International Journal o  f
Molecular Sciences
Article
Obesity Modifies the Proteomic Profile of the
Periodontal Ligament
Andressa V. B. Nogueira 1,2,* , Maria Eduarda S. Lopes 2, Camila C. Marcantonio 2 , Cristiane R. Salmon 3 ,
Luciana S. Mofatto 4 , James Deschner 1, Francisco H. Nociti-Junior 3,5 and Joni A. Cirelli 2,*
1 Department of Periodontology and Operative Dentistry, University Medical Center of the Johannes
Gutenberg University, 55131 Mainz, Germany
2 Department of Diagnosis and Surgery, School of Dentistry at Araraquara, São Paulo State University—UNESP,
Araraquara 14801-903, São Paulo, Brazil
3 Department of Prosthodontics and Periodontics, Division of Periodontics, Piracicaba Dental School,
University of Campinas—UNICAMP, Piracicaba 13414-903, São Paulo, Brazil
4 Department of Genetics, Evolution, Microbiology, and Immunology, Institute of Biology,
University of Campinas—UNICAMP, Campinas 13083-862, São Paulo, Brazil
5 São Leopoldo Mandic Research Center, Campinas 13045-755, São Paulo, Brazil
* Correspondence: a.nogueira@uni-mainz.de (A.V.B.N.); joni.cirelli@unesp.br (J.A.C.);
Tel.: +49-0-6131-17-7091 (A.V.B.N.); +55-16-3301-6375 (J.A.C.)
Abstract: This study aimed to assess the obesity effects on the proteomic profile of the periodontal
ligament of rats submitted to obesity induction by a high-fat diet. Eight Holtzman rats were divided
into control (n = 3) and obese (n = 5) groups. The maxillae were histologically processed for laser
capture microdissection of the periodontal ligament of the first maxillary molars. Peptide mixtures
were analyzed by LC-MS/MS. A total of 1379 proteins were identified in all groups. Among them,
335 (24.30%) were exclusively detected in the obese group, while 129 (9.35%) proteins were uniquely
found in the control group. Out of the 110 (7.98%) differentially abundant proteins, 10 were more
abundant and 100 had decreased abundance in the obese group. A gene ontology analysis showed
some proteins related to obesity in the “extracellular exosome” term among differentially identified
Citation: Nogueira, A.V.B.; Lopes, proteins in the gene ontology cellular component terms Prelp, Sec13, and Sod2. These three proteins
M.E.S.; Marcantonio, C.C.; Salmon, were upregulated in the obese group (p < 0.05), as shown by proteomic and immunohistochemistry
C.R.; Mofatto, L.S.; Deschner, J.; analyses. In summary, our study presents novel evidence that the proteomic profile of the periodontal
Nociti-Junior, F.H.; Cirelli, J.A. ligament is altered in experimental obesity induction, providing a list of differentially abundant
Obesity Modifies the Proteomic proteins associated with obesity, which indicates that the periodontal ligament is responsive to
Profile of the Periodontal Ligament. obesity.
Int. J. Mol. Sci. 2023, 24, 1003.
https://doi.org/10.3390/ Keywords: obesity; periodontal ligament; proteomics; prolargin; protein Sec13 homolog;
ijms24021003
superoxide dismutase
Academic Editor: Shun-Fa Yang
Received: 15 November 2022
Revised: 22 December 2022
1. Introduction
Accepted: 24 December 2022
Published: 5 January 2023 Obesity is a chronic, complex, and inflammatory disease defined by exaggerated or
abnormal fat accumulation that impairs an individual’s health [1–3]. In recent years, the
prevalence of obesity has increased significantly worldwide. The main cause of obesity is
the energy imbalance between calorie intake and expenditure. In addition, other factors
Copyright: © 2023 by the authors. contribute to obesity, such as a sedentary lifestyle, genetic factors, hormonal dysfunctions,
Licensee MDPI, Basel, Switzerland. industrialization, and food production [4]. Body mass index (BMI) has been used to
This article is an open access article measure obesity and is calculated as the ratio of body weight to height. According to the
distributed under the terms and WHO, a person with a BMI greater than or equal to 30 kg/m2 is considered obese. In
conditions of the Creative Commons addition, recognizing the importance of visceral fat as a health risk factor, measurements
Attribution (CC BY) license (https:// of waist circumference or waist-to-hip ratio have also been used [5]. These measures
creativecommons.org/licenses/by/
are important to evaluate the risk of comorbidities. Thus, increased body weight, as
4.0/).
Int. J. Mol. Sci. 2023, 24, 1003. https://doi.org/10.3390/ijms24021003 https://www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2023, 24, 1003 2 of 19
measured by BMI, waist circumference, or waist-to-hip ratio, is a critical risk factor for some
non-communicable diseases such as cardiovascular diseases, diabetes, musculoskeletal
disorders, and cancer [6–8]. Lately, obesity has been considered by the new classification
of periodontal diseases and conditions as one of the systemic conditions that negatively
influences the periodontal support tissues by influencing the pathogenesis of periodontal
diseases [9]. The pathomechanistic link between obesity and other chronic diseases may
be due to the low-grade systemic inflammation in obesity. Thus, many adipokines are
produced not only in the adipose tissue by adipocytes but also by other cells and tissues
during obesity. Interestingly, adipokines have also been found in periodontal ligament
(PDL) cells and tissues [10–13].
The periodontium is the tooth-supporting tissue comprised of the gingiva, PDL, root
cementum, and alveolar bone. The PDL is a specialized connective tissue that attaches
the root cementum to the alveolar bone [14]. It is essential in attenuating the occlusal
stresses through the transmission and absorption of mechanical stresses. Interestingly, the
PDL has a strong and flexible three-dimensional structure capable of tolerating multiaxial
loading and functioning properly under different forces such as tension, compression,
shear, and torsion [15]. Furthermore, PDL is a highly vascularized and cellularized tissue,
containing many fibroblasts, a high metabolic rate, and cellular turnover [14]. Its capacity
for regeneration has been investigated, as it contains undifferentiated mesenchymal stem
cells [16].
Proteins are produced under physiological and pathological conditions through the
metabolism of cells and tissues in the human body. In the last three decades, proteomic
analysis has been used extensively. It involves a large-scale study of proteins based on
the systematic identification and quantification of the complete proteome of a biological
system such as cells, tissues, and organs. The protein abundance through proteomics
enables the identification of the main proteins present in a sample and also differentially
abundant proteins in different samples. Lately, proteomic analysis has been used widely
in different challenged oral cells, tissues, and fluids, such as enamel [17], pulp [18,19],
dental cementum [20–22], PDL [23,24], alveolar bone [22], periodontal ligament cells [25,26],
gingival fibroblasts [27], gingival tissue [28,29], saliva [30], and gingival crevicular fluid [31],
enabling researchers to better understand biological processes. Therefore, characterizing
the PDL protein profile in obesity conditions becomes important in identifying possible
biological markers different from those in health conditions. In this context, the present
study was designed to assess the effect of obesity on the proteomic profile of the PDL in
rats for the first time.
2. Results
2.1. General Proteomic Profile of PDL in the Control and Obese Groups
A total of 1379 proteins were identified in all groups and their distribution. Among
the identified proteins, 335 (24.30%) were exclusively detected in the obese group, while
129 (9.35%) proteins were uniquely found in the control group. In addition, 915 (66.35%)
proteins were commonly identified in both the control and obese groups (Figure 1A). A
principal component analysis (PCA) was performed to transform and group the datasets of
the control and obese groups in an unsupervised method (Figure 1B). The analysis revealed
that the first principal component (PC1) presented 50.50% of the variance within the dataset,
whereas the second principal component (PC2) showed 29.20%. In other words, the rat
samples with similar protein profiles were clustered together, leading to two different
clusters of the two experimental groups, showing a distinction between the proteomes of
the control and obese groups. The volcano plot shows the differential abundance of proteins
between the control and obese groups (Figure 1C). Proteins with a 1.5-fold difference and
statistical significance (p < 0.05) were plotted. The control group presented a higher amount
of significantly abundant proteins compared to the obese group. Of the total proteins
identified, the abundance of 110 (7.98 %) proteins was significantly (p < 0.05) altered, 10
were more abundant, and 100 had a decreased abundance in the obese group (Table S1).
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 3 of 19 
 
