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Article

Changes in Proteins in Saliva and Serum in Equine Gastric Ulcer Syndrome Using a Proteomic Approach

1
Clinic for Internal Diseases, Faculty of Veterinary Medicine, University of Zagreb, Heinzelova 55, 10000 Zagreb, Croatia
2
Interdisciplinary Laboratory of Clinical Analysis of the University of Murcia (Interlab-UMU), Department of Animal Medicine and Surgery, Veterinary School, Regional Campus of International Excellence Mare Nostrum, Campus de Espinardo, University of Murcia, 30100 Murcia, Spain
3
Department of Animal Medicine & Surgery, Veterinary School, Regional Campus of International Excellence Mare Nostrum, Campus de Espinardo, University of Murcia, 30100 Murcia, Spain
4
Medicine Animal, Faculty of Veterinary Medicine of Cáceres, University of Extremadura, Avenida de la Universidad S-N, 10002 Cáceres, Spain
5
BioQuant, Faculty of Biosciences, Heidelberg University, Im Neuenheimer Feld 267, 69117 Heidelberg, Germany
6
Department of Veterinary Clinical Sciences, Section Medicine and Surgery, University of Copenhagen, Agrovej 8, 2630 Taastrup, Denmark
*
Author to whom correspondence should be addressed.
Animals 2022, 12(9), 1169; https://doi.org/10.3390/ani12091169
Submission received: 22 March 2022 / Revised: 21 April 2022 / Accepted: 29 April 2022 / Published: 2 May 2022
(This article belongs to the Collection Clinical Pathology in Animals)

Abstract

:

Simple Summary

Equine gastric ulcer syndrome (EGUS) is a highly prevalent disease with a major clinical importance due to its negative effects on the welfare and performance of horses. EGUS can be distinguished into two different diseases depending on which anatomical region is affected: equine glandular gastric disease (EGGD), in which there is a lesion in the glandular stomach, and equine squamous gastric disease (ESGD), in which the alteration appears in the non-glandular stomach. EGUS has nonspecific clinical signs, and its underlying mechanism has not been completely elicited. Therefore, it would be of interest to clarify the pathophysiology and identify potential biomarkers of this syndrome. This study detected differences in the salivary and serum proteome between horses with EGUS and healthy horses, and also between horses with EGGD and ESGD. The most upregulated proteins in EGGD were related to the immune activation whereas, in horses with ESGD, the proteins with the most significant changes were associated with the squamous cell regulation and growth. Compared to serum, saliva had a higher number of proteins showing significant changes and also showed a different pattern of changes, indicating that the proteins in both fluids show a different response to the disease and can provide complementary information.

Abstract

Changes in the salivary proteome in 12 horses with the two diseases included in equine gastric ulcer syndrome (EGUS), equine glandular gastric disease (EGGD) (n = 6) and equine squamous gastric disease (ESGD) (n = 6), were evaluated using a high-resolution LC-MS/MS analysis of TMT-labelled peptides and compared to 10 healthy control horses. Serum was also analysed for comparative purposes. The comparison between the horses with EGGD and controls showed significant changes in 10 salivary proteins, whereas 36 salivary proteins were differently abundant between ESGD and control groups. The most upregulated proteins in the case of EGGD were related to immune activation whereas, in horses with ESGD, the most significantly changed proteins were associated with squamous cell regulation and growth. Compared to serum, saliva showed a higher number of proteins with significant changes and a different pattern of changes. The proteins identified in our study, in addition to providing new information about the pathophysiological mechanisms in these diseases, could have the potential to be novel biomarkers for the diagnosis or monitoring of EGGD and ESGD.

1. Introduction

Equine gastric ulcer syndrome (EGUS) is a highly prevalent disease with a high importance due to its negative effects on the welfare and performance of horses [1,2]. According to the European College of Equine Internal Medicine (ECEIM) Consensus Statement, EGUS can be distinguished into two different diseases, depending on which anatomical region is affected: equine squamous gastric disease (ESGD), in which there is a lesion in the non-glandular stomach; and equine glandular gastric disease (EGGD), in which the alteration appears in the glandular stomach [1,3]. EGUS has nonspecific clinical signs and its mechanism has not been totally elicited. The most common clinical signs that can be observed include poor appetite, loss of weight, yawning, bruxism, anorexia, salivation, abdominal discomfort, and reduced performance [2]. The only antemortem diagnostic method currently validated and considered as a gold standard is the evaluation of the entire stomach using gastroscopy, in order to visualize the specific lesions and their locations [4]. Due to the current lack of specific haematological or biochemical markers to diagnose EGUS, it would be of interest to discover analytes that could potentially serve as biomarkers of this syndrome [2,5,6].
Proteomics allows the identification of a high number of proteins simultaneously, thus being useful in the identification of proteins that can change in a selected disease [7]. Tandem mass tag (TMT) is a labelling procedure that allows the quantification of different peptides, which are marked and identified [8]. This technique has been used previously to investigate the salivary proteome in horses with acute abdominal syndrome [9]. In a previous study, an evaluation of changes in the serum proteins of horses with ESGD using gel electrophoresis and mass spectrometry has been performed [3]. In this report, 10 serum proteins were found to be possible biomarkers for ESGD. However, to the best of the current authors’ knowledge, there are no proteomic studies that evaluate changes in the proteome of saliva in EGUS, and the possible differences between ESGD and EGGD.
The hypothesis of this study is that ESGD and EGGD could produce changes in the proteins in saliva that could be detected by gel-free proteomics. Therefore, the objective was to evaluate the changes produced in saliva proteins of horses with ESGD, EGGD, and healthy horses through proteomic analysis using TMT. Serum was also analysed for comparative purposes. This data can contribute to a better understanding of the pathophysiological changes that occur in these diseases and to identify new potential biomarkers of these conditions.

2. Materials and Methods

2.1. Animals

Saliva and serum samples from 10 healthy horses and 12 horses with EGUS were included. In the EGUS group, there were 6 horses with each of the different presentation of the disease (EGGD and ESGD). Therefore, the animals included in this study were:
  • EGGD horses (n = 6; 3 geldings and 3 mares; mean age = 14.6 years (range 5–20); warmblood breeds (n = 4) and ponies (n = 2)). The presence of EGGD was diagnosed based on gastroscopic examination and the presence of compatible lesions in the glandular mucosa region of the stomach;
  • ESGD horses (n = 6; 3 geldings and 3 mares; mean age = 11 years (range 5–12); warmblood breeds (n = 4), trotter breed (n = 1), and Tinker breed (n = 1)). All horses had at least a grade 2/4 lesion identified during gastroscopy [1,10] according to the ECEIM Consensus Statement 1).
  • Healthy horses (n = 10; 7 geldings and 3 mares; mean age = 9.6 years (range 4–22); warmblood breeds (n = 3), Spanish pure breed (n = 1), ponies (n = 2), trotter breed (n = 1), Icelandic horse (n = 1), and crossbreds (n = 2)). These horses were found healthy based on history, clinical examination, complete blood count (CBC), and serum biochemistry profile. In addition, a gastroscopy study was performed to rule out EGUS.
All animals included in this study visited the Large Animal Teaching Hospital at the University of Copenhagen between February 2020 and October 2021. The diseased horses were diagnosed at that hospital by an internist with 10 years of experience from a specialized equine hospital (S.H.).
EGUS was suspected in horses based on compatible symptomatology (weight loss, pain behaviours, changes in temperament or reduced performance) and were referred to the hospital the day before the gastroscopy. Horses fasted for 12 h before the gastroscopy, which was performed as previously described [2]. Images from gastroscopy were used for the EGUS diagnosis [11] and classification according to the ECEIM Consensus Statement [1]. Based on this, horses were stratified into ESGD using the 4-point scale gradation or EGGD. Three of the horses diagnosed with ESGD showed 3 points of severity and three horses showed 2 points of severity.
The healthy horses were privately owned horses with no signs of illness. Gastroscopy was performed to rule out EGUS (using ESGD grading system equal to 0, meaning that epithelium is intact with no hyperkeratotic areas nor any glandular lesions). Horses were included in the healthy population if they had normal results upon physical examination (heart rate, respiratory rate, rectal temperature, colour of mucous membranes, capillary refill time, borborygmi) and they were free of any organ-related pathology based on haematological or biochemical findings.

