Campus de Araçatuba Araçatuba 2023 Caroline Loureiro TESE DE DOUTORADO Perfil proteômico das infecções endodônticas em pacientes diabéticos Campus de Araçatuba Araçatuba 2023 CAROLINE LOUREIRO Perfil proteômico das infecções endodônticas em pacientes diabéticos Tese apresentada à Faculdade de Odontologia de Araçatuba, Universidade Estadual Paulista “Júlio de Mesquita Filho” – UNESP, como parte dos requisitos para obtenção do título de Doutor em Ciência Odontológica, área de concentração em Endodontia. Orientador: Prof. Ass. Dr. Rogério de Castilho Jacinto Catalogação na Publicação (CIP) Diretoria Técnica de Biblioteca e Documentação – FOA / UNESP Loureiro, Caroline. L892p Perfil proteômico das infecções endodônticas em pacientes diabéticos / Caroline Loureiro. - Araçatuba, 2023 73 f. : il. ; tab. Tese (Doutorado) – Universidade Estadual Paulista, Faculdade de Odontologia de Araçatuba Orientador: Prof. Rogério de Castilho Jacinto 1. Periodontite periapical 2. Diabetes Mellitus 3. Endodontia 4. Interações hospedeiro-patógeno 5. Proteômica I. T. Black D24 CDD 617.67 Claudio Hideo Matsumoto – CRB-8/5550 Dados Curriculares Caroline Loureiro Nascimento 16/09/1992 Filiação Maria Iracema Chagas de Brito Loureiro Jaime Aparecido Loureiro 2012-2016 Graduação Curso de Graduação em Odontologia Faculdade de Odontologia de Araçatuba – UNESP 2017-2019 Especialização Especialização em Endodontia Faculdade de Odontologia de Araçatuba - UNESP 2017-2019 Mestrado Mestrado em Ciência Odontológica - Área de Endodontia Faculdade de Odontologia de Araçatuba – UNESP 2019-2023 Doutorado Doutorado em Ciência Odontológica - Área de Endodontia Faculdade de Odontologia de Araçatuba UNESP Agradecimentos Aos familiares Aos meus pais, Jaime Aparecido Loureiro e Maria Iracema Chagas de Brito Loureiro, agradeço pelo apoio, pelo esforço e pela renúncia. Pelo apoio, em todas as minha decisões sempre estiveram ao meu lado. Pelo esforço, asseguraram que meus dias fossem os melhores com todo amparo emocional e financeiro. Pela renúncia, abdicaram da convivência por longos anos para que eu pudesse ter acesso a todo conhecimento que desfruto hoje. Se hoje sou quem sou, devo tudo à vocês. Agradeço pelo empenho na minha criação e educação, pelo cuidado, carinho e amor. Obrigada por confiarem nos meus sonhos e sonharem junto comigo. Amo muito vocês. Ao meu namorado, Lucas Marcondes de Mello, agradeço pelo amor, pela companhia e cumplicidade. Obrigada pela ajuda ao longo desses anos de pós-graduação, seu acolhimento foi essencial para eu ter chegado forte até aqui. Agradeço pelo incentivo em todas as vezes que pensei em desistir. Aos meus irmãos, Gustavo Loureiro e Rodrigo Loureiro, e à minha cunhada, Jessica de Oliveira Loureiro pelo apoio e pela torcida. Obrigada por acreditarem em mim e nos meus sonhos. Aos mestres Ao meu orientador, Prof. Rogério de Castilho Jacinto, agradeço pela confiança, pela convivência e pela amizade construída. Tive o privilégio de ter sido orientada por alguém como o senhor, gentil, acolhedor, humano e sempre disposto a ajudar. Sou grata por todas as vezes em que o senhor encontrou solução onde eu não via e que me estimulou quando eu me via desanimada. Hoje colho os frutos que o senhor semeou em 2017 da melhor forma, com sensação de dever cumprido, conhecimento e maturidade. Obrigada por compartilhar sua sabedoria e por ter tornado tão leve esse processo de aprendizado. Minha eterna gratidão. Ao Prof. João Eduardo Gomes Filho agradeço por estar sempre presente nos meus momentos de conquista e por cada palavra de acolhimento nos momentos de angústia. Obrigada pelo estímulo, pela disponibilidade e pelo apoio durante todos esses anos. Ao Prof. Elói Dezan Junior, pela amizade criada desde a graduação. Obrigada por sempre ter uma palavra amiga e um bom conselho para qualquer situação. Seu incentivo foi primordial para essa conquista. Obrigada por tudo professor. Aos professores Luciano Ângelo Tavares Cintra e Gustavo Sivieri de Araújo agradeço pela confiança, amizade e momentos de descontração. Sou grata pelo incentivo recebido durante a pós-graduação e por estarem sempre dispostos a ajudar e dar conselhos. Vocês tiveram uma contribuição importante na minha formação acadêmica, me espelho em vocês. Ao Prof. Juliano Pelim Pessan agradeço pela parceria e pelo estímulo desde o início da minha trajetória. Muito obrigada por compartilhar tanto conhecimento com leveza e humildade. Sou grata por ter confiado em mim, por todos os momentos compartilhados e pelas conversas descontraídas. Obrigada por me coorientar. À Prof. Marília Afonso Rabelo Buzalaf pelo acolhimento, desde o nosso primeiro contato me recepcionou muito bem, abriu a porta da sua casa e com a leveza de quem domina e ama o que faz, me ajudou em tudo que precisei. Muito obrigada pela parceria e ajuda. Ao Prof. Francisco Montagner agradeço pela contribuição com nosso trabalho e por ter