presented a higher amount of significantly abundant proteins compared to the obese 
group. Of the total proteins identified, the abundance of 110 (7.98 %) proteins was 
Int. J. Mol. Sci. 2023, 24, 1003 significantly (p < 0.05) altered, 10 were more abundant, and 100 had 3ao fd19ecreased 
abundance in the obese group (Table S1). Among those ten proteins more abundant in the 
obese group, five were exclusive: Prelp, Arpc5, I6l9g6, Prof2, and A3fm27 (p < 0.05). From 
thAe m10o0n pgrtohtoesientse nmporroet eainbsumndoraenat biunn tdhaen ctoin trhoelo gbreosue pgr, osuixp ,wfievreew eexrceluexscivlues:i vGem: Ppreplap,, B2rza9, 
A0Aar1p4c05t,aIa64l9,g M6,0Prrdo2f20,, aQn6d3A0032fm, a2n7d( pC<ps0f.055 ()p.  <F r0o.m05)th. e 100 proteins more abundant in
the control group, six were exclusive: Gmppa, B2rza9, A0a140taa4, M0rd20, Q63002, and
 Cpsf5 (p < 0.05).
B 
 
C  
 
FigFuigruer 1e.1 S.uSmummmaarryy ooff tthhee pprrooteteoommicipcr porfiolefiolef  PoDf LPDinLth ine  ctohnet rcoolnatnrdolo abensde ogrboeuspes g. (rAo)uDpiss.t (rAibu) tDioinstribution 
of otfhteh etototatall 11337799p prortoetienisnisd eindteifinetdifiinedb oitnh gbrootuhp sg(r9o1u5p) asn (d9e1x5c)l uasnivde leyxicnltuhseivcoenlytr oinl ( 1t2h9e) acnodnotrboels e(129) and 
ob(e3s3e5 )(3g3ro5u) pgsr,oaus pshso, wasn sbhyoVwennn bdyia Vgreanmn. d(Bia) gDriaffmer.e n(Bce) bDetiwffeeerenntchee sbaemtwpleeesno fththee scaomntrpolle(sC oTfR tLh)e control 
(CaTnRdLo)b aensedg orobuepses agnrdouthpess aimndila trhitey sbiemtwileaernittyh ebseatmwpeleens wthieth sinameapchlegsr wouipth, ains eexahcihb igterdobuypP, aCsA e. xhibited 
by( CP)CDAis. t(rCib)u Dtioisntroifbsuigtinoinfi coafn stliygndiifffiecraennttliyal ldyifrfeegruelnatteiadllpyr orteeginusla(pte<d0 p.0r5o)tieninthse (Pp D<L 0.o0f5t)h ienc othnetr oPlDL of the 
con(letrftosl id(lee)fta nsdidoeb)e asne d(r iogbhet ssied (er)iggrhotu spisddee) tgerrmouinpesd dbeyttehrembientae-db ibnyom thiael sbteattais-tbicianlotemstia(pl s<ta0.t0is5t),icaasl test (p < 
0.0i5ll)u,s atrsa itleldusbtyraatveodl cbayn oap vlootl.cano plot. 
2.2. Differentially Identified Proteins in PDL
2.2. DiHffeireernartcihalilcya lIcdleunstteifriiendg Porfottheeintosp in5 0PdDifLfe rentially abundant proteins showed a distinc-
tionHbietrwarecehnitchael ccolnutsrtoelraindg oobfe tsheeg rtooupp s50in daihffeeartemntaipalalnya alybsuisn(dFiagnutr ep2r)o.teins showed a dis-
tinctionA bGeetnweeOennt othloeg cyo(nGtOro)le annridch ombeenste agnraolyuspiss uinsi nag htehaet Dmaatapb aasneafloyrsAisn (nFoigtautiroen 2, )V. i- 
sualization, and Integrated Discovery platform (DAVID v 6.8, accessed on 15 May 2019
https://david.ncifcrf.gov) was performed on the proteins differentially identified in the
control and obese groups to evaluate the functional meaning of the results. The proteins
were analyzed according to the GO terms (level 2): cellular component (CC), molecular func-
tion (MF), and biological process (BP). As shown in Figure 3A, the differentially detected
proteins in the GO BP terms were related to “translation” (GO:0006412), “cell-cell adhesion”
 
Int. J. Mol. Sci. 2023, 24, 1003 4 of 19
(GO:0098609), “mRNA processing” (GO:0006397), and “RNA splicing” (GO:0008380). In
addition, in the GO MF terms, there were 12 significant terms and the majority of differ-
entially identified proteins were involved with “poly (A) RNA binding” (GO:0044822),
“protein binding” (GO:0005515), “RNA binding” (GO:0003723), and “structural constituent
of ribosome” (GO:0003735) (Figure 3B). The GO CC terms were the largest category of
differentially identified proteins, which were related to 24 enriched terms, such as “extra-
Int. J. Mol. Sci. 2023, 24, x FOR PEEceRl RluElVaIrEWex   osome” (GO:0070062), “cytoplasm” (GO:0005737), “nucleus” (GO:0005643 4of) ,1a9n  d
“membrane” (GO:0016020) (Figure 3C).
 
