Targeted metabolic profiling of wounds in diabetic and nondiabetic mice Ravi F. Sood, MD1; Haiwei Gu, PhD2; Danijel Djukovic, PhD2; Lingli Deng, BS2; Maricar Ga1; Lara A. Muffley, BS1; Daniel Raftery, PhD2,3; Anne M. Hocking, PhD1 1. Department of Surgery, Harborview Research & Training Building, University of Washington, 2. Northwest Metabolomics Research Center, Department of Anesthesiology & Pain Medicine, University of Washington, 3. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
Reprint requests: Anne M. Hocking, PhD, University of Washington, Department of Surgery, Campus Box 359796, 300 9th Avenue, Seattle, Washington 98104. Tel: 1 206 897 5922; Fax: 1 206 897 5343; Email:[email protected] Manuscript received: December 17, 2014 Accepted in final form: April 2, 2015 DOI:10.1111/wrr.12299 This work should be attributed to University of Washington Department of Surgery, Harborview Research & Training Building, Seattle, WA 98104, USA.
ABSTRACT While cellular metabolism is known to regulate a number of key biological processes such as cell growth and proliferation, its role in wound healing is unknown. We hypothesized that cutaneous injury would induce significant metabolic changes and that the impaired wound healing seen in diabetes would be associated with a dysfunctional metabolic response to injury. We used a targeted metabolomics approach to characterize the metabolic profile of uninjured skin and full-thickness wounds at day 7 postinjury in nondiabetic (db/-) and diabetic (db/db) mice. By liquid chromatography mass spectrometry, we identified 129 metabolites among all tissue samples. Principal component analysis demonstrated that uninjured skin and wounds have distinct metabolic profiles and that diabetes alters the metabolic profile of both uninjured skin and wounds. Examining individual metabolites, we identified 62 with a significantly altered response to injury in the diabetic mice, with many of these, including glycine, kynurenate, and OH-phenylpyruvate, implicated in wound healing for the first time. Thus, we report the first comprehensive analysis of wound metabolic profiles, and our results highlight the potential for metabolomics to identify novel biomarkers and therapeutic targets for improved wound healing outcomes.
In the 21st century, metabolism has been rediscovered and redefined. It is no longer considered to operate in isolation of other biological processes, but instead it is now recognized to play a central role in the regulation of cell signaling pathways critical for cell survival, growth, and proliferation.1 Metabolism impacts these signaling pathways not only by maintaining energy homeostasis but also because metabolites serve as substrates for epigenetic modifications of chromatin2 and post-translational modifications important for protein localization and activity.1 Underscoring the interaction between metabolism and cell signaling pathways is the association of distinctive metabolisms with specific cell states. For example, cancer cell metabolism is different from that of normal cells,3 and the metabolic program of quiescent stem cells appears to be distinct from that of dividing stem cells.4 We therefore hypothesize that metabolism and specific metabolites will play a central role in the regulation of cell survival, growth, and proliferation during cutaneous wound repair. The major barrier for advancing research on wound metabolism is our very limited understanding of the wound metabolic profile. We do not know which metabolites are present in the wound, much less their concentrations or functions in wound repair. Previous studies in the field of wound healing have focused on individual metabolites and have been limited by sensitivity and resolution of available tools and databases.5–7 However, recent innovations in C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
nuclear magnetic resonance spectroscopy and mass spectrometry coupled with the development of metabolite databases and rigorous biostatistics have now made it possible to simultaneously analyze hundreds of metabolites in a single biological sample.8 Metabolomics is the term often used for this comprehensive metabolic profiling.8,9 Like the other ’omics, metabolomics is being used to identify biomarkers and novel therapeutic targets. However, it has an important advantage because it measures the net result of gene expression and protein activity.10 It also reflects what is happening in real time as metabolites turn over in seconds or minutes, in contrast to gene expression, which may elicit its final downstream effect hours after transcription. In the field of skin biology, metabolomics approaches have been used in the study of psoriasis,11 epidermolysis bullosa acquisita,12 melanoma,13 low dose ionizing radiation,14 and skin expanders.15 However, to date, metabolomics has not been used to study wound repair. In this study, we measured 129 metabolites using a targeted metabolomics approach to determine the metabolic profile of cutaneous wounds in the db/db mouse model of type 2 diabetes mellitus. Wound healing in the db/db mouse is impaired with delayed wound closure, decreased granulation tissue formation and decreased vascularity.16 Our results show that diabetic and nondiabetic wounds have different metabolic profiles, and 62 metabolites were 423
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identified with an altered response to injury in diabetic wounds.
MATERIALS AND METHODS Murine excisional wound model
The University of Washington’s Institutional Animal Care and Use Committee approved this study (Protocol Number: 4069-05). All surgery was performed under isoflurane anesthesia, and all efforts were made to minimize suffering. Female db/db and db/- (BKS.Cg-Dock7m1/1 Leprdb/ J) mice were purchased from The Jackson Laboratory (Bar Harbor, ME). The excisional wound surgery was performed on 8-week old mice and has been previously described.17,18 In brief, a single 1.5-cm2 square fullthickness wound was made on the mouse dorsum after shaving with clippers and depilating with Nair (Church & Dwight Co., Inc., Ewing, NJ). Each wound was then photographed and covered with the transparent semi-occlusive dressing Tegaderm (3M, St. Paul, MN), using a thin layer of Mastisol (Ferndale Laboratories, Ferndale, MI) to ensure adhesion for 7 days. Buprenex was injected subcutaneously during the preoperative period to minimize pain and an intraperitoneal injection of saline was administered postoperatively to minimize risk of dehydration. After postoperative recovery, animals were housed individually, maintained on a 12-hour light/dark cycle, and allowed ad libitum access to food and water. Wound healing kinetics
Wounds were photographed on days 0 and 7. Digital images were imported into Adobe Photoshop containing the FoveaPro image analysis tool kit plug in (Reindeer Graphics, Ashville, NC). Wound area was measured at each time point. Percent wound closure was calculated as follows: % closure5
day 0 area2day 7 area 3100: day 0 area
The Student’s t test (two-tailed, assuming unequal variances) was used to determine statistical significance, and a p-value less than 0.05 was considered significant. Wound processing
Animals were euthanized, and wounds were collected on day 7 after injury. We chose this time point anticipating that both db/- and db/db wounds would still be open and that the size of the wound would provide enough wound tissue for processing for metabolomics analysis. In addition, we anticipated significant differences in the percent area closed between db/- and db/db wounds at day 7 postinjury, a key consideration given our goal of obtaining metabolic profiles of wounds with different healing kinetics. Wounds were excised with 0.5 cm of surrounding uninjured skin. Uninjured skin samples were those excised on day 0 to generate the wound. Both wound and uninjured skin samples were full thickness. For liquid chromatography mass spectrometry (LC-MS), metabolites were extracted from the wounds and uninjured skin using high424
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performance liquid chromatography (HPLC) grade methanol and water (Sigma-Aldrich Corp., St Louis, MO). Tissue was pulverized using a liquid nitrogen-cooled mortar and pestle and then homogenized using a FastPrep homogenizer with lysing matrix D ceramic beads (both from MP Biomedicals, Santa Ana, CA). After homogenization, 20 mg tissue in 600 lL of 80:20 methanol:water was incubated on dry ice for 30 minutes and then centrifuged at 13,000 rpm for 5 minutes at 4 C. The soluble extract was stored on dry ice and the pellet resuspended in 200 ll of 80:20 methanol:water chilled to 4 C. This suspension was incubated on wet ice for 30 minutes and then spun at 13,000 rpm for 5 minutes at 4 C. This second soluble extract was pooled with the first extract and stored on dry ice. For the final extraction, the pellet was resuspended in 200 lL of 80:20 methanol:water chilled to 4 C, vortexed, sonicated in an ice bath for 15 minutes, and centrifuged at 13,000 rpm for 5 minutes at 4 C. This last soluble extract was combined with the pooled extracts on dry ice. The pooled extracts were dried under vacuum at 30 C and reconstituted in 200 lL of 5 mM ammonium acetate in 40% water/60% acetonitrile 1 0.2% acetic acid, and filtered through 0.45 lm polyvinylidene difluoride (PVDF) filters (Phenomenex, Torrance, CA) prior to LC-MS analysis. Liquid chromatography mass spectrometry
The following reagents were used for LC-MS: deionized/ purified water (provided in-house by a Synergy UV system, EMD Millipore, Billerica, MA), LC-MS grade acetonitrile (Sigma-Aldrich, Saint Louis, MO), LC-MS grade ammonium acetate (Sigma-Aldrich, Saint Louis, MO), and LC-MS grade acetic acid (Sigma-Aldrich, Saint Louis, MO). Each sample was injected twice: 10 lL for analysis using negative ionization mode and 2 lL for analysis using positive ionization mode. Both chromatographic separations were performed in hydrophilic interaction liquid chromatography (HILIC) mode on two SeQuant ZICcHILIC columns (150 3 2.1 mm, 3.0 lm particle size, Merck KGaA, Darmstadt, Germany). The separation was carried out on an Agilent 1260 LC system (Agilent Technologies, Santa Clara, CA). While one column was performing the separation, the other column was reconditioned and prepared for the next injection. The flow rate was 0.300 mL/min, autosampler temperature was kept at 4 C, the column compartment was set at 40 C, and total separation time for both ionization modes was 20 minutes. The mobile phase was composed of Solvents A (5 mM ammonium acetate in 90%H2O/10% acetonitrile 1 0.2% acetic acid) and B (5 mM ammonium acetate in 90%acetonitrile/10% H2O 1 0.2% acetic acid). The LC gradient was as follows: 0–2 minutes, 75% Solvent B constant; 2–5 minutes, 75–30% Solvent B; 5–9 minutes, 30% Solvent B constant; 9–11 minutes, 30–75% Solvent B; 11– 20 minutes, 75% Solvent B constant. After the chromatographic separation, MS ionization and data acquisition were performed using an AB Sciex QTrap 5500 mass spectrometer (AB Sciex, Toronto, ON, Canada) equipped with an electrospray ionization source. The instrument was controlled by Analyst 1.5 software (AB Sciex, Toronto, ON, Canada). Targeted data acquisition was performed in multiple-reaction-monitoring C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
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Figure 2. PCA scores plots based on 129 metabolites comparing all samples from both db/- and db/db mice.
for db/- uninjured skin; N 5 5 for db/- day 7 wounds; N 5 8 for db/db uninjured skin; and N 5 4 for db/db day 7 wounds.