aceito participar da minha banca de defesa de tese. Sou grata pelo seu tempo e por todas as suas considerações. À professora Dóris Hissako Matsushita, agradeço por fazer parte do meu exame de qualificação. Fico extremamente agradecida pelo tempo dedicado em colaborar com nosso projeto. Aos amigos Agradeço à Flávia Plazza, irmã que a FOA-UNESP me deu. Tenho sorte em poder contar com você durante minha caminhada. Sou grata pela nossa amizade e por você nunca ter medido esforços para me ajudar e me acolher. Ao nosso grupo de pesquisa, agradeço pela convivência e pela amizade que começou no laboratório de microbiologia e que cultivamos além dele. Lembro de cada experimento em que passávamos horas sentados, compartilhando nossas histórias e criando novos capítulos. Obrigada por tudo Julia Guerra de Andrade, Ana Paula Fernandes Ribeiro e Gladiston William Lobo Rodrigues, vocês fizeram com que meus dias na pós-graduação fossem mais leves e felizes. Contem sempre comigo. Aos alunos de iniciação científica, Juliana, Larissa, Yana, Sthefanie, Michely, Felipe e Gabriel, que tive o prazer de co-orientar e conviver, vocês foram muito importantes na minha trajetória acadêmica. Aprendi muito com cada um de vocês e agradeço por ter participado de alguma forma da formação de vocês. Aos amigos da pós-graduação, Cristiane Cantiga, Nathália Machado, Carol Barros, Ana Maria Veiga, Ana Cláudia, Flávio Duarte, Pedro Chaves, Henrique Banci e aos demais pela convivência, ajuda mútua e por todos momentos de descontração. Aprendi muito com vocês durantes esses anos, obrigada por estarem sempre dispostos a me ajudar. À Talita Ventura e Vinicius Pelá do laboratório de Bioquímica da FOB- USP por todo apoio durante o desenvolvimento do projeto. Vocês foram essenciais para o sucesso da nossa linha de pesquisa. Muito obrigada. Aos funcionários À toda equipe do Departamento de Odontologia Preventiva e Restauradora, em especial ao Carlos e ao Jorge, agradeço por toda ajuda e pela boa convivência. Às funcionárias da Seção de pós-graduação, Cristiane e Valéria, obrigada pela paciência e ajuda com os prazos e documentações. Ao apoio das agências de fomento Agradeço à Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) pela concessão da minha bolsa de doutorado (Processo nº 2019/14995-0). O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Código de Financiamento 001. Resumo Loureiro C. Perfil proteômico das infecções endodônticas em pacientes diabéticos. 2023. 73 f. Tese - Faculdade de Odontologia de Araçatuba, UNESP - Universidade Estadual Paulista, Araçatuba, Brasil, 2023. RESUMO Este estudo teve como objetivo determinar quantitativa e qualitativamente o perfil proteômico da periodontite apical (PA) em pacientes com Diabetes Mellitus tipo 2 (DMT2) em comparação com pacientes não comprometidos sistemicamente e correlacionar a expressão proteica de ambos os grupos com suas funções biológicas. A amostra foi composta por 18 pacientes com PA assintomática divididos em dois grupos de acordo com a presença de DMT2: grupo diabético - pacientes com DMT2 (n = 9) e grupo controle - pacientes sistemicamente saudáveis (n = 9). Após a coleta, as amostras do canal radicular foram preparadas para análise proteômica usando cromatografia líquida de fase reversa e espectrometria de massa. A análise proteômica quantitativa sem marcadores foi realizada pelo software Protein Lynx Global Service. A diferença na expressão proteica entre os grupos foi calculada através do teste t (p < 0,05). As funções biológicas foram analisadas usando o banco de dados Homo sapiens do UniProt. Um total de 727 proteínas humanas foram identificadas em todas as amostras. Entre elas, foram quantificadas 124 proteínas comuns aos dois grupos, das quais 65 proteínas do grupo diabético apresentaram diferenças significativas em relação ao grupo controle: 43 proteínas suprarreguladas (p < 0,05) e 22 subreguladas (p < 0,05). Nenhuma diferença significativa foi observada na expressão proteica das 59 proteínas restantes (p > 0,05). A maioria das proteínas com diferenças na expressão estavam relacionadas à resposta imune/inflamatória. Lipocalina associada à gelatinase de neutrófilos, Plastin- 2, Lactotransferrina e 13 isoformas de imunoglobulinas foram reguladas positivamente. Em contrapartida, a proteína S100-A8, a proteína S100-A9, a histona H2B, a defensina 1 de neutrófilos, a defensina 3 de neutrófilos e a proteína induzível por prolactina foram reguladas negativamente. Foram demonstradas diferenças quantitativas na expressão de proteínas comuns aos grupos diabético e controle, principalmente relacionadas à resposta imune, estresse oxidativo, apoptose e proteólise. Esses achados revelaram vias biológicas que fornecem a base para apoiar os achados clínicos sobre a relação entre PA e DMT2. Palavras-chave: periodontite apical, diabetes mellitus, endodontia, interação hospedeiro-patógeno, proteômica. Abstract Loureiro C. Proteomic profile of endodontic infections in diabetic patients. 