FigFuirgeu2re.  H2. iHeriaercahrcihcaiclacll culsutsetreirninggo offt htheet toopp 550 protteiins off iinccrreeaasseedd aanndd rerdeducuecde dabaubnudnadnacen cine tinhet he
conctoronltr(oClT (RCLTR) aLn) danodb eosbeegsero gurposu,pass, sahs oswhonwbny bthye thhee ahtemata mp.aDp.a Dtaawta ewreercela cslsaisfiseifdiebdy bpy- vpa‐vluaelu(ep (<p 0<. 05)
and0.e0s5t)i manadte desutimsinatgedth  eusZin-sgc othree cZa‐lsccuolraet iocanlcounlaLtioogn2  osnp eLcotgra2l  scpoeucntrtavl aclouuens,t avpaplulyesin, gapthpelyEinugc ltihdee an
Euclidean distance and average linkage method. 
distance and average linkage method.
A Gene Ontology  (GO)  enrichment  analysis  using  the Database  for Annotation, 
Visualization, and Integrated Discovery platform (DAVID v 6.8, accessed on 15 May 2019 
https://david.ncifcrf.gov) was performed on  the proteins differentially  identified  in  the 
control and obese groups to evaluate the functional meaning of the results. The proteins 
were analyzed according to the GO terms (level 2): cellular component (CC), molecular 
function  (MF),  and  biological process  (BP). As  shown  in  Figure  3A,  the differentially 
detected proteins in the GO BP terms were related to “translation” (GO:0006412), “cell‐
cell  adhesion”  (GO:0098609),  “mRNA processing”  (GO:0006397),  and  “RNA  splicing” 
 
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW  5  of  19 
 
(GO:0008380). In addition, in the GO MF terms, there were 12 significant terms and the 
majority of differentially identified proteins were involved with “poly (A) RNA binding” 
(GO:0044822),  “protein  binding”  (GO:0005515),  “RNA  binding”  (GO:0003723),  and 
“structural constituent of ribosome” (GO:0003735) (Figure 3B). The GO CC terms were the 
largest category of differentially  identified proteins, which were related  to 24 enriched 
Int. J. Mol. Sci. 2023, 24, 1003 terms,  such  as  “extracellular  exosome”  (GO:0070062),  “cytoplasm”  (GO:0050o5f71397), 
“nucleus” (GO:0005634), and “membrane” (GO:0016020) (Figure 3C). 
 
FFiigguurree 33.. GGOO aannaallyyssiiss ooff aallll ddiiffffeerreennttiaialllylya abbuunnddaannt tp prortoetienisnisd iednetnifitiefdieidn itnh ethPeD PLDoLf othf ethceo nctornotlraonl dand 
oobbeessee ggrroouuppss.. ((AA)) GGrraapphh rreepprreesseennttininggt htheed dififfefreernentitailalyllyd edteetcetectdedpr porteoitnesinins itnh ethGeO GBOP BtPer tmersmresl areteladted 
ttoo  “t“rtarnasnlastliaotnio”n(”G O(:0G0O06:040120)6,4“1c2e)l,l -ce“lcl ealdl‐hceeslilo  n”ad(GheOs:i0o0n9”8 609(G),O“m:0R09N8A60p9r)o, ce“ssminRgN” A(G O:p0r0o0c6e3s9s7i)n,g” 
(aGnOd:“0R00N6A39s7p),l icaindg ”“(RGNOA:0 00sp83li8c0in).g(”B )(GOra:p0h00d8e3m80o)n. s(tBra)t iGngratphhe  ddifefmeroentsitarlalytinagb utnhdea ndtifpfreorteenintisally 
ainbuthnedaGnOt pMroFtteeirnms sine sthpe cGiaOll yMrFel atetermd tso e“spoeclyia(Ally)  RreNlaAtebdi ntod i“npgo”ly(G(AO): 0R0N44A8 2b2i)n,d“ipnrgo”te (iGnObi:n00d4in4g82”2), 
(GO:0005515), “RNA binding” (GO:0003723), and “structural constituent of ribosome” (GO:0003735).
(C) Differentially, proteins were detected in the GO CC terms, which were the largest category of
  differentially identified proteins, as they were related to 24 enriched terms, such as “extracellular
exosome” (GO:0070062), “cytoplasm” (GO:0005737), “nucleus” (GO:0005634), and “membrane”
(GO:0016020). The number of proteins associated with each GO enrichment term is shown.
Int. J. Mol. Sci. 2023, 24, 1003 6 of 19
Among the differentially identified proteins in the GO CC terms, there are some
proteins related to obesity in the GO term “extracellular exosome”, as shown in Table 1, in-
cluding prolargin (Prelp), protein Sec13 homolog (Sec13), and superoxide dismutase (Sod2).
Prelp, Sec13, and Sod2 were significantly more abundant in the obese group (p < 0.05).
Table 1. List of differentially abundant proteins related to obesity in the GO term “extracellular
exosome” in the PDL of control and obese groups. Average peak area values of each group, p-values
(t-test) and fold-change are shown.
Extracellular Exosome (GO:0070062)
Average Peak Area Values
Protein Name Uniprot Accession Protein Symbol Control Obese p-Value Fold-Change
Prolargin Q9EQP5 Prelp 0.00 1.20 × 107 0.014 * 1.2 × 107
Protein SEC13 homolog Q5XFW8 Sec13 7.40 × 105 4.60 × 106 0.014 * 6.10
Superoxide dismutase P07895 Sod2 2.60 × 106 7.80 × 106 0.040 * 3.00
* Significant (p < 0.05) difference.
The KEGG pathways of differentially abundant proteins were analyzed by DAVID
software and the following pathways were identified: “ribosome”, “spliceosome”, “pro-
tein processing in the endoplasmic reticulum”, and “endocytosis” (Figure 4A). Only the
“ribosome” pathway was statistically (p < 0.05) significant, and a detailed expression of
the 11 proteins of this pathway is shown in Table S1. In addition, the complete ribosome
pathway and a gene report with these 11 proteins are shown in Figures 4B and S1. The
ribosomal proteins of the ribosome pathway were all more abundant in the control group
compared to the obesity group. Protein-protein interactions of differentially abundant
proteins in the PDL of the control and obese groups were assessed using the Protein-Protein
Interaction Networks Functional Enrichment Analysis platform (STRING v 11.5, released
12 August 2021, accessed on 6 December 2022: https://string-db.org), as illustrated in
Figure 5. A high confidence level of 0.700 was used and a strong protein-protein interaction
could be observed. Sod2, Sec13, and Actin-related protein 2/3 complex subunit 5 (Arpc5)
were the most abundant proteins in the obese group compared to the control group, and
had a strong interaction with some proteins. Sod2, for example, showed a strong interac-
tion with Peroxiredoxin 6 (Prdx6), and both are antioxidant enzymes. Furthermore, Sod2
strongly interacted with several ribosomal proteins (Rpl). In addition, Sec13 showed strong
interactions with Ubiquitin-conjugating enzyme E2 L3 (Ube2l3), Ubiquitin-conjugating
enzyme E2 variant 1 (Ube2v1), 60S ribosomal protein L7-A (Rpl7a), Heat shock cognate
71 kDa protein (Hspa8), and Clathrin heavy chain 1 (Cltc). Proteins lacking interactions
did not appear in the network.
2.3. Immunolocalization of the Selected Proteins Prelp, Sec13, and Sod2 in PDL
The selected proteins Prelp, Sec13, and Sod2 identified by the proteomic analysis were
chosen for their relevant role in obesity and different abundance between groups. Their
presence was confirmed in periodontal tissues by immunohistochemistry (IHC) staining.
An increased number of positive cells to Prelp, Sec13, and Sod2 proteins was observed
in the obese group compared to the control group, but only the expression of Prelp and
Sod2 was statistically significant (p < 0.05). The IHC analysis confirms the proteomic
analysis (Figure 6A,B).
2.4. Exclusively Identified Proteins in PDL
GO enrichment analysis in exclusively identified proteins in the PDL of obese and con-
trol rats revealed that proteins were related to GO CC terms (Figures 7A and 8A). The most
enriched GO CC terms were determined by a t-test followed by the Benjamini-Hochberg
test with p < 0.05. There were six terms; among them, five terms were significant (p < 0.05)
in the GO CC terms of the obese group, which were: “extracellular exosome” (GO:0070062)
Int. J. Mol. Sci. 2023, 24, 1003 7 of 19
with 123 proteins, “mitochondrion” (GO:0005739) with 48 proteins, “proteasome complex”
(GO:0000502) with seven proteins, “retromer complex” (GO:0030904) with five proteins, and
“membrane” (GO:0016020) with 56 proteins (Figure 7A). Among the proteins detected in
the GO term “extracellular exosome”, we can highlight adiponectin (A0a0g2k845), cadherin
11 (F1mah6), cathepsin G (G3v9q7), cathepsin L1 (P07154), fatty acid synthase (P12785),
programmed cell death 5 (D4adf5), prostaglandin E synthase 3 (P83868), metalloproteinase
inhibitor 2 (P30121), and prolargin (Prelp) (Figure 7B). In the control group, there were
Int. J. Mol. Sci. 2023, 24, x FOR1 P9EtEeRrm REsV, bIEuWt  only 3 significant (p < 0.05) enriched GO CC terms, which were: “extracellular 7  of  19 
  exosome” (GO:0070062) with 40 proteins; “membrane” (GO:0016020) with 28 proteins; and
“cytoplasm” (GO:0005737) with 48 proteins (Figure 8A).
 