2.2. Sampling of Saliva and Serum Specimens

The saliva samples were collected in all horses before intravenous sedation and gastroscopy, immediately after the horses were placed in the examination stand, as previously reported [12,13]. Blood samples were obtained by jugular venepuncture after the saliva collection. All tubes with saliva and all blood samples were centrifuged at 3000× g for 10 min at 4 °C and stored at −80 °C until analysis. All serum samples did not have visual gross haemolysis and all saliva samples did not have evidence of blood contamination according to the colour scale previously reported [14].

2.3. Proteomic Analysis

2.3.1. Sample Preparation

Proteomic analysis of saliva and serum samples was performed using the tandem mass tag (TMT)-based quantitative approach as previously described, with minor modifications [15]. Briefly, protein concentration was determined using bicinchoninic acid assay (BCA, Thermo Scientific, Rockford, IL, USA). Afterwards, 35 µg of the samples and internal standards (a pool of equal protein amounts from all samples) were reduced with 200 mM dithiothreitol (DTT, Sigma-Aldrich, St. Louis, MO, USA), alkylated with 375 mM iodoacetamide (Sigma-Aldrich, St. Louis, MO, USA), and precipitated with ice-cold acetone (VWR, Radnor, PA, USA) overnight. Protein pellets were collected subsequently by centrifugation (9000× g, 4 °C), dissolved in 50 μL of 0.1 M triethyl ammonium bicarbonate (TEAB, Thermo Scientific, Rockford, IL, USA) and digested using 1 μL of trypsin (1 mg/mL, Promega; trypsin-to-protein ratio 1:35, at 37 °C overnight). The TMT 6 plex reagents (Thermo Scientific, Rockford, IL, USA) were prepared according to the manufacturer’s procedure. Then, reaction was quenched using 5% hydroxylamine (Sigma-Aldrich, St. Louis, MO, USA).

2.3.2. Liquid Chromatography-Tandem Mass Spectrometry Analysis

TMT-labelled peptides were analysed by LC-MS/MS in high resolution using an Ultimate 3000 RSLCnano system (Dionex, Germering, Germany) coupled to a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) as previously described [16].
The SEQUEST algorithm implemented in Proteome discoverer (version 2.3., Thermo Fisher Scientific) was used for protein identification and quantification from the Acquired MS/MS spectra. A database searching against Equus caballus FASTA files was made as previously described [9].

2.4. Statistics and Bioinformatics

Fold changes between groups were calculated as the log2(Mean(Group2)/Mean(Group1)). Due to the small number of individuals, normal distribution of values was not assumed; thus, the Mann–Whitney U test was used to assess the statistical significance of differentially expressed proteins between EGUS and control horses, and the Kruskal–Wallis H test was used to search for significant differences in protein expression between the three different groups (EGGD, ESGD, and control). For those proteins, Dunn’s multiple comparison test was used in a post hoc analysis to differentiate the specific variation between these groups. Statistical significance was considered when p < 0.05. All statistical analyses were implemented using Python3 and the SciPy [17] library.
Proteins were mapped to UniProt [18] entries and then annotated with their recommended gene names and descriptions. For functional characterization of the differentially expressed proteins, gene ontology (GO) enrichment analysis was performed with the ClueGo/CluePedia plugin for Cytoscape [19] and its functionalities to fuse and group functionally related terms to reduce redundancy.

3. Results

3.1. Proteomic Changes in the Saliva of Horses with Equine Gastric Ulcer Syndrome

The comparison between horses with EGUS and healthy controls is shown in Table 1. Twenty-five proteins showed upregulation in horses with EGUS, while four proteins were downregulated. The three proteins with the highest upregulation were transmembrane protease serine (TMPRSS11D), WD repeat Domain 1 (WDR1), and Serpin B5. The most downregulated were alpha-2-HS-glycoprotein (AHSG), jacalin-type lectin domain-containing protein (JAC) and 6-phosphogluconate dehydrogenase decarboxylating (PGD).
GO enrichment analysis showed that nine GO terms were upregulated in the saliva of horses with EGUS (Supplementary Figure S1). Among them, the most significant were the regulation of actin filament-based process and protein processing (Supplementary Table S1).
The comparison of horses with EGGD and healthy horses showed upregulation of 10 salivary proteins (Table 2), the most significant being transmembrane protease serine, protein S100-A9 (PSA-9), and peptidase S1 domain-containing protein, which according to our GO analysis were mostly associated with serine-type endopeptidase activity (Supplementary Figure S2 and Supplementary Table S2). No proteins were downregulated in horses with EGGD compared to healthy horses.
The comparison of the ESGD and healthy horses revealed 36 salivary proteins that were significantly differentially expressed (Table 3). From these, the proteins most upregulated in ESGD were serpin B5, WDR1, and glutaredoxin (GLRX) and transmembrane protease serine, of which the latter two showed changes of the same statistical significance. Two proteins were downregulated: deoxyribonuclease-1 (DNASE1) and PGD. The GO terms most related to ESGD were eicosanoid metabolic process, unsaturated fatty acid biosynthetic process, and protein processing (Supplementary Figure S3 and Supplementary Table S3).
Additionally, when comparing EGGD and ESGD groups, fourteen proteins were differentially expressed (Table 4). The most upregulated proteins in the ESGD group were arginase (ARG1), serpin B5, and the keratins 15 (KRT15) and 4 (KRT4). One protein was downregulated in ESGD compared to EGGD: the peptidase S1 domain-containing protein. The GO enrichment analysis between the significantly expressed proteins in the comparison of the EGGD and ESGD groups were characterized by nine different GO terms upregulated in the ESGD group compared to EGGD group (Supplementary Figure S4). The most significant were the adaptative immune response, T cell activation, and regulation of cell population proliferation (Supplementary Table S4).

3.2. Proteomic Changes in the Serum of Horses with Equine Gastric Ulcer Syndrome

The comparison between horses with EGUS and healthy horses is shown in Table 5. Five proteins were upregulated, while two were downregulated in horses with EGUS. The most upregulated proteins were fibrinogen alpha chain (FGA), complement factor I (CFI), and C3/C5 convertase (C2), and the only two downregulated proteins were complement component 8 subunit beta (C8B) and pregnancy zone protein. No GO terms were highlighted in the GO term enrichment analysis for the global comparison of horses with EGUS and controls.
Ten proteins were differentially abundant in the serum of horses with EGGD and healthy horses (Table 6). The three proteins that were most upregulated were FGA, C2, and hyaluronan binding protein 2 (HABP2). The most downregulated proteins were complement factor H (CFH), pregnancy zone protein, complement subcomponent C1r (C1R), and adiponectin A (C1QB). GO enrichment analysis revealed these downregulated proteins to be associated with the functional terms complement activation classical pathway and systemic lupus erythematosus (Supplementary Figure S5 and Supplementary Table S5).
In horses with ESGD, expression of nine proteins was significantly increased compared to healthy horses (Table 7). The most upregulated proteins were actin cytoplasmic 1 (ACTB), serpin family A member 6 (SERPINA6), and alpha-1-antiproteinase 2-like (SPI2). The most downregulated proteins were histidine-rich glycoprotein (HRG), C8B, and plasminogen (PLG). A total of six different GO terms were altered in this comparison (Supplementary Figure S6). The most significant upregulated GO terms were negative regulation of molecular function, serine-type endopeptidase inhibitor activity, and cellular protein metabolic process (Supplementary Table S6).
The comparison between the two EGUS groups showed eight proteins differently expressed, seven being upregulated and one downregulated in ESGD compared to EGGD (Table 8). The most relevant proteins with upregulated expression were C1r, ACTB, and EGF containing fibulin extracellular matrix protein 1, while only alpha-2-HS glycoprotein was found downregulated. GO terms analysis showed the enrichment of the postsynapse, synapse organization, and phagosome GO terms within these upregulated proteins (Supplementary Figure S7 and Supplementary Table S7).

