were prepared according to the method of Gou et al. (2017) with minor modifications. 100 μL of thawed serum sample was accurately aspirated, and 10 μL of L-2-chloro-phenylalanine solution (0.3 mg/mL) dissolved in methanol was added as an internal standard, and the mixture was vortexed for 10 s. 300 μL of pre-cooled methanol–acetonitrile (2/1, v/v) was subsequently added, and the mixture was vortexed for 60 s and sonicated for 10 min in an ice water bath. The mixture was allowed to stand at −20 °C for 30 min, and then centrifuged at 13,000 rpm and 4 °C for 15 min. The supernatant was collected and filtered through a 0.22 μm syringe filter into an LC injection vial for LC-MS/MS analysis. 30 mg of thawed liver sample was transferred to an 1.5 mL Eppendorf tube containing two small steel balls, and 20 μL of L-2chloro-phenylalanine solution (0.3 mg/mL) dissolved in methanol was added as an internal standard. 400 μL of an aqueous methanol solution (CH3OH:H2O = 4:1, v:v) was subsequently added. After standing in a −80 °C freezer for 2 min, the mixture was ground by an automatic rapid grinder (JXFSTPRP-24/32, Shanghai, China) at 60 Hz for 2 min. After sonication for 10 min in an ice water bath, the mixture was allowed to stand at −20 °C for 30 min. Finally, the samples were centrifuged at 13,000 rpm and 4 °C for 15 min. The supernatant was collected and filtered through a 0.22 μm syringe filter into an LC injection vial for LC-MS analysis. A quality control (QC) sample was used for method validation in this experiment. The QC sample was prepared by mixing the aliquots of all samples (Dunn, Wilson, Nicholls, & David, 2012).

2. Materials and methods 2.1. Materials AOS-iron was prepared according to a previously described method (He et al., 2019) and 14.03% was determined as the iron content in AOS-iron. 2.2. Experimental design Thirty-six male Sprague-Dawley rats with an initial body weight of 55 ± 5 g were purchased from Shanghai SLAC Laboratory Animal Co., Ltd (Shanghai, China). The rats were raised under controlled conditions at a temperature of 23 ± 2 °C with humidity of 50 ± 10% and a light–dark cycle of 12 h/12 h. All animal experiments were approved by the Laboratory Animal Ethics Committee of the College of Food Science, Fujian Agriculture and Forestry University. Following a basal diet for 5 days, the rats were randomly divided into the normal control group (n = 12) and model group (n = 24). The normal group was fed a normal feed (45 mg Fe/kg diet) produced according to the American AIN93 standard. The model group was fed a low iron feed (12 mg Fe/kg diet) produced based on the American AIN93 standard. All rats were allowed free access to food and deionized water. Stainless steel cages and plastic drinking water tanks were used to avoid iron contamination. Blood was collected from the orbit of rats in the model group every weekend to measure the Hb content. At the end of the 4th week, the IDA rat model was successfully established (He et al., 2019). Rats in the model group (n = 24) were then divided into the anemia model group and AOS-iron group, with 12 rats in each group. At 9 am each day for 4 weeks, rats in the AOS-iron group were intragastrically administered 2 mg Fe/kg·bw AOS-iron solution, while rats in the

2.4.2. LC-MS/MS analysis The samples were subjected to LC-MS/MS analysis according to the method of Ji et al. (2018) with minor modifications. An ACQUITY UHPLC system (Waters Corporation, Milford, USA) coupled with an AB SCIEX Triple TOF 5600 System (AB SCIEX, Framingham, MA) was used to analyze the metabolic profiling in both ESI positive and negative ion modes. Chromatographic separation was carried out on an ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 μm, Waters) equipped with a binary solvent system (solvent A: 0.1% formic acid in water; solvent B: 0.1% formic acid in acetonitrile). The following gradient elution procedure was used: 0–2 min, 5–20% B; 2–4 min, 20–60% B; 4–11 min, 60–100% B; 11–13 min, held at 100% B; 13–13.5 min, 100–5% B; held for 1 min at 5% B to equilibrate the column. The flow rate was 0.4 mL/min, the injection volume was 5 μL, and the column temperature was maintained at 45 °C. Data acquisition was performed in full scan mode (m/z ranges: 2