2023. 73 f. PhD Thesis – Araçatuba School of Dentistry, UNESP – São Paulo State University, Araçatuba, Brazil, 2023. ABSTRACT This study aimed to quantitatively and qualitatively determine the proteomic profile of apical periodontitis (AP) in Type 2 Diabetes Mellitus (T2DM) patients in comparison to systemically non-compromised patients, and to correlate the protein expression of both groups with their biological functions. The sample consisted of 18 patients with asymptomatic AP divided into two groups according to the presence of T2DM: diabetic group - patients with T2DM (n = 9) and control group - systemically healthy patients (n = 9). After sample collection, the root canal samples were prepared for proteomic analysis using reverse-phase liquid chromatography mass spectrometry. Label-free quantitative proteomic analysis was performed by Protein Lynx Global Service software. Differences in protein expression between groups were calculated using t-test (p < 0.05). Biological functions were analyzed using the Homo sapiens UniProt database. A total of 727 human proteins were identified in all samples. Among them, 124 proteins common to both groups were quantified, out of which 65 proteins from the diabetic group showed significant differences compared with the control: 43 up- regulated (p < 0.05) and 22 down-regulated (p < 0.05) proteins. No significant differences in protein expression were seen for the remaining 59 proteins (p > 0.05). Most proteins with differences in expression were related to immune/inflammatory response. Neutrophil gelatinase-associated lipocalin, Plastin-2, Lactotransferrin, and 13 isoforms of immunoglobulins were up- regulated. In contrast, Protein S100-A8, Protein S100-A9, Histone H2B, Neutrophil defensin 1, Neutrophil defensin 3, and Prolactin-inducible protein were down-regulated. Quantitative differences were demonstrated in the expression of proteins common to diabetic and control groups, mainly related to immune response, oxidative stress, apoptosis, and proteolysis. These findings revealed biological pathways that provide the basis to support clinical findings on the relationship between AP and T2DM. Keywords: apical periodontitis, diabetes mellitus, endodontics, host–pathogen interactions, proteomics. Lista de Figuras LISTA DE FIGURAS pág. Figure 1 Recruitment flowchart 3 Figure 2 Venn diagram of exclusive and common human proteins identified in both groups, and the difference in expression of quantified common proteins 11 Figure 3 Interactions between differentially expressed common proteins comparing diabetic and control groups (string database). Protein-protein interaction (ppi) enrichment p value < 1.0 e-16. Biological processes with significative e-value were selected: immune system process (red), response to stress (blue), cellular oxidant detoxification (purple) and antimicrobial humoral response (green) 17 Lista de Tabelas LISTA DE TABELAS pág. Table 1 Demographic data of patients and teeth included in the study 10 Table 2 Description and biological function classification of differentially expressed human proteins, comparing diabetic with the control group 12 Table 3 Biological function classification of exclusive human proteins in the diabetic and control groups, and of common proteins (↑, ↓, SE in the diabetic group in relation to the control group 16 Sumário ARTIGO pág. 1. INTRODUCTION 1 2. MATERIAL AND METHODS 2.1. Study design 2.2. Patient selection 2.3. Sample collection 2.4.Root canal sample preparation 2.5. Shotgun label-free quantitative proteomic analysis 2.6. Bioinformatics analysis and protein classification 3. RESULTS 4. DISCUSSION 5. CONCLUSIONS 6. REFERENCES 3 3 4 5 6 7 9 9 18 23 23 ANEXOS pág. Anexo 1 – Guia de submissão revista International Endodontic Journal 33 Anexo 2 – Comitê de Ética em Pesquisa em Humanos 48 Artigo 1 1. Introduction Diabetes mellitus (DM) is a complex metabolic disease caused by the absence or deficiency of pancreatic insulin production. The vast majority of DM cases can be categorised as resulting from either the destruction of β cells, which usually leads to absolute insulin deficiency (type 1), or from a combination of insulin resistance and inadequate response to compensatory insulin secretion (type 2) (Zimmet et al. 2016). Type 2 DM (T2DM) accounts for 90–95% of DM cases, and requires the use of insulin and/or other drugs to control blood glucose levels (American Diabetes 2013, DeFronzo et al. 2015). Diabetic patients often have an impaired immune system and inflammatory response, which, combined with changes in blood microcirculation associated with DM, results in an increased susceptibility to bacterial infections (Bender & Bender 2003, Chakravarthy 2013). Oral health studies have shown that DM influences both the pathogenesis and prognosis of pulp and periapical diseases (Fouad 2003). Diabetes has been associated with an increased risk of endodontic infections, including apical periodontitis (AP) (Segura-Egea et al. 2005, Lopez-Lopez et al. 2011, Marotta et al. 2012), the presence of periapical radiolucencies in endodontically-treated teeth (Segura-Egea et al. 2016) and the extraction of teeth with root canal treatments (Cabanillas-Balsera et al. 2019). Furthermore, the presence of local inflammation caused by AP can increase blood glucose, thus intensifying DM and producing an uncontrolled diabetic state in the patient (Schulze et al. 2007). 