4. Discussion

Our results provide preliminary evidence of EGUS-induced changes in the salivary protein profile, which could further differentiate between the two EGUS clinical forms, EGGD and ESGD. The number of differentially expressed proteins was more than three times higher in ESGD than in EGGD. Furthermore, the changes were not uniform when the horses with EGGD and ESGD were independently compared with the healthy horses. Therefore, it could be a better approach to assess the changes in saliva in EGGD and ESGD separately, instead of in all horses with EGUS.
Many of the proteins with the increased abundance of saliva in EGGD (e.g., TMPRSS11D, S100-A9, joining (J) chain, and adenosylhomocysteinase) share a common feature as they are involved in the regulation and activation of the immune system. This is in line with a recent report indicating the involvement of immune-mediated mechanisms and lymphoplasmacytic infiltration of the glandular mucosal inflammation in EGGD [5].
TMPRSS11D is a transmembrane serine protease and, as such, it is deeply involved in the inflammation and immune system reaction due to its ability to cleave the peptide bonds. Serine protease activation has been associated with an exacerbation of the immune response [20]. In addition, it has been reported in humans that Helicobacter pylori, a bacterium that could be involved in EGGD pathogenesis [1], can activate serine proteases, impairing the cellular repair and leading to connective tissue and extracellular matrix degradation [20]. However, to date, there remains conflict in the literature as to the role of bacteria in EGGD and there could be other factors that could activate serine proteases in this disease. For example, vascular and circulatory diseases can activate proteases [21]. This could potentially occur in EGGD, where circulatory disorders within the stomach leading to a reduced gastric glandular blood flow wall have been described as a possible cause [22].
S100-A9, also known as MRP14, is a Ca2 + binding protein belonging to the S100 family that forms a complex with S100-A8 called calprotectin. The upregulation of S100-A9 occurs in multiple immune system dysfunction diseases that result in excessive immune responses leading to autoimmune diseases and hypersensitivity reactions [23].
The joining (J) chain is a small polypeptide expressed by mucosal and glandular plasma cells, which regulates polymer formation of immunoglobulin (Ig) A and IgM. J chain shows a high affinity for the polymeric Ig receptor (pIgR) also known as transmembrane secretory component (SC). This epithelial glycoprotein mediates active external transfer of IgA and pentameric IgM to exocrine secretions. Therefore, it is a key protein in mucosal immunity [24].
The adenosylhomocysteinase, also named S-adenosylhomocysteine hydrolase, is an enzyme that converts S-adenosylhomocysteine into homocysteine and adenosine [25]. The in vivo activity of this enzyme depends on the function of another enzyme, adenosine deaminase (ADA). Both are located in the same chromosome and it seems that S-adenosylhomocysteine hydrolase, a eukaryotic enzyme, evolutionarily appeared later than ADA, which occurs in prokaryotes as well as eukaryotes [26,27]. Therefore, it could be postulated that the increase in adenosylhomocysteinase could also imply an increase in ADA, which is an enzyme related to immune function. This would be in line with the recent report on the increase in ADA in saliva in EGGD [28].
In the GO analysis of EGGD there was an activation of the pathway of the serine-type endopeptidase activity, which is in line with the increase found in the serine protease TMPRSS11D that is directly related to this GO term.
Saliva from horses diagnosed with ESGD showed a higher number of proteins with significant expression changes than the horses with EGGD. Some of those, such as serpin B5, WDR1, phosphoglycerate kinase 1 (PGK1), and keratins 15 and 4, have as a common feature the regulation of the growth of squamous epithelial cells, which represent the cytological substrate for ESGD [1].
Serpin B5 (also called maspin) is an intracellular serine protease inhibitor expressed in squamous epithelial cells, and increases in its expression have been described in ulcerative colitis in humans. This protein is involved in epithelial cell proliferation and resistance to apoptosis [29]. In addition, serpin B5 expression could be a marker of disease activity, since it was shown to be increased in over 90% of patients with active inflammatory bowel disease, being correlated to the activity of the disease [30]. This protein has been detected in saliva in humans associated to oral squamous cell carcinoma [31]. This could indicate that this protein is involved in alterations of the normal function, regulation, and metabolism of squamous cells that can occur in ESGD.
WDR1 is a protein that is involved in epithelial development and membrane epithelial cell junctions [32].
PGK1 has been found to increase in epithelial cells in the situation of anoxia and cellular damage and it has even been related to squamous cell neoplasm [33].
KRT15 and KRT8 are present in basal keratinocytes of all stratified epithelia, being also involved in epithelial cell proliferation [34]. In a previous study, other members of the keratin family, keratins 5 and 10, were found increased in the serum of horses with ESGD [3].
Besides serpin B5, WDR1, PGK1, and keratins 15 and 4, our results identify additional proteins involved in epithelial regulation that have a higher abundance in ESGD than in EGGD, thus implicating the presence of a complex disturbance in the squamous cell regulation. One of these proteins was arginase, which is involved in the growth of squamous cells. The upregulation of arginase activity has been described in gastric, breast, renal cell, and head and neck squamous cell carcinomas, thus making the inhibition of this enzyme a possible target for the treatment of these neoplasms [35]. On the other hand, arginase was lower in EGGD. Decreases in arginase activity in gastric ulcers have been reported in mice [36]. In addition to this, arginase has a regulatory role in gastrointestinal inflammation and low values could be related to an impaired tissue repair since upregulation of arginase increases the level of polyamines, which play a significant role in wound healing [37].
Overall, the changes in proteins in ESGD indicated an alteration of the squamous mucosa cells. This alteration has been found to be caused by acid and results in hyperkeratosis, erosions, and ulceration. Many of the risks for development of squamous disease relate to factors that allow or promote a more acidic gastric pH or increase exposure of the squamous mucosa to this acid [21].
In our study, the GO terms altered in horses with ESGD showed an activation of the pathways related to epithelium metabolism. Overall, when ESGD and EGGD were compared, the GO analysis in ESGD showed a tendency for activation of the pathways related to epithelium development and activation, while serine-type endopeptidase activity was more related to EGGD, as indicated above.
Serum of horses with ESGD and EGGD, showed a lower number of proteins with significant changes than saliva. Additionally, the GO analysis showed that the changes present in saliva mirror a higher number of upregulated processes. In addition, there was no substantial match between the pattern of proteome changes in the saliva and serum. This discrepancy in the number and types of protein changing between saliva and serum has also been observed in other diseases in different species, such as canine pyometra or mammary tumours [38,39], or in cows with mastitis [40]. Those results would indicate that changes in the composition of both fluids show a different response to the disease and can provide complementary information. These differences could be explained in part because there are analytes present in saliva that are locally produced in the salivary glands [41].
In a previous proteomic study analysing the saliva of horses with colic of intestinal aetiology and using the same technology, different proteins were identified compared to the current report [9]. These proteins were mostly related to a protective effect against inflammation and an impaired immune defence and antimicrobial capacity of the mucosa. This would reflect a different pathophysiological mechanism of this disease in comparison to ESGD or EGGD.
It is important to point out that this should be considered a pilot study. The sine qua non for the large-scale validation of these results should be the development of high-throughput assays for the quantitation of the main proteins that had different abundance levels between the groups. This large-scale validation should include a larger number of horses with ESGD and EGGD, as well as horses with other diseases, in order to determine the clinical sensitivity and specificity of these proteins, and to evaluate their possible use as biomarkers for diagnostic or monitoring purposes. In addition, since the gastroscopy exam of the horses with EGUS required a period of fasting, it would be interesting to investigate the possible effect of fasting in salivary protein variations.