Figure 1. Wound closure kinetics for db/- and db/db mice. (A) Representative photos of wounds on days 0 and 7. (B) Image analysis of the wound photos was used to determine the percent wound closure as [(Area of day 0 wound 2 Area of day 7 wound)/Area of day 0 wound] 3 100. *Indicates that p-value < 0.05 when comparing db/db to db/- wounds.
(MRM) mode. We monitored 98 and 59 MRM transitions in negative and positive mode, respectively (157 transitions total). The extracted MRM peaks were integrated using MultiQuant 2.1 software (AB Sciex). Statistical analyses of metabolite levels
Principal component analysis (PCA) was carried out after autoscaling using Stata 13.0 (StataCorp, College Station, TX). All other analyses were performed using R version 3.0.2. Sample sizes for each group were as follows: N 5 8 C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
Figure 3. PCA scores plots based on 129 metabolites comparing wounds and uninjured skin. (A) Nondiabetic samples; (B) diabetic samples.
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The effects of diabetes and wounding on metabolite levels were analyzed by fitting the following linear regression model for each metabolite: E½Am jW; D 5 b0;m 1 b1;m W 1 b2;m D 1 b3;m W D;
where E½Am jW; D is the estimated mean abundance A of metabolite m, W is a dummy variable indicating whether the sample was from a wound (vs. uninjured skin), D is a dummy variable indicating the mouse genotype (db/db vs. db/-), and bi,m are regression coefficients for metabolite m. For each comparison of interest, statistical inference was based on a partial F-test of the relevant coefficient estimate(s): b2,m for db/db vs. db/- uninjured skin; b1,m for db/- wound vs. db/- uninjured skin; b1,m 1 b3,m for db/db wound vs. db/db uninjured skin; and b3,m to test whether diabetes modified the response to wounding. To account for multiple testing, p-values were adjusted to control the false-discovery rate by the method of Benjamini and Hochberg19; p-values less than 0.05 were considered to be statistically significant.
RESULTS The metabolic profile of a cutaneous wound is distinct from that of uninjured skin
Analysis of wound closure kinetics confirmed previous studies reporting that wound closure is delayed in the db/ db mouse.16 In this study at day 7, the average percent wound closure for the db/db mice was 15.3 6 3.9% compared to 51.6 6 7.1% in the db/- mice (Figure 1; p < 0.001). Of the 154 metabolites targeted using LC-MS, 129 were detected among all tissues analyzed (Table S1). Notably, none of the 129 metabolites detected was specific to uninjured skin or day 7 wounds. There was also no
Figure 4. PCA scores plots based on 129 metabolites comparing diabetic and nondiabetic samples. (A) Uninjured skin; (B) wounds.
Table 1. Top 10 metabolites up-regulated in response to injury Nondiabetic (db/-) Metabolite Uracil‡ cGMP Orotate Trytophan‡ dCMP Cytidine‡ Cytosine‡ Succinate/methylmalonate§ Reduced glutathione Urate
Diabetic (db/db) †
Fold change*
p-value
68.3 20.8 10.8 5.4 5.3 4.8 4.7 4.6 4.5 4.4
1.3 3 1028 2.6 3 1024 3.3 3 1025 8.0 3 10210 2.3 3 1023 2.1 3 1028 2.5 3 1028 1.3 3 1028 1.8 3 1022 3.0 3 1028
Metabolite
Fold change*
p-value†
29.4 14.9 5.5 4.8 4.4 4.2 4.2 4.1 4.1 4.0
8.2 3 1024 7.6 3 1027 5.1 3 1023 1.4 3 1022 8.9 3 1023 5.0 3 1024 5.6 3 1024 1.2 3 1023 1.2 3 1022 5.9 3 1023
Uracil‡ Linolenic acid‡ Xanthosine Sucrose Uridine Cytosine‡ Cytidine‡ GDP Xanthine Malonic acid/3HBA§
*Metabolites ranked by mean fold change (wound/uninjured). † Based on linear regression and adjusted to control the false discovery rate. ‡ Significant difference in response to injury between diabetic and nondiabetic wounds; see Table 5. § Metabolites not separated by LC-MS because they have the same precursor and fragment ions.
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Table 2. Metabolites down-regulated significantly in response to injury Nondiabetic (db/-) Metabolite Epinephrine 4-Pyridoxic acid Creatine
Diabetic (db/db) †
Fold change*
p-value
0.7 0.9 0.9
2.2 3 1022 1.5 3 1022 3.8 3 1022
Metabolite Adenosine Cystathionine Serine Epinephrine 4-Pyridoxic acid
Fold change*
p-value†
0.04 0.4 0.5 0.7 0.9
1.4 3 1022 9.1 3 1023 3.3 3 1023 2.3 3 1022 1.3 3 1022
*Metabolites ranked by mean fold change (wound/uninjured). † Based on linear regression and adjusted to control the false discovery rate.
specificity to either the db/db or db/- tissue. To identify groupings of samples, we performed PCA, an unsupervised multivariate analysis that assumes no prior knowledge of
the dataset. Each point on a PCA scores plot represents a single tissue sample. Points that cluster together have similar metabolic profiles whereas points that are far apart
Figure 5. Metabolites with significant response to injury in the nondiabetic but not the diabetic wounds. (A) Arginine (nondiabetic response to injury: fold change 5 1.5; p 5 3.8 3 1022); (B) gamma-aminobutyrate (nondiabetic response to injury: fold change 5 1.8; p 5 2.8 3 1024); (C) glycine (nondiabetic response to injury: fold change 5 1.5; p 5 3.9 3 1023); and (D) kynurenate (nondiabetic response to injury: fold change 5 2.0; p 5 2.0 3 1023). Fold change 5 wound/uninjured. *Indicates that adjusted p-value < 0.05 for nondiabetic wounds compared to nondiabetic uninjured skin.
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Figure 6. Metabolites with significant response to injury in the diabetic but not the nondiabetic wounds. (A) Carnitine (diabetic response to injury: fold change 5 2.1; p 5 1.8 3 1024); (B) glucose (diabetic response to injury: fold change 5 1.9; p 5 3.0 3 1024); (C) 3-nitrotyrosine (diabetic response to injury: fold change 5 1.5; p 5 1.3 3 1022); and (D) OH-phenylpyruvate (diabetic response to injury: fold change 5 1.5; p 5 7.9 3 1023). Fold change 5 wound/uninjured. *Indicates that adjusted p-value < 0.05 for diabetic wounds compared to diabetic uninjured skin.
have different profiles.20 Using the 129 detected metabolites, a PCA scores plot was generated for all samples (Figure 2). The first two principal components accounted for 67.6% of the total variance, which suggests that wounding was largely responsible for the observed metabolic variability instead of inherent variation between animals.21 The PCA scores plot showed clear separation of uninjured skin from wounds. Similar results were observed when individual PCA scores plots were generated for the db/- (Figure 3A) and db/db samples (Figure 3B). Overall, these data suggest that the metabolic profile of a wound is different from the metabolic profile of uninjured skin in both nondiabetic and diabetic mice. Diabetes alters the metabolic profile of both uninjured skin and wounds
We next determined whether diabetes alters the metabolic profile of either uninjured skin or wounds. Similar to the 428
PCA comparing uninjured skin and wounds, 129 metabolites were again used to generate the PCA scores plots. For uninjured skin, the db/db samples separated from the db/samples (Figure 4A). Similarly, the db/db wound grouped separately from the db/-wounds (Figure 4B). These results suggest that in this murine model, diabetic uninjured skin has a different metabolic profile than that of nondiabetic uninjured skin and diabetic wounds have a metabolic profile distinct from that of nondiabetic wounds. The metabolite response to injury is impaired in diabetic wounds
Individual metabolite responses to injury were determined with p-values adjusted to control for multiple testing.19 In the db/- mice, 88 of the 129 detected metabolites had a significant response to injury with 85 up-regulated and 3 down-regulated (Table S2). In the db/db mice, 81 metabolites had a significant response to injury with 76 upC 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
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Figure 7. Metabolites with different magnitude of response to injury between nondiabetic and diabetic wounds. (A) Histidine (nondiabetic response to injury: fold change 5 3.1; diabetic response to injury: fold change 5 1.6; p 5 1.8 3 1023); (B) 2aminoadipate (nondiabetic response to injury: fold change 5 4.1; diabetic response to injury: fold change 5 2.8; p 5 9.4 3 1023); (C) methylsuccinate (nondiabetic response to injury: fold change 5 3.7; diabetic response to injury: fold change 5 2.2; p 5 1.8 3 1023); and (D) linolenic acid (nondiabetic response to injury: fold change 5 4.0; diabetic response to injury: fold change 5 14.9; p 5 1.0 3 1022). Fold change 5 wound/uninjured. *Adjusted p-value < 0.05 for nondiabetic wounds compared to nondiabetic uninjured skin or for diabetic wounds compared to diabetic uninjured skin. †Adjusted p-value < 0.05 for difference in magnitude of change in response to wounding between nondiabetic and diabetic samples.