2 DM and endodontic infections share fundamental molecular mechanisms in the development of a chronic inflammatory state (Sasaki et al. 2016). Immune/inflammatory pattern alterations characterised by neutrophil dysfunction, a proinflammatory macrophage profile and the elevated release of inflammatory mediators can produce a hyperresponsive immune/inflammatory system in diabetic patients (Bender & Bender 2003, Sasaki et al. 2016). Moreover, a hyperglycaemic state promotes the formation of advanced glycation end-products (AGEs), which increase cell apoptosis and the production of reactive oxygen species (ROS) (Graves et al. 2006). The proteomic profile of endodontic infections has recently been investigated using mass spectrometry, as infected root canal contents allow for the study of the pathogen-host relationship by analysing human and bacterial protein expression. Bacterial proteomic profile of endodontic infections was addressed for the first time by Nandakumar et al. (2009), while human proteomic profile by Provenzano et al. (2013), using liquid chromatography-mass spectrometry. Human protein characterisation in endodontic infections of healthy patients has been determined by: the progression of endodontic pathogenesis (Loureiro et al. 2020, Silva et al. 2020); asymptomatic and symptomatic AP (Provenzano et al. 2013, Loureiro et al. 2021); post-treatment AP infections (Provenzano et al. 2016, Francisco et al. 2019); and acute apical abscesses (Provenzano et al. 2013, Alfenas et al. 2017). However, no study has yet used proteomic tools to investigate the molecular pathways of AP in patients with systemic disorders, such as diabetes. The objective of this study was 3 therefore to quantitatively and qualitatively determine the proteomic profile of AP in T2DM patients, compared with systemically non-compromised patients. 2. Material and Methods This cross-sectional study was approved by the Research Ethics Committee of the School of Dentistry, Araçatuba - UNESP (N° 34694620.7.0000.5420). All patients were informed about the research procedures and agreed to participate by signing an informed consent form. This study followed the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines (https://www.strobe-statement.org). 2.1. Study design Figure 1 presents the recruitment flowchart showing the study design from patient screening to proteomic assessment. Figure 1. Recruitment flowchart. 4 2.2. Patient selection The sample consisted of 18 patients of both genders, aged between 18– 70 years, who sought endodontic treatment at the School of Dentistry, Araçatuba - UNESP from July 2018 to December 2019. The study was divided into two groups according to the presence of T2DM: a diabetic group with patients diagnosed with T2DM (n = 9), and a control group with systemically healthy patients (n = 9). The sample size was based on our previous clinical study (Loureiro et al. 2021), in which proteomic analysis of root canal contents from patients with symptomatic or asymptomatic AP was performed. Glycated haemoglobin (HbA1c) was recorded on the day of treatment using data from recent laboratory tests (up to 3 months); patients with a metabolic control status of HbA1c ≥ 7.5% were included in the diabetic group (American Diabetes Association 2019). Patients without T2DM who were reportedly healthy (i.e., without any systemic disease) were included in the control group. HbA1c was also recorded in control group patients through recent exams (up to 3 months) or exam requests. Medical records, anamnesis and clinical/radiographic examinations were analysed for patient selection. Patients reporting smoking habits, uncontrolled arterial hypertension, autoimmune diseases, pregnancy, chronic liver disease, chronic kidney disease, transplant, use of antibiotics in the past 3 months or presenting with severe periodontal disease were excluded. T2DM patients presenting with controlled arterial hypertension, or who were overweight or obese could be included in the diabetic group. 