Descriptive transcriptome analysis of tendon derived fibroblasts following in-vitro exposure to advanced glycation end products.

BACKGROUND: Tendon pathologies affect a large portion of people with diabetes. This high rate of tendon pain, injury, and disease appears to manifest independent of well-controlled HbA1c and fasting blood glucose. Advanced glycation end products (AGEs) are elevated in the serum of those with diabetes. In vitro, AGEs severely impact tendon fibroblast proliferation and mitochondrial function. However, the extent that AGEs impact the tendon cell transcriptome has not been evaluated.
OBJECTIVE: The purpose of this study was to investigate transcriptome-wide changes that occur to tendon-derived fibroblasts following treatment with AGEs. We propose to complete a descriptive approach to pathway profiling to broaden our mechanistic understanding of cell signaling events that may contribute to the development of tendon pathology.
METHODS: Rat Achilles tendon fibroblasts were treated with glycolaldehyde-derived AGEs (200μg/ml) for 48 hours in normal glucose (5.5mM) conditions. In addition, total RNA was isolated, and the PolyA+ library was sequenced.
RESULTS: We demonstrate that tendon fibroblasts treated with 200μg/ml of AGEs differentially express 2,159 gene targets compared to fibroblasts treated with an equal amount of BSA-Control. Additionally, we report in a descriptive and ranked fashion 21 implicated cell-signaling pathways.
CONCLUSION: Our findings suggest that AGEs disrupt the tendon fibroblast transcriptome on a large scale and that these pathways may contribute to the development and progression of diabetic tendinopathy. Specifically, pathways related to cell cycle progression and extracellular matrix remodeling were affected in our data set and may play a contributing role in the development of diabetic tendon complications.

Introduction

Tendon degeneration and impaired biomechanical function result in significant reductions in mobility and quality of life for the majority of the ~30 million Americans living with diabetes, resulting in a substantial economic burden to individuals and society. Compounding the problem, human [1-3] and rodent [4] studies indicate that improving blood glucose levels does not normalize tendon properties in individuals with diabetes. Any new approach to enhance tendon health in people with diabetes is hindered by a poor understanding of the underlying etiology of tendon degeneration and impaired biomechanical properties [1, 2, 5–7].

Our previous cell culture work implicated advanced glycation end-products (AGEs) as a potential mechanism driving tendon degeneration [8]. AGEs can form non-enzymatic crosslinks with collagen [9], a mechanism that has traditionally been the focus of tendon complications in persons with diabetes [10]. Yet, recent studies of tendons from humans with diabetes have found no evidence of greater collagen crosslinking than those without diabetes [1, 11] and no relationship between tendon AGE content and tensile mechanics [11]. A less explored mechanism of AGE-mediated effects is the interaction of serum AGEs with AGE receptors (RAGE). AGEs accumulate in the serum of patients with diabetes [12-14] and our cell culture data suggest that AGEs can impact tendon cells. Specifically, treatment of cells with AGEs dose-dependently reduced cell proliferation and mitochondrial ATP production.

A thorough understanding of the cell signaling events contributing to the development of AGE-mediated diabetic tendinopathies will assist in exploring alternative areas of thought and developing therapeutic options to target this large patient population. Therefore, to better understand the effect of AGEs on tendon cells, we sought to characterize the alterations to the tendon fibroblast transcriptome following exposure to AGEs. Although many of these pathways have already been implicated with AGEs from analysis of non-tendon tissues, the primary goal of this study was to establish a descriptive and ranked evaluation of pathway disruptions that occur to tendon fibroblasts following an AGE insult.

Materials and methods

Animal protocol

Animals utilized in this study were from a previous investigation [8]. The study was approved by the Purdue University Institutional Animal Care and Use Committee. All animals were cared for per the recommendations in the Guide for the Care and Use of Laboratory Animals. Five eight-week-old female Sprague-Dawley rats were purchased from Charles River Laboratories (Wilmington, MA) and maintained for an additional eight weeks. Rats were housed on a 12-hour light-dark cycle and provided standard rat chow and water ad libitum. At sixteen weeks (Final Weights: 256.43±5.19 g), rats were euthanized by decapitation after CO2 inhalation.

Tendon fibroblast isolation and cell culture

Tendon-derived fibroblasts utilized in this study were from a previous investigation [8]. Briefly, Achilles tendons were rinsed with sterile PBS, minced, placed in DMEM containing 0.2% type I collagenase, and incubated in a 37°C shaking water bath for four hours. After digestion, the cell suspension was filtered through a 100μm mesh filter, pelleted by centrifugation, and resuspended in 5.5mM glucose DMEM containing 10% FBS, 1% sodium pyruvate (Sigma, St. Louis, MO), and 1% penicillin/streptomycin (Thermo Scientific, Waltham, MA). Samples were then plated in 100mm collagen-coated dishes. After reaching confluency, tendon fibroblasts were split and seeded (100,000 cells) in 100mm collagen-coated culture plates. Each donor animal’s (n = 5) tendon fibroblasts were treated separately with 200μg/ml of BSA-Control or AGE-BSA for 48 hours for downstream paired DESeq2 analysis. Tendon fibroblasts treated at passages 2–4 were used for RNA isolation and RNA-sequencing (RNAseq).