regulated and 5 down-regulated (Table S3). The top 10 metabolites up-regulated with the greatest fold change in the db/- and db/db wounds are listed in Table 1. Of note, six metabolites in Table 1 had significant differences in the magnitude of response to injury observed between nondiabetic and diabetic wounds. Uracil, cGMP, tryptophan, and cytidine had a greater fold changes in the nondiabetic wounds, whereas linolenic acid and cytosine had a greater fold changes in the diabetic wounds (Tables 1 and 5 and Table S2). Metabolites down-regulated in response to injury are listed in Table 2. Only 2 metabolites, epinephrine and 4-pyridoxic acid were down-regulated significantly in both diabetic and nondiabetic wounds (Table 2). Interestingly, there were 62 unique metabolites with differential responses to injury between nondiabetic and diaC 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
betic wounds. These metabolites met at least one of the following three criteria: (1) significant response to injury in nondiabetic but not in diabetic wounds (25 metabolites; Table 3); (2) significant response to injury in diabetic but not nondiabetic wounds (18 metabolites; Table 4); and/or (3) significant difference in magnitude of response to injury between diabetic and nondiabetic wounds (32 metabolites; Table 5). Arginine, gamma-aminobutyrate, glycine, and kynurenate are examples of metabolites that had a significant response to injury in nondiabetic wounds but not in diabetic wounds (Figure 5), whereas carnitine, glucose, 3-nitrotyrosine, and OH-phenylpyruvate are examples of metabolites that had a significant response to injury in diabetic wounds but not in nondiabetic wounds (Figure 6). The majority of metabolites with differences in the 429
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Table 3. Metabolites with significant response to injury in nondiabetic but not in diabetic wounds Metabolite 1-Methylguanosine Aconitate Adenlyosuccinate Alpha-ketoglutaric acid Arginine cGMP Creatine Cystamine D-Leucic acid dTMP Gamma-aminobutyrate Glycine Guanosine Histidine Hydroxyproline/Aminolevulinate‡ Hypoxanthine Inosine Kynurenate N-Acetylglycine Ornithine Orotate Proline Pyroglutamic acid Shikimic acid Trimethylamine-N-oxide
Fold change* 3.3 3.5 2.8 1.7 1.5 20.8 0.9 2.1 1.6 1.2 1.8 1.5 2.6 3.1 1.6 2.6 1.9 2.0 4.0 1.6 10.8 1.5 1.8 3.3 2.8
p-value†
uninjured
skin
(fold
change 5 0.6;
DISCUSSION 27
6.2 3 10 1.7 3 1025 1.3 3 1027 3.4 3 1024 3.8 3 1022 2.6 3 1024 3.8 3 1022 1.8 3 1023 1.2 3 1022 1.1 3 1024 2.8 3 1024 3.9 3 1023 2.0 3 1022 2.1 3 1028 9.7 3 1025 1.3 3 1022 3.3 3 1022 2.0 3 1023 2.4 3 1025 3.6 3 1022 3.3 3 1025 3.6 3 1023 1.2 3 1024 1.9 3 1025 1.2 3 1025
*Mean fold change (wound/uninjured) in db/- mice. † Based on linear regression and adjusted to control the false discovery rate; in db/db mice, the adjusted p-values for these metabolites were greater than 0.05. ‡ Metabolites not separated by LC-MS because they have the same precursor and fragment ions.
magnitude of response to injury between diabetic and nondiabetic wounds displayed a blunted response in diabetic wounds (Table 5). Histidine, 2-aminoadipate, and methylsuccinate are examples of metabolites with a blunted response to injury in the diabetic wounds (Figure 7A–C). Compared to the nondiabetic wounds, the magnitude of response was significantly less in diabetic wounds. In contrast, the magnitude of response for linolenic acid in diabetic wounds was greater than in nondiabetic wounds (Figure 7D). Of the 129 metabolites detected, only kynurenate and hydroxyproline/aminolevulinate had significantly different levels in uninjured skin between nondiabetic and diabetic animals. There was twofold more kynurenate measured in the diabetic uninjured skin compared to the nondiabetic uninjured skin (fold change 5 1.97; p 5 0.028). In contrast, hydroxyproline/aminolevulinate levels were significantly reduced in diabetic uninjured skin compared to 430
the nondiabetic p 5 0.028).
Targeted metabolic profiling is a powerful approach for defining the wound metabolome. It has the potential to create opportunities for translational research as has happened in the field of oncology.22,23 For example, metabolic profiling has the potential to identify novel biomarkers for wound diagnosis and prognosis. Metabolomics may also identify new therapeutic targets for treatment of wounds with delayed healing. In this study, targeted metabolomics has provided detailed information about the response to injury of 129 metabolites in murine cutaneous wounds. To our knowledge, this is the first comprehensive analysis of the metabolic profile of wounds. Importantly, it has allowed the identification of metabolites that discriminate the metabolic signature of a diabetic wound from a nondiabetic wound in the db/db mouse model of type 2 diabetes mellitus. Our metabolomics data are consistent with welldocumented impaired responses to injury in diabetic wounds including reduced collagen synthesis and reduced nitric oxide (NO) production.24 Known indices of collagen synthesis, proline and hydroxyproline, had no response to injury in diabetic wounds (Table 3), which may contribute Table 4. Metabolites with significant response to injury in diabetic but not nondiabetic wounds Metabolite 3-Nitrotyrosine Adenosine Adipic acid Carnitine Cystathionine D-GA3P/DHAP‡ F16BP/F26BP/G16BP‡ Galactose Glucose Glyceraldehyde Glycerol-3-P GTP Inositol Margaric acid OH-phenylpyruvate Pentothenate Ribose-5-phosphate Serine
Fold change* 1.5 0.04 2.1 2.1 0.4 1.7 1.8 2.2 1.9 1.8 1.6 2.8 2.3 1.7 1.5 3.6 2.5 0.5
p-value† 1.3 3 1022 1.4 3 1022 9.3 3 1023 1.8 3 1024 9.1 3 1023 3.1 3 1022 3.3 3 1022 5.7 3 1023 3.0 3 1024 5.6 3 1024 2.5 3 1022 1.9 3 1022 7.2 3 1023 1.4 3 1023 7.9 3 1023 2.6 3 1023 7.3 3 1023 3.3 3 1023
*Mean fold change (wound/uninjured) in db/db mice. † Based on linear regression and adjusted to control the false discovery rate; in db/- mice, the adjusted p-values for these metabolites were greater than 0.05. ‡ Metabolites not separated by LC-MS because they have the same precursor and fragment ions. C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
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Table 5. Metabolites with significant differences in the magnitude of response to injury between diabetic and nondiabetic mice Fold change* Metabolite
Nondiabetic (db/-)
Diabetic (db/db)
1-Methylguanosine 2-Aminoadipate 3-Nitrotyrosine Aconitate Adenylosuccinate Allantoin Asparagine Aspartic acid Carnitine cGMP Cystine Cytidine Cytosine dTMP Glucoronate Glucose Histidine Homocysteine Linolenic acid Lysine Methionine Methylsuccinate N-Acetylglycine OH-phenylpyruvate Phenylalanine Shikimic acid Sorbitol Threonine Trimethylamine-N-oxide Tryptophan Tyrosine Uracil
3.3 4.1 0.8‡ 3.5 2.8 2.4 3.3 2.3 1.3‡ 20.8 3.8 4.8 4.7 1.2 2.0 1.2‡ 3.1 3.3 4.0 2.8 3.2 3.7 4.0 0.9‡ 4.3 3.3 3.4 2.8 2.8 5.4 3.4 68.3
2.0§ 2.8 1.5 2.5§ 1.8§ 2.2 2.4 1.5 2.1 2.2§ 2.2 4.2 4.2 0.9§ 1.4 1.9 1.6 2.6 14.9 2.4 2.3 2.2 2.0§ 1.5 2.5 1.6§ 2.3 2.0 1.6§ 3.5 2.3 29.4
p-value† 8.8 3 1023 9.4 3 1023 4.0 3 1022 4.4 3 1022 2.3 3 1023 1.9 3 1022 6.5 3 1023 1.8 3 1023 3.6 3 1022 4.0 3 1022 9.4 3 1023 2.6 3 1022 3.5 3 1022 6.2 3 1023 4.9 3 1022 3.5 3 1022 1.8 3 1023 3.5 3 1022 1.1 3 1022 6.5 3 1023 1.7 3 1022 1.8 3 1023 4.9 3 1022 4.0 3 1022 1.8 3 1023 1.7 3 1022 6.2 3 1023 1.8 3 1023 8.8 3 1023 1.8 3 1023 6.5 3 1023 1.0 3 1022
*Mean fold change (wound/uninjured). † Test of interaction between sample type (wound vs. uninjured skin) and mouse genotype (db/db vs. db/-), with p-values adjusted to control the false discovery rate. ‡ Nonsignificant response to injury (adjusted p-value>0.05) in db/- mice. § Nonsignificant response to injury (adjusted p-value >0.05) in db/db mice. # Metabolites not separated by LC-MS because they have the same precursor and fragment ions.