5 Only asymptomatic teeth with primary endodontic infections that were associated with the presence of a periapical lesion visible on the radiograph and were diagnosed as chronic AP were included. Periapical radiography was performed using a digital radiographic sensor with measurement software (DentalMaster DICOM, V 1.0.11, Micro Imagem, Rio de Janeiro, RJ, Brazil). Only teeth with periapical lesions ≥ 2.5 mm in diameter as measured by the software were included. The exclusion criteria consisted of teeth with spontaneous pain and/or pain when biting/eating, and tenderness to percussion, teeth that could not be isolated with a rubber dam, teeth with root canals exposed to the oral cavity or sinus tract, a history of dental trauma or the presence of oedema in periapical tissues, advanced periodontal disease or incomplete root formation. 2.3. Sample collection Samples were taken from root canal contents according to a protocol described in previous studies (Jacinto et al. 2008, Loureiro et al. 2021). After administration of local anaesthesia with 2% lidocaine and epinephrine at 1 : 100 000 (Alphacaine; Nova DFL Industria e Comercio S/A, Curicica, RJ, Brazil), the tooth was isolated using a rubber dam, followed by sealing of the crown/rubber dam interface with a light-cured gingival barrier (TopDam; FGM, Joinville, SC, Brazil). Disinfection of the tooth and the operative field was performed using 30% hydrogen peroxide (Merck KGaA, Darmstadt, Germany) and 2.5% sodium hypochlorite (Rioquímica) for 30 s each. Neutralisation was then performed using 5% sodium thiosulfate (Merck KGaA). Access cavity preparation was conducted using sterile high-speed diamond burs, without water spray. Cooling during this 6 stage was performed manually with sterile saline solution. Before accessing the pulp chamber, contaminants (restoration/carious tissue) were removed without exposing the root canals. Samples were collected only from broad and rectilinear canals (minimum initial diameter of a size 15 K-File). Before root canal sample collection, a sterile K-file was introduced with minimal instrumentation to confirm root canal access. Three sterile paper points were then placed 1 mm from the apex and held in place for 60 s each. If the canal was completely dry, a drop of sterile saline was placed before removing the paper point. After collection, the paper points were stored in sterile, DNA- and RNA-free cryotubes, which were frozen at –80 °C until they were used for proteomic analysis. 2.4. Root canal sample preparation Sample preparation for proteomic analysis was performed based on the protocol used by previous studies (Ventura et al. 2018, Loureiro et al. 2020, Loureiro et al. 2021). The paper points of each sample were cut and pooled in biological triplicate (samples from 3 patients in the same group were combined in a single tube, comprising three pools per group). For protein extraction, an extraction solution containing 6 mol L–1 urea and 2 mol L–1 thiourea in 50 mmol L-1 NH4HCO3 pH 7.8 was added until the paper points were covered. The samples were vortexed for 10 min at 4 °C, then sonicated at 4 °C for 5 min and finally centrifuged at 20 817 g for 10 min at 4 °C. The supernatant was then collected, and this step was repeated twice. The pellets were placed in filter tubes 7 (Corning® Costar® Spin-X® Plastic Centrifuge Tube Filters; Sigma-Aldrich, New York, USA) and centrifuged at 20 817 g for 10 min at 4 °C. The total solution recovered was approximately 160 µL in each biological triplicate. After, 50 mmol L–1 NH4HCO3 (volume corresponding to 1.5 × the sample volume) were added to the samples, which were placed in Falcon Amicon Ultra-4 10k tubes (Merck Millipore, Tullagreen, CO, Ireland) and centrifuged at 4500 g at 4 °C to approximately 150 µL. For protein reduction, samples were incubated with 5 mmol L–1 dithiothreitol at 37 °C for 40 min, and subsequently with 10 mmol L–1 iodoacetamide at 37 °C for 30 min in the dark. The tryptic digestion was performed for 14 h at 37 °C by adding 2% (w/w) trypsin (Promega, Madison, WI, USA). 5% formic acid was then added to inhibit the action of trypsin, and the samples were desalted and purified with a C18 spin column (Thermo Scientific, Rockford, IL, USA). 1 µL of each sample was used for protein quantification according to the Bradford method (Bio-Rad Bradford Assays). The remnants were dried to approximately 1 µL in SpeedVac (Thermo Scientific), and were resuspended in 3% acetonitrile and 0.1% formic acid for submission for Nano Liquid Chromatography Electron Spray Ionization Tandem Mass Spectrometry – LC-ESI-MS/MS (Waters, Manchester, UK). 