DOI: 10.5061/dryad.n2z34tn7c
title: Effect of fecal microbiota transplantation on diabetic wound healing through the IL-17A-mTOR-HIF1α signaling axis

description: Diabetes is the third most common chronic disorder worldwide. Diabetic
 wounds are a severe complication that is costly and often results in
 non-traumatic lower limb amputation. Recent investigations have
 demonstrated that the gut microbiota as a "virtual organ" can
 regulate metabolic diseases like diabetes. Fecal microbiota
 transplantation (FMT) is an innovative therapeutic approach for promoting
 wound healing, but its function remains incompletely defined. A
 diabetes model was established by supplying mice with a high-fat diet and
 performing an intraperitoneal injection of streptozotocin. Diabetic wounds
 were then created, followed by bacterial transplantation. The relevant
 indexes of wound healing were evaluated to verify the promoting effect of
 FMT on diabetic wounds. Human skin keratinocytes were also cultured, and
 cell scratch experiments were conducted to further investigate the
 underlying mechanism. The FMT regulated the levels of specific
 bacteria in the diabetic mice and helped restore the balance of intestinal
 microbes. This transplantation also enhanced wound healing in diabetic
 mice by augmenting the closure rate, accelerating re-epithelialization,
 and boosting collagen deposition in skin wounds. Furthermore, FMT promoted
 the production of IL-17A, which significantly enhanced the growth and
 movement of human keratinocytes. Inhibiting molecules related to the
 IL-17A-mTOR-HIF1α signaling axis were shown to hinder wound
 re-epithelialization. This study clarifies the function of the
 IL-17A-mTOR-HIF1α signaling axis in the utilization of FMT in diabetic
 wound healing, providing a new therapeutic method and target for promoting
 the healing of diabetic wounds.
description: <strong>Diabetic wound healing
 experiment</strong> Once the mice were fully
 anesthetized through an intraperitoneal injection of 1% sodium
 pentobarbital at a dosage of 0.3 mg/kg, the fur on their backs was shaved
 and disinfected. A circular full-thickness skin wound measuring 5 mm in
 diameter was then made on the posterior back near the hip joint using a
 punch tool. After the creation of the wound, each mouse was housed
 separately in a single cage to prevent self-scratching. One hour prior to
 wound creation, as illustrated in Fig. 6, mice in the SECU group received
 a subcutaneous injection of 10 mg/kg of the IL-17A inhibitor secukinumab
 (SECU, S412013, Aladdin), while the remaining groups were administered an
 equivalent amount of sterile saline solution. The injection was
 administered once a week until sacrifice. Wound healing was assessed using
 Image J on days 0, 3, 7, and 14 after the wound was created. The wound
 closure (%) was calculated as (W<sub>0</sub> –
 W<sub>n</sub>) / W<sub>0</sub> × 100%;
 W<sub>0</sub> and W<sub>n</sub> indicate the wound
 area on days 0 and n. Two weeks later, the mice were sacrificed under
 general anesthesia, and colon, serum, and skin specimens were gathered for
 corresponding tests. <strong>Hematoxylin and
 eosin staining and Masson’s trichrome staining</strong>
 Following general anesthesia, the mice were perfused through the
 heart with a 0.9% saline solution, followed by a perfusion of 0.1 M
 phosphate buffer with 4% paraformaldehyde. The colon and skin tissues were
 carefully excised and promptly placed in 4% paraformaldehyde for 24 hours.
 These tissues were subsequently embedded in paraffin and cut into 5 μm
 sections. The embedding medium was removed using solvents such as xylene.