to the low collagen content detected in diabetic wounds.25,26 Similarly, the NO precursor arginine failed to respond to injury in diabetic wounds (Table 3), which supports previous studies demonstrating reduced NO synthesis in diabetic wounds.27 It may also explain why arginine supplementation improves wound healing in diabetes.7 The metabolomics data also support studies suggesting that oxidative stress is elevated in diabetic wounds.24 Evidence of C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
oxidative stress is provided by the novel finding that 3nitrotyrosine, a marker of oxidative damage,28 was elevated specifically in diabetic wounds. Our lactate data are also consistent with extensive literature reporting that lactate accumulates in wounds5,29,30 (Tables S2 and S3). Collectively these observations validate the use of metabolomics to define metabolic signatures of diabetic wounds. 431
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Novel metabolic responses to injury were identified for gamma-aminobutyrate (GABA), glucose, glycine, kynurenate, and OH-phenylpyruvate. Importantly, all of these responses were dysregulated in a diabetic wound model. To our knowledge, only GABA, kynurenate, and glucose have been studied in the context of cutaneous wound repair. The neurotransmitter GABA has been reported to regulate dermal fibroblast synthesis of hyaluronic acid in addition to enhancing dermal fibroblast tolerance to oxidative stress.31 Topical administration of GABA also accelerates wound closure.32 Two recent studies suggest that kynurenate may play a role in both inflammation and fibroproliferation during wound repair. Kynurenine, a precursor of kynurenate, had anti-inflammatory activity in mouse excisional wounds and antifibrotic effects in rabbit model of hypertrophic scarring.33,34 Despite intense interest in the contribution of hyperglycemia to the impaired healing of diabetic ulcers, there have been only a few studies investigating glucose levels in wounds, and to our knowledge all of these studies have analyzed glucose in wound fluid rather than wound tissue.35,36 This present study is the first to report that glucose levels are not significantly different between uninjured diabetic and nondiabetic murine skin, which suggests that glucose concentrations in diabetic skin do not reflect systemic hyperglycemia. This is supported by studies showing a similar lack of correlation in glucose levels between wound fluid and serum in humans35 and diabetic pigs.36 In addition, our observation that glucose levels increased significantly in response to injury specifically in diabetic murine wounds is novel. Further investigation is needed to define the temporal and spatial regulation of glucose in wounds during healing. Little is known about the role of glycine or OH-phenylpyruvate during wound repair. The glycine response to injury is especially intriguing, as recent metabolic profiling of cancer cells has correlated this amino acid with increased cell proliferation.37 It remains to be determined whether glycine is required for cell proliferation during wound repair and whether a lack of glycine has a detrimental effect on cell proliferation in diabetic wounds. Further work is also needed to determine the biological relevance of down-regulated metabolites and metabolites that had significant differences in the magnitude of response to injury between diabetic and nondiabetic wounds. Overall, these data underscore the power of metabolomics for both hypothesis generation and identification of novel biomarkers predicting wound outcome. This was a pilot study demonstrating the power and feasibility of metabolomics to discern differences between different wound phenotypes. Further studies are needed to determine the metabolic profile of wounds over time. Specifically, metabolic profiling of wounds from different times during wound repair may provide insight into whether different metabolic programs are associated with different phases of wound repair such as hemostasis, inflammation, epithelialization, and tissue remodeling. Accordingly, it may be possible that the metabolic profile differences we observed between the diabetic and nondiabetic wounds may be due to these wounds being in different phases of the wound healing process. For any metabolomics study, it is also important to note that metabolite levels are a function of both production and consumption.38 Consequently, metabolic flux experiments 432
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are necessary to determine the activity of specific metabolic pathways, as it is challenging to make conclusions about pathway activity based on metabolite level alone. Despite these limitations, our data show robust metabolic differences both in response to injury and between diabetic and nondiabetic wounds, which underscores the need for an in-depth investigation of wound metabolism. Another limitation of this study is that the metabolite profile of a rodent wound is likely different from that of a human wound due to species-specific differences in wound healing biology and skin anatomy.39 In this first study of wound metabolomics, we chose to use an experimental mouse model rather than human samples because we wanted to precisely control for sample-specific factors such as age, genetic background, wound location, wound size, wound depth, and time since injury in order to minimize biological variability between samples and thus maximize statistical power to detect differences in metabolite levels between experimental groups. Our results from the murine model establish the feasibility of wound metabolic profiling and serve as a reference for future studies using human samples, which will be ultimately required for clinical translation of wound-healing metabolomics research. In summary, we have used targeted metabolomics to define the metabolic profile of cutaneous wounds in the db/db murine model of type 2 diabetes. Diabetic and nondiabetic murine wounds have distinct metabolic profiles with 62 metabolites having an impaired response to injury in diabetic wounds. Application of metabolomics to the field of wound repair may identify both new biomarkers of wound outcome and novel therapeutic targets for wounds with delayed healing.
ACKNOWLEDGMENTS Source of Funding: This work was supported by University of Washington’s Department of Surgery Research Reinvestment Fund (AMH). Conflict of Interest: Daniel Raftery holds equity and an executive position at Matrix-Bio, Inc. All other authors have no conflict of interest.
REFERENCES 1. Wellen KE, Thompson CB. A two-way street: reciprocal regulation of metabolism and signalling. Nat Rev Mol Cell Biol 2012; 13: 270–6. 2. Kaelin WG Jr, McKnight SL. Influence of metabolism on epigenetics and disease. Cell 2013; 153: 56–69. 3. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 2009; 324: 1029–33. 4. Takubo K, Nagamatsu G, Kobayashi CI, Nakamura-Ishizu A, Kobayashi H, Ikeda E, et al. Regulation of glycolysis by Pdk functions as a metabolic checkpoint for cell cycle quiescence in hematopoietic stem cells. Cell Stem Cell 2013; 12: 49–61. 5. Hunt TK, Conolly WB, Aronson SB, Goldstein P. Anaerobic metabolism and wound healing: an hypothesis for the initiation and cessation of collagen synthesis in wounds. Am J Surg 1978; 135: 328–32. 6. Shi HP, Most D, Efron DT, Witte MB, Barbul A. Supplemental L-arginine enhances wound healing in diabetic rats. Wound Repair Regen 2003; 11: 198–203. C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
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7. Witte MB, Thornton FJ, Tantry U, Barbul A. L-Arginine supplementation enhances diabetic wound healing: involvement of the nitric oxide synthase and arginase pathways. Metabolism 2002; 51: 1269–73. 8. Gowda GA, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics. Expert Rev Mol Diagn 2008; 8: 617–33. 9. Kinross JM, Holmes E, Darzi AW, Nicholson JK. Metabolic phenotyping for monitoring surgical patients. Lancet 2011; 377: 1817–19. 10. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB. Metabolomics applied to diabetes research: moving from information to knowledge. Diabetes 2009; 58: 2429–43. 11. Sitter B, Johnsson MK, Halgunset J, Bathen TF. Metabolic changes in psoriatic skin under topical corticosteroid treatment. BMC Dermatol 2013; 13: 8. 12. Schonig S, Recke A, Hirose M, Ludwig RJ, Seeger K. Metabolite analysis distinguishes between mice with epidermolysis bullosa acquisita and healthy mice. Orphanet J Rare Dis 2013; 8: 93. 13. Abaffy T, Moller MG, Riemer DD, Milikowski C, Defazio RA. Comparative analysis of volatile metabolomics signals from melanoma and benign skin: a pilot study. Metabolomics 2013; 9: 998–1008. 14. Hu ZP, Kim YM, Sowa MB, Robinson RJ, Gao X, Metz TO, et al. Metabolomic response of human skin tissue to low dose ionizing radiation. Mol Biosyst 2012; 8: 1979–86. 15. Lutz NW, Confort-Gouny S, Casanova D, Andrac-Meyer L, Magalon G, Cozzone PJ. Conditions of wound healing and cutaneous growth affect metabolic performance of skin following plastic surgery. Wound Repair Regen 2007; 15: 491–6. 16. Michaels JT, Churgin SS, Blechman KM, Greives MR, Aarabi S, Galiano RD, et al. db/db mice exhibit severe wound-healing impairments compared with other murine diabetic strains in a silicone-splinted excisional wound model. Wound Repair Regen 2007; 15: 665–70. 17. Fathke C, Wilson L, Hutter J, Kapoor V, Smith A, Hocking A, et al. Contribution of bone marrow-derived cells to skin: collagen deposition and wound repair. Stem Cells 2004; 22: 812–22. 18. Muffley LA, Zhu KQ, Engrav LH, Gibran NS, Hocking AM. Spatial and temporal localization of the melanocortin 1 receptor and its ligand alpha-melanocyte-stimulating hormone during cutaneous wound repair. J Histochem Cytochem 2011; 59: 278–88. 19. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995: 289–300. 20. Smolinska A, Blanchet L, Buydens LM, Wijmenga SS. NMR and pattern recognition methods in metabolomics: from data acquisition to biomarker discovery: a review. Anal Chim Acta 2012; 750: 82–97. 21. Kinross JM, Alkhamesi N, Barton RH, Silk DB, Yap IK, Darzi AW, et al. Global metabolic phenotyping in an experimental laparotomy model of surgical trauma. J Proteome Res 2011; 10: 277–87. 22. Gu H, Gowda GA, Raftery D. Metabolic profiling: are we en route to better diagnostic tests for cancer? Future Oncol 2012; 8: 1207–10. 23. Hiller K, Metallo CM. Profiling metabolic networks to study cancer metabolism. Curr Opin Biotechnol 2013; 24: 60–8.