FigureF4i.gAunrea l4y.s Aisnoaflyasllisd oifff earlel ndtiifaflelryeanbtiuanlldya anbtupnrdotaenint sprinottehiensP iDnL thoef PthDeLc oonf ttrhoel caonndtrooble asnedg roobuepsse. groups. 
(A) KE(GAG) KpaEtGhwG apyasthowf daiyffse oref ndtiifaflelyreanbtiuanlldya anbtupnrodtaenint sparontdeitnhse annudm tbheer noufmprboetre ionfs porfoeteaicnhs KoEf eGaGch KEGG 
pathwapyatthewrma.y (tBe)rmRi. b(oBs)o Rmiablopsoamthawl apyaitnhfwoarmy aintifoonrmgeantieorna tgedenbeyraDteAdV bIyD DsoAftVwIDar es.oRftewdarseq.u Rareeds squares 
represernetptrheeseenlet vthene erilbevoseonm riablopsroomteainl ps rtohtaetiwnse trheamt woreeraeb munodrea nabtuinntdhaenct oinn ttrhoel  gcoronutrpocl ogmropuapr ecdomtopared to 
the obetshiety ogbreosuitpy. group. 
 
Int. J.InMt.o Jl.. MScoi.l.2 S0c2i3. ,22042,31, 02043, x FOR PEER REVIEW  8 o8f  1o9f  19  
 
Figure 5. Protein-protein interaction network analysis (high-confidence of 0.700 with hidden discon-
nectFedignuored e5s. Pinrothteeinn‐eptrwotoerikn)  ionftethraecdtiiofnfe rneentwtiaolrlky aabnualnydsiasn (thpigroht‐ecionnsfiidnetnhcee PoDf L0.7o0f0t hweictho nhtirdodl eann ddis‐
connected nodes in the network) of the differentially abundant proteins in the PDL of the control 
obesaengdr oobuepsse. gRroeudpcsi.r cRleeds creirpcrleess ernept rtehsreenet pthroreteei pnrsottheaintsw thearet wmeorere maobruen adbaunntdianntth ine tohbee osibteysigtryo gurpoup 
comcpoamrepdatroedth teo ctohne tcroonl tgrroolu gpr:oSuopd: 2S,oSde2c, 1S3e,ca1n3d, aAndrp Ac5r.pc5. 
2.3. Immunolocalization of the Selected Proteins Prelp, Sec13, and Sod2 in PDL 
The  selected proteins Prelp, Sec13, and Sod2  identified by  the proteomic analysis 
were chosen for their relevant role in obesity and different abundance between groups. 
Their presence was  confirmed  in periodontal  tissues by  immunohistochemistry  (IHC) 
staining. An  increased number of positive cells  to Prelp, Sec13, and Sod2 proteins was 
observed in the obese group compared to the control group, but only the expression of 
Prelp and Sod2 was statistically significant (p < 0.05). The IHC analysis confirms the pro‐
teomic analysis (Figure 6A,B). 
 