2.5. Shotgun label-free quantitative proteomic analysis Identification of peptides was performed using a nanoACQUITY UPLC- Xevo QTof MS system (Waters, Manchester, UK). The nanoACQUITY UPLC was equipped with nanoACQUITY HSS T3, an analytical reverse-phase column (75 μm × 150 mm, 1.8 μm particle size (Waters, Manchester, UK). The column was 8 equilibrated with mobile phase A (0.1% formic acid in water). The peptides were then separated with a linear gradient of 7–85% mobile phase B (0.1% formic acid in acetonitrile) for 70 min at a flow rate of 0.35 μL min–1. The column temperature remained at 55 °C. The Xevo G2 Q-TOF mass spectrometer was operated in positive nanoelectrospray ion mode, and data were collected using the MSE method at elevated energy (19–45 V), which allows data acquisition of both precursor and fragment ions in one injection. Source conditions used included capillary voltage of 2.5 kV, sample cone at 30 V, extraction cone at 5.0 V and source temperature at 80 °C. Data acquisition occurred over 70 min and the scan range was 50–2000 Da. The lock spray, used to ensure accuracy and reproducibility, was run with a [Glu1] fibrinopeptide solution (1 pmol/ μL min–1) at a flow rate of 1 μL min–1, as a reference ion in positive mode at m/z 785.8427. Protein Lynx Global Server (PLGS) version 3.0 software (Waters, Manchester, UK) was used to process and search for LC-MSE continuum data. Proteins were identified using the embedded ion accounting algorithm in the software and a search of the Homo sapiens database (UniProtKB/Swiss-Prot) downloaded on January 2020 from UniProtKB (http://www.uniprot.org/). For label-free quantitative proteomic analysis, three raw MS files from the diabetic and control groups were analysed using the PLGS software. All proteins identified that had a score with confidence greater than 95% were included in the quantitative statistical analysis. Identical peptides from each technical triplicate sample were grouped based on mass accuracy (< 10 ppm) and time of retention tolerance (< 0.25 min), using the clustering software embedded in the PLGS. Differences in expression between the diabetic and control groups were 9 analysed using the t-test statistic, and the downregulation or up-regulation of protein expression was determined (p < 0.05). 2.6. Bioinformatics analysis and protein classification For data analysis, reviewed and unreviewed proteins were analysed according to their accession number using the UniProt database (http://www.uniprot.org). Repeated proteins and fragments were excluded from the analysis, as were reverse proteins. The biological functions analyses were performed using the Homo sapiens proteome database (UniProtKB/Swiss-Prot). A Venn diagram was created using the Bioinformatics & Evolutionary Genomics platform (http://bioinformatics.psb.ugent.be/webtools/Venn/). For the interaction networks, the STRING® database (https://string- db.org/cgi/network.pl) was used to illustrate the interactions between common proteins showing different expression levels in the diabetic and control groups. Protein classification was based on the methods of previous studies (Eckhardt et al. 2014; Loureiro et al. 2021). The identified proteins were divided into the following categories: metabolism and energy pathways, immune/inflammatory response, transport, structure, DNA/RNA regulation and repair, cell communication and signal transduction, cell growth and/or maintenance, differentiation of neural cells, apoptosis, stress response, and proteins of unknown function. 3. Results A total of 18 patients participated in this study (11 of whom were male), and the demographics of these individuals are provided in Table 1. The mean age 10 of patients in the diabetic group was 60.3 ± 8.8 years, and 44.3 ± 11.4 years in the control group. In diabetic patients, the mean HbA1c was 8.75 ± 0.9. With the exception of one patient, all individuals in the diabetic group had controlled arterial hypertension associated with DM. Table 1. Demographic data of patients and teeth included in the study. Patient Group Medications Body mass index (Kg/m2) Arterial hypertension Age Gender HbA1c (%) Teeth P1 Diabetic Captopril, metformin 24.4 Yes 48 Male 8.9 Canine P2 Diabetic Simvastatin, metformin 30.6 No 49 Female 7.5 Molar P3 Diabetic Losartan, metformin 23.0 Yes 68 Male 9.4 Canine P4 Diabetic Losartan, metformin 23.0 Yes 68 Male 9.4 Canine P5 Diabetic Captopril, metformin 22.1 Yes 68 Male 10.1 Incisor P6 Diabetic Losartan, metformin 32.4 Yes 52 Female 7.8 Incisor P7 Diabetic Losartan, metformin 23.6 Yes 70 Male 9.8 Incisor P8 Diabetic Olmesartan, metformin 23.2 Yes 60 Male 7.8 Premolar P9 Diabetic Losartan, metformin 25.2 Yes 60 Male 8.1 Premolar P10 Control - 33.9 No 46 Female 5.5 Premolar P11 Control - 24.5 No 32 Male 4.8 Incisor P12 Control - 20.3 No 40 Female 5.1 Premolar P13 Control - 23.1 No 35 Male 4.8 Incisor P14 Control - 22.4 No 43 Male 5.0 Incisor P15 Control - 24.5 No 42 Female 4.7 Premolar P16 Control - 23.7 No 36 Female 4.9 Canine P17 Control - 26.1 No 58 Male 5.4 Canine P18 Control - 27.0 No 67 Female 5.5 Premolar Proteomic analysis of the samples revealed a total of 1901 accession numbers. After the exclusion of duplicates, human proteins were selected from the raw data obtained by nLC-ESI-MS/MS, as previously described by Loureiro et al. (2021). Following selection, all analysed samples accounted for 727 proteins of human origin in both groups. Among them, 350 proteins were present in the diabetic group, and 501 in the control group. The complete list of human 11 proteins identified is presented in the supplementary information (Table S1). The distribution of proteins in each group is shown in Figure 2. Figure 2. Venn diagram of exclusive and common human proteins identified in both groups, and the difference in expression of quantified common proteins. All 124 common proteins were quantified for both groups. 