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were clustered in the PCA score plot, indicating that the data quality was reliable. R2X is the main parameter for determining the quality of the PCA model. As shown in Table 1, both R2X of serum (R2X = 0.574) and liver (R2X = 0.51) were greater than 0.5, indicating that the model was reliable. In addition, all data from serum and liver were within 95% confidence intervals, and the PCA score plots for total serum (Fig. 1A) and liver (Fig. 1B) showed that the clustering of rat serum and liver endogenous metabolites in model group changed significantly compared with the normal group. After AOS-iron supplementation, the metabolic profiles of serum and liver in IDA rats changed to normal, indicating that AOS-iron effectively alleviated IDA in rats. OPLS-DA is a supervised pattern recognition method that facilitates the classification of samples and eliminates uncorrelated noise information in the dataset (Su et al., 2013). OPLS-DA was established to further validate separation of the metabolic profiles between the control and model groups, and the AOS-iron and model groups. The OPLSDA model parameters (R2X, R2Y and Q2) of serum and liver are shown in Table 1. In the serum and liver, the parameters R2Y and Q2 between the model and control groups, and the AOS-iron and model groups were greater than 0.5, indicating that the OPLS-DA model was well established. From the OPLS-DA score plots, the serum and liver samples between either the control and model groups (Fig. 2A and C) or model and AOS-iron groups (Fig. 2B and D) were completely separated, further indicating that there were significant differences in the metabolic characteristics of serum and liver.

70–1000) combined with Information Dependent Acquisition (IDA) mode. The mass spectrometry parameters were as follows: precursors per cycle, 550 ms; nebulizer gas, 40 PSI; auxiliary gas, 40 PSI; curtain gas, 35 PSI; ion source temperature, 550 °C (+) and 550 °C (−); ion spray voltage, 5,500 V (+) and 4,500 V (−); declustering potential, 100 V (+) and − 100 V (−); collision energy, 10 eV (+) and − 10 eV (−), and interface heater temperature, 550 °C (+) and 600 °C (−). For IDA analysis, the range of m/z was set as 50–1000, the collision energy was set at 30 eV to identify selected compounds. 2.4.3. Multivariate data analysis and identification of potential biomarkers Raw LC-MS/MS data were processed by Progenesis QI software (Waters Corporation, Milford, MA, USA) to perform baseline filtering, peak identification, retention time correction, peak alignment and normalization, and a data matrix containing retention time, mass-tocharge ratio, and peak intensity was obtained. Precusor tolerance was set at 5 ppm and fragment tolerance was set at 10 ppm, and retention time tolerance was set at 0.02 min. The data matrices of positive and negative ion modes were combined and exported into the SIMCA software (Version 14.0, Umetrics, Umeå, Sweden). After unit variance (UV) and Pareto (Par) scaling, unsupervised principal component analysis (PCA) and supervised orthogonal partial least-squares discriminant analysis (OPLS-DA) were performed to visualize metabolic changes between experimental groups. The Hotelling's T2 region, shown as an ellipse in score plots of the models, defined the 95% confidence interval of the modeled variation. In the OPLS-DA analysis, the variable importance in the projection (VIP) was sorted according to the overall contribution of each variable to the OPLS-DA model, and those variables with a VIP > 1 were considered significant. In this study, the default 7-fold cross-validation and 200-times permutation test were applied to validate the reliability of the model, with 1/7 of the samples being excluded from the mathematical model in each round to prevent over-fitting. Using SIMCA and SPSS software, the selected metabolites were considered to be potential biomarkers for group discrimination based on the screening criteria which were set as VIP values of OPLSDA model greater than 1 and P values of ANOVA less than 0.05. The ratio of the average content of metabolites between two groups was compared by fold-change (FC) analysis. The metabolites were identified based on public databases such as LipidMaps (http://www.lipidmaps. org/), KEGG (http://www.genome.jp/kegg/) and HMDB (http://www. hmdb.ca) databases, and metabolic pathway enrichment analysis of the identified biomarkers was performed by KEGG (M. Liu et al., 2016; Qi, Lu, Guo, Guo, & Li, 2017). Those pathways with P < 0.05 were considered and filtered out as major metabolic pathways. In addition, heatmap was constructed based on the relative level of identified metabolites was drawn by R with gplots.