Int. JI.nMt. Jo.l M. Socli. .S2c0i.2 230,2234, ,2140, 0x3 FOR PEER REVIEW  9  of  199o  f 19
 
 
FiFgiugruere6 .6.( A(A))G Grraapphhss with mean andd ssttaannddaardrd ddeveivaitaiotino nshsohwoiwngin tghet hneumnubmerb oefr coefllcs epllossiptiovsei ttiov e to
PrPerlepl,pS, Seecc1133,, aanndd SSoodd22,, aass aannaalylyzezde dbyb IyHICH sCtasintainingi n(pg <( 0p.0<5)0. .*0 5S)ig. n*ifSicigannti fi(pc a<n 0t.0(5p) <di0ff.e0r5e)ndceif. f(eBr)e nce.
(BR)eRperpesreensetanttiavtei vime aimgeasg (e4s00(4×0 m0×agmnifaigcantiifiocna) toiof nIH) oCf sItHaiCninstga ifnorin Pgreflopr, PSerecl1p3,, Sanecd1 S3o, dan2 dprSootedi2nsp, rroet‐eins,
spectively, in tissue sections from the control and obese groups. 
respectively, in tissue sections from the control and obese groups.
2.4. Exclusively Identified Proteins in PDL   
GO enrichment analysis in exclusively identified proteins in the PDL of obese and 
control rats revealed that proteins were related to GO CC terms (Figures 7A and 8A). The 
most  enriched GO CC  terms were determined  by  a  t‐test  followed  by  the Benjamini‐
Hochberg test with p < 0.05. There were six terms; among them, five terms were significant 
(p < 0.05) in the GO CC terms of the obese group, which were: “extracellular exosome” 
(GO:0070062) with 123 proteins, “mitochondrion” (GO:0005739) with 48 proteins, “pro‐
teasome complex” (GO:0000502) with seven proteins, “retromer complex” (GO:0030904) 
with five proteins, and “membrane” (GO:0016020) with 56 proteins (Figure 7A). Among 
the proteins detected in the GO term “extracellular exosome”, we can highlight adiponec‐
tin  (A0a0g2k845), cadherin 11  (F1mah6), cathepsin G  (G3v9q7), cathepsin L1  (P07154), 
fatty acid synthase (P12785), programmed cell death 5 (D4adf5), prostaglandin E synthase 
3 (P83868), metalloproteinase  inhibitor 2 (P30121), and prolargin (Prelp) (Figure 7B). In 
the control group, there were 19 terms, but only 3 significant (p < 0.05) enriched GO CC 
terms, which were: “extracellular exosome” (GO:0070062) with 40 proteins; “membrane” 
 
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW  10  of  19 
 
(GO:0016020) with 28 proteins; and “cytoplasm” (GO:0005737) with 48 proteins (Figure 
Int. J. Mol. Sci. 2023, 24, 1003 10 of 19
8A). 
Figure 7. (A) Graph containing the GO enrichment analysis of exclusively identified proteins in the  
FigureP 7D. L(Aof)o Gbersaepahni mcoanlstuasiinnignDgA tVhIeD GsoOftw eanrre,icwhhmichenretv aeanlaedlythsiast tohfe epxroctleuinssivweelrye riedlaetnedtitfoietdhe proteins in the 
PDL oft eormbe“scee lalunlaimr caomlsp uonsienngt” D(CAC)V. (IBD)  Lsiostftowf saormee, wprohtiecinhs rdeevteecateldedin tthhaetG tOheC Cprteortmei“nesx twraecerle- related to the 
lular exosome” are highlighted: adiponectin (A0A0G2K845), cadherin 11 (F1MAH6), cathepsin G
term “c(Gel3lVu9lQar7 )c,ocamthpeposnineLn1t”(P (0C71C54).) ,(fBat)t yLaicsitd osfy nstohmasee (pPr1o27t8e5i)n, sp rdogertaemctmeedd icne ltlhdeea GthO5  (CDC4A tDeFr5m), “extracellular 
exosompreo”s taaglraen dhinigEhsylingthhatseed3:( Pa83d8i6p8)o,nmeectatlilnop  r(oAtei0nAas0eGin2hiKbi8to4r52),( P3c0a1d21h),earnind  pr1o1la rg(Fin1(MQ9AEQHP65)).,  cathepsin  G 
(G3V9Q7), cathepsin L1 (P07154), fatty acid synthase (P12785), programmed cell death 5 (D4ADF5), 
prostaglandin  E  synthase  3  (P83868),  metalloproteinase  inhibitor  2  (P30121),  and  prolargin 
(Q9EQP5). 
 
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW  11  of  19 
  Int. J. Mol. Sci. 2023, 24, 1003 11 of 19
Figure 8. (A) Graph containing the GO enrichment analysis of exclusively identified proteins in PDL 
of control animals using DAVID software, which revealed that the proteins were related to the term
Figure “8c.e (llAul)a rGcroampphon ceonnt”ta(CinCi).n(gB )tLhiest GofOth eenprroictehinms oefntth eanGaOlyCsCiste ormf e“xmcelumsbirvaneely”. identified proteins in PDL 
of control animals using DAVID software, which revealed that the proteins were related to the term 
“cellular component” (CC). (B) List of the proteins of the GO CC term “membrane”. 
3. Discussion 
The present study provides novel evidence that obesity affects the PDL at a molecular 
level. Our results reveal a critical difference in the proteomic profile between the PDL of 
the obese and control groups. Moreover, among the differentially abundant proteins, most 
of the proteins that were more abundant in the control group had decreased abundance 
in the obese group, suggesting that important molecules could mediate the detrimental 
effects of obesity on periodontal tissues, such as PDL.   
Obesity  is defined as abnormal or excessive  fat accumulation, and has become an 
increasing public health problem worldwide [1,3]. As a consequence of obesity, systemic 
inflammation occurs [32]. Thus, not only adipose cells but also other cells and tissues in 
 