5. Conclusions

In our study, horses with ESGD and EGGD showed different protein profiles in saliva when analysed by proteomics with a TMT-based approach. The most upregulated proteins in EGGD were related to immune activation whereas, in horses with ESGD, the proteins that changed were related to squamous cell regulation and growth. The proteins identified in our study, in addition to providing novel information about the pathophysiological mechanisms in these diseases, could have the potential to be new biomarkers for diagnosis or monitoring of EGGD and ESGD. Furthermore, our data provide additional evidence that EGGD and ESGD have two different pathophysiological mechanisms of disease and suggest that they should be considered two different diseases instead combining them into a single syndrome of EGUS.

Supplementary Materials

The following supporting information can be downloaded at: www.mdpi.com/article/10.3390/ani12091169/s1, Figure S1: Interactome of GO terms differentially expressed in the saliva of horses with EGUS, and their intermediate proteins. Figure S2: Interactome of GO terms differentially expressed in the saliva of horses with EGGD compared with controls, and their intermediate proteins. Figure S3: Interactome of GO terms differentially expressed in the saliva of horses with ESGD compared with controls, and their intermediate proteins. Figure S4: Interactome of GO terms differentially expressed in the saliva of horses with ESGD compared with horses with EGGD, and their intermediate proteins. Figure S5: Interactome of GO terms differentially expressed in the serum of horses with EGGD compared with controls, and their intermediate proteins. Figure S6: Interactome of GO terms differentially expressed in the serum of horses with ESGD compared with controls, and their intermediate proteins. Figure S7: Interactome of GO terms differentially expressed in the serum of horses with ESGD compared with horses with EGGD, and their intermediate proteins. Table S1: Groups of most representative GO terms in the saliva of horses with EGUS compared with controls. Table S2: Groups of most representative GO terms in the saliva of horses with EGGD compared with controls. Table S3: Groups of most representative GO terms in the saliva of horses with ESGD compared with controls. Table S4: Groups of most representative GO terms in the saliva of horses with ESGD compared with EGGD. Table S5: Groups of most representative GO terms in the serum of horses with EGGD compared with controls. Table S6: Groups of most representative GO terms in the serum of horses with ESGD compared with controls. Table S7: Groups of most representative GO terms in the serum of horses with ESGD compared with EGGD.

Author Contributions

Conceptualization, A.M.-P., J.J.C.,V.M. and S.H.; methodology, A.M.-P., M.D.C.-A., J.J.C., M.M.-C., S.J. and F.T.; software, A.M.-P., J.C.G.-S., J.K. and A.B.; validation, A.M.-P., J.J.C. and F.T.; formal analysis, A.M.-P., J.K., A.B. and I.R.; investigation, A.M.-P., M.D.C.-A., J.J.C., V.M. and F.T.; resources, J.J.C., I.A., V.M. and F.T.; data curation, A.M.-P., J.C.G.-S., J.K. and A.B.; writing—original draft preparation, A.M.-P. and J.J.C.; writing—review and editing, A.M.-P., M.D.C.-A., J.J.C., I.A., M.M.-C., J.C.G.-S., S.J., J.K., A.B., I.R., V.M., F.T. and S.H.; visualization, J.J.C., I.A., S.J., V.M., F.T. and S.H.; supervision, A.M.-P., J.J.C., V.M. and F.T.; project administration, J.J.C., V.M. and F.T.; funding acquisition, J.J.C., I.A., S.J., V.M., F.T. and S.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by a grant from the Program for Research Groups of Excellence of the Seneca Foundation, Murcia, Spain (Grant 19894/GERM/15) and by the European Structural and Investment Funds (Grant Agreement KK.01.1.16.0004). A.M.-P. is funded by the University of Murcia through a postdoctoral grant (Margarita Salas) within the mark of “Ayudas en el marco del Programa para la Recualificación del Sistema Universitario Español” through the European Union Next Generation fund.

Institutional Review Board Statement

The animal study protocol was approved by the Murcia University Ethics Committee (protocol code CEEA 288/2017) and by the local committee of the Large Animal Teaching Hospital of University of Copenhagen (protocol code # 2020-020).

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; the collection, analyses, or interpretation of data; the writing of the manuscript; nor in the decision to publish the results.