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24. Blakytny R, Jude E. The molecular biology of chronic wounds and delayed healing in diabetes. Diabet Med 2006; 23: 594–608. 25. Caskey RC, Zgheib C, Morris M, Allukian M, DorsettMartin W, Xu J, et al. Dysregulation of collagen production in diabetes following recurrent skin injury: contribution to the development of a chronic wound. Wound Repair Regen 2014; 22: 515–20. 26. Goodson WH 3rd, Hunt TK. Wound collagen accumulation in obese hyperglycemic mice. Diabetes 1986; 35: 491–5. 27. Schaffer MR, Tantry U, Efron PA, Ahrendt GM, Thornto FJ, Barbul A. Diabetes-impaired healing and reduced wound nitric oxide synthesis: a possible pathophysiologic correlation. Surgery 1997; 121: 513–9. 28. Murray J, Oquendo CE, Willis JH, Marusich MF, Capaldi RA. Monitoring oxidative and nitrative modification of cellular proteins; a paradigm for identifying key disease related markers of oxidative stress. Adv Drug Deliv Rev 2008; 60: 1497–1503. 29. Hehenberger K, Heilborn JD, Brismar K, Hansson A. Inhibited proliferation of fibroblasts derived from chronic diabetic wounds and normal dermal fibroblasts treated with high glucose is associated with increased formation of l-lactate. Wound Repair Regen 1998; 6: 135–41. 30. Britland S, Ross-Smith O, Jamil H, Smith AG, Vowden K, Vowden P. The lactate conundrum in wound healing: clinical and experimental findings indicate the requirement for a rapid point-of-care diagnostic. Biotechnol Prog 2012; 28: 917–24. 31. Ito K, Tanaka K, Nishibe Y, Hasegawa J, Ueno H. GABAsynthesizing enzyme, GAD67, from dermal fibroblasts: evidence for a new skin function. Biochim Biophys Acta 2007; 1770: 291–6. 32. Han D, Kim HY, Lee HJ, Shim I, Hahm DH. Wound healing activity of gamma-aminobutyric acid (GABA) in rats. J Microbiol Biotechnol 2007; 17: 1661–9. 33. Li Y, Kilani RT, Rahmani-Neishaboor E, Jalili RB, Ghahary A. Kynurenine increases matrix metalloproteinase1 and -3 expression in cultured dermal fibroblasts and improves scarring in vivo. J Invest Dermatol 2014; 134: 643–50. 34. Salimi Elizei S, Poormasjedi-Meibod MS, Baradar Jalili R, Ghahary A. Effects of kynurenine on CD31 and macrophages in wound healing. Wound Repair Regen 2014. Dec 22 [Epub ahead of print]. 35. Trengove NJ, Langton SR, Stacey MC. Biochemical analysis of wound fluid from nonhealing and healing chronic leg ulcers. Wound Repair Regen 1996; 4: 234–9. 36. Velander P, Theopold C, Hirsch T, Bleiziffer O, Zuhaili B, Fossum M, et al. Impaired wound healing in an acute diabetic pig model and the effects of local hyperglycemia. Wound Repair Regen 2008; 16: 288–93. 37. Jain M, Nilsson R, Sharma S, Madhusudhan N, Kitami T, Souza AL, et al. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 2012; 336: 1040–4. 38. Metallo CM, Vander Heiden MG. Understanding metabolic regulation and its influence on cell physiology. Mol Cell 2013; 49: 388–98. 39. Wong VW, Sorkin M, Glotzbach JP, Longaker MT, Gurtner GC. Surgical approaches to create murine models of human wound healing. J Biomed Biotechnol 2011; 2011: 969618.
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Supporting Information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site Table S1. Metabolites (with relative peak areas) detected in uninjured skin and wounds from db/db and db/- mice. Table S2. Metabolites regulated in response to injury in db/- mice. Metabolites with p-value less than or equal
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to 0.05 ranked by fold change. 1Mean fold change (wound/ uninjured). 2Based on linear regression and adjusted to control the false discovery rate. Table S3. Metabolites regulated in response to injury in db/db mice. Metabolites with adjusted p-value less than or equal to 0.05 ranked by fold change. 1Mean fold change (wound/uninjured). 2Based on linear regression and adjusted to control the false discovery rate.
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Reprint requests: Anne M. Hocking, PhD, University of Washington, Department of Surgery, Campus Box 359796, 300 9th Avenue, Seattle, Washington 98104. Tel: 1 206 897 5922; Fax: 1 206 897 5343; Email:[email protected] Manuscript received: December 17, 2014 Accepted in final form: April 2, 2015 DOI:10.1111/wrr.12299 This work should be attributed to University of Washington Department of Surgery, Harborview Research & Training Building, Seattle, WA 98104, USA.
ABSTRACT While cellular metabolism is known to regulate a number of key biological processes such as cell growth and proliferation, its role in wound healing is unknown. We hypothesized that cutaneous injury would induce significant metabolic changes and that the impaired wound healing seen in diabetes would be associated with a dysfunctional metabolic response to injury. We used a targeted metabolomics approach to characterize the metabolic profile of uninjured skin and full-thickness wounds at day 7 postinjury in nondiabetic (db/-) and diabetic (db/db) mice. By liquid chromatography mass spectrometry, we identified 129 metabolites among all tissue samples. Principal component analysis demonstrated that uninjured skin and wounds have distinct metabolic profiles and that diabetes alters the metabolic profile of both uninjured skin and wounds. Examining individual metabolites, we identified 62 with a significantly altered response to injury in the diabetic mice, with many of these, including glycine, kynurenate, and OH-phenylpyruvate, implicated in wound healing for the first time. Thus, we report the first comprehensive analysis of wound metabolic profiles, and our results highlight the potential for metabolomics to identify novel biomarkers and therapeutic targets for improved wound healing outcomes.
In the 21st century, metabolism has been rediscovered and redefined. It is no longer considered to operate in isolation of other biological processes, but instead it is now recognized to play a central role in the regulation of cell signaling pathways critical for cell survival, growth, and proliferation.1 Metabolism impacts these signaling pathways not only by maintaining energy homeostasis but also because metabolites serve as substrates for epigenetic modifications of chromatin2 and post-translational modifications important for protein localization and activity.1 Underscoring the interaction between metabolism and cell signaling pathways is the association of distinctive metabolisms with specific cell states. For example, cancer cell metabolism is different from that of normal cells,3 and the metabolic program of quiescent stem cells appears to be distinct from that of dividing stem cells.4 We therefore hypothesize that metabolism and specific metabolites will play a central role in the regulation of cell survival, growth, and proliferation during cutaneous wound repair. The major barrier for advancing research on wound metabolism is our very limited understanding of the wound metabolic profile. We do not know which metabolites are present in the wound, much less their concentrations or functions in wound repair. Previous studies in the field of wound healing have focused on individual metabolites and have been limited by sensitivity and resolution of available tools and databases.5–7 However, recent innovations in C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
nuclear magnetic resonance spectroscopy and mass spectrometry coupled with the development of metabolite databases and rigorous biostatistics have now made it possible to simultaneously analyze hundreds of metabolites in a single biological sample.8 Metabolomics is the term often used for this comprehensive metabolic profiling.8,9 Like the other ’omics, metabolomics is being used to identify biomarkers and novel therapeutic targets. However, it has an important advantage because it measures the net result of gene expression and protein activity.10 It also reflects what is happening in real time as metabolites turn over in seconds or minutes, in contrast to gene expression, which may elicit its final downstream effect hours after transcription. In the field of skin biology, metabolomics approaches have been used in the study of psoriasis,11 epidermolysis bullosa acquisita,12 melanoma,13 low dose ionizing radiation,14 and skin expanders.15 However, to date, metabolomics has not been used to study wound repair. In this study, we measured 129 metabolites using a targeted metabolomics approach to determine the metabolic profile of cutaneous wounds in the db/db mouse model of type 2 diabetes mellitus. Wound healing in the db/db mouse is impaired with delayed wound closure, decreased granulation tissue formation and decreased vascularity.16 Our results show that diabetic and nondiabetic wounds have different metabolic profiles, and 62 metabolites were 423
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identified with an altered response to injury in diabetic wounds.