3.2. Screening and identification of potential serum and liver biomarkers 228 serum features and 201 liver features were identified as discriminated variables, and these were significantly affected by iron deficiency or AOS-iron supplementation. After comparison with the LipidMaps, KEGG and HMDB databases, 21 serum metabolites (Table S1) and 12 liver metabolites (Table S2) were identified as potential biomarkers and visualized in a heat map (Fig. 3). Interestingly, treatment of IDA rats with AOS-iron allowed the levels of most potential biomarkers affected by IDA to return to normal or near normal levels. Compared with the normal group, in the serum (Table S1), SM(d18:0/ 16:1(9Z) and trichloroethanol glucuronide showed significantly decreased levels in anemia model group, and the other 19 metabolites showed significantly increased levels. All metabolite levels in AOS-iron group were correspondingly restored to normal. Similarly, in the liver (Table S2), 6 metabolites levels (docosapentaenoic acid, palmitic acid, linoleic acid, glycocholic acid, 4-androstenediol and 17.alpha.-hydroxypregnenolone) in anemia model group were significantly higher, and the other 6 metabolites were significantly lower compared with the normal group. All metabolite levels in AOS-iron group were correspondingly restored to normal, except docosahexaenoic acid.

2.5. Statistical analysis 3.3. Pathway enrichment and metabolic pathway analysis of potential biomarkers

All data were analyzed using one-way analysis of variance (ANOVA) and Duncan's multiple range tests. P < 0.05 was considered statistically significant, and P < 0.01 was considered extremely significant. All data were expressed as mean ± standard deviation (SD).

Metabolic pathway enrichment analysis was performed by the KEGG database to investigate the metabolic mechanism of potential biomarkers that were affected by AOS-iron during IDA. Ten major metabolic pathways were identified in serum, including biosynthesis of unsaturated fatty acids, sphingolipid metabolism, glycerophospholipid metabolism, linoleic acid metabolism, Fcγ receptor (FcγR)-mediated phagocytosis, pancreatic cancer metabolism, regulation of autophagy, gonadotropin releasing hormone (GnRH) signaling pathway, fatty acid metabolism, and fatty acid biosynthesis, while six major metabolic pathways were identified in liver, including biosynthesis of saturated and unsaturated fatty acids, pantothenate and CoA biosynthesis, glutathione metabolism, glycerophospholipid metabolism and primary bile acid biosynthesis. A total of 17 key significant metabolites were identified in the major metabolic pathways in serum. Compared with the normal group, SM

3. Results 3.1. Multivariate analysis of serum and liver metabolites Multivariate analysis, mainly including PCA and OPLS-DA (Xu et al., 2015), was used to analyze the differences in serum and liver metabolites in rats into the three groups. PCA is an unsupervised statistical method used to observe the overall distribution between samples and the stability of the entire analytical process through the relationship of original variables (Dhouha & Bérengère, 2011). In this study, test data on serum + QC and liver + QC samples and samples were separately uploaded into SIMCA for PCA analysis. As shown in Fig. 1, the QCs data 3

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Fig. 1. PCA score plots of rat serum (A) and liver (B) samples in the three groups.

18:0), PE(18:2(9Z,12Z)/24:1(15Z)), PA(16:0/16:0), LysoPC(18:0) and palmitoylcarnitine were all increased (Table 2). These key significant metabolites were restored to normal levels in AOS-iron group compared with anemia group. With regard to the major metabolic pathways in liver, 8 key significant metabolites were identified. Compared with the normal group, the levels of docosahexaenoic acid, pantetheine 4′phosphate, glutathione, and glycerophosphocholine were significantly decreased in the anemia group, while the levels of docosapentaenoic acid, palmitic acid, linoleic acid, and glycocholic acid were significantly increased (Table 3). Furthermore, key significant metabolites in the AOS-iron group, except for docosahexaenoic acid, were restored to normal levels compared to the anemia group.

Table 1 The R2X, R2Y and Q2 of PCA and OPLS-DA in rat serum and liver samples in the three groups. Item

R2X (cum)

R2Y (cum)

Q2 (cum)

PCA (all) OPLS-DA (model vs control) OPLS-DA (AOS-iron vs model) Liver PCA (all) OPLS-DA (model vs control) OPLS-DA (AOS-iron vs model)