Age preparation

Details on the preparation of AGEs have been reported previously [8, 15]. Briefly, sterile filtered 30% BSA solution (Sigma, St. Louis, MO) was incubated with 70mM glycolaldehyde dimer (Sigma) in sterile PBS for three days at 37°C. After incubation, the AGE product was dialyzed against sterile PBS for 24 hours at 4°C using gamma-irradiated 10kDa cut-off cassettes (Thermo Scientific, Waltham, MA) to remove unreacted glycolaldehyde. Unmodified control BSA was prepared similarly, without the addition of glycolaldehyde dimer. Protein concentration was determined by BCA assay (Thermo Scientific) and absence of endotoxin (<0.25Eu/ml) was confirmed via the LAL gel-clot assay (GenScript, Piscataway, NJ).

The extent of BSA modification was confirmed by fluorescence, absorbance, and loss of primary amines [15-18]. AGE-BSA and Control-BSA were diluted to 1mg/ml in PBS and fluorescent spectra and absorbance were recorded at 335nm excitation/420nm emission and 340nm, respectively (Molecular Devices, San Jose, CA). For determination of loss of primary amines AGE-BSA and Control-BSA were diluted to 0.2mg/ml in PBS. An equal volume of ortho-phthalaldehyde solution (Sigma) was added and fluorescent spectrum was recorded at 340nm excitation/455nm emission (Molecular Devices). Absorbance readings were completed to determine the extent of glycation. AGE-BSA showed increased glycation with absorbance readings of 0.682 AU compared to 0.01 AU for control BSA. AGE-BSA primary amine terminals underwent complete modification (-0.03% accessible amine terminals remaining), while control BSA retained 81.48% of accessible amine terminals. Negative values were interpreted as zero, and extent of modification was similar to previous reports [15].

RNA sequencing

Total RNA was isolated as previously described [8]. Briefly, RNA was isolated after BSA-Control or AGE-BSA treatment using the Direct-zol RNA Miniprep kit (Zymo Research, Irvine, CA). On-column DNase digestion was completed on all samples before elution of RNA. Total RNA from BSA-Control (n = 5) and AGE-BSA (n = 5) treated tendon fibroblasts was submitted to the Purdue University Genomics Core Facility (West Lafayette, IN) for PolyA+ library construction. The integrity of input total RNA was assessed using a Bioanalyzer RNA Nano chip (Agilent 2100, Santa Clara, CA). Libraries from 500ng of input total RNA were constructed as directed by the Nugen Universal Plus mRNA-Seq + UDI kit (PN#9144–96), but the RNA fragmentation time was decreased from 8 minutes to 4 minutes. Final library products were subjected to a 0.7 Ampure:1 Sample ratio purification to reduce lower molecular weight amplicons. The resulting libraries were assessed with an Agilent DNA High Sensitivity Chip for yield and quality and sequenced by Novogene (Sacramento, CA). Ten libraries were pooled and evenly distributed across a single HiSeq lane to generate ~40,000,000 2X150bp reads on the HiSeq 4000 platform (Illumina, San Diego, CA).

Bioinformatics

RNAseq raw data set quality and analysis was completed using Basepair software (New York, NY) pipelines. Reads were first aligned to the transcriptome derived from rn6 genome assembly using STAR with default parameters [19]. Next, read counts for each transcript were measured using featureCounts, and differentially expressed genes were determined using DESeq2 using a paired analysis [20, 21]. An adjusted p-value cut-off of 0.05 (corrected for multiple hypotheses testing) was used. Finally, GSEA was performed on normalized gene expression counts, using gene permutations for calculating p-value. A log2 fold change cut-off of 1.5 was enforced.

Descriptive pathway profiling

To preserve unbiased gene target selection and maintain a hypothesis-driven pathway selection, GeneGlobe (Qiagen, Hilden, Germany) pathway database was utilized to complete a descriptive approach to pathway analysis. We generated heat maps based on GeneGlobe RT2 profiler arrays independent of whether those gene targets were significantly altered in our dataset. Gene targets in the RT2 profilers but not in our dataset were excluded from heat maps. The percentage of significantly altered genes, both increased and decreased, was calculated based on the number of total genes included in each pathway’s respective heat map to rank the most implicated pathways. This systematic approach was employed to maintain an objective view of the global data.

Pathway analysis

RNAseq data were imported into Ingenuity Pathway Analysis (IPA, Qiagen) to determine select pathways and biological functions that were altered in response to AGE-BSA treatment.

Results and discussion

Overview

A total of 2,159 genes within our data set met the criteria of q<0.05 and fold change of greater than or less than 1.5 (log2 fold change greater than or less than 0.584). One thousand forty-six genes were significantly increased, and 1,113 were significantly decreased (Fig 1).

Fig 1

Volcano plot overview of RNA sequencing results.

Each point represents a single gene target. Red (n = 1046) indicates significant increase in gene expression. Blue (n = 1113) indicates significant decrease in gene expression. Black (n = 10,648) indicates gene targets that were either unaltered or did not meet our thresholds of q<0.05 and fold change of greater that 1.5 or less than -1.5.