MATERIALS AND METHODS Murine excisional wound model
The University of Washington’s Institutional Animal Care and Use Committee approved this study (Protocol Number: 4069-05). All surgery was performed under isoflurane anesthesia, and all efforts were made to minimize suffering. Female db/db and db/- (BKS.Cg-Dock7m1/1 Leprdb/ J) mice were purchased from The Jackson Laboratory (Bar Harbor, ME). The excisional wound surgery was performed on 8-week old mice and has been previously described.17,18 In brief, a single 1.5-cm2 square fullthickness wound was made on the mouse dorsum after shaving with clippers and depilating with Nair (Church & Dwight Co., Inc., Ewing, NJ). Each wound was then photographed and covered with the transparent semi-occlusive dressing Tegaderm (3M, St. Paul, MN), using a thin layer of Mastisol (Ferndale Laboratories, Ferndale, MI) to ensure adhesion for 7 days. Buprenex was injected subcutaneously during the preoperative period to minimize pain and an intraperitoneal injection of saline was administered postoperatively to minimize risk of dehydration. After postoperative recovery, animals were housed individually, maintained on a 12-hour light/dark cycle, and allowed ad libitum access to food and water. Wound healing kinetics
Wounds were photographed on days 0 and 7. Digital images were imported into Adobe Photoshop containing the FoveaPro image analysis tool kit plug in (Reindeer Graphics, Ashville, NC). Wound area was measured at each time point. Percent wound closure was calculated as follows: % closure5
day 0 area2day 7 area 3100: day 0 area
The Student’s t test (two-tailed, assuming unequal variances) was used to determine statistical significance, and a p-value less than 0.05 was considered significant. Wound processing
Animals were euthanized, and wounds were collected on day 7 after injury. We chose this time point anticipating that both db/- and db/db wounds would still be open and that the size of the wound would provide enough wound tissue for processing for metabolomics analysis. In addition, we anticipated significant differences in the percent area closed between db/- and db/db wounds at day 7 postinjury, a key consideration given our goal of obtaining metabolic profiles of wounds with different healing kinetics. Wounds were excised with 0.5 cm of surrounding uninjured skin. Uninjured skin samples were those excised on day 0 to generate the wound. Both wound and uninjured skin samples were full thickness. For liquid chromatography mass spectrometry (LC-MS), metabolites were extracted from the wounds and uninjured skin using high424
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performance liquid chromatography (HPLC) grade methanol and water (Sigma-Aldrich Corp., St Louis, MO). Tissue was pulverized using a liquid nitrogen-cooled mortar and pestle and then homogenized using a FastPrep homogenizer with lysing matrix D ceramic beads (both from MP Biomedicals, Santa Ana, CA). After homogenization, 20 mg tissue in 600 lL of 80:20 methanol:water was incubated on dry ice for 30 minutes and then centrifuged at 13,000 rpm for 5 minutes at 4 C. The soluble extract was stored on dry ice and the pellet resuspended in 200 ll of 80:20 methanol:water chilled to 4 C. This suspension was incubated on wet ice for 30 minutes and then spun at 13,000 rpm for 5 minutes at 4 C. This second soluble extract was pooled with the first extract and stored on dry ice. For the final extraction, the pellet was resuspended in 200 lL of 80:20 methanol:water chilled to 4 C, vortexed, sonicated in an ice bath for 15 minutes, and centrifuged at 13,000 rpm for 5 minutes at 4 C. This last soluble extract was combined with the pooled extracts on dry ice. The pooled extracts were dried under vacuum at 30 C and reconstituted in 200 lL of 5 mM ammonium acetate in 40% water/60% acetonitrile 1 0.2% acetic acid, and filtered through 0.45 lm polyvinylidene difluoride (PVDF) filters (Phenomenex, Torrance, CA) prior to LC-MS analysis. Liquid chromatography mass spectrometry
The following reagents were used for LC-MS: deionized/ purified water (provided in-house by a Synergy UV system, EMD Millipore, Billerica, MA), LC-MS grade acetonitrile (Sigma-Aldrich, Saint Louis, MO), LC-MS grade ammonium acetate (Sigma-Aldrich, Saint Louis, MO), and LC-MS grade acetic acid (Sigma-Aldrich, Saint Louis, MO). Each sample was injected twice: 10 lL for analysis using negative ionization mode and 2 lL for analysis using positive ionization mode. Both chromatographic separations were performed in hydrophilic interaction liquid chromatography (HILIC) mode on two SeQuant ZICcHILIC columns (150 3 2.1 mm, 3.0 lm particle size, Merck KGaA, Darmstadt, Germany). The separation was carried out on an Agilent 1260 LC system (Agilent Technologies, Santa Clara, CA). While one column was performing the separation, the other column was reconditioned and prepared for the next injection. The flow rate was 0.300 mL/min, autosampler temperature was kept at 4 C, the column compartment was set at 40 C, and total separation time for both ionization modes was 20 minutes. The mobile phase was composed of Solvents A (5 mM ammonium acetate in 90%H2O/10% acetonitrile 1 0.2% acetic acid) and B (5 mM ammonium acetate in 90%acetonitrile/10% H2O 1 0.2% acetic acid). The LC gradient was as follows: 0–2 minutes, 75% Solvent B constant; 2–5 minutes, 75–30% Solvent B; 5–9 minutes, 30% Solvent B constant; 9–11 minutes, 30–75% Solvent B; 11– 20 minutes, 75% Solvent B constant. After the chromatographic separation, MS ionization and data acquisition were performed using an AB Sciex QTrap 5500 mass spectrometer (AB Sciex, Toronto, ON, Canada) equipped with an electrospray ionization source. The instrument was controlled by Analyst 1.5 software (AB Sciex, Toronto, ON, Canada). Targeted data acquisition was performed in multiple-reaction-monitoring C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
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Figure 2. PCA scores plots based on 129 metabolites comparing all samples from both db/- and db/db mice.
for db/- uninjured skin; N 5 5 for db/- day 7 wounds; N 5 8 for db/db uninjured skin; and N 5 4 for db/db day 7 wounds.
Figure 1. Wound closure kinetics for db/- and db/db mice. (A) Representative photos of wounds on days 0 and 7. (B) Image analysis of the wound photos was used to determine the percent wound closure as [(Area of day 0 wound 2 Area of day 7 wound)/Area of day 0 wound] 3 100. *Indicates that p-value < 0.05 when comparing db/db to db/- wounds.
(MRM) mode. We monitored 98 and 59 MRM transitions in negative and positive mode, respectively (157 transitions total). The extracted MRM peaks were integrated using MultiQuant 2.1 software (AB Sciex). Statistical analyses of metabolite levels
Principal component analysis (PCA) was carried out after autoscaling using Stata 13.0 (StataCorp, College Station, TX). All other analyses were performed using R version 3.0.2. Sample sizes for each group were as follows: N 5 8 C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
Figure 3. PCA scores plots based on 129 metabolites comparing wounds and uninjured skin. (A) Nondiabetic samples; (B) diabetic samples.
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The effects of diabetes and wounding on metabolite levels were analyzed by fitting the following linear regression model for each metabolite: E½Am jW; D 5 b0;m 1 b1;m W 1 b2;m D 1 b3;m W D;
where E½Am jW; D is the estimated mean abundance A of metabolite m, W is a dummy variable indicating whether the sample was from a wound (vs. uninjured skin), D is a dummy variable indicating the mouse genotype (db/db vs. db/-), and bi,m are regression coefficients for metabolite m. For each comparison of interest, statistical inference was based on a partial F-test of the relevant coefficient estimate(s): b2,m for db/db vs. db/- uninjured skin; b1,m for db/- wound vs. db/- uninjured skin; b1,m 1 b3,m for db/db wound vs. db/db uninjured skin; and b3,m to test whether diabetes modified the response to wounding. To account for multiple testing, p-values were adjusted to control the false-discovery rate by the method of Benjamini and Hochberg19; p-values less than 0.05 were considered to be statistically significant.
RESULTS The metabolic profile of a cutaneous wound is distinct from that of uninjured skin
Analysis of wound closure kinetics confirmed previous studies reporting that wound closure is delayed in the db/ db mouse.16 In this study at day 7, the average percent wound closure for the db/db mice was 15.3 6 3.9% compared to 51.6 6 7.1% in the db/- mice (Figure 1; p < 0.001). Of the 154 metabolites targeted using LC-MS, 129 were detected among all tissues analyzed (Table S1). Notably, none of the 129 metabolites detected was specific to uninjured skin or day 7 wounds. There was also no
Figure 4. PCA scores plots based on 129 metabolites comparing diabetic and nondiabetic samples. (A) Uninjured skin; (B) wounds.
Table 1. Top 10 metabolites up-regulated in response to injury Nondiabetic (db/-) Metabolite Uracil‡ cGMP Orotate Trytophan‡ dCMP Cytidine‡ Cytosine‡ Succinate/methylmalonate§ Reduced glutathione Urate
Diabetic (db/db) †
Fold change*
p-value
68.3 20.8 10.8 5.4 5.3 4.8 4.7 4.6 4.5 4.4
1.3 3 1028 2.6 3 1024 3.3 3 1025 8.0 3 10210 2.3 3 1023 2.1 3 1028 2.5 3 1028 1.3 3 1028 1.8 3 1022 3.0 3 1028
Metabolite
Fold change*
p-value†
29.4 14.9 5.5 4.8 4.4 4.2 4.2 4.1 4.1 4.0
8.2 3 1024 7.6 3 1027 5.1 3 1023 1.4 3 1022 8.9 3 1023 5.0 3 1024 5.6 3 1024 1.2 3 1023 1.2 3 1022 5.9 3 1023
Uracil‡ Linolenic acid‡ Xanthosine Sucrose Uridine Cytosine‡ Cytidine‡ GDP Xanthine Malonic acid/3HBA§
*Metabolites ranked by mean fold change (wound/uninjured). † Based on linear regression and adjusted to control the false discovery rate. ‡ Significant difference in response to injury between diabetic and nondiabetic wounds; see Table 5. § Metabolites not separated by LC-MS because they have the same precursor and fragment ions.