0.574 0.73 0.727

0.937 0.973

0.372 0.883 0.842

0.51 0.607 0.575

0.977 0.994

0.277 0.936 0.899

Serum

4. Discussion

(d18:0/16:1(9Z)) level was decreased in the anemia model group, and adrenic acid, docosahexaenoic acid, eicosadienoic acid, linoleic acid, palmitic acid, stearic acid, cis-8, 11, 14-eicosatrienoic acid, d-erythrosphinganine, glucosylceramide(d18:1/16:0), d-erythro-sphingosine-1phosphate, 3-O-sulfogalactosyl-ceramide(d18:1/18:0), PC(16:1(9Z)/P-

Our previous studies have indicated that AOS-iron has better iron supplementation effect for IDA rats compared to ferrous gluconate and FeSO4 (He et al., 2019). In this study, we further investigate the changes in serum and liver metabolites in IDA rats and the rats treated by AOS-

Fig. 2. OPLS-DA score plots of rat serum (A, B) and liver (C, D) samples in the three groups. 4

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Fig. 3. Heat map analysis of potential serum (A) and liver (B) biomarkers in the normal control, anemia model and AOS-iron groups. The degree of change is marked by different colors, and red or blue represent the relatively increased or decreased levels of the metabolites, respectively. Each column represents an individual sample, and each row represents a biomarker. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 2 Significant metabolites in rat serum samples. No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

m/z

333.28 329.25 307.26 279.23 255.23 283.26 305.25 725.56 302.31 722.55 378.24 788.54 744.59 848.62 629.45 524.37 400.34

RT (min)

9.49 8.24 9.98 8.74 9.56 10.87 9.15 12.81 5.23 13.25 5.13 13.30 14.86 14.28 8.54 7.43 5.83

Metabolites

Adrenic acid Docosahexaenoic acid Eicosadienoic acid Linoleic acid Palmitic acid Stearic acid cis-8,11,14-Eicosatrienoic acid SM(d18:0/16:1(9Z)) D-erythro-Sphinganine Glucosylceramide(d18:1/16:0) D-erythro-Sphingosine-1-phosphate 3-O-Sulfogalactosylceramide (d18:1/18:0) PC(16:1(9Z)/P-18:0) PE(18:2(9Z,12Z)/24:1(15Z)) PA(16:0/16:0) LysoPC(18:0) Palmitoylcarnitine

Anemia model vs Normal control

AOS-iron vs Anemia model

VIP

Trend

VIP

Trend

2.583 3.568 1.386 8.260 3.200 1.365 1.379 10.421 1.737 1.798 3.355 1.635 2.026 1.851 2.181 13.117 2.588

↑** ↑** ↑** ↑** ↑** ↑** ↑** ↓* ↑** ↑** ↑** ↑** ↑** ↑** ↑** ↑* ↑**

2.715 4.171 1.360 8.231 3.276 1.464 1.565 5.176 1.593 2.046 3.097 1.492 1.794 1.648 2.524 24.380 2.339

↓** ↓** ↓** ↓** ↓** ↓** ↓** ↑* ↓** ↓** ↓** ↓** ↓** ↓** ↓** ↓** ↓**

Metabolic pathway

Biosynthesis of unsaturated fatty acids Biosynthesis of unsaturated fatty acids Biosynthesis of unsaturated fatty acids Linoleic acid metabolism Fatty acid biosynthesis Fatty acid biosynthesis Linoleic acid metabolism Sphingolipid metabolism Sphingolipid metabolism Sphingolipid metabolism FcγR-mediated phagocytosis Sphingolipid metabolism Glycerophospholipid metabolism Regulation of autophagy Pancreatic cancer Glycerophospholipid metabolism Fatty acid metabolism

Note: The up (FC > 1) or down (FC < 1) arrows represent the relatively increased or decreased levels of the metabolites, respectively. Metabolites showing significant differences (*P < 0.05) and extremely significant differences (**P < 0.01) between groups as determined by Duncan's multiple range test.

included biosynthesis of unsaturated fatty acids, fatty acid metabolism, fatty acid biosynthesis, sphingolipid metabolism, glycerophospholipid metabolism, and linoleic acid metabolism. Compared with the normal group, SM(d18:0/16:1(9Z)) level in anemia group was significantly decreased, and lipid substances levels including adrenic acid, docosahexaenoic acid, eicosadienoic acid, linoleic acid, palmitic acid and stearic acid were significantly increased in serum. In liver, the levels of docosahexaenoic acid and glycerophosphocholine in anemia group

iron. LC-MS-based metabolomics combined with multivariate analysis were used to identify potential biomarkers. The major metabolic pathways disturbed by IDA and the possible metabolic mechanism of AOS-iron treatment are summarized in Fig. 4. Lipids are important nutrients in the human body and are closely related to material transport, energy metabolism and metabolic regulation (Price, 2017; Rostovtseva, Hoogerheide, Rovini, & Bezrukov, 2017). In this study, metabolic pathways related to lipid metabolism 5

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Table 3 Significant metabolites in rat liver samples. No.