Most affected gene targets

The top ten increased, and the top ten decreased gene targets within our data set were identified based on our log2 fold change and adjusted p-value thresholds. The top ten increased gene targets in order of highest to lowest positive log2 fold change were Cyp1a1, Pipox, Btc, Slc22a14, Tbxas1, Itgb2, Slc13a3, Cldn1, Ncf1, and Tnfrsf17 (Table 1). The top ten decreased gene targets in order of highest to lowest negative log2 fold change were Pimreg, Pmch, E2f7, Pbk, Parpbp, Ube2c, Troap, Cenpf, Cldn23, and Ccnb2 (Table 1).

Table 1

Most affected gene targets.

Gene	log2 Fold Change	q Value
Cyp1a1	7.07	6.77E-07
Pipox	4.78	7.59E-04
Btc	4.70	1.87E-05
Slc22a14	4.66	2.49E-04
Tbxas1	4.08	1.46E-02
Itgb2	4.06	3.42E-02
Slc13a3	4.05	4.78E-03
Cldn1	4.03	3.80E-06
Ncf1	4.01	1.19E-03
Tnfrsf17	3.94	9.65E-03
Pimreg	-4.94	1.01E-12
Pmch	-4.83	1.49E-33
E2f7	-4.45	8.80E-08
Pbk	-4.29	3.99E-05
Parpbp	-4.24	3.47E-23
Ube2c	-4.19	7.41E-24
Troap	-4.13	3.23E-07
Cenpf	-4.12	8.15E-11
Cldn23	-4.10	4.98E-03
Ccnb2	-4.08	5.44E-25

A total of 21 GeneGlobe (Qiagen) pathways were explored. Pathway selection was based on the literature, hypotheses that we have explored previously, and hypotheses we plan to explore in future studies. Select pathways strongly associated with AGE/RAGE biology were also included. Pathways were ranked strictly based on the percentage of significantly altered genes within that respective pathway. Pathways explored, in order from most to least implicated, were cell cycle (51.2%, Fig 2), extracellular matrix (ECM) and tenogenic markers (48.4%, Fig 3), DNA damage (40.3%, Fig 4), cellular senescence (39.2%, Fig 5), p53 signaling (38.7%, Fig 6), TGF-β signaling (32.4%, Fig 7), fibrosis (29.2%, Fig 8), oxidative stress (28.1%, Fig 9), wound healing (23.8%, Fig 10), growth factors (21.9%, Fig 11), transcription factors (20.6%, Fig 12), cytoskeleton (16%, Fig 13), cytokines (14.9%, Fig 14), innate and adaptive immunity (13.2%, Fig 15), NF-κB signaling (11.3%, Fig 16), cellular stress responses (10%, Fig 17), mitochondria (9.5%, Fig 18), apoptosis (8.5%, Fig 19), glycosylation (8.2%, Fig 20), inflammasomes (7.8%, Fig 21), and mitochondrial energy metabolism (2.6%, Fig 22). Pathways, listed in order of most implicated and respective figure numbers for heat maps, are summarized in Table 2.

Fig 2

Cell cycle heat map.

Bold text indicates significantly altered gene targets.

Fig 3

ECM and tenogenic markers heat map.

Bold text indicates significantly altered gene targets.

Fig 4

DNA damage heat map.

Bold text indicates significantly altered gene targets.

Fig 5

Cellular senescence heat map.

Bold text indicates significantly altered gene targets.

Fig 6

p53 signaling heat map.

Bold text indicates significantly altered gene targets.

Fig 7

TGF-β signaling heat map.

Bold text indicates significantly altered gene targets.

Fig 8

Fibrosis heat map.

Bold text indicates significantly altered gene targets.

Fig 9

Oxidative stress heat map.

Bold text indicates significantly altered gene targets.

Fig 10

Wound healing heat map.

Bold text indicates significantly altered gene targets.

Fig 11

Growth factors heat map.

Bold text indicates significantly altered gene targets.

Fig 12

Transcription factors heat map.

Bold text indicates significantly altered gene targets.

Fig 13

Cytoskeleton heat map.

Bold text indicates significantly altered gene targets.

Fig 14

Cytokines heat map.

Bold text indicates significantly altered gene targets.

Fig 15

Innate and adaptive immunity heat map.

Bold text indicates significantly altered gene targets.

Fig 16

NF-κB signaling heat map.

Bold text indicates significantly altered gene targets.

Fig 17

Cellular stress responses heat map.

Bold text indicates significantly altered gene targets.

Fig 18

Mitochondria heat map.

Bold text indicates significantly altered gene targets.

Fig 19

Apoptosis heat map.

Bold text indicates significantly altered gene targets.

Fig 20

Glycosylation heat map.

Bold text indicates significantly altered gene targets.

Fig 21

Inflammasomes heat map.

Bold text indicates significantly altered gene targets.

Fig 22

Mitochondrial energy metabolism heat map.

Bold text indicates significantly altered gene targets.

Table 2

Descriptive pathway profiling.