References

  1. Sykes, B.W.; Hewetson, M.; Hepburn, R.J.; Luthersson, N.; Tamzali, Y. European College of Equine Internal Medicine Consensus Statement-Equine Gastric Ulcer Syndrome in Adult Horses. J. Vet. Intern. Med. 2015, 29, 1288–1299. [Google Scholar] [CrossRef] [PubMed]
  2. Shawaf, T.; El-Deeb, W.M.; Elgioushy, M. The Contribution of Specific and Nonspecific Biomarkers in Diagnosis of Equine Gastric Ulcer Syndrome (EGUS) Under Field Condition. J. Equine Vet. Sci. 2020, 84, 102853. [Google Scholar] [CrossRef] [PubMed]
  3. Tesena, P.; Yingchutrakul, Y.; Roytrakul, S.; Taylor, J.; Angkanaporn, K.; Wongtawan, T. Searching for Serum Protein Markers of Equine Squamous Gastric Disease Using Gel Electrophoresis and Mass Spectrometry. Equine Vet. J. 2019, 51, 581–586. [Google Scholar] [CrossRef] [PubMed]
  4. Bell, R.J.W.; Mogg, T.D.; Kingston, J.K. Equine Gastric Ulcer Syndrome in Adult Horses: A Review. N. Z. Vet. J. 2007, 55, 1–12. [Google Scholar] [CrossRef] [PubMed]
  5. Banse, H.E.; Andrews, F.M. Equine Glandular Gastric Disease: Prevalence, Impact and Management Strategies. Vet. Med. Res. Rep. 2019, 10, 69–76. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Busechian, S.; Sgorbini, M.; Orvieto, S.; Pisello, L.; Zappulla, F.; Briganti, A.; Nocera, I.; Conte, G.; Rueca, F. Evaluation of a Questionnaire to Detect the Risk of Developing ESGD or EGGD in Horses. Prev. Vet. Med. 2021, 188, 105285. [Google Scholar] [CrossRef]
  7. Bilić, P.; Kuleš, J.; Galan, A.; Gomes de Pontes, L.; Guillemin, N.; Horvatić, A.; Festa Sabes, A.; Mrljak, V.; Eckersall, P.D. Proteomics in Veterinary Medicine and Animal Science: Neglected Scientific Opportunities with Immediate Impact. Proteomics 2018, 18, e1800047. [Google Scholar] [CrossRef]
  8. Baeumlisberger, D.; Arrey, T.N.; Rietschel, B.; Rohmer, M.; Papasotiriou, D.G.; Mueller, B.; Beckhaus, T.; Karas, M. Labeling Elastase Digests with TMT: Informational Gain by Identification of Poorly Detectable Peptides with MALDI-TOF/TOF Mass Spectrometry. Proteomics 2010, 10, 3905–3909. [Google Scholar] [CrossRef]
  9. Muñoz-Prieto, A.; Escribano, D.; Contreras-Aguilar, M.D.; Horvatić, A.; Guillemin, N.; Jacobsen, S.; Cerón, J.J.; Mrljak, V. Tandem Mass Tag (TMT) Proteomic Analysis of Saliva in Horses with Acute Abdominal Disease. Animals 2021, 11, 1304. [Google Scholar] [CrossRef]
  10. Rendle, D.; Bowen, M.; Brazil, T.; Conwell, R.; Hallowell, G.; Hepburn, R.; Hewetson, M.; Sykes, B. Recommendations for the Management of Equine Glandular Gastric Disease. UK-Vet Equine 2018, 2, 2–11. [Google Scholar] [CrossRef] [Green Version]
  11. Wise, J.C.; Wilkes, E.J.A.; Raidal, S.L.; Xie, G.; Crosby, D.E.; Hale, J.N.; Hughes, K.J. Interobserver and Intraobserver Reliability for 2 Grading Systems for Gastric Ulcer Syndrome in Horses. J. Vet. Intern. Med. 2021, 35, 571–579. [Google Scholar] [CrossRef] [PubMed]
  12. Contreras-Aguilar, M.; Martínez-Subiela, S.; Cerón, J.; Martín-Cuervo, M.; Tecles, F.; Escribano, D. Salivary Alpha-Amylase Activity and Concentration in Horses with Acute Abdominal Disease: Association with Outcome. Equine Vet. J. 2019, 51, 569–574. [Google Scholar] [CrossRef] [PubMed]
  13. Contreras-Aguilar, M.; Escribano, D.; Martínez-Subiela, S.; Martín-Cuervo, M.; Lamy, E.; Tecles, F.; Cerón, J.J. Changes in Saliva Analytes in Equine Acute Abdominal Disease: A Sialochemistry Approach. BMC Vet. Res. 2019, 15, 187. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Contreras-Aguilar, M.D.; Hevia, M.L.; Escribano, D.; Lamy, E.; Tecles, F.; Cerón, J.J. Effect of Food Contamination and Collection Material in the Measurement of Biomarkers in Saliva of Horses. Res. Vet. Sci. 2020, 129, 90–95. [Google Scholar] [CrossRef]
  15. Horvatic, A.; Guillemin, N.; Kaab, H.; McKeegan, D.; O’Reilly, E.; Bain, M.; Kules, J.; Eckersall, P.D. Integrated Dataset on Acute Phase Protein Response in Chicken Challenged with Escherichia Coli Lipopolysaccharide Endotoxin. Data Br. 2018, 21, 684–699. [Google Scholar] [CrossRef]
  16. Kuleš, J.; Lovrić, L.; Gelemanović, A.; Beer Ljubić, B.; Rubić, I.; Bujanić, M.; Konjević, D. Complementary Liver and Serum Protein Profile in Wild Boars Infected by the Giant Liver Fluke Fascioloides Magna Using Tandem Mass Tags Quantitative Approach. J. Proteom. 2021, 247, 104332. [Google Scholar] [CrossRef]
  17. Virtanen, P.; Gommers, R.; Oliphant, T.E.; Haberland, M.; Reddy, T.; Cournapeau, D.; Burovski, E.; Peterson, P.; Weckesser, W.; Bright, J.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat. Methods 2020, 17, 261–272. [Google Scholar] [CrossRef] [Green Version]
  18. Bateman, A.; Martin, M.J.; Orchard, S.; Magrane, M.; Agivetova, R.; Ahmad, S.; Alpi, E.; Bowler-Barnett, E.H.; Britto, R.; Bursteinas, B.; et al. UniProt: The Universal Protein Knowledgebase in 2021. Nucleic Acids Res. 2021, 49, D480–D489. [Google Scholar] [CrossRef]
  19. Bindea, G.; Mlecnik, B.; Hackl, H.; Charoentong, P.; Tosolini, M.; Kirilovsky, A.; Fridman, W.-H.; Pagès, F.; Trajanoski, Z.; Galon, J. ClueGO: A Cytoscape Plug-in to Decipher Functionally Grouped Gene Ontology and Pathway Annotation Networks. Bioinformatics 2009, 25, 1091–1093. [Google Scholar] [CrossRef] [Green Version]
  20. Patel, S. A Critical Review on Serine Protease: Key Immune Manipulator and Pathology Mediator. Allergol. Immunopathol. 2017, 45, 579–591. [Google Scholar] [CrossRef]
  21. Slack, M.A.; Gordon, S.M. Protease Activity in Vascular Disease. Arterioscler. Thromb. Vasc. Biol. 2019, 39, E210–E218. [Google Scholar] [CrossRef]
  22. Hallowell, G. Pathogenesis of Equine Squamous and Glandular Gastric Disease. UK-Vet Equine 2018, 2, 70–75. [Google Scholar] [CrossRef]
  23. Wang, S.; Song, R.; Wang, Z.; Jing, Z.; Wang, S.; Ma, J. S100A8/A9 in Inflammation. Front. Immunol. 2018, 9, 1298. [Google Scholar] [CrossRef]
  24. Johansen, F.E.; Braathen, R.; Brandtzaeg, P. Role of J Chain in Secretory Immunoglobulin Formation. Scand. J. Immunol. 2000, 52, 240–248. [Google Scholar] [CrossRef]
  25. Palmer, J.; Abeles, R. The Mechanism of Action of S-Adenosylhomocysteinase. J. Bio. Chem. 1979, 254, 1217–1226. [Google Scholar] [CrossRef]
  26. Hershfield, M.S.; Francke, U. The Human Genes for S-Adenosylhomocysteine Hydrolase and Adenosine Deaminase Are Syntenic on Chromosome 20. Science 1982, 216, 739–742. [Google Scholar] [CrossRef]
  27. Kloor, D.; Osswald, H. S-Adenosylhomocysteine Hydrolase as a Target for Intracellular Adenosine Action. Trends Pharmacol. Sci. 2004, 25, 294–297. [Google Scholar] [CrossRef]