 The sections were rehydrated with graded alcohol solutions. Hematoxylin
 and eosin were used for HE staining, and Weigert's iron hematoxylin
 staining solution was employed for Masson staining. The experimental
 results were then observed under an optical microscope.<strong>
 </strong> <strong>Fecal 16S rDNA
 sequencing</strong> Each mouse was placed in a
 separate sterile cage, and fresh fecal pellets were collected in sterile
 EP tubes. The samples were immediately chilled and stored at -80 °C for
 subsequent analysis. Total DNA was extracted from microbial community
 samples using the CTAB method. DNA integrity was assessed by
 electrophoresis, and quantification was done with a UV-Vis
 spectrophotometer. The V3-V4 hypervariable region of the bacterial 16S
 rDNA gene was selected for PCR amplification. The forward primer 341F
 (5'-CCTACGGGNGGCWGCAG-3') and the reverse primer 805R
 (5'-GACTACHVGGGTATCTAATCC-3') were used. PCR products were
 confirmed via 2% agarose gel electrophoresis. Purification was performed
 using AMPure XT beads (Beckman Coulter Genomics), followed by
 quantification with Qubit (Invitrogen). Purified products were recovered
 using an AMPure XT bead recovery kit. The refined PCR products were
 evaluated using an Agilent 2100 Bioanalyzer and a Kapa Biosciences
 Illumina library quantification kit. A qualified sequencing library, with
 unique index sequences, underwent gradient dilution and proportional
 mixing based on required sequencing amounts. The library was denatured
 into single strands for sequencing on a NovaSeq 6000 sequencer, with
 paired-end sequencing at 2×250 bp using the NovaSeq 6000 SP Reagent Kit
 (500 cycles), followed by data analysis. Sequencing was performed on the
 Illumina NovaSeq platform. Paired-end reads were assigned to samples using
 unique barcodes and processed by removing barcode and primer sequences.
 The paired-end reads were assembled with FLASH. Raw reads were filtered to
 generate high-quality clean tags using fqtrim (v0.94). Chimeric sequences
 were identified and removed with Vsearch (v2.3.4). After dereplication
 with DADA2, we generated an ASV feature table and extracted feature
 sequences. Alpha and beta diversity metrics were computed based on
 randomly subsampled sequences to ensure equal sequence depth using QIIME2,
 the Chao1 estimator estimates total species richness in a community, the
 Shannon index is derived from the information entropy of a community. the
 beta diversity was visualized through principal coordinate analysis
 (PCoA), ANOSIM (Analysis of Similarities) is a non-parametric test that
 evaluates whether differences between groups, based on a Jaccard
 index-derived distance matrix, are statistically significant. Feature
 abundance was normalized by relative abundance within each sample using
 the SILVA classifier (release 138). Graphs were generated using R
 (v3.5.2). Differentially abundant taxa were identified using LEfSe with
 default parameters. Microbial metabolic functions were analyzed with
 PICRUSt2 to infer functional profiles, which were then mapped against the
 KEGG database for pathway abundance values. Additional diagrams were also
 created using R (v3.5.2).
 <strong>Immunofluorescence
 staining</strong> The sections were sequentially
 immersed in a series of xylene and ethanol solutions for dewaxing,
 followed by antigen retrieval with citric acid (pH 6.0). A 3% hydrogen
 peroxide solution was prepared using water, and the sections were placed
 in this solution in an incubator under ambient temperature for 20 minutes.
 Blocking was conducted with 10% goat serum at 37 °C for half an hour. The
 primary antibodies, IL-17A (1:200 dilution, PTG, catalog number
 26163-1-AP) and HIF-1α (1:200 dilution, bioss, catalog number bs-0737R),
 were diluted in an antibody dilution buffer and allowed to incubate
 overnight at 4 °C. Goat anti-rabbit IgG-CY3 conjugated with HRP (1:300
 dilution, Servicebio) was prepared in PBST and kept at 37 °C for one hour.
 DAPI solution was utilized to stain cell nuclei. Representative images
 were obtained using fluorescence microscopy (Nikon Eclipse C1, Tokyo,
 Japan). The number of positive cells was evaluated using Image Pro Plus
 version 6.0 software. <strong>Enzyme-linked
 immunosorbent assay</strong> The enzyme-linked
 immunosorbent assay (ELISA) kit (ELM-IL17-1, Raybio) was used to measure
 IL-17A levels in the mouse serum. The experimental procedures followed the
 guidelines provided by the manufacturer. Absorbance readings were taken
 with an ELISA reader, and a standard curve was generated based on these
 values. <strong>Cell culture and cell wound
 scratch assay</strong> Human keratinocytes
 (hacat) were purchased from IMMOCELL (Xiamen, Fujian, China). In the
 normal medium group, cells were grown in DMEM/F12 medium enriched with 10%
 fetal bovine serum (FBS) and 1% penicillin-streptomycin liquid. In the
 high-glucose medium group, an additional 25 mM of glucose was added to the
 medium. In group I, 100 ng/ml exogenous IL-17A (CM018-20HP, Chamot
 Biotechnology Co, Ltd, China) was added. In group D, 100 ng/ml exogenous
 IL-17A and 40 mM glycolysis inhibitor 2-Deoxy-D-glucose(2-DG) (CD4251-1g,
 Coolaber) were added. In group B, 100 ng/ml exogenous IL-17A and 10 μM
 HIF1α inhibitor BAY87-2243 (87-2243, MCE) were incorporated. Following a
 24-hour incubation period, the cells covered the six-well culture plate.