Figure	GeneGlobe Pathway	Altered Genes in Pathway	Total Genes in Pathway	Percent of Affected Genes
2	Cell Cycle	42	82	51.2
3	ECM and Tenogenic Markers	31	64	48.4
4	DNA Damage	29	72	40.3
5	Cellular Senescence	31	79	39.2
6	p53 Signaling	29	75	38.7
7	TGF-β Signaling	24	74	32.4
8	Fibrosis	19	65	29.2
9	Oxidative Stress	18	64	28.1
10	Wound Healing	15	63	23.8
11	Growth Factors	14	64	21.9
12	Transcription Factors	14	68	20.6
13	Cytoskeleton	13	81	16
14	Cytokines	7	47	14.9
15	Innate and Adaptive Immunity	7	53	13.2
16	NF-κB Signaling	8	71	11.3
17	Cellular Stress Responses	7	70	10
18	Mitochondria	7	74	9.5
19	Apoptosis	6	71	8.5
20	Glycosylation	6	73	8.2
21	Inflammasomes	5	64	7.8
22	Mitochondrial Energy Metabolism	2	77	2.6

Ten pathways or biological functions were selected using the IPA disease and function tool. Apoptosis (Z Score: 4.70), morbidity and mortality (Z Score: 4.53), organismal death (Z Score: 4.47), DNA damage (Z Score: 3.36), and diabetes mellitus (Z Score: 2.24) were selected as activated pathways. Cell survival (Z Score: -4.91), cell viability (Z Score: -4.62), repair of DNA (Z Score: -3.85), cell proliferation (Z Score: -3.67), and growth of connective tissue (Z Score: -3.02) were selected as inhibited pathways. IPA pathways are summarized in Table 3 with respective p-values and activation Z-scores.

Table 3

Select IPA pathway analysis.

Pathway	p Value	Activation Z Score
Apoptosis	1.45E-33	4.70
Morbidity or Mortality	4.62E-34	4.53
Organismal Death	2.06E-33	4.47
DNA Damage	7.32E-09	3.36
Diabetes Mellitus	1.27E-13	2.24
Cell Survival	4.74E-25	-4.91
Cell Viability	5.59E-23	-4.62
Repair of DNA	7.15E-15	-3.85
Cell Proliferation (Fibroblast)	7.59E-12	-3.67
Growth of Connective Tissue	5.10E-23	-3.02

Diabetes-related complications, such as those implicating connective tissue, create a large healthcare burden and reduce quality of life. Our knowledge of diabetes-related tendon degeneration has primarily been limited to macroscopic and structural changes with minimal molecular insight exists. Previous work from our laboratory has demonstrated that AGEs induce severe limitations to tendon fibroblast proliferative capacity and mitochondrial function while increasing mitochondrial DNA content [8]. We have followed up on these previous findings by completing a descriptive transcriptome profile of Achilles tendon-derived fibroblasts following AGE exposure. The goal of this study was to identify and rank pathways that were most implicated following AGE exposure, thus providing a more precise mechanistic exploration of AGE-mediated effects on tendon-derived cells.

Using a clinically-relevant concentration of AGEs [12, 22], we have previously demonstrated incorporation of synthetic nucleoside 5-ethynyl-2´-deoxyuridine (EdU) in tendon-derived fibroblasts to be ~3% following AGE-BSA (200μg/ml) exposure as compared to ~53% in the BSA-Control exposed group, which proliferate normally [8]. Further, we noted a reduction in proliferative gene markers, Mybl2 and Pcna, and reduced absorbance values of cytostatic MTT with AGE-BSA treatment in tendon fibroblasts. Our RNAseq data corroborated our previous findings of reduced Mybl2 and Pcna gene expression and revealed several additional genes responsible for cell cycle progression to be significantly impacted (Fig 2). In fact, our transcriptome analysis revealed that genes associated with the cell cycle are the most impacted by AGE treatment (Fig 2 and Table 2). Tendon fibroblast proliferation is vital for tendon development and adaptation [23, 24]. The inability of tenocytes to proliferate in the presence of AGEs could precipitate the development of tendon degeneration by limiting adaptations to loading [25]. Tendon healing requires a phase of increased cellular proliferation [23, 24, 26], thus AGE-induced limitations in cell proliferation could contribute to delayed in healing noted in those with diabetes [27-29]. In fact, would healing was identified as one of the top 10 GeneGlobe Pathways impacted by AGE treatment (Table 2 and Fig 10).

Gene targets associated with ECM maintenance and remodeling were also dramatically affected in our dataset (Fig 3). The ECM is vital to tendon tissue health and serves several vital functions, including cell adhesion, communication, and differentiation. Additionally, the ECM provides structural and biochemical support to the surrounding resident cell population. The tendon ECM consists primarily of type I and type III collagen fibers surrounded by proteoglycans that assist collagen fibrils’ assembly and stability [30]. A precise and linear arrangement of collagen fibrils is vital to tissue integrity and, therefore, mechanical function [31]. The inclusion of multiple collagen isoforms allows the ECM to specialize and adapt to specific mechanical loading and functional responses [32]. For instance, type I collagen (Col1a1) is a stronger collagen isoform. In contrast, type III collagen (Col3a1) is weaker and generally upregulated in the early stages of tissue remodeling following exercise or during the initial stages of healing [33, 34]. Col3a1 can provide temporary tensile strength to the tissue assembly until it is later replaced by stronger Col1a1 [35]. Although Col1a1 mRNA was unaffected in our RNAseq data set, Col3a1 mRNA expression was increased (Fig 3). Similarly, our previous report indicated Col3a1 mRNA expression increased with 50μg/ml and 100μg/ml AGE exposure compared to an equal dose of BSA-Control [8]. This increase in Col3a1 mRNA expression is likely in response to the AGE insult and an attempt to maintain the ECM environment.