  28. Contreras-Aguilar, M.D.; Rubio, C.P.; González-Arostegui, L.G.; Martín-Cuervo, M.; Cerón, J.J.; Ayala, I.; Henriksen, I.M.H.; Jacobsen, S.; Hansen, S. Changes in Oxidative Status Biomarkers in Saliva and Serum in the Equine Gastric Ulcer Syndrome and Colic of Intestinal Aetiology: A Pilot Study. Animals 2022, 12, 667. [Google Scholar] [CrossRef]
  29. Planell, N.; Lozano, J.J.; Mora-Buch, R.; Masamunt, M.C.; Jimeno, M.; Ordás, I.; Esteller, M.; Ricart, E.; Piqué, J.M.; Panés, J.; et al. Transcriptional Analysis of the Intestinal Mucosa of Patients with Ulcerative Colitis in Remission Reveals Lasting Epithelial Cell Alterations. Gut 2013, 62, 967–976. [Google Scholar] [CrossRef] [Green Version]
  30. Cao, D.; Wilentz, R.E.; Abbruzzese, J.L.; Ho, L.; Maitra, A. Aberrant Expression of Maspin in Idiopathic Inflammatory Bowel Disease Is Associated with Disease Activity and Neoplastic Transformation. Int. J. Gastrointest. Cancer 2005, 36, 39–46. [Google Scholar] [CrossRef]
  31. Csősz, É.; Márkus, B.; Darula, Z.; Medzihradszky, K.F.; Nemes, J.; Szabó, E.; Tőzsér, J.; Kiss, C.; Márton, I. Salivary Proteome Profiling of Oral Squamous Cell Carcinoma in a Hungarian Population. FEBS Open Bio. 2018, 8, 556–569. [Google Scholar] [CrossRef] [Green Version]
  32. The Human Protein Atlas. Available online: https://www.proteinatlas.org/ENSG00000071127-WDR1 (accessed on 20 March 2022).
  33. Zhang, Y.; Cai, H.; Liao, Y.; Zhu, Y.; Wang, F.; Hou, J. Activation of PGK1 under Hypoxic Conditions Promotes Glycolysis and Increases Stem Cell-like Properties and the Epithelial-Mesenchymal Transition in Oral Squamous Cell Carcinoma Cells via the AKT Signalling Pathway. Int. J. Oncol. 2020, 57, 743. [Google Scholar] [CrossRef]
  34. Bose, A.; Teh, M.T.; Mackenzie, I.C.; Waseem, A. Keratin K15 as a Biomarker of Epidermal Stem Cells. Int. J. Mol. Sci. 2013, 14, 19385–19398. [Google Scholar] [CrossRef] [Green Version]
  35. Mittal, A.; Wang, M.; Vidyarthi, A.; Yanez, D.; Pizzurro, G.; Thakral, D.; Tracy, E.; Colegio, O.R. Topical Arginase Inhibition Decreases Growth of Cutaneous Squamous Cell Carcinoma. Sci. Rep. 2021, 11, 10731. [Google Scholar] [CrossRef]
  36. Yadav, S.K.; Adhikary, B.; Maity, B.; Bandyopadhyay, S.K.; Chattopadhyay, S. The Gastric Ulcer-Healing Action of Allylpyrocatechol Is Mediated by Modulation of Arginase Metabolism and Shift of Cytokine Balance. Eur. J. Pharmacol. 2009, 614, 106–113. [Google Scholar] [CrossRef]
  37. Satriano, J. Arginine Pathways and the Inflammatory Response: Interregulation of Nitric Oxide and Polyamines: Review Article. Amino Acids 2004, 26, 321–329. [Google Scholar] [CrossRef]
  38. Franco-Martínez, L.; Horvatić, A.; Gelemanović, A.; Samardžija, M.; Mrljak, V.; Contreras-Aguilar, M.D.; Martínez-Subiela, S.; Dąbrowski, R.; Tvarijonaviciute, A. Changes in the Salivary Proteome Associated with Canine Pyometra. Front. Vet. Sci. 2020, 7, 277. [Google Scholar] [CrossRef]
  39. Franco-Martínez, L.; Gelemanović, A.; Horvatić, A.; Contreras-Aguilar, M.D.; Dąbrowski, R.; Mrljak, V.; Cerón, J.J.; Martínez-Subiela, S.; Tvarijonaviciute, A. Changes in Serum and Salivary Proteins in Canine Mammary Tumors. Animals 2020, 10, 741. [Google Scholar] [CrossRef]
  40. Franco-Martínez, L.; Muñoz-Prieto, A.; Contreras-Aguilar, M.D.; Želvytė, R.; Monkevičienė, I.; Horvatić, A.; Kuleš, J.; Mrljak, V.; Cerón, J.J.; Escribano, D. Changes in Saliva Proteins in Cows with Mastitis: A Proteomic Approach. Res. Vet. Sci. 2021, 140, 91–99. [Google Scholar] [CrossRef]
  41. Cerón, J.J.; Aguilar, M.D.C.; Escribano, D.; Miró, S.M.; Martínez, M.J.L. Basics for the Potential Use of Saliva to Evaluate Stress, Inflammation, Immune System, and Redox Homeostasis in Pigs. BMC Vet. Res. 2022, 18, 81. [Google Scholar] [CrossRef]
Table 1. Differentially abundant salivary proteins between horses with equine gastric ulcer syndrome (EGUS) (n = 12) and control group (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and EGUS group, Log2(FC) between EGUS and control groups, and p-value derived from Mann–Whitney analysis.
Table 1. Differentially abundant salivary proteins between horses with equine gastric ulcer syndrome (EGUS) (n = 12) and control group (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and EGUS group, Log2(FC) between EGUS and control groups, and p-value derived from Mann–Whitney analysis.
Accession (UniProt)Protein NameMean and SD ControlMean and SD EGUSLog2(FC)p-ValueRegulation in EGUS
F6QSG9Transmembrane protease serine 0.63 ± 0.391.32 ± 0.791.040.003UP
A0A3Q2I4F4WD repeat domain 1 0.45 ± 0.270.79 ± 0.400.790.029UP
F6YDZ0Serpin B5 0.61 ± 0.391.06 ± 0.510.790.030UP
F6W039Rho GDP dissociation inhibitor alpha 0.59 ± 0.410.950.390.690.041UP
P00559Phosphoglycerate kinase 1 0.60 ± 0.380.94 ± 0.420.650.041UP
F6Q818Arachidonate 15-lipoxygenase type B 0.66 ± 0.351.02 ± 0.410.620.019UP
F6SG30Protein S100-A9 1.03 ± 0.381.560.460.600.006UP
F7DMA1Carboxypeptidase M 0.69 ± 0.321.02 ± 0.290.570.013UP
F6YA47Alpha-2-macroglobulin like 1 0.88 ± 0.401.31 ± 0.360.570.018UP
A0A0B4J1C4Joining chain of multimeric IgA and IgM 0.82 ± 0.161.20 ± 0.510.540.021UP
F6T8J7LY6/PLAUR domain containing 3 0.90 ± 0.251.31 ± 0.310.540.004UP
F6WR95Sulfhydryl oxidase 0.91 ± 0.201.29 ± 0.190.510.001UP
F6SP0214-3-3 protein theta 0.85 ± 0.341.21 ± 0.430.500.047UP
A0A5F5PSG4Desmoglein 2 0.95 ± 0.341.30 ± 0.360.460.047UP
A0A3Q2KJH8Desmocollin 2 1.03 ± 0.371.42 ± 0.360.450.023UP
F6S6J4Peroxiredoxin-1 0.88 ± 0.481.19 ± 0.350.430.041UP
A0A5F5PRI0Nucleoside diphosphate kinase 0.86 ± 0.421.14 ± 0.440.400.041UP
Q28372Gelsolin 0.79 ± 0.301.04 ± 0.270.390.018UP
F7DW69Heat shock 70 kDa protein 1A 0.94 ± 0.311.23 ± 0.360.380.041UP
A0A3Q2GYE5Kallikrein related peptidase 12 0.85 ± 0.311.09 ± 0.340.360.041UP
A0A3Q2LCR2Glucose-6-phosphate isomerase 0.88 ± 0.241.13 ± 0.280.360.047UP
A0A5S7NAP8Chloride channel accessory 2 0.97 ± 0.241.23 ± 0.300.350.035UP
A0A3Q2HWQ6C3-beta-c 0.89 ± 0.321.10 ± 0.270.300.041UP
F6PH38Fibrinogen beta chain 0.87 ± 0.211.07 ± 0.260.300.041UP
A0A5F5PF02Tropomyosin alpha-4 chain 0.93 ± 0.251.14 ± 0.230.290.041UP
Q4AEE3Deoxyribonuclease-1 0.98 ± 0.260.79 ± 0.28−0.310.035DOWN
F7C450Alpha-2-HS-glycoprotein 1.33 ± 0.600.91 ± 0.35−0.540.041DOWN
A0A3Q2I8J7Jacalin-type lectin domain-containing protein 1.19 ± 0.450.79 ± 0.36−0.580.030DOWN
F7D9176-phosphogluconate dehydrogenase decarboxylating 1.12 ± 0.750.55 ± 0.25−1.030.007DOWN
SD: standard deviation; EGUS: equine gastric ulcer syndrome; FC: fold change.