 Subsequently, a cell scratch assay was conducted, and the scratch patterns
 of the cells at 0 and 24 hours were recorded. The scratch area was
 calculated using Image J. The percentage of the scratch area (%) was
 calculated as (A<sub>0</sub> － A<sub>24</sub>) /
 A<sub>0</sub> × 100%, where A<sub>0</sub> and
 A<sub>24</sub> indicate the area of the scratch at 0 and 24
 hours. The levels of pathway-related proteins were assessed using Western
 blot (WB). <strong>Western
 blot</strong> The efficient RIPA lysing buffer
 for tissue and cell samples (with PMSF) (SL1020-100mL, Coolaber) was used
 to extract cell proteins. The lysate was subsequently subjected to
 centrifugation at 12,000 g and 4 °C for 20 minutes to isolate the total
 protein. The protein concentration was measured using the BCA protein
 assay kit (EC0001, SparkJade). The membranes were moved and allowed to
 incubate overnight at 4 °C with the specified primary antibodies: mouse
 monoclonal AKT (1:25,000, 60203-2-Ig, Proteintech), mouse monoclonal p-AKT
 (1:5,000, 66444-1-Ig, Proteintech), mouse monoclonal mTOR (1:25,000,
 66888-1-Ig, Proteintech), rabbit monoclonal p-mTOR (1:1,000, 5536T, CST),
 Rabbit polyclonal antibody anti-HIF1α (1:1000, D222477-0025, Sangon
 Biotech), Rabbit polyclonal antibody anti-HK2 (1:25,000; 22029-1-AP;
 Proteintech), mouse monoclonal HIF-1 (1:5,000, 66730-1-Ig, Proteintech),
 mouse monoclonal HK2 (1:10,000; 66974-1-Ig; Proteintech), and β-actin
 (1:20,000; T0022; Affinity). Afterward, the membranes were treated with
 the appropriate secondary antibodies, including HRP-conjugated goat
 anti-rabbit antibody (1:1000; A0208; Beyotime) and HRP-conjugated goat
 anti-mouse antibody (1:10, 000; SA00001-１; Proteintech) at room
 temperature for two hours. Visualization of the blots was performed using
 the LAS4000 chemiluminescence system from Fujifilm in Tokyo, Japan, and
 the gray values of the films were analyzed using IPP software.
 <strong>Statistical analysis</strong>
 An analysis of the statistical data was carried out utilizing
 SPSS 20.0. Data are presented as the mean ± standard deviation (SD). In
 the case of multiple comparisons, one-way analysis of variance (ANOVA) was
 employed and subsequently analyzed using the least significant difference
 (LSD) post hoc test. The nonparametric Kruskal–Wallis test was applied to
 evaluate the pole test performance, grip strength test results,
 histological scores, ZO-1 integrity scores, and cell counts, with
 Mann–Whitney U post hoc testing. Comparisons between the two groups were
 conducted using independent <em>t</em>-tests. Spearman
 correlation analysis was performed with R (version 3.5.1) to assess
 correlations across different experiments. The results were considered
 statistically significant if <em>P</em> ＜ 0.05.