Further, the most abundant tendon proteoglycan gene expression of decorin (Dcn) increased in our RNAseq data set (Fig 3). Dcn aids in the maintenance and regulation of collagen fibril structure and resident fibroblast proliferation [31]. As a critical regulator in matrix assembly, loss of Dcn would likely prove to be unfavorable to the strength of the tendon assembly, which would decrease the tissue’s ability to withstand sudden strain [31]. Our observed increase in Dcn gene expression may be a compensatory response that results in response to the AGE insult. However, impacts to Dcn content and gene expression would need to be externally validated in a whole diabetic tendon.

Lysine and hydroxylysine are found within the collagen amino acid sequence and play an essential role in cross-link formation. Oxidation of lysine and hydroxylysine by lysyl oxidase (Lox) forms cross-links within collagen fibrils, contributing to tissue integrity by increasing tensile strength and stabilizing the collagen fibril assembly. Strength and stability of the tissue assembly are essential, especially given the high contractile forces tendons are responsible for transmitting from muscle to bone. Our dataset revealed Lox gene expression to be significantly reduced following AGE exposure (Fig 3). If reduced mRNA expression of Lox coincides with reduced enzymatic cross-link formation, AGEs may contribute to a weakened tendon assembly due to loss of enzymatic cross-links between adjacent collagen fibrils. Tendons of diabetic animals generally have a reduced load to failure capacity, which may be a result of greater tissue degeneration at the macroscopic level [4, 11, 28, 36]. More work is needed to determine the impact of AGEs on the whole tendon fibril assembly.

Remodeling of the ECM is primarily regulated by enzymes known as matrix metalloproteinases (MMPs), which are responsible for the degradation portion of ECM remodeling. Collagenases such as MMP-1 and MMP-13 cleave type I collagen molecules in the ECM. Similarly, gelatinases, such as MMP-2 and MMP-9, degrade collagen isoforms in the ECM. MMPs are transcribed and translated as proenzymes and then secreted into the ECM, where they are activated through proteolytic cleavage of the N-terminal. Although MMP activity is degenerative, it facilitates ECM remodeling and tendon tissue adaptation. In turn, MMP activity can be reversibly inhibited by a group of enzymes known as tissue inhibitors of metalloproteinases (TIMPs). TIMPs play an essential role in ECM remodeling by limiting MMP activity and preventing excessive degradation. Counter-regulation via TIMP activity tightly regulates the breakdown and synthesis of collagen in response to external stresses, such as mechanical loading. Loss of ECM regulation, such as favoring degradation over synthesis, could alter the ECM responses to damage the tissue assembly. It is no surprise that the dysregulation of degenerative enzymes, such as MMPs, has been thought to play an essential role in developing tendon pathology in diabetes as overexpression of MMPs may favor ECM degradation [37]. Similarly, if inhibitory TIMPs are less expressed, the environment may also favor degradation by allowing MMPs to act on the ECM for a more extended period. Previous reports have indicated that AGEs increase MMP -2, -3, -9, and -13 secretion and expression in chondrocytes with 100μg/ml of AGEs [38, 39]. Further, mRNA expression of MMP -1, -3, and -13 in porcine chondrocytes was increased with 100μg/ml of AGE exposure [40]. Our previous work in Achilles tendon-derived fibroblasts demonstrated an increase in MMP -2 and -3 but no significant changes to MMP-9 and -13 [8]. Our RNAseq analysis confirmed MMP -2 and -3 to be elevated, along with MMP -15 and -17. However, we did not observe any changes to TIMP -1, -2, -3, or -4 in our RNAseq dataset, suggesting that MMPs may be exerting their function in an unorganized fashion that would favor a degenerative ECM environment. MMP gene expression data is limited in scope as it does not account for ECM secretion and N-terminal cleavage. However, the large impact that AGE exposure has on the dysregulation of ECM-related gene expression is further evidence that elevated serum AGEs may be contributing to the development of connective tissue pathology in diabetic populations (Fig 3).

Delayed and abnormal healing is a common complication of types I and II diabetes [27, 41]. Not only does it appear that diabetic patients are at risk of developing tendon tears, but healing post-repair is also impaired [42-44]. Interestingly, transforming growth factor (TGF)β1 expression was significantly reduced in our RNAseq data (Fig 7). In addition to TGFβ1 being one of the affected genes in the wound-healing pathway (Fig 10), the GeneGlobe TGFβ signaling pathway was also strongly influenced by AGE treatment (Table 2 and Fig 7). TGFβ is a critical factor in fibrosis and modulation of ECM homeostasis [45]. It has previously been demonstrated that TGFβ levels are significantly reduced in diseased human rotator cuff tendon samples [45]. In addition, TGFβ is known to modulate inflammatory responses by influencing fibroblast recruitment and stimulating collagen production [46, 47].