Int. J. Mol. Sci. 2023, 24, 1003 12 of 19
3. Discussion
The present study provides novel evidence that obesity affects the PDL at a molecular
level. Our results reveal a critical difference in the proteomic profile between the PDL of
the obese and control groups. Moreover, among the differentially abundant proteins, most
of the proteins that were more abundant in the control group had decreased abundance
in the obese group, suggesting that important molecules could mediate the detrimental
effects of obesity on periodontal tissues, such as PDL.
Obesity is defined as abnormal or excessive fat accumulation, and has become an
increasing public health problem worldwide [1,3]. As a consequence of obesity, systemic
inflammation occurs [32]. Thus, not only adipose cells but also other cells and tissues in
the body are responsible for regulating inflammation and immunity associated with the
dysregulated release of a variety of pro-inflammatory and anti-inflammatory molecules,
such as adipokines, cytokines, and chemokines [33]. This inflammatory state present in
obesity may act as a pathomechanistic link between obesity and other non-communicable
diseases such as cardiovascular diseases, diabetes, musculoskeletal disorders, cancer, and
periodontitis [6–8,34]. Interestingly, our group has recently demonstrated that obesity
modifies the proteomic profile of PDL of rats subjected to experimental periodontitis,
suggesting a critical effect of obesity on the PDL response to inflammation and tissue
remodeling. Our study showed that obesity modulated the abundance of proteins related
to periodontitis, such as spondin1, tartrate-resistant acid phosphatase, and vinculin [23].
Moreover, in another study, we observed the detrimental effects of obesity on the protein
abundance of the PDL of rats exposed to orthodontic movement. It suggested that the
chronic inflammation in obesity might interfere with the aseptic inflammation induced
by the mechanical loading by the increase of vinculin, osteopontin, and cathepsin D [24].
Therefore, it is exciting to see how proteomic analysis enables us to observe that a systemic
condition or disease can interfere with a microenvironment, such as PDL.
The GO term mostly found by the GO enrichment analysis and, consequently, the
one that obtained the highest number of proteins was the CC term “extracellular exosome”
(GO:0070062). Also known as exosomes, extracellular exosomes are extracellular vesicles
produced and released by most cells. They have different functions, and are involved in
intracellular regulatory processes such as cellular homeostasis and autophagy, for example
Furthermore, they act as mediators in intercellular communication, immune responses, and
infection. Interestingly, exosomes are considered as potential biomarkers for the diagnosis
and treatment of various diseases [35]. Exosomes in obesity become altered with different
miRNAs and enriched with molecules involved in inflammation, immune competence,
and cellular activation. Furthermore, they are released by adipocytes, which can induce a
pro-inflammatory state in adipose tissue and promote insulin resistance [36].
A KEGG pathway analysis of the differentially abundant proteins revealed an enrich-
ment of the ribosome pathway. The eleven proteins found in this pathway significantly
decreased in their abundance in the obese group compared to the control group, showing
that obesity affects the function of ribosomes. Ribosomal proteins are responsible for the
translational process of cells and act in ribosomal biogenesis. They are key players during
normal cell physiology, such as cell growth, proliferation, and development. They play an
important role in cellular responses to internal and external stimuli and the pathogenesis
of diseases. Ribosomal proteins have been demonstrated to regulate gene expression, the
translation of proteins, and the cell cycle. Any dysregulation of the ribosome biogenesis can
result in atypical cell proliferation and clinical manifestations of diseases and conditions
such as cancer and metabolic disorders. In obesity, cellular functions are required due to
the hyperplasia and hypertrophy of adipocytes, and are dependent on increased ribosomal
proteins [37]. Furthermore, there is an increased metabolic demand in obesity, which could
enhance ribosomal expression due to the increased protein synthesis in blood cells [38]. This
evidence shows that the ribosomal pathway is upregulated in obese subjects, as reported in
the whole-genome expression of whole blood [37,38]. However, corroborating our result,
we found some studies [39–41] that observed a negative correlation between the ribosome
Int. J. Mol. Sci. 2023, 24, 1003 13 of 19
pathway and obesity. Remarkably, a previous proteomic analysis of human skeletal muscle
demonstrated a reduced abundance of proteins related to ribosome activity in obesity.
Surprisingly, bariatric surgery could reverse the negative effects of obesity by increasing the
expression of ribosome proteins [39]. Van der Kolk et al. [41] observed the downregulation
of the ribosome pathway in adipose tissue and in the skeletal muscle of heavier co-twins.
Unfortunately, these studies do not comment on the role of the ribosome pathway in obesity.
The 11 ribosomal proteins of the ribosome pathway found in the present study are localized
in the cell cytoplasm and could perform different extraribosomal function(s). More studies
are necessary to better comprehend the role of ribosomal proteins in obesity.
Proline/arginine-rich end leucine-rich repeat protein (Prelp) is a protein present in the
extracellular matrices of collagen-rich tissues. [42] It has been demonstrated to be expressed
in the ligaments, cartilage, bone matrices, tendon, skin, sclera, lungs, and heart [43–45].
Prelp is responsible for anchoring basement membranes to the underlying connective
tissues [43]. It has been reported that Prelp reconstructs ruptured ligament tissue through
the differentiation of stem cells into ligament tissue [46]. Furthermore, Prelp is involved in
bone metabolism, as the N-terminal peptide of Prelp has been shown to inhibit osteoclast
differentiation and activity in addition to bone loss [47]. In our study, as assessed by
proteomic analysis and IHC, the production of Prelp was significantly increased in the
PDL of high-fat diet-induced obesity rats compared to the control group. A study recently
reported that Prelp expression is increased in adipocytes of obese mice. Interestingly, in this
same study, they developed adipocyte-specific Prelp transgenic mice and could observe
that the overexpression of Prelp resulted in considerable adipose tissue fibrosis and insulin
resistance. Their results suggested that this protein may play an important role in obesity
concerning adipose tissue remodeling [48]. Furthermore, Prelp belongs to the small leucine-
rich repeat proteoglycans (SLRPs). Evidence has shown that SLRPs are involved in insulin
resistance related to visceral secretome. In addition, they positively correlate with BMI and
central obesity and mediate metabolic inflammation, suggesting that extracellular matrix
components are critical to the pathogenesis of obesity [49].
Superoxide dismutases (Sods) are a family of enzymatic antioxidants that neutralize