description: # Effect of fecal microbiota transplantation on diabetic wound healing
 through the IL-17A-mTOR-HIF1α signaling axis
 [https://doi.org/10.5061/dryad.n2z34tn7c](https://doi.org/10.5061/dryad.n2z34tn7c) ## Description of the data and file structure All the data are mainly divided into two files. The file named "**data**" mainly contains the results of experiments such as HE and Masson’s trichrome staining, immunofluorescence staining, enzyme-linked immunosorbent assay, cell culture and cell wound scratch assay, and western blot. The file named "**16S rDNA**" mainly contains the data results of 16S rDNA sequencing. All the data are provided to support our article titled “Effect of Fecal Microbiota Transplantation on Diabetic Wound Healing Through the IL-17A-mTOR-HIF1α Signaling Axis”. The data mainly consists of the experimental results of four groups of mice experiments and six groups of keratinocyte cell experiments involving experimental reagents. The abbreviations in the document represent the experimental groups. Twenty-four 4-week-old male C57BL/6J mice were randomly assigned to four groups (n=6 per group): blank control group (**Blank**), Type 2 diabetes mellitus (T2DM) model group (**DM**), T2DM model with fecal microbiota transplantation group (**FMT**), and IL-17A inhibitor group (**SECU**). Human skin keratinocytes were cultured and divided into eight groups: normal cells (**NC**), normal cells treated with IL-17A (**NI**), normal cells treated with HIF inhibitor BAY87-2243 (**NB**), normal cells treated with glycolysis inhibitor 2-DG (**ND**), high glucose-induced cells (**HC**), high glucose-induced cells treated with IL-17A (**HI**), high glucose-induced cells treated with HIF inhibitor BAY87-2243 (**HB**), and high glucose-induced cells treated with glycolysis inhibitor 2-DG (**HD**).  file in "**data**": figure1B,C; figure1FGH; figure4D; figure4E; figure4G, H; figure5A-F; figure5H; figure6B; figure6D file in"**16S rDNA**": Data; Summary (1-raw data, 2-clean data, 3-ASV profiling, 4-alpha diversity, 5-Beta diversity, 6-taxonomy community, 7-advanced analyse) ### Files and variables #### File: data.zip **Description:** The file contains the original data of all the images in the article. The main contents include the area measured during the wound healing process of mice, the experimental results of HE staining, Masson staining, and immunofluorescence staining of skin collected from mice 7 and 14 days after wound formation, the experimental results of HE staining and immunofluorescence staining of the colon of mice on the 14th day, the results of IL-17A in mouse serum measured by ELISA on the 14th day, the measurement results of human keratinocyte scratch experiments at 0 and 24 hours, and the WB results of mouse skin and keratinocytes.  variables in Figures 1B, C: Wound healing charts on days 0, 3, 7, and 14 in each group, and the wound **area** at each time point was measured (figure 1B), wound healing rates on day 7 in each group (figure 1C), the **wound closure** (%) was calculated as (W0 - W7) / W0 × 100%, W0 and W7 indicate the wound area on days 0 and 7. variables in Figures 1F, G, and H: HE staining on days 7 and 14 in each group **wound length** measurement on day 7 (figure 1F), and **number of dermal appendages** on day 14 (figure 1G). Masson staining of the skin wounds of mice in each group and the proportion of collagen fibers in the wounds on day 7 (figure 1H), **collagen fiber area**(%)=area of fibrosis/ the total area of the tissue× 100%. variables in Figure 4D: ELISA showed **IL-17A content** in mouse serum (figure 4D) variables in Figure 4E: **The number of IL-17A-positive cells** in the intestinal tract of the mice from each group as observed under an immunofluorescence staining microscope (figure 4E). variables in Figures 4G, and H: Immunofluorescence staining of the skin of mice in each group on the 14th day of wound healing, **number of** **IL-17A-positive cells** (figure 4G) and **number of HIF1α-positive cells** (figure4H) under the immunofluorescence staining microscope. variables in Figure 5A-F: Conduct a correlation analysis. Association between *Alloprevotella* and wound closure (%) (figure 5A). Correlation analysis between *Clostridium* and IL-17A+ cell number in the wound (figure 5B), correlation analysis between *Clostridium* and wound closure (%) (figure 5C); analysis of the correlation between wound closure (%) and collagen fiber area (%) (figure5D), analysis of the correlation between wound closure (%) and IL-17A+ cell number in the wound (figure5E), correlation network diagram between each indicator (figure5F). variables in Figure 5H: WB detection of pathway proteins in the skin of each group of mice on the 14th day (figure 5H). variables in Figure 6B: Experimental records of human keratinocytes in each group at 0 and 24 h after scratch and comparison of their mobility (figure 6B), the **scratch area**(%) was calculated as (A0 - A24) / A0 × 100%, A0 and A7 indicate the area on hour 0 and 7. variables in Figure 6D: WB detection of pathway proteins in cells (figure 6D). #### File: 16S\_rDNA.zip **Description:** Collect the feces of mice after successful microbiota transplantation and conduct 16s rDNA sequencing. The sequencing result data are as shown in the file. 16S rDNA is a technology for high-throughput sequencing of all bacteria in a specific environment (or a specific habitat) sample to study the composition of microbial communities in environmental samples, interpret the diversity, richness, and population structure of microbial communities, and explore the relationship between microorganisms and the environment or host. Traditional microbial research relies on laboratory culture. The rise of high-throughput sequencing such as 16S amplicons fills the research gap for microorganisms that cannot be cultured in traditional laboratories, expands the utilization space of microbial resources, and provides an effective tool for studying microbial interactions. 