Table 2. Differentially abundant salivary proteins between horses with equine glandular gastric disease (EGGD) (n = 6) and control group (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and EGGD groups, Log2(FC) between EGGD and control groups, and p-value derived from Kruskal–Wallis analysis.
Table 2. Differentially abundant salivary proteins between horses with equine glandular gastric disease (EGGD) (n = 6) and control group (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and EGGD groups, Log2(FC) between EGGD and control groups, and p-value derived from Kruskal–Wallis analysis.
Accession (UniProt)Protein NameMean and SD ControlMean and SD EGGDLog2(FC)p-ValueRegulation in EGGD
F6QSG9Transmembrane protease serine 0.63 ± 0.391.49 ± 1.041.230.025UP
F6SG30Protein S100-A9 1.03 ± 0.381.66 ± 0.490.690.01UP
A0A3Q2HN65Peptidase S1 domain-containing protein 1.08 ± 0.371.70 ± 0.600.640.019UP
F6Q818Arachidonate 15-lipoxygenase type B 0.65 ± 0.350.99 ± 0.380.590.043UP
F6YA47Alpha-2-macroglobulin like 1 0.88 ± 0.411.30 ± 0.400.560.043UP
A0A0B4J1C4Joining chain of multimeric IgA and IgM 0.82 ± 0.161.16 ± 0.290.500.019UP
F6T8J7LY6/PLAUR domain containing 3 0.90 ± 0.251.26 ± 0.340.480.019UP
F6 × 058Transmembrane protease serine 0.89 ± 0.311.24 ± 0.290.470.033UP
F6WR95Sulfhydryl oxidase0.91 ± 0.201.24 ± 0.210.440.007UP
A0A5F5PFG3Adenosylhomocysteinase1.08 ± 0.221.39 ± 0.240.290.049UP
SD: standard deviation; EGGD: equine glandular gastric disease; FC: fold change.
Table 3. Differentially abundant salivary proteins between horses with equine squamous gastric disease (ESGD) (n = 6) and control group (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and ESGD groups, Log2(FC) between ESGD and control groups, and p-value derived from Kruskal-Wallis analysis.
Table 3. Differentially abundant salivary proteins between horses with equine squamous gastric disease (ESGD) (n = 6) and control group (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and ESGD groups, Log2(FC) between ESGD and control groups, and p-value derived from Kruskal-Wallis analysis.
Accession (UniProt)Protein NameMean and SD ControlMean and SD ESGDLog2(FC)p-ValueRegulation in ESGD
F6YDZ0Serpin B5 0.61 ± 0–391.37 ± 0.511.160.011UP
A0A3Q2I4F4WD repeat domain 1 0.46 ± 0.270.92 ± 0.471.010.025UP
F6QR81Glutaredoxin 0.95 ± 0.351.71 ± 1.070.850.044UP
F6QSG9Transmembrane protease serine 0.64 ± 0.391.14 ± 0.340.840.008UP
P00559Phosphoglycerate kinase 1 0.60 ± 0.381.07 ± 0.450.830.033UP
F6 × 058Transmembrane protease serine 0.90 ± 0.311.54 ± 0.410.780.004UP
F7CJ82Arginase 0.98 ± 0.521.60 ± 0.910.700.044UP
F6Q1M4Keratin 15 0.63 ± 0.221.01 ± 0.290.690.020UP
A0A5F5PRI0Nucleoside diphosphate kinase 0.86 ± 0.421.38 ± 0.450.680.020UP
Q8HZM6Annexin A1 0.84 ± 0.361.34 ± 0.430.680.034UP
F7D5 × 8Keratin 4 0.91 ± 0.431.44 ± 0.560.670.034UP
F7DMA1Carboxypeptidase M 0.69 ± 0.321.09 ± 0.280.660.019UP
F6Q818Arachidonate 15-lipoxygenase type B 0.66 ± 0.351.04 ± 0.440.650.049UP
F6SP0214-3-3 protein theta 0.85 ± 0.341.34 ± 0.430.650.034UP
Q28372Gelsolin 0.79 ± 0.301.19 ± 0.270.600.011UP
F6T8J7LY6/PLAUR domain containing 3 0.90 ± 0.251.36 ± 0.260.590.011UP
A0A3Q2KJH8Desmocollin 2 1.03 ± 0.371.55 ± 0.420.580.020UP
A0A3Q2GYE5Kallikrein related peptidase 12 0.85 ± 0.311.27 ± 0.340.580.026UP
F6YA47Alpha-2-macroglobulin like 1 0.88 ± 0.401.31 ± 0.320.570.044UP
A0A3Q2HC63Calmodulin like 5 0.97 ± 0.311.44 ± 0.440.570.026UP
F6S6J4Peroxiredoxin-1 0.88 ± 0.481.31 ± 0.270.570.034UP
F6WR95Sulfhydryl oxidase 0.91 ± 0.201.35 ± 0.170.570.002UP
F6RGN2Fatty acid binding protein 5 1.05 ± 0.471.55 ± 0.400.560.026UP
F6TZS9Triosephosphate isomerase 0.72 ± 0.331.02 ± 0.360.510.029UP
F6SG30Protein S100-A9 1.03 ± 0.381.46 ± 0.400.500.034UP
F6SX07Galectin 1.12 ± 0.691.58 ± 0.420.490.044UP
A0A3Q2HWQ6C3-beta-c 0.89 ± 0.321.25 ± 0.250.490.011UP
F7DW69Heat shock 70 kDa protein 1A 0.94 ± 0.311.31 ± 0.270.470.026UP
F6PH38Fibrinogen beta chain 0.87 ± 0.211.20 ± 0.220.470.011UP
A0A3Q2HT63Serpin B3 0.94 ± 0.351.26 ± 0.310.430.034UP
F6VJR6Alpha-1B-glycoprotein 0.82 ± 0.221.05 ± 0.160.360.034UP
B7XH73L-lactate dehydrogenase 0.91 ± 0.481.16 ± 0.180.350.044UP
F7BPX8Cathepsin L1 0.96 ± 0.351.20 ± 0.220.330.011UP
A0A5F5PF02Tropomyosin alpha-4 chain 0.93 ± 0.251.17 ± 0.200.320.044UP
Q4AEE3Deoxyribonuclease-1 0.98 ± 0.260.74 ± 0.18−0.410.044DOWN
F7D9176-phosphogluconate dehydrogenase decarboxylating 1.12 ± 0.750.46 ± 0.10−1.270.008DOWN
SD: standard deviation; ESGD: equine squamous gastric disease; FC: fold change.
Table 4. Differentially abundant salivary proteins between horses with equine glandular gastric disease (n = 6) and equine squamous gastric disease (n = 6). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of EGGD and ESGD groups, Log2(FC) between ESGD and EGGD groups, and p-value derived from Kruskal–Wallis analysis.
Table 4. Differentially abundant salivary proteins between horses with equine glandular gastric disease (n = 6) and equine squamous gastric disease (n = 6). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of EGGD and ESGD groups, Log2(FC) between ESGD and EGGD groups, and p-value derived from Kruskal–Wallis analysis.
Accession (UniProt)Protein NameMean and SD EGGDMean and SD ESGDLog2(FC)p-ValueRegulation in ESGD
F7CJ82Arginase 0.80 ± 0.461.60 ± 0.911.000.023UP
F6YDZ0Serpin B5 0.75 ± 0.281.37 ± 0.510.880.023UP
F6Q1M4Keratin 15 0.55 ± 0.071.01 ± 0.290.880.003UP
F7D5 × 8Keratin 4 0.81 ± 0.181.44 ± 0.560.830.023UP
A0A5F5PRI0Nucleoside diphosphate kinase 0.89 ± 0.241.38 ± 0.450.640.015UP
A0A3Q2LHM4SEC14-like protein 4 0.84 ± 0.171.23 ± 0.250.550.015UP
F6RGN2Fatty acid binding protein 5 1.07 ± 0.331.55 ± 0.400.530.015UP
Q8HZM6Annexin A1 0.95 ± 0.361.34 ± 0.430.500.046UP
A0A3Q2GYE5Kallikrein related peptidase 12 0.92 ± 0.221.27 ± 0.340.470.046UP
Q28372Gelsolin 0.88 ± 0.171.19 ± 0.240.440.033UP
A0A3Q2HWQ6C3-beta-c 0.95 ± 0.211.25 ± 0.250.410.015UP
F7BPX8Cathepsin L1 0.92 ± 0.111.20 ± 0.220.390.010UP
A0A3Q2GTU4Fructose-bisphosphate aldolase 1.05 ± 0.201.35 ± 0.120.370.033UP
A0A3Q2HN65Peptidase S1 domain-containing protein 1.70 ± 0.601.30 ± 0.71−0.390.046DOWN
SD: standard deviation; EGGD: equine glandular gastric disease; ESGD: equine squamous gastric disease; FC: fold change.
Table 5. Differentially abundant serum proteins between horses with equine gastric ulcer syndrome (EGUS) (n = 12) and controls (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and EGUS groups, Log2(FC) between EGUS and control groups, and p-value derived from Mann–Whitney analysis.