1 2 3 4 5 6 7 8

m/z

331.26 329.25 255.23 279.23 359.10 308.09 296.07 488.30

RT(min)

6.86 8.24 9.56 8.74 1.26 0.78 0.56 4.09

Metabolites

Docosapentaenoic acid Docosahexaenoic acid Palmitic acid Linoleic acid Pantetheine 4′-phosphate Glutathione Glycerophosphocholine Glycocholic acid

Anemia model vs Normal control

AOS-iron vs Anemia model

VIP

Trend

VIP

Trend

1.336 2.748 1.330 2.773 2.121 4.678 1.439 1.647

↑** ↓** ↑* ↑* ↓** ↓** ↓** ↑*

1.530 2.771 2.297 4.557 3.427 4.515 1.341 2.351

↓** ↓** ↓** ↓** ↑** ↑** ↑* ↓**

Metabolic pathway

Biosynthesis of unsaturated fatty acids Biosynthesis of unsaturated fatty acids Fatty acid biosynthesis Biosynthesis of unsaturated fatty acids Pantothenate and CoA biosynthesis Glutathione metabolism Glycerophospholipid metabolism Primary bile acid biosynthesis

Note: The up (FC > 1) or down (FC < 1) arrows represent the relatively increased or decreased levels of the metabolites, respectively. Metabolites showing significant differences (*P < 0.05) and extremely significant differences (**P < 0.01) between groups as determined by Duncan's multiple range test.

Fig. 4. Schematic diagram of the disturbed metabolic pathways related to IDA and AOS-iron treatment. Solid and dashed arrows indicate the single and multiple steps between two metabolites. Solid arrows, direct; dashed arrows, indirect, respectively. Colored items are disturbed metabolites. Red metabolites, increased in the anemia model group compared with the normal control group and decreased in the AOS-iron group compared with the anemia model group; blue metabolites, decreased in the anemia model group compared with the normal control group and increased in the AOS-iron group compared with the anemia model group. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

and 3-O-sulfogalactosylceramide(d18:1/18:0)). Therefore, sphingolipid metabolism disorders led to hair loss in IDA rats, which further validates the clinical phenomenon of IDA-induced hair loss in rats. Following supplementation with AOS-iron, changes in sphingolipid metabolites were contrary to those in anemia group, indicating that AOSiron improved the sphingolipid metabolism disorders caused by IDA. In addition, lysophosphatidylcholine is a degradation product of phospholipids, which is mainly related to the hydrolysis of cell membranes. It can rupture the membranes of red blood cells and other cells. Lysophosphatidylcholine is rarely present in normal blood, but rapidly increases in ischemic tissues (Corr & Yamada, 1995; P; Lin, Welch, Gao, Malik, & Ye, 2005). An increase in the level of lysophosphatidylcholine induces oxidative stress and inflammatory responses in vascular endothelial cells, leading to endothelial cell dysfunction (Chen et al., 2017). The results of this study showed that serum lysophosphatidylcholine (LysoPC(18:0)) level was increased in anemia group compared with normal group (Table 2), which was related to erythrocyte membrane damage caused by IDA; thus, a large amount of LysoPC(18:0) was released into the blood. Studies have shown that choline and its related compounds (phosphocholine and glycerophosphocholine) are essential nutrients for many major biological metabolic pathways (Moukarzel et al., 2017). We found that the increased levels of serum LysoPC(18:0) and PC(16:1(9Z)/P-18:0) (Table 2) and decreased level of liver glycerophosphocholine were observed (Table 3)