superoxide anions. The isozyme Sod2 is mainly found in mitochondria but may also be
located in the extracellular compartment [50]. Sod2 is involved in the response of peri-
odontal cells and tissues exposed to bacterial and mechanical signals [51–54]. Furthermore,
evidence shows an association between obesity, increased reactive oxygen species (ROS),
and intracellular oxidative stress. Therefore, the antioxidant system is necessary to elimi-
nate ROS or minimize their harmful effects. Our results show that the Sod2 protein was
significantly more abundant in the obese group compared to the control group, probably
due to the increase in the number of superoxide anions, suggesting a preventive role of
Sod2 in inflammation progression that occurred as a result of obesity. A previous study that
focused on the presence of Sod2 in saliva showed a greater total amount of Sod2 in subjects
with obesity than in healthy controls [55]. Regarding the evaluation of Sod2 expression in
the adipose tissue of obese children, Sod2 was significantly increased, whereas its activity
was reduced [56]. This finding suggests that obesity impairs the function of PDL in relation
to protein synthesis/secretion.
The protein Sec13 is encountered in the nuclear pore complex (NPC) inside the nu-
cleus, and is a structural component of the coat protein II (COPII) coated vesicles in the
cytoplasm [57]. It is involved in the transport of protein from the endoplasmic reticulum to
the Golgi apparatus [58]. In inflammation, Sec13 modulates the expression of key immune
factors [57]. Food intake in high-fat mice has been shown to upregulate the expression
of Sec13 in the hypothalamus [59]. This observation corroborates our results that higher
levels of Sec13 were observed in the PDL of the obese group as compared to the healthy
control group.
Evaluating protein-protein interactions using STRING analysis, we could observe
that Sod2 has a strong interaction with Prdx6. This protein belongs to the peroxiredoxin
family, which are antioxidant enzymes capable of catalyzing the reduction of hydrogen
Int. J. Mol. Sci. 2023, 24, 1003 14 of 19
peroxide (H2O2). In addition to its peroxidase activity, Prdx6 has phospholipase A2 activity
and can hydrolyze phospholipids. The absence of Prdx6 was shown to be related to a
higher level of pro-inflammatory markers in the liver, skeletal muscle, and white adipose
tissue. Furthermore, in this study, the authors observed that mice lacking Prdx6 were more
likely to develop severe liver dysfunction [60]. Corroborating the link between Prdx6 and
inflammation, pancreatic beta cells exposed to TNF-alpha and/or IFN-gamma decreased
the level of Prdx6 protein [61]. In the present study, Prdx6 was significantly decreased in
the obese group compared to the control group, showing that this antioxidant enzyme has
an important role in the harmful effects of obesity in some tissues, such as PDL. Future
studies must further explore the protein-protein interaction network and its impact on
clinical conditions.
In this context, this study contributed by highlighting PDL proteins affected by obesity,
suggesting that future clinical studies should investigate these proteins in human oral
fluids and in vitro or pre-clinical studies to unravel the mechanisms of their involvement
in periodontal health and disease conditions.
A limitation of the present study is that not all significantly abundant proteins found by
proteomic analysis could be validated by IHC analysis. Future studies should be performed
to validate these proteins and to better explore their role in obesity.
Taken together, our results clearly show the effects of obesity on relevant GOs, such
as the GO CC term “extracellular exosome”. Furthermore, the present study exhibits
novel evidence that the PDL is affected by obesity at a molecular level. Moreover, the
proteomic profile of PDL is altered in experimental obesity induction, providing a list
of differentially abundant proteins associated with obesity, which indicates that PDL is
responsive to obesity and that critical molecules might mediate the harmful effects of
obesity on periodontal tissues.
4. Materials and Methods
4.1. Experimental Protocol
The Ethical Committee on Animal Experimentation of the Sao Paulo State University-
UNESP, School of Dentistry at Araraquara, Brazil (protocol number 16/2015) approved the
experimental protocol. A total of eight male adult Holtzman rats with an average weight
of 300 g were kept in the animal facility of the university in plastic cages, under controlled
temperature (22–25 ◦C), and exposed to a 12-h light/dark cycle. The animals received
a standard laboratory diet (Labina/Purina, Ribeirão Preto, Brazil) or a high-fat diet in
addition to water ad libitum. They were randomly divided into two experimental groups:
control animals (n = 3) and obese animals (n = 5), which comprised animals subjected to
obesity induction by a high-fat diet, as previously described [23,24]. This high-fat diet had
about 3.82 kcal/g and consisted of a mixture of standard rat chow and milk chocolate,
peanuts, and sweet biscuits in a 3:2:2:1 ratio and contained 20 g of protein, 20 g of total
fat, 48 g of carbohydrates, and 4 g of fiber per 100 g of diet. In contrast, the standard diet
contained approximately 2.25 kcal/g and 22 g of protein, 48 g of carbohydrate, 4 g of total
fat, 8 g of fiber, and 200 mg of sodium per 100 g of diet. The high-fat diet was replaced
daily. After 90 days, all animals were euthanized by anesthetic overdose and the maxillae
were collected.
4.2. Laser Capture Microdissection (LCM) and Protein Extraction
As previously described [21], the maxillae were histologically processed to obtain
serial sections in a buccolingual direction. They were fixed in 10% buffered formalin (Fisher
Diagnostics, Middletown, VA, USA) at 4 ◦C for 24 h, rinsed three times in phosphate-
buffered saline (PBS, pH 7.4, Applied Biosystems, Foster City, CA, USA) for 30 min at
4 ◦C, and decalcified in 20% ethylenediamine tetraacetic acid (EDTA, Merck, Millipore)
for 30 days at 4 ◦C under agitation. The decalcified maxillae were washed three times in
PBS for 30 min at 4 ◦C, dehydrated in three changes of 100% ethanol for 30 min at 4 ◦C,
diaphanized in two changes of xylene for 20 min at room temperature (RT), and, finally,
Int. J. Mol. Sci. 2023, 24, 1003 15 of 19
embedded in paraffin and stored at −20 ◦C. Serial 5 µm thick buccolingual sections of
the first molar were obtained and mounted on polyethylene naphthalate membrane glass
slides (PEN, Applied Biosystems). Sections were deparaffinized in two changes of xylene
for 2 min and one change for 5 min, and then air dried for 5 min. Unstained sections
allowed the visualization of the periodontal ligament architecture and were immediately
used for microdissection. PDL was microdissected, as previously described [62]. Eight to
ten histological sections per sample were microdissected, and the total captured area was
calculated and used to normalize the amount of tissue/protein for LC-MS/MS analysis.
Caps containing the captured tissues were incubated with 30 µL of 8 M urea for 30 min
at RT for protein extraction. Samples were sonicated and centrifuged briefly. Whole
protein extracts were reduced by incubation in 5 mM dithiothreitol, alkylated with 14 mM