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Table 2. Metabolites down-regulated significantly in response to injury Nondiabetic (db/-) Metabolite Epinephrine 4-Pyridoxic acid Creatine
Diabetic (db/db) †
Fold change*
p-value
0.7 0.9 0.9
2.2 3 1022 1.5 3 1022 3.8 3 1022
Metabolite Adenosine Cystathionine Serine Epinephrine 4-Pyridoxic acid
Fold change*
p-value†
0.04 0.4 0.5 0.7 0.9
1.4 3 1022 9.1 3 1023 3.3 3 1023 2.3 3 1022 1.3 3 1022
*Metabolites ranked by mean fold change (wound/uninjured). † Based on linear regression and adjusted to control the false discovery rate.
specificity to either the db/db or db/- tissue. To identify groupings of samples, we performed PCA, an unsupervised multivariate analysis that assumes no prior knowledge of
the dataset. Each point on a PCA scores plot represents a single tissue sample. Points that cluster together have similar metabolic profiles whereas points that are far apart
Figure 5. Metabolites with significant response to injury in the nondiabetic but not the diabetic wounds. (A) Arginine (nondiabetic response to injury: fold change 5 1.5; p 5 3.8 3 1022); (B) gamma-aminobutyrate (nondiabetic response to injury: fold change 5 1.8; p 5 2.8 3 1024); (C) glycine (nondiabetic response to injury: fold change 5 1.5; p 5 3.9 3 1023); and (D) kynurenate (nondiabetic response to injury: fold change 5 2.0; p 5 2.0 3 1023). Fold change 5 wound/uninjured. *Indicates that adjusted p-value < 0.05 for nondiabetic wounds compared to nondiabetic uninjured skin.
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Figure 6. Metabolites with significant response to injury in the diabetic but not the nondiabetic wounds. (A) Carnitine (diabetic response to injury: fold change 5 2.1; p 5 1.8 3 1024); (B) glucose (diabetic response to injury: fold change 5 1.9; p 5 3.0 3 1024); (C) 3-nitrotyrosine (diabetic response to injury: fold change 5 1.5; p 5 1.3 3 1022); and (D) OH-phenylpyruvate (diabetic response to injury: fold change 5 1.5; p 5 7.9 3 1023). Fold change 5 wound/uninjured. *Indicates that adjusted p-value < 0.05 for diabetic wounds compared to diabetic uninjured skin.
have different profiles.20 Using the 129 detected metabolites, a PCA scores plot was generated for all samples (Figure 2). The first two principal components accounted for 67.6% of the total variance, which suggests that wounding was largely responsible for the observed metabolic variability instead of inherent variation between animals.21 The PCA scores plot showed clear separation of uninjured skin from wounds. Similar results were observed when individual PCA scores plots were generated for the db/- (Figure 3A) and db/db samples (Figure 3B). Overall, these data suggest that the metabolic profile of a wound is different from the metabolic profile of uninjured skin in both nondiabetic and diabetic mice. Diabetes alters the metabolic profile of both uninjured skin and wounds
We next determined whether diabetes alters the metabolic profile of either uninjured skin or wounds. Similar to the 428
PCA comparing uninjured skin and wounds, 129 metabolites were again used to generate the PCA scores plots. For uninjured skin, the db/db samples separated from the db/samples (Figure 4A). Similarly, the db/db wound grouped separately from the db/-wounds (Figure 4B). These results suggest that in this murine model, diabetic uninjured skin has a different metabolic profile than that of nondiabetic uninjured skin and diabetic wounds have a metabolic profile distinct from that of nondiabetic wounds. The metabolite response to injury is impaired in diabetic wounds
Individual metabolite responses to injury were determined with p-values adjusted to control for multiple testing.19 In the db/- mice, 88 of the 129 detected metabolites had a significant response to injury with 85 up-regulated and 3 down-regulated (Table S2). In the db/db mice, 81 metabolites had a significant response to injury with 76 upC 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
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Figure 7. Metabolites with different magnitude of response to injury between nondiabetic and diabetic wounds. (A) Histidine (nondiabetic response to injury: fold change 5 3.1; diabetic response to injury: fold change 5 1.6; p 5 1.8 3 1023); (B) 2aminoadipate (nondiabetic response to injury: fold change 5 4.1; diabetic response to injury: fold change 5 2.8; p 5 9.4 3 1023); (C) methylsuccinate (nondiabetic response to injury: fold change 5 3.7; diabetic response to injury: fold change 5 2.2; p 5 1.8 3 1023); and (D) linolenic acid (nondiabetic response to injury: fold change 5 4.0; diabetic response to injury: fold change 5 14.9; p 5 1.0 3 1022). Fold change 5 wound/uninjured. *Adjusted p-value < 0.05 for nondiabetic wounds compared to nondiabetic uninjured skin or for diabetic wounds compared to diabetic uninjured skin. †Adjusted p-value < 0.05 for difference in magnitude of change in response to wounding between nondiabetic and diabetic samples.
regulated and 5 down-regulated (Table S3). The top 10 metabolites up-regulated with the greatest fold change in the db/- and db/db wounds are listed in Table 1. Of note, six metabolites in Table 1 had significant differences in the magnitude of response to injury observed between nondiabetic and diabetic wounds. Uracil, cGMP, tryptophan, and cytidine had a greater fold changes in the nondiabetic wounds, whereas linolenic acid and cytosine had a greater fold changes in the diabetic wounds (Tables 1 and 5 and Table S2). Metabolites down-regulated in response to injury are listed in Table 2. Only 2 metabolites, epinephrine and 4-pyridoxic acid were down-regulated significantly in both diabetic and nondiabetic wounds (Table 2). Interestingly, there were 62 unique metabolites with differential responses to injury between nondiabetic and diaC 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
betic wounds. These metabolites met at least one of the following three criteria: (1) significant response to injury in nondiabetic but not in diabetic wounds (25 metabolites; Table 3); (2) significant response to injury in diabetic but not nondiabetic wounds (18 metabolites; Table 4); and/or (3) significant difference in magnitude of response to injury between diabetic and nondiabetic wounds (32 metabolites; Table 5). Arginine, gamma-aminobutyrate, glycine, and kynurenate are examples of metabolites that had a significant response to injury in nondiabetic wounds but not in diabetic wounds (Figure 5), whereas carnitine, glucose, 3-nitrotyrosine, and OH-phenylpyruvate are examples of metabolites that had a significant response to injury in diabetic wounds but not in nondiabetic wounds (Figure 6). The majority of metabolites with differences in the 429
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Table 3. Metabolites with significant response to injury in nondiabetic but not in diabetic wounds Metabolite 1-Methylguanosine Aconitate Adenlyosuccinate Alpha-ketoglutaric acid Arginine cGMP Creatine Cystamine D-Leucic acid dTMP Gamma-aminobutyrate Glycine Guanosine Histidine Hydroxyproline/Aminolevulinate‡ Hypoxanthine Inosine Kynurenate N-Acetylglycine Ornithine Orotate Proline Pyroglutamic acid Shikimic acid Trimethylamine-N-oxide
Fold change* 3.3 3.5 2.8 1.7 1.5 20.8 0.9 2.1 1.6 1.2 1.8 1.5 2.6 3.1 1.6 2.6 1.9 2.0 4.0 1.6 10.8 1.5 1.8 3.3 2.8
p-value†
uninjured
skin
(fold
change 5 0.6;
DISCUSSION 27
6.2 3 10 1.7 3 1025 1.3 3 1027 3.4 3 1024 3.8 3 1022 2.6 3 1024 3.8 3 1022 1.8 3 1023 1.2 3 1022 1.1 3 1024 2.8 3 1024 3.9 3 1023 2.0 3 1022 2.1 3 1028 9.7 3 1025 1.3 3 1022 3.3 3 1022 2.0 3 1023 2.4 3 1025 3.6 3 1022 3.3 3 1025 3.6 3 1023 1.2 3 1024 1.9 3 1025 1.2 3 1025
*Mean fold change (wound/uninjured) in db/- mice. † Based on linear regression and adjusted to control the false discovery rate; in db/db mice, the adjusted p-values for these metabolites were greater than 0.05. ‡ Metabolites not separated by LC-MS because they have the same precursor and fragment ions.
magnitude of response to injury between diabetic and nondiabetic wounds displayed a blunted response in diabetic wounds (Table 5). Histidine, 2-aminoadipate, and methylsuccinate are examples of metabolites with a blunted response to injury in the diabetic wounds (Figure 7A–C). Compared to the nondiabetic wounds, the magnitude of response was significantly less in diabetic wounds. In contrast, the magnitude of response for linolenic acid in diabetic wounds was greater than in nondiabetic wounds (Figure 7D). Of the 129 metabolites detected, only kynurenate and hydroxyproline/aminolevulinate had significantly different levels in uninjured skin between nondiabetic and diabetic animals. There was twofold more kynurenate measured in the diabetic uninjured skin compared to the nondiabetic uninjured skin (fold change 5 1.97; p 5 0.028). In contrast, hydroxyproline/aminolevulinate levels were significantly reduced in diabetic uninjured skin compared to 430
the nondiabetic p 5 0.028).