were lower than those in normal group, while the levels of docosapentaenoic acid, palmitic acid, and linoleic acid were higher than those in normal group. Changes in these lipid metabolites indicated that IDA caused lipid metabolism disorders. Aktas et al. (2016) also found changes in the fatty acid composition of erythrocyte membranes in premenopausal IDA patients using gas chromatography. However, compared with the anemia group, except for docosahexaenoic acid in liver in AOS-iron group, which did not return to normal, the other lipid metabolites were restored to normal levels, indicating that AOS-iron had a regulatory effect on lipid metabolism in IDA rats. Specifically, we found that the levels of sphingomyelin metabolites in serum in anemia group were abnormal (Table 2), and SM(d18:0/16:1(9Z)) showed significantly decreased levels compared with the normal group. Sphingomyelin (SM(d18:0/16:1(9Z))) is a major component of cell membranes, and sphingomyelin and its metabolites are essential for maintaining cell structure and function, as well as for cell growth, survival and apoptosis (D'Angelo, Moorthi, & Luberto, 2018; Si, Zhen, Shuang, Yuan, & Yan, 2018). Damage of hair follicle stem cells can lead to hair loss (Hwang et al., 2016). Lin et al. (2017) reported that ceramide (glucosylceramide (d18:1/16:0)) was very important for epidermis homeostasis and played a crucial role in the structure and function of hair follicles. Abnormal ceramide levels could cause hair loss. In this study, IDA caused the level of sphingomyelin decreased and increased of its metabolites (e.g., d-erythro-sphinganine, glucosylceramide(d18:1/16:0), 6

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Weschawalit, Thongthip, Phutrakool, & Asawanonda, 2017). Leonid et al. (2005) found that glutathione protected human red blood cells from oxidative stress. In this study, glutathione in anemia group was significantly reduced compared with the normal group (Table 3), which resulted in glutathione metabolic disorder and reduced the antioxidant activity of the liver, which is consistent with previous antioxidant studies in rats (He et al., 2019). In summary, with the exception of docosahexaenoic acid, other metabolites related to the above liver metabolic pathways in AOS-iron group were restored to normal levels (Table 3). Therefore, we speculate that AOS-iron could restore the status of IDA rats by intervening in the metabolic pathways of pantothenate and CoA biosynthesis, primary bile acid biosynthesis and glutathione metabolism in the liver.

in anemia group as compared to normal group, indicating that glycerophospholipid metabolism were disturbed by IDA. Following supplementation with AOS-iron, the metabolites involved in the glycerophospholipid metabolism were correspondingly changed to normal levels compared with the anemia group, indicating that AOS-iron had a repairing effect on the glycerophospholipid metabolism disorder. This study suggested that in serum the GnRH signaling pathway and the FcγR-mediated phagocytosis metabolic pathway were identified as the major metabolic pathways of IDA. Phospholipid (PA(16:0/16:0)) is the differential metabolite in above both metabolic pathways. PA(16:0/ 16:0) is a basic component of cell membranes (Liu et al., 2017; Testerink & Munnik, 2011). It is not only the precursor of many physiologically active substances, but is also involved in signal transduction and metabolism in cells. The data from this study (Table 2) showed a significant increase in serum PA(16:0/16:0) levels in rats in anemia group, indicating that IDA affected the GnRH signaling pathway and the FcγR-mediated phagocytosis metabolic pathway. Studies have also reported that FcγR-mediated phagocytosis metabolic pathway is associated with anemia (Berney et al., 1992; Yamada et al., 2013). Moreover, regulation of the autophagy metabolic pathway was also significantly different in serum (P < 0.05). Basal autophagy indicates that 1–1.5% of metabolites are degraded in cells each hour. However, autophagy intensity increases with increased stress, such as hunger, metabolic imbalance, hypoxia, oxidative stress, and oncogene activation (Simon, Friis, Tait, & Ryan, 2017). Rockenfeller et al. reported that the abundance of phosphatidylethanolamine positively regulates autophagy (Rockenfeller et al., 2015). In this study we found that PE (18:2(9Z,12Z)/24:1(15Z)) (phosphatidylethanolamine) level in serum was increased in anemia group compared with normal group, which meant that the stress of IDA caused cell autophagy, resulting in metabolic disorders of autophagy regulation. Following supplementation with AOS-iron, the above all metabolic pathways disturbed by IDA were restored to normal levels. The liver is the largest digestive gland in the human body, and is also an important organ for energy storage and metabolic regulation, which maintains the balance between anabolism and catabolism (Hashimoto, 2016; Tarasenko & Mcguire, 2017). In this study, in addition to the above-mentioned pathways associated with lipid metabolism disorders, pantothenate and CoA biosynthesis, primary bile acid biosynthesis and glutathione metabolism were also identified as major metabolic pathways in the liver. Pantothenate and CoA biosynthesis are critical for energy metabolism (Liu et al., 2018), and pantetheine 4′phosphate is the intermediates of CoA biosynthesis (Dupr, Rosei, Bellussi, Grosso, & Cavallini, 2005). Via ATP + pantetheine ⇌ ADP + pantetheine 4′-phosphate, energy storage and release are achieved by the interconversion of ATP and ADP; thus, ensuring the energy supply for various life activities in cells. Metabolic disorders in pantothenate and CoA biosynthesis may result in disruption of energy supply. The data from this study (Table 3) showed that pantetheine 4′phosphate level was significantly decreased in anemia group; thus, disorders in the pantothenate and CoA biosynthesis pathways in IDA rats might affect energy metabolism. Glycocholic acid associated with the primary bile acid biosynthesis pathway is one of the major components of bile acids and is a conjugate of cholic acid with glycine in the liver (Cui et al., 2017). Under normal conditions, the liver can effectively absorb glycocholic acid. However, when lesions or damage develop in liver, the function of absorption and excretion of glycocholic acid is impaired and the glycocholic acid level can increase (Collazos, 1993; Liu, Jiang, & Shen, 2016). In this study we found that the levels of glycocholic acid in liver tissues of IDA rats were significantly increased, which may be related to liver function damage caused by IDA, leading to the accumulation of glycocholic acid in liver due to its slow excretion. Liver is the main site of glutathione synthesis, and glutathione plays an important role in liver biochemical metabolism. The main physiological functions of glutathione are scavenging free radicals, antioxidation, anti-aging and detoxification (Takujiro & Junichi, 2015;