Inconsistent with known effect of TGFβ on collagen production [46, 47], Col1a1 was unchanged in our RNAseq dataset, and Col3a1 was increased (Fig 3). However, mRNA expression of Col5a1, Col5a2, and Col5a3 expression was significantly reduced in our RNAseq dataset. Type V is a fibrillar collagen isoform found less abundantly in a tendon but exists to provide support to tissues that do contain high levels of type V collagen isoforms [48]. While the wound healing GeneGlobe pathway was not as affected as other pathways, it is still likely that these gene targets contribute in some manner to the delayed healing response that is commonly observed following tendon injury in diabetic patients.

Conclusions

Several studies have shown that the risk of developing tendinopathy is greater in those with diabetes mellitus [42–44, 49]. Our new data highlights cell-signaling pathways that may assist with expanding our understanding of diabetic tendon pathology and failed healing responses. While our discussion is limited in scope, and we provide only transcriptome data, the purpose of this study was to complete a descriptive profile of the AGE insult to tendon fibroblasts. This work is the first data set to utilize RNAseq methodology to study the tendon fibroblast transcriptome following AGE exposure. These data will be helpful for further elucidation of the diabetic tendon disease process.

26 Apr 2022

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I congratulate authors to extend their previous study with a comprehensive gene profiling in rat tendon derived fibroblasts. AGEs exposure mainly affects pathways involved cell survival and proliferation as well as ECM remodeling. The data help to understand diabetic tendinopathy mechanisms to ask whether AGEs are the causal for the complication.

I do have a critique that authors used only one concentration of AGEs. I understand authors picked up the concentration from previous study. Cell death and damage pathways were strongly shown up in the analysis. I am worried that too severe damages can be a confounding factor to evaluate transcriptome changes. Meaning, mechanical stress inducing cell death may cause all the transcriptome changes. Are authors sure that the AGE concentration used here is clinically relevant concentration compared to diabetes patient serum? These discussions should be included in the manuscript.

Another minor points are:

• Please add GEO accession number if it is available in cover pages.

• Please spell out AGE-BSA vendor and catalog number in method section. AGEs consist of heterogenic products. Authors want to make the study reproducible for others.

• Ingenuity pathway analysis enables to determine upstream pathways. Authors may mitigate the question above too much AGEs mediated cell death.

Reviewer #2: In this manuscript, Patel et al. analyzed the transcriptome change of rat tendon-derived fibroblasts treated with AGEs. The top 21 implicated cell-signaling pathways are described and show that AGEs disrupt the transcriptome of tendon fibroblasts on a large scale, which may have some implications for future research in diabetic tendinopathy. However, due to the lack of further experimental verification and the small sample size of RNAseq, this study is of little significance. I think the authors should have further studies in this manuscript based on the RNAseq results rather than just analyzing the RNAseq results of a few samples.

In addition, I also have the following comments/suggestions for the authors’ consideration.

1. The sentence in the results section: “A total of 2,159 genes within our data set met the criteria of q<0.05 and fold change of greater than or less than 1.5 (log2 fold change greater than or less than 0.584)” needs clarification. The “q” is p-value, right? This problem also arises in Figure 1. What does “fold change of greater than or less than 1.5” mean?

2. The gene Cyp1a1 (log2 Fold Change is 7.07) does not show in Figure 1. What gene does the point on about log2 Fold Change 5 of Figure 1 represent? The gene names in Table1 should be shown in Figure 1.

3. The data in the table1 should be discussed in the discussion section.

4. The expression level of some genes, which were significant differences in RNAseq results and mentioned in the discussion section, should be re-validated by another experimental technique, such as QPCR.

5. In implicated cell-signaling pathways, network analysis of dysregulated genes needs to be supplemented to clarify the interaction of the genes.

6. The first paragraph of the discussion section repeats a lot of information already presented in the introduction section. These should be shortened.

7. The first ECM should be given its full name.

8. Some studies have shown that the expression level of Tnmd is decreased, but the expression level of this gene is increased in Figure3, why? I think this data needs to be verified again.

9. Some unimportant Figures and Table should be attached in Supplementary Information. The original sequencing results should also be uploaded.

Reviewer #3: This a beneficial study that investigate transcriptome-wide changes that occur to

tendon-derived fibroblasts following treatment with AGEs. There were some doubts about the number of rats in treated (exposed) and control and need clarification. Several varieties were evaluated that showed in 22 figures. Except numerous number of figures, I didn’t find any fault in the manuscript.

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Reviewer #1: Yes: Yuichiro Adachi

Reviewer #2: No

Reviewer #3: No

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28 Jun 2022

Reviewer 1

I congratulate the authors on extending their previous study with a comprehensive gene profiling in rat tendon-derived fibroblasts. AGEs exposure mainly affects pathways involved in cell survival and proliferation, and ECM remodeling. The data help understand diabetic tendinopathy mechanisms to ask whether AGEs are the causal for the complication.