Targeted metabolic profiling is a powerful approach for defining the wound metabolome. It has the potential to create opportunities for translational research as has happened in the field of oncology.22,23 For example, metabolic profiling has the potential to identify novel biomarkers for wound diagnosis and prognosis. Metabolomics may also identify new therapeutic targets for treatment of wounds with delayed healing. In this study, targeted metabolomics has provided detailed information about the response to injury of 129 metabolites in murine cutaneous wounds. To our knowledge, this is the first comprehensive analysis of the metabolic profile of wounds. Importantly, it has allowed the identification of metabolites that discriminate the metabolic signature of a diabetic wound from a nondiabetic wound in the db/db mouse model of type 2 diabetes mellitus. Our metabolomics data are consistent with welldocumented impaired responses to injury in diabetic wounds including reduced collagen synthesis and reduced nitric oxide (NO) production.24 Known indices of collagen synthesis, proline and hydroxyproline, had no response to injury in diabetic wounds (Table 3), which may contribute Table 4. Metabolites with significant response to injury in diabetic but not nondiabetic wounds Metabolite 3-Nitrotyrosine Adenosine Adipic acid Carnitine Cystathionine D-GA3P/DHAP‡ F16BP/F26BP/G16BP‡ Galactose Glucose Glyceraldehyde Glycerol-3-P GTP Inositol Margaric acid OH-phenylpyruvate Pentothenate Ribose-5-phosphate Serine
Fold change* 1.5 0.04 2.1 2.1 0.4 1.7 1.8 2.2 1.9 1.8 1.6 2.8 2.3 1.7 1.5 3.6 2.5 0.5
p-value† 1.3 3 1022 1.4 3 1022 9.3 3 1023 1.8 3 1024 9.1 3 1023 3.1 3 1022 3.3 3 1022 5.7 3 1023 3.0 3 1024 5.6 3 1024 2.5 3 1022 1.9 3 1022 7.2 3 1023 1.4 3 1023 7.9 3 1023 2.6 3 1023 7.3 3 1023 3.3 3 1023
*Mean fold change (wound/uninjured) in db/db mice. † Based on linear regression and adjusted to control the false discovery rate; in db/- mice, the adjusted p-values for these metabolites were greater than 0.05. ‡ Metabolites not separated by LC-MS because they have the same precursor and fragment ions. C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
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Table 5. Metabolites with significant differences in the magnitude of response to injury between diabetic and nondiabetic mice Fold change* Metabolite
Nondiabetic (db/-)
Diabetic (db/db)
1-Methylguanosine 2-Aminoadipate 3-Nitrotyrosine Aconitate Adenylosuccinate Allantoin Asparagine Aspartic acid Carnitine cGMP Cystine Cytidine Cytosine dTMP Glucoronate Glucose Histidine Homocysteine Linolenic acid Lysine Methionine Methylsuccinate N-Acetylglycine OH-phenylpyruvate Phenylalanine Shikimic acid Sorbitol Threonine Trimethylamine-N-oxide Tryptophan Tyrosine Uracil
3.3 4.1 0.8‡ 3.5 2.8 2.4 3.3 2.3 1.3‡ 20.8 3.8 4.8 4.7 1.2 2.0 1.2‡ 3.1 3.3 4.0 2.8 3.2 3.7 4.0 0.9‡ 4.3 3.3 3.4 2.8 2.8 5.4 3.4 68.3
2.0§ 2.8 1.5 2.5§ 1.8§ 2.2 2.4 1.5 2.1 2.2§ 2.2 4.2 4.2 0.9§ 1.4 1.9 1.6 2.6 14.9 2.4 2.3 2.2 2.0§ 1.5 2.5 1.6§ 2.3 2.0 1.6§ 3.5 2.3 29.4
p-value† 8.8 3 1023 9.4 3 1023 4.0 3 1022 4.4 3 1022 2.3 3 1023 1.9 3 1022 6.5 3 1023 1.8 3 1023 3.6 3 1022 4.0 3 1022 9.4 3 1023 2.6 3 1022 3.5 3 1022 6.2 3 1023 4.9 3 1022 3.5 3 1022 1.8 3 1023 3.5 3 1022 1.1 3 1022 6.5 3 1023 1.7 3 1022 1.8 3 1023 4.9 3 1022 4.0 3 1022 1.8 3 1023 1.7 3 1022 6.2 3 1023 1.8 3 1023 8.8 3 1023 1.8 3 1023 6.5 3 1023 1.0 3 1022
*Mean fold change (wound/uninjured). † Test of interaction between sample type (wound vs. uninjured skin) and mouse genotype (db/db vs. db/-), with p-values adjusted to control the false discovery rate. ‡ Nonsignificant response to injury (adjusted p-value>0.05) in db/- mice. § Nonsignificant response to injury (adjusted p-value >0.05) in db/db mice. # Metabolites not separated by LC-MS because they have the same precursor and fragment ions.
to the low collagen content detected in diabetic wounds.25,26 Similarly, the NO precursor arginine failed to respond to injury in diabetic wounds (Table 3), which supports previous studies demonstrating reduced NO synthesis in diabetic wounds.27 It may also explain why arginine supplementation improves wound healing in diabetes.7 The metabolomics data also support studies suggesting that oxidative stress is elevated in diabetic wounds.24 Evidence of C 2015 by the Wound Healing Society Wound Rep Reg (2015) 23 423–434 V
oxidative stress is provided by the novel finding that 3nitrotyrosine, a marker of oxidative damage,28 was elevated specifically in diabetic wounds. Our lactate data are also consistent with extensive literature reporting that lactate accumulates in wounds5,29,30 (Tables S2 and S3). Collectively these observations validate the use of metabolomics to define metabolic signatures of diabetic wounds. 431
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Novel metabolic responses to injury were identified for gamma-aminobutyrate (GABA), glucose, glycine, kynurenate, and OH-phenylpyruvate. Importantly, all of these responses were dysregulated in a diabetic wound model. To our knowledge, only GABA, kynurenate, and glucose have been studied in the context of cutaneous wound repair. The neurotransmitter GABA has been reported to regulate dermal fibroblast synthesis of hyaluronic acid in addition to enhancing dermal fibroblast tolerance to oxidative stress.31 Topical administration of GABA also accelerates wound closure.32 Two recent studies suggest that kynurenate may play a role in both inflammation and fibroproliferation during wound repair. Kynurenine, a precursor of kynurenate, had anti-inflammatory activity in mouse excisional wounds and antifibrotic effects in rabbit model of hypertrophic scarring.33,34 Despite intense interest in the contribution of hyperglycemia to the impaired healing of diabetic ulcers, there have been only a few studies investigating glucose levels in wounds, and to our knowledge all of these studies have analyzed glucose in wound fluid rather than wound tissue.35,36 This present study is the first to report that glucose levels are not significantly different between uninjured diabetic and nondiabetic murine skin, which suggests that glucose concentrations in diabetic skin do not reflect systemic hyperglycemia. This is supported by studies showing a similar lack of correlation in glucose levels between wound fluid and serum in humans35 and diabetic pigs.36 In addition, our observation that glucose levels increased significantly in response to injury specifically in diabetic murine wounds is novel. Further investigation is needed to define the temporal and spatial regulation of glucose in wounds during healing. Little is known about the role of glycine or OH-phenylpyruvate during wound repair. The glycine response to injury is especially intriguing, as recent metabolic profiling of cancer cells has correlated this amino acid with increased cell proliferation.37 It remains to be determined whether glycine is required for cell proliferation during wound repair and whether a lack of glycine has a detrimental effect on cell proliferation in diabetic wounds. Further work is also needed to determine the biological relevance of down-regulated metabolites and metabolites that had significant differences in the magnitude of response to injury between diabetic and nondiabetic wounds. Overall, these data underscore the power of metabolomics for both hypothesis generation and identification of novel biomarkers predicting wound outcome. This was a pilot study demonstrating the power and feasibility of metabolomics to discern differences between different wound phenotypes. Further studies are needed to determine the metabolic profile of wounds over time. Specifically, metabolic profiling of wounds from different times during wound repair may provide insight into whether different metabolic programs are associated with different phases of wound repair such as hemostasis, inflammation, epithelialization, and tissue remodeling. Accordingly, it may be possible that the metabolic profile differences we observed between the diabetic and nondiabetic wounds may be due to these wounds being in different phases of the wound healing process. For any metabolomics study, it is also important to note that metabolite levels are a function of both production and consumption.38 Consequently, metabolic flux experiments 432
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are necessary to determine the activity of specific metabolic pathways, as it is challenging to make conclusions about pathway activity based on metabolite level alone. Despite these limitations, our data show robust metabolic differences both in response to injury and between diabetic and nondiabetic wounds, which underscores the need for an in-depth investigation of wound metabolism. Another limitation of this study is that the metabolite profile of a rodent wound is likely different from that of a human wound due to species-specific differences in wound healing biology and skin anatomy.39 In this first study of wound metabolomics, we chose to use an experimental mouse model rather than human samples because we wanted to precisely control for sample-specific factors such as age, genetic background, wound location, wound size, wound depth, and time since injury in order to minimize biological variability between samples and thus maximize statistical power to detect differences in metabolite levels between experimental groups. Our results from the murine model establish the feasibility of wound metabolic profiling and serve as a reference for future studies using human samples, which will be ultimately required for clinical translation of wound-healing metabolomics research. In summary, we have used targeted metabolomics to define the metabolic profile of cutaneous wounds in the db/db murine model of type 2 diabetes. Diabetic and nondiabetic murine wounds have distinct metabolic profiles with 62 metabolites having an impaired response to injury in diabetic wounds. Application of metabolomics to the field of wound repair may identify both new biomarkers of wound outcome and novel therapeutic targets for wounds with delayed healing.
ACKNOWLEDGMENTS Source of Funding: This work was supported by University of Washington’s Department of Surgery Research Reinvestment Fund (AMH). Conflict of Interest: Daniel Raftery holds equity and an executive position at Matrix-Bio, Inc. All other authors have no conflict of interest.
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Supporting Information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site Table S1. Metabolites (with relative peak areas) detected in uninjured skin and wounds from db/db and db/- mice. Table S2. Metabolites regulated in response to injury in db/- mice. Metabolites with p-value less than or equal
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to 0.05 ranked by fold change. 1Mean fold change (wound/ uninjured). 2Based on linear regression and adjusted to control the false discovery rate. Table S3. Metabolites regulated in response to injury in db/db mice. Metabolites with adjusted p-value less than or equal to 0.05 ranked by fold change. 1Mean fold change (wound/uninjured). 2Based on linear regression and adjusted to control the false discovery rate.
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