5. Conclusion In this study, a total of 22 endogenous metabolites in serum and liver were identified as potential biomarkers for IDA in rats, and related metabolic pathways involved the biosynthesis of saturated and unsaturated fatty acids, sphingolipid metabolism, glycerophospholipid metabolism, linoleic acid metabolism, FcγR-mediated phagocytosis, pancreatic cancer metabolism, regulation of autophagy, GnRH signaling pathway, fatty acid metabolism, pantothenate and CoA biosynthesis, glutathione metabolism and primary bile acid biosynthesis. AOS-iron restored the major metabolites (adrenic acid, docosahexaenoic acid, eicosadienoic acid, linoleic acid, palmitic acid, stearic acid, cis-8,11,14-Eicosatrienoic acid, SM(d18:0/16:1(9Z)), d-erythroSphinganine, glucosylceramide(d18:1/16:0), d-erythro-Sphingosine-1phosphate, 3-O-Sulfogalactosylceramide (d18:1/18:0), PC(16:1(9Z)/P18:0), PE(18:2(9Z,12Z)/24:1(15Z)), PA(16:0/16:0), lysoPC(18:0), palmitoylcarnitine, docosapentaenoic acid, pantetheine 4′-phosphate, glutathione, glycerophosphocholine, glycocholic acid) in relevant pathways disturbed by IDA to normal levels. This is the first study on the metabolic pathways for IDA and the therapeutic mechanism of AOSiron on IDA rats by serum and liver metabolomics. The identification of potential biomarkers and major biochemical pathways not only provides further valuable evidence for the intervention with AOS-iron in IDA rats, but also contributes to improve IDA therapy strategies and develop new iron supplement in the future. Author contributions Hong He: author make substantial contributions to conception and design, and/or acquisition of data, and/or analysis and interpretation of data, participate in drafting the article or revising it critically for important intellectual content and give final approval of the version to be submitted and any revised version. Fengping An: author make substantial contributions to acquisition of data, participate in drafting the article and give final approval of the version to be submitted. Qun Huang: author make substantial contributions to acquisition of data, participate in drafting the article and give final approval of the version to be submitted. Yuting Kong and Dan He: authors make substantial contributions to acquisition of data, participate in drafting the article and give final approval of the version to be submitted. Lei Chen, author make substantial contributions to conception and design and analysis and interpretation of data, participate in drafting the article or revising it critically for important intellectual contente and give final approval of the version to be submitted and any revised version. Hongbo Song: author make substantial contributions to design, participate in drafting the article or revising it critically for important intellectual contente and give final approval of the version to be submitted. Declaration of Competing Interest The authors declared that there is no conflict of interest. 7

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Acknowledgements

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