65 proteins showed significant differences in expression in the comparison between the diabetic group and the control group: 43 proteins were up-regulated and 22 were down-regulated in the diabetic group, compared to the control group (Table 2). No significant difference in expression was observed in 59 quantified proteins (Figure 2). Most proteins that were differentially expressed belonged to the immune/inflammatory response category. Neutrophil gelatinase-associated lipocalin (NGAL), Plastin-2, Lactotransferrin and 13 isoforms of immunoglobulins were among the proteins up-regulated in the diabetic group. In contrast, Protein S100-A8, Protein S100-A9, Histone H2B, Neutrophil defensin 1, Neutrophil defensin 3 and Prolactin-inducible protein were down-regulated. The greatest increases in expression were observed for Haemoglobin subunit beta (26-fold 12 higher), Haemoglobin subunit zeta (15-fold higher), Haemoglobin subunit delta (9-fold higher) and Serine/threonine-protein kinase 31 (6-fold higher). Table 2. Description and biological function classification of differentially expressed human proteins, comparing diabetic with the control group. Expression differences Ratio D/C Accession number Description Biological function ↑ 26.05 P68871 Hemoglobin subunit beta Transport C ↑ 15.80 P02008 Hemoglobin subunit zeta Transport C ↑ 9.12 P02042 Hemoglobin subunit delta Transport C ↑ 6.23 Q9BXU1 Serine/threonine-protein kinase 31 Catabolic process A ↑ 5.81 P01877 Immunoglobulin heavy constant alpha 2 Adaptive immunity B ↑ 5.05 P01876 Immunoglobulin heavy constant alpha 1 Adaptive immunity B ↑ 4.44 A0M8Q6 Immunoglobulin lambda constant 7 Adaptive immunity B ↑ 4.35 P0CG04 Immunoglobulin lambda constant 1 Adaptive immunity B ↑ 4.26 B9A064 Immunoglobulin lambda-like polypeptide 5 Defense response to bacterium B ↑ 4.22 P0DOY3 Immunoglobulin lambda constant 3 Defense response to bacterium B ↑ 4.18 P0DOY2 Immunoglobulin lambda constant 2 Defense response to bacterium B ↑ 4.10 A0A075B6Z2 T cell receptor alpha joining 56 Unknown K ↑ 3.67 P0CF74 Immunoglobulin lambda constant 6 Adaptive immunity B ↑ 3.13 Q14980 Nuclear mitotic apparatus protein 1 Cell division G ↑ 2.66 Q06830 Peroxiredoxin-1 Response to oxidative stress J ↑ 2.56 S4R460 Immunoglobulin heavy variable 3/OR16-9 Adaptive immunity B ↑ 2.53 A0A4W8ZXM2 Immunoglobulin heavy variable 3-72 Adaptive immunity B ↑ 2.53 G3V1N2 HCG1745306_ isoform CRA_a Transport C ↑ 2.51 P32119 Peroxiredoxin-2 Response to oxidative stress J ↑ 2.23 Q8WXA9 Splicing regulatory glutamine/lysine-rich 1 mRNA processing E ↑ 1.86 P13796 Plastin-2 Interleukin-12-mediator B ↑ 1.86 A6NIW5 Peroxiredoxin 2_ isoform CRA_a Response to oxidative stress J ↑ 1.84 P13797 Plastin-3 Cytoskeleton component D ↑ 1.77 P11678 Eosinophil peroxidase Response to oxidative stress J ↑ 1.70 P04264 Keratin_ type II cytoskeletal 1 Structural activity D ↑ 1.60 P01023 Alpha-2-macroglobulin Differentiation/Protease inhibitor G ↑ 1.55 P05164 Myeloperoxidase Response to oxidative stress J ↑ 1.55 X6R8F3 Neutrophil gelatinase-associated lipocalin Neutrophil degranulation B ↑ 1.55 Q5TEC6 Histone H3 DNA binding E ↑ 1.54 P60709 Actin_ cytoplasmic 1 Constituent of cytoskeleton D ↑ 1.54 P63261 Actin_ cytoplasmic 2 Constituent of cytoskeleton D 13 ↑ 1.52 P62736 Actin_ aortic smooth muscle Constituent of cytoskeleton D ↑ 1.51 Q6S8J3 POTE ankyrin domain family member E Unknown K ↑ 1.42 P07737 Profilin-1 Cytoskeleton organization D ↑ 1.40 P02788 Lactotransferrin Immunity, Osteogenesis B ↑ 1.39 P62805 Histone H4 Metabolic process A ↑ 1.39 P0CG39 POTE ankyrin domain family member J Unknown K ↑ 1.36 P01860 Immunoglobulin heavy constant gamma 3 Defense response to bacterium B ↑ 1.30 P14618 Pyruvate kinase PKM Catalytic activity A ↑ 1.26 P68133 Actin_ alpha skeletal muscle Constituent of cytoskeleton D ↑ 1.21 P04406 Glyceraldehyde-3-phosphate dehydrogenase Oxidoreductase activity J ↑ 1.20 P01857 Immunoglobulin heavy constant gamma 1 Defense response to bacterium B ↑ 1.13 P01834 Immunoglobulin kappa constant Adaptive immunity B ↓ 0.79 P02768 Serum albumin Apoptotic process I ↓ 0.78 C9JKR2 Albumin_ isoform CRA_k Transport C ↓ 0.77 P12273 Prolactin-inducible protein Immune system process B ↓ 0.76 P06733 Alpha-enolase Glycolytic process A ↓ 0.63 P68032 Actin_ alpha cardiac muscle 1 Constituent of cytoskeleton D ↓ 0.61 P63267 Actin_ gamma-enteric smooth muscle Constituent