Table 5. Differentially abundant serum proteins between horses with equine gastric ulcer syndrome (EGUS) (n = 12) and controls (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and EGUS groups, Log2(FC) between EGUS and control groups, and p-value derived from Mann–Whitney analysis.
Accession (UniProt)Protein NameMean and SD ControlMean and SD EGUSLog2(FC)p-ValueRegulation in EGUS
A0A3Q2HTG2Fibrinogen alpha chain 0.90 ± 0.081.20 ± 0.540.420.022UP
F7APU2Complement factor I 0.98 ± 0.071.13 ± 0.300.200.049UP
F6PPQ0C3/C5 convertase 0.94 ± 0.071.08 ± 0.060.190.006UP
F7DRS2Serpin family A member 6 0.97 ± 0.081.08 ± 0.110.150.024UP
A0A3Q2I8Y6Alpha-2-glycoprotein 1 zinc-binding 0.99 ± 0.061.05 ± 0.070.080.036UP
A0A3Q2LBD1Complement component 8 subunit beta1.01 ± 0.050.91 ± 0.06−0.140.000DOWN
F6REX3Pregnancy zone protein 1.11 ± 0.360.85 ± 0.23−0.390.042DOWN
SD: standard deviation; EGUS: equine gastric ulcer syndrome; FC: fold change.
Table 6. Differentially abundant serum proteins between horses with equine glandular gastric disease (n = 6) and controls (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and EGGD groups, Log2(FC) between EGGD and control groups, and p-value derived from Kruskal–Wallis analysis.
Table 6. Differentially abundant serum proteins between horses with equine glandular gastric disease (n = 6) and controls (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and EGGD groups, Log2(FC) between EGGD and control groups, and p-value derived from Kruskal–Wallis analysis.
Accession (UniProt)Protein NameMean ControlMean EGGDLog2(FC)p-ValueRegulation in EGGD
A0A3Q2HTG2Fibrinogen alpha chain 1.09 ± 0.080.90 ± 0.230.290.029UP
F6PPQ0C3/C5 convertase 1.08 ± 0.070.94 ± 0.070.190.005UP
F6VZ73Hyaluronan binding protein 2 1.05 ± 0.090.95 ± 0.100.140.046UP
F6PKE1Inhibitor of carbonic anhydrase 1.05 ± 0.050.98 ± 0.060.090.041UP
F6ZR63Complement factor H 0.87 ± 0.040.95 ± 0.06−0.110.029DOWN
A0A3Q2LBD1Complement component 8 subunit beta 0.93 ± 0.051.01 ± 0.03−0.120.004DOWN
Q95M34Immunoglobulin gamma 1 heavy chain constant region (Fragment) 0.87 ± 0.100.99 ± 0.07−0.190.013DOWN
F7BUV8Adiponectin A 0.96 ± 0.171.12 ± 0.08−0.220.046DOWN
F6Z5L1Complement subcomponent C1r 0.69 ± 0.320.92 ± 0.31−0.430.037DOWN
F6REX3Pregnancy zone protein 0.82 ± 0.361.11 ± 0.16−0.440.046DOWN
SD: standard deviation; EGGD: equine glandular gastric disease; FC: fold change.
Table 7. Differentially abundant serum proteins between horses with equine squamous gastric disease (ESGD) (n = 6) and controls (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and ESGD groups, Log2(FC) between ESGD and control groups, and p-value derived from Kruskal–Wallis analysis.
Table 7. Differentially abundant serum proteins between horses with equine squamous gastric disease (ESGD) (n = 6) and controls (n = 10). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of control and ESGD groups, Log2(FC) between ESGD and control groups, and p-value derived from Kruskal–Wallis analysis.
Accession (UniProt)Protein NameMean ControlMean ESGDLog2(FC)p-ValueRegulation in EGSD
A0A3Q2KTQ9Actin cytoplasmic 1 1.14 ± 0.210.94 ± 0.190.280.049UP
F7DRS2Serpin family A member 6 1.10 ± 0.080.97 ± 0.110.170.029UP
F7CSL8Alpha-1-antiproteinase 2-like 1.13 ± 0.081.00 ± 0.120.170.049UP
F7CZW9Serpin family G member 1 1.03 ± 0.090.93 ± 0.050.150.049UP
A0A3Q2I8Y6Alpha-2-glycoprotein 1 zinc-binding 1.06 ± 0.060.99 ± 0.060.100.049UP
F7BM31Serpin family D member 1 0.92 ± 0.060.99 ± 0.03−0.100.025DOWN
F6USP9Plasminogen 0.92 ± 0.051.02 ± 0.07−0.140.029DOWN
A0A3Q2LBD1Complement component 8 subunit beta 0.89 ± 0.051.01 ± 0.07−0.180.003DOWN
F6ZI35Histidine rich glycoprotein 0.89 ± 0.131.00 ± 0.10−0.180.049DOWN
SD: standard deviation; ESGD: equine squamous gastric disease; FC: fold change.
Table 8. Differentially abundant serum proteins between horses with equine squamous gastric disease (ESGD) (n = 6) and equine glandular gastric disease (EGGD) (n = 6). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of EGGD and ESGD groups, Log2(FC) between ESGD and EGGD groups, and p-value derived from Kruskal–Wallis analysis.
Table 8. Differentially abundant serum proteins between horses with equine squamous gastric disease (ESGD) (n = 6) and equine glandular gastric disease (EGGD) (n = 6). Columns indicate the accession number obtained from UniProt database, protein name, mean and SD abundances of EGGD and ESGD groups, Log2(FC) between ESGD and EGGD groups, and p-value derived from Kruskal–Wallis analysis.
Accession (UniProt)Protein NameMean EGGDMean ESGDLog2(FC)p-ValueRegulation in EGSD
F6Z5L1Complement subcomponent C1r 0.68 ± 0.290.93 ± 0.310.440.041UP
A0A3Q2KTQ9Actin cytoplasmic 1 0.87 ± 0.111.14 ± 0.190.380.041UP
F6PVG3EGF containing fibulin extracellular matrix protein 1 0.92 ± 0.111.14 ± 0.160.300.041UP
F7CSL8Alpha-1-antiproteinase 2-like 0.94 ± 0.151.12 ± 0.120.240.041UP
Q95M34Immunoglobulin gamma 1 heavy chain constant region (Fragment) 0.86 ± 0.070.99 ± 0.080.190.028UP
F7BUV8Adiponectin A 0.96 ± 0.081.06 ± 0.080.150.041UP
F6ZR63Complement factor H 0.87 ± 0.060.95 ± 0.040.110.028UP
F7C450Alpha-2-HS-glycoprotein 0.95 ± 0.110.85 ± 0.08−0.160.040DOWN
SD: standard deviation; EGGD: equine glandular gastric disease; ESGD: equine squamous gastric disease; FC: fold change.
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Muñoz-Prieto, A.; Contreras-Aguilar, M.D.; Cerón, J.J.; Ayala, I.; Martin-Cuervo, M.; Gonzalez-Sanchez, J.C.; Jacobsen, S.; Kuleš, J.; Beletić, A.; Rubić, I.; et al. Changes in Proteins in Saliva and Serum in Equine Gastric Ulcer Syndrome Using a Proteomic Approach. Animals 2022, 12, 1169. https://doi.org/10.3390/ani12091169

AMA Style

Muñoz-Prieto A, Contreras-Aguilar MD, Cerón JJ, Ayala I, Martin-Cuervo M, Gonzalez-Sanchez JC, Jacobsen S, Kuleš J, Beletić A, Rubić I, et al. Changes in Proteins in Saliva and Serum in Equine Gastric Ulcer Syndrome Using a Proteomic Approach. Animals. 2022; 12(9):1169. https://doi.org/10.3390/ani12091169

Chicago/Turabian Style

Muñoz-Prieto, Alberto, Maria Dolores Contreras-Aguilar, Jose Joaquín Cerón, Ignacio Ayala, Maria Martin-Cuervo, Juan Carlos Gonzalez-Sanchez, Stine Jacobsen, Josipa Kuleš, Anđelo Beletić, Ivana Rubić, and et al. 2022. "Changes in Proteins in Saliva and Serum in Equine Gastric Ulcer Syndrome Using a Proteomic Approach" Animals 12, no. 9: 1169. https://doi.org/10.3390/ani12091169

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