I do have a critique that the authors used only one concentration of AGEs. I understand the authors picked up the concentration from the previous study. Cell death and damage pathways were strongly shown up in the analysis. I am worried that too severe damages can be a confounding factor to evaluate transcriptome changes. Meaning, mechanical stress inducing cell death may cause all the transcriptome changes. Are authors sure that the AGE concentration used here is clinically relevant concentration compared to diabetes patient serum? These discussions should be included in the manuscript.

Our initial report established a clear dose-response relationship between AGE concentration and effects on tendon cells. Our dose-response data from 0-200 �  g/ml) suggest no inflection point. All doses produce a similar direction in gene changes. We believe that the strong effects of AGEs on cells only strengthen the need further to determine the long-term impact of AGEs on tendon properties and explore the mechanisms of these effects. Including multiple doses would confound the RNA sequencing analysis and would like not to reveal any additional information, i.e., we would see similar trends in pathway activation/suppression just to a lower magnitude. Cells were not mechanically stressed, and the concentration used fits within the range of AGEs concentrations seen in an individual with diabetes (1, 2).

Another minor points are:

• Please add GEO accession number if it is available in cover pages. • Please spell out AGE-BSA vendor and catalog number in method section. AGEs consist of heterogenic products. Authors want to make the study reproducible for others.

• Ingenuity pathway analysis enables to determine upstream pathways. Authors may mitigate the question above too much AGEs mediated cell death.

1. We have uploaded our data to GEO (GSE204714).

2. The AGE-BSA is made “in-house,” as we have reported previously. (3) We have included additional details in the Methods and materials. Thank you.

3. In our previous data, 50 ug/ml was sufficient to induce limitations to cell proliferation and mitochondrial function. However, to maintain external validity, we choose a dose clinically relevant in the context of type 2 diabetes.

Reviewer 2

1. In this manuscript, Patel et al. analyzed the transcriptome change of rat tendon-derived fibroblasts treated with AGEs. The top 21 implicated cell-signaling pathways are described and show that AGEs disrupt the transcriptome of tendon fibroblasts on a large scale, which may have some implications for future research in diabetic tendinopathy. However, due to the lack of further experimental verification and the small sample size of RNAseq, this study is of little significance. I think the authors should have further studies in this manuscript based on the RNAseq results rather than just analyzing the RNAseq results of a few samples.

We thank the reviewer for their comments but respectfully disagree on the issue with sample size. It's challenging to determine a required sample size a priori for in vitro RNAseq studies. However, a general approach is to take a minimum sample size of N=4 and evaluate the coefficient of variation of key genes thought to be central to this process. Based on previous RNAseq studies using cultured tendon fibroblasts (4, 5), a sample size of N=4 provides low coefficients of variation and is sufficient to detect differences in key tenogenic, cell proliferation, and apoptotic genes, changes that match observations in the whole tendon. In this study, we used N=5 for each group and therefore feel we have an adequate sample size to test the central hypotheses of this manuscript. Further, studies have shown that 15 million reads (we used 40 million in our study) are adequate for power (6) and a sample size of five will yield sufficient statistical power (7).

2. The sentence in the results section: “A total of 2,159 genes within our data set met the criteria of q<0.05 and fold change of greater than or less than 1.5 (log2 fold change greater than or less than 0.584)” needs clarification. The “q” is p-value, right? This problem also arises in Figure 1. What does “fold change of greater than or less than 1.5” mean?

We used a very standard approach for RNAseq experimentation and data analysis based on the guidance of the bioinformatics experts in our genomics core. The q value is the false discovery rate (FDR) adjusted p-value. An FDR correction is applied to adjust for the multiple observations made. Setting a requirement for a gene to have a q-value < 0.05 and a 1.5-fold change difference before it is considered significantly different is standard. Because the volcano plot graphs are displayed on a log scale, we log transform the 1.5-fold difference (log2 1.5 = 0.584).

3. The gene Cyp1a1 (log2 Fold Change is 7.07) does not show in Figure 1. What gene does the point on about log2 Fold Change 5 of Figure 1 represent? The gene names in Table1 should be shown in Figure 1.

Thank you for the comments. Any x-y data point outside the boundary of the volcano plot is represented directly on the y axis. We included Table 1 to highlight the top gene changes. The volcano plot functions as a method to visualize the overall spread of the RNA sequencing data, and the tables highlight individual changes in gene expression.

4. The data in the table1 should be discussed in the discussion section.

To preserve unbiased gene target selection and maintain a hypothesis-driven pathway selection, GeneGlobe (Qiagen, Hilden, Germany) pathway database was utilized to complete a descriptive approach to pathway analysis. Our study aimed to complete a pathway analysis of genes that were relevant based on our previous data and knowledge of diabetic tendon pathology. The top 10 altered genes are provided to establish the overall impact of AGEs on fibroblast for reference for future studies.

5. The expression level of some genes, which were significant differences in RNAseq results and mentioned in the discussion section, should be re-validated by another experimental technique, such as QPCR.