iodoacetamide, and trypsin digested (2 µg) as described previously [21]. The resulting
tryptic peptide samples were equilibrated to pH 2.0 with formic acid. Peptide mixtures
were desalted and purified using microcolumns ZipTip® C18, P10 Pipette Tips (Merck
Millipore, Billerica, MA, USA), dried in a vacuum concentrator, and reconstituted in 0.1%
formic acid for analysis.
4.3. LC-MS/MS and Bioinformatics Analysis
A mass spectrometry analysis was performed in the BIOMASS Core Facility for Scien-
tific Research of the University of São Paulo, Brazil. Peptide mixtures were analyzed on an
LTQ-Orbitrap Velos ETD (Thermo Scientific, Waltham, MA, USA) mass spectrometer cou-
pled with an Easy NanoLC II (Thermo Scientific) ion source. The peptides were separated
by a 2–95% acetonitrile gradient in 0.1% formic acid using a C18 PicoFrit analytical column
(C18 PepMap, 75 µm id× 10 cm, 3.5 µm particle size, 100 Å pore size; New Objective,
Ringoes, NJ, USA) at a flow rate of 300 nL/min for 105 min. The LTQ-Orbitrap Velos
was operated in positive ion mode with a data-dependent acquisition. The full scan was
obtained in the Orbitrap, with an automatic gain control (AGC) target value of 106 ions
and a maximum fill time of 500 ms. Each precursor ion scan was acquired at a resolu-
tion of 60,000 FWHM in the 400–1500 m/z mass range. Peptide ions were fragmented by
CID MS/MS using a normalized collision energy of 35. The 20 most abundant peptides
were selected for MS/MS and dynamically excluded for 30 s. The samples were analyzed
in two technical replicates combined to perform the database search. All raw data were
accessed in Xcalibur software (Thermo Fisher Scientific) and processed using Proteome
Discoverer (version 1.4.0.288, Thermo Finnigan, San Jose, CA, USA). The MS/MS spectra
(msf) generated from raw files were searched against the UniProtKBSwiss-Prot Protein
Database (released 2017_01 of 18 January 2017; 553,474 entries), assuming trypsin digestion
with parameters set to a maximum of two missing cleavages, with a parent ion tolerance
of 20.0 ppm for the MS search and a fragment ion mass tolerance of 0.6 Da. Oxidation of
methionine (+16 Da) was set as a dynamic modification, and carbamidomethylation in cys-
teine residues (+57 Da) was set as a static modification. For label-free protein quantification,
the data files were analyzed in MaxQuant software. The quantitative value (normalized
spectral counts) was obtained with the protein thresholds set at a minimum 99% probability
to achieve a false discovery rate (FDR) < 1% and containing at least one peptide with thresh-
olds established at a minimum 60% probability, XCorr cutoffs +1 > 1.8, +2 > 2.2, +3 > 2.5, and
+4 > 3.5. The resulting peak area values were used to analyze the distribution of identified
proteins throughout the samples. An independent samples t-test was applied to test for dif-
ferences in protein intensities between the control and obese sample groups. Protein ratios
were calculated from the average peak area values of the control and obese groups. A PCA
was performed to determine sample variability between the proteome profiles of control
and obese PDLs. A PCA analysis, volcano plot and a heat map graphic organization were
performed using MetaboAnalist software (https://www.metaboanalyst.ca/home.xhtml,
accessed on 7 June 2018). A GO enrichment analysis and KEGG pathways assignment were
performed using DAVID software. The differential abundance of proteins was determined
by a t-test followed by the Benjamini-Hochberg multiple test correction. The fold-change
Int. J. Mol. Sci. 2023, 24, 1003 16 of 19
between differentially regulated proteins was also determined. All statistical tests were
calculated using a p < 0.05, and only statistically significantly enriched GO terms were
reported. Furthermore, the protein-protein interactions of differentially abundant proteins
were assessed using STRING software with a high confidence level of 0.700.
4.4. IHC
Three proteins identified in the PDL by proteomic analysis were selected. Their pres-
ence was confirmed by IHC using additional histological sections from the same animals as-
sessed by LC-MS/MS. IHC was performed on paraffin sections using an avidin-biotinylated
peroxidase enzyme complex- (ABC) based kit (Vector Labs, Burlingame, CA, USA) with
3-amino-9-ethylcarbazole (AEC) chromogenic substrate (Vector Labs, Burlingame, CA,
USA), as described previously [63]. The primary antibodies included were Prelp (bs13707R,
Bioss, Hong Kong, China), Sec13 (ORB312990, Biorbyt, Cambridge, UK), and Sod2
(PA530604, Invitrogen). Samples were stained with 3,3′-diaminobenzidine (DAB, Dako,
Glostrup, Denmark) and counterstained with Carazzi’s hematoxylin. Negative controls
lacking primary antibodies were performed. Representative photomicrographs of each
sample’s mesial, distal, and furcation areas per group were taken at 400x magnification
using a digital camera (DP-71, Olympus, Hong Kong, China) attached to a light microscope
(BX-51, Olympus). The analysis was performed by an experienced examiner blinded to the
experimental groups by counting positive cells.
4.5. Statistical Analysis
The statistical t-test with a Bonferroni test correction was used (p < 0.05) to determine
the differential protein abundances on average peak area values of the control and obese
groups. The fold-change was calculated as the ratio of obese/control values. For the IHC
analysis, data were tested for normality using the Shapiro-Wilk test and an intergroup
comparison was performed using the t-test (Graph-Pad Prism 9 Software Inc., San Diego,
CA, USA) at a significance level of 5%.
Supplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/ijms24021003/s1.
Author Contributions: Conceptualization, A.V.B.N., J.D., F.H.N.-J., and J.A.C.; methodology, A.V.B.N.,
M.E.S.L., C.C.M., C.R.S., and L.S.M.; software, A.V.B.N., C.C.M., C.R.S. and L.S.M.; validation,
M.E.S.L. and C.C.M..; formal analysis, A.V.B.N., M.E.S.L., C.C.M., C.R.S., and L.S.M.; investigation,
A.V.B.N., M.E.S.L., C.C.M. and C.R.S.; resources, F.H.N.-J. and J.A.C..; data curation, M.E.S.L. and
C.R.S.; writing—original draft preparation, A.V.B.N.; writing—review and editing, A.V.B.N., M.E.S.L.,
C.C.M., C.R.S., L.S.M., J.D., F.H.N.-J. and J.A.C.; visualization, A.V.B.N., M.E.S.L., C.C.M. and C.R.S.;
supervision, J.A.C.; project administration, J.D. and J.A.C.; funding acquisition, J.D. and J.A.C. All
authors have read and agreed to the published version of the manuscript.
Funding: The authors would like to thank the São Paulo Research Foundation (FAPESP), grant
numbers 2014/20715-7 and 2017/07137-2, and the German Research Foundation (DFG), grant number
DE 1593/5-1 for financial support.
Institutional Review Board Statement: The animal study protocol was approved by the Ethical
Committee on Animal Experimentation of the Sao Paulo State University-UNESP, School of Dentistry
at Araraquara, Brazil (protocol code 16/2015).
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: The authors would like to thank BIOMASS-Core Facility for Scientific Research
(CEFAP-USP), São Paulo-SP, Brazil, for the Mass Spectrometry and Proteome Research analysis.
Conflicts of Interest: The authors declare no conflict of interest.
Int. J. Mol. Sci. 2023, 24, 1003 17 of 19
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