16S rDNA is located on the small subunit of the prokaryotic ribosome, which refers to the DNA sequence corresponding to the coding ribosomal 16S rDNA molecule in the genome, that is, the coding gene of 16S rDNA. The whole length of the gene is about 1542bp, and it is composed of 9 variable regions and 10 conserved regions (the variable regions are V1 to V9). The conserved regions reflect the genetic relationship among species, while the variable regions indicate the differences among species. The degree of variation is closely related to the phylogeny of bacteria, which is considered to be the most suitable indicator for bacterial phylogenetic and taxonomic identification. variables in Data: After the on-machine sequencing was completed, we obtained the raw off-machine data **RawData**. The double-end data were spliced using overlap, and quality control and chimera filtering were performed to obtain high-quality **CleanData**. variables in Summary: 1-**raw data**, 2-**clean data**; 3-ASV profiling: Instead of clustering by sequence similarity, DADA2 (Divisive Amplicon Denoising Algorithm) uses steps such as "Dereplication" (clustering by 100% similarity) to obtain representative sequences with single-base accuracy. The data accuracy and species resolution were greatly improved. The core of DADA2 is denoising, and then using the concept of **ASV**s (Amplicon Sequence Variants) to build Operational Taxonomic Units (OTU) tables to obtain the final ASV feature table and feature sequence. We further performed diversity analysis, species classification annotation, and difference analysis. 4-alpha diversity: Alpha diversity refers to the diversity within a given environment or ecosystem and is mainly used to reflect species richness and evenness and sequencing depth. Alpha diversity mainly reflects richness and evenness through indices such as Chao1 and Shannon. **Chao1** was mainly used to estimate the number of species in the community. The **Shannon** index is derived from information entropy, and a larger Shannon index indicates greater uncertainty. The greater the uncertainty, the more unknown factors in the community, that is, the higher the diversity. 5-Beta diversity: Beta diversity refers to the species divergence between different environmental communities. Beta diversity, together with alpha diversity, constitutes the overall diversity or the biological heterogeneity of a given environmental community. Beta diversity analysis usually begins by calculating the distance matrix between environmental samples, which contains the distance between any two samples, mainly through Principal coordinates analysis (PCoA), Analysis of similarities (ANOSIM), and other methods used to observe the differences between samples. **PCoA** is a kind of dimensionality reduction sorting method similar to PCA, which maximizes the relationship information between samples by rearranging samples in a visual low-dimensional space (usually two-dimensional). **ANOSIM** is a nonparametric test to test whether the differences between groups (two or more groups) are significantly greater than the differences within groups, and thus determine whether grouping is meaningful. 6-taxonomy community: The ASV feature sequence is the source file of our species classification. To analyze the species composition more accurately, we used SILVA (Release 138, [https://www.arb-silva.de/documentation/release-138/](https://www.arb-silva.de/documentation/release-138/)) and the NT - 16 s database do species classification and subsequent analysis, to ensure complete and accurate annotation results. Annotation threshold: confidence greater than 0.7. According to the ASV annotation results and the ASV abundance tables of each sample, species abundance tables at the **genus** level were obtained, and species composition of different samples (groups) was analyzed according to the species abundance tables at different levels. 7-advanced analyse:  **taxonomy community** and relative abundance at the genus level are shown in different sample groups;  LEfSe (LDA Effect Size) analysis is widely used to compare two or more groups and find species (**biomarker**) with significant differences in abundance between different groups. LEfSe plot is a more intuitive display of different species at all levels between groups, and it is also the most frequently used display plot for different species. The analysis steps were divided into three steps: Step 1: All characteristic species were detected by the Kruskal-Wallis rank sum test, and significantly different species were obtained by detecting the difference in species abundance between different groups. Step 2: The Wilcoxon rank-sum test was used to test whether all subspecies of significantly different species obtained in the previous step converged to the same taxonomic level. Step3: Using linear discriminant analysis (LDA) to obtain the final differential species (biomarker);  Based on the abundance and variation of different species in each sample, the correlation between species was calculated, and the related species were found through certain conditions. Based on this analysis, **correlation** (positive or negative) between different microbial groups can be found. PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) is a software that predicts functions based on the sequence of marker genes. PICRUSt establishes "mapping" between microbiota and **function**. ## Code/software The data is stored in EXCEL format.