We don't feel that qPCR is a necessary validation step for RNAseq. We used standard Illumina reagents and supplies that have been used in hundreds of thousands of previous studies. We have previously performed RNAseq in several tendon studies, and qPCR validation for these studies provided no different results than what was observed in RNAseq. Further, genes that we have assessed previously after AGE treatment in our cell culture model (3) followed similar patterns in our RNA sequencing studies. qPCR could be useful for looking at spliced isoform versions of specific genes where specific exon boundaries may not be picked up in all sequencing runs. But in the absence of this, we do not feel there is any additional value in performing qPCR experiments.

6. In implicated cell-signaling pathways, network analysis of dysregulated genes needs to be supplemented to clarify the interaction of the genes.

We used the IPA disease and function tool for bioinformatics analysis instead of direct gene network analysis. We felt this approach would help us extrapolate the in vitro data to provide better translational insight into the in vivo physiology of diabetic tendon disease.

7. The first paragraph of the discussion section repeats a lot of information already presented in the introduction section. These should be shortened.

Thank for the feedback. We have shortened this section of the Discussion.

8. The first ECM should be given its full name.

Thank you. We have made this correction.

9. Some studies have shown that the expression level of Tnmd is decreased, but the expression level of this gene is increased in Figure3, why? I think this data needs to be verified again.

The difference between Tnmd in our paper and others may be due to differences in experimental conditions. In previous papers, we have frequently measured Tnmd in qPCR and RNAseq, as discussed above, and have found the qPCR data to match the RNAseq data. Therefore, we are confident that the presented data adequately measures Tnmd expression. We are also unaware of any previous reports in tendon tissue or tendon cells assessing Tnmd expression after AGE exposure.

10. Some unimportant Figures and Table should be attached in Supplementary Information. The original sequencing results should also be uploaded.

We have moved several figures to Supplementary information. We have also uploaded our data to GEO.

Reviewer 3

This a beneficial study that investigated transcriptome-wide changes that occur to

tendon-derived fibroblasts following treatment with AGEs. There were some doubts about the number of rats in treated (exposed) and control and need clarification. Several varieties were evaluated that showed in 22 figures. Except numerous figures, I didn’t find any fault in the manuscript.

Thank you for your comments. We have added clarification on the source animals used for our study. Cells were isolated from five donor rats. Experiments were completed in a paired fashion such that each donor rat’s tendon fibroblast was exposed to AGE-BSA and BSA-only conditions.

REFERENCES

1. Boehm BO, Schilling S, Rosinger S, Lang GE, Lang GK, Kientsch-Engel R, et al. Elevated serum levels of N(epsilon)-carboxymethyl-lysine, an advanced glycation end product, are associated with proliferative diabetic retinopathy and macular oedema. Diabetologia. 2004;47(8):1376-9.

2. Yoshida N, Okumura K, Aso Y. High serum pentosidine concentrations are associated with increased arterial stiffness and thickness in patients with type 2 diabetes. Metabolism. 2005;54(3):345-50.

3. Patel SH, Yue F, Saw SK, Foguth R, Cannon JR, Shannahan JH, et al. Advanced Glycation End-Products Suppress Mitochondrial Function and Proliferative Capacity of Achilles Tendon-Derived Fibroblasts. Sci Rep. 2019;9(1):12614.

4. Gumucio JP, Schonk MM, Kharaz YA, Comerford E, Mendias CL. Scleraxis is required for the growth of adult tendons in response to mechanical loading. JCI Insight. 2020;5(13).

5. Disser NP, Ghahramani GC, Swanson JB, Wada S, Chao ML, Rodeo SA, et al. Widespread diversity in the transcriptomes of functionally divergent limb tendons. J Physiol. 2020;598(8):1537-50.

6. Liu Y, Zhou J, White KP. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics. 2014;30(3):301-4.

7. Ching T, Huang S, Garmire LX. Power analysis and sample size estimation for RNA-Seq differential expression. RNA. 2014;20(11):1684-96.

Submitted filename: PLOSONE RNA Seq Response.docx

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7 Jul 2022

Descriptive Transcriptome Analysis of Tendon Derived Fibroblasts Following In-Vitro Exposure to Advanced Glycation End Products

PONE-D-22-07074R1

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Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I congratulate authors. All comments have been addressed properly. The manuscript is a good shape for publication.

Reviewer #3: Dear author

This study could be one of novel studies that investigate the transcriptome-wide changes in tendon-

derived fibroblasts after treatment with AGEs.The revision was done. Althoguh it could be better if the number of animals clearly noted at animal protocol section that were described five-six wk old rats were selected,than Tendon fibroblast isolation and cell culture section: that number of donor animals were shown in parenthesis (5). However, I think the method were written fluent and clear.

Regards

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Reviewer #1: Yes: Yuichiro Adachi

Reviewer #3: Yes: Akefeh Ahmadiafshar M.D

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15 Jul 2022

PONE-D-22-07074R1

Descriptive transcriptome analysis of tendon derived fibroblasts following in-vitro exposure to advanced glycation end products

Dear Dr. Carroll:

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