of cytoskeleton D ↓ 0.52 P59665 Neutrophil defensin 1 Cellular response to LPS B ↓ 0.52 P59666 Neutrophil defensin 3 Cellular response to LPS B ↓ 0.50 P06899 Histone H2B type 1-J Antibacterial response B ↓ 0.41 Q5T3N0 Annexin Adaptive immune response B ↓ 0.40 P0CG38 POTE ankyrin domain family member I Unknown K ↓ 0.38 P69905 Hemoglobin subunit alpha Transport C ↓ 0.34 Q96AB3 Isochorismatase domain-containing protein 2 Catalytic activity A ↓ 0.29 Q562R1 Beta-actin-like protein 2 Constituent of cytoskeleton D ↓ 0.24 P06702 Protein S100-A9 Inflammatory response B ↓ 0.16 A5A3E0 POTE ankyrin domain family member F Unknown K ↓ 0.14 P69891 Hemoglobin subunit gamma-1 Transport C ↓ 0.14 P05109 Protein S100-A8 Inflammatory response B ↓ 0.13 P69892 Hemoglobin subunit gamma-2 Transport C ↓ 0.09 P02100 Hemoglobin subunit epsilon Transport C ↓ 0.04 P11532 Dystrophin Response to growth factor G ↓ 0.02 A0A2R8Y7X9 GLOBIN domain-containing protein Oxygen transport C Differences in expression amongst the groups were expressed as ↑ for up-regulated proteins and ↓ for down-regulated proteins (p < 0.05); Ratio D/C = ratio between diabetic and control group proteins. Superscript letters indicate the biological function of each protein. A Metabolism and energy pathways; B Immune/inflammatory response; C Transport; D Structural; E Regulation and repair of DNA/RNA; F Cellular communication and signal transduction; G Cell growth and 14 maintenance; H Differentiation of neural cells; I Apoptosis; J Stress response; K Unknown. Human proteins that were more than 2-fold higher or lower are in bold. Two hundred and twenty-six proteins were found to be exclusive to the diabetic group, and had distinct biological functions. This group had several proteins related to immune/inflammatory response processes (e.g. Homeobox protein Hox-A2, Interleukin-7, Interferon gamma, Arachidonate 15-lipoxygenase, Centrosomal protein of 192 kDa and Centrosomal protein of 290 kDa); oxidative stress (e.g. Egl nine homolog 1, Fe2OG dioxygenase domain-containing protein, Lysine-specific demethylase 4C, Methylenetetrahydrofolate reductase, Putative oxidoreductase GLYR1 and Ribosomal protein S6 kinase alpha-4); apoptotic processes (e.g. Bcl-2-modifying factor, Caspase recruitment domain-containing protein 14, Nischarin, Programmed cell death 6-interacting protein, Protein Wnt- 10b and Rho GTPase-activating protein 10); and proteolytic processes, such as proteases (e.g. Tripeptidyl-peptidase 2, Dipeptidyl peptidase 3, Ubiquitin carboxyl-terminal hydrolase 34, Ubiquitin carboxyl-terminal hydrolase 40, Probable ubiquitin carboxyl-terminal hydrolase FAF-X and Probable ubiquitin carboxyl-terminal hydrolase FAF-Y) and protease inhibitors (e.g. Serpin E3, Calpastatin, Serine protease inhibitor Kazal-type 2 and Submaxillary gland androgen-regulated protein 3B). Most of the identified proteins were involved in biological processes related to cellular communication and signal transduction, the immune/inflammatory response and metabolism and energy pathways, while common proteins were primarily involved in the immune/inflammatory response, transport and structural function (Table 3). In terms of localisation, 26.7% were 15 discovered in the cytoplasm/cytoskeleton, 25.0% in the nucleus, 17.7% in the plasma membrane and 13.9% in the extracellular region. Figure 3 shows the interaction network between differentially expressed common proteins, and considers all active interaction sources with a medium confidence score (0.4) (STRING® database) to show the strength of protein-protein interactions (p value < 1.0e-16). Regarding the biological processes revealed by protein-protein interaction, up-regulated proteins showed functional enrichment of immune system process and response to stress (Figure 3a), while down-regulated proteins describe cellular oxidant detoxification and antimicrobial humoral response (Figure 3b). 16 Table 3. Biological function classification of exclusive human proteins in the diabetic and control groups, and of common proteins (↑, ↓, SE in the diabetic group in relation to the control group. Exclusive proteins Common proteins Biological function classification Diabetic Control ↑ ↓ NSE Metabolism and energy pathways 24 (10.6) 47 (12.5) 3 (7.0) 2 (9.1) 4 (6.8) Immune/inflammatory response 28 (12.4) 48 (12.7) 16 (37.2) 7 (31.8) 16 (27.1) Transport 23 (10.2) 25 (6.6) 4 (9.3) 6 (27.3) 3 (5.1) Structure 13 (5.8) 53 (14.1) 7 (16.3) 3 (13.6) 3 (5.1) DNA/RNA regulation and repair 15 (6.6) 36 (9.5) 2 (4.7) 0 (0) 17 (28.8) Cellular communication and signal transduction 53 (23.5) 62 (16.4) 0 (0) 0 (0) 6 (10.2) Growth and/or cell maintenance 24 (10.6) 37 (9.8) 2 (4.7) 1 (4.5) 2 (3.4) Differentiation of neural cells 8 (3.5) 10 (2.7) 0 (0) 0 (0) 1 (1.7) Apoptosis 13 (5.8) 15 (4.0) 0 (0) 1 (4.5) 2 (3.4) Stress response 9 (4.0) 19 (5.0) 6 (14.0) 0 (0) 2 (3.4) Unknown 16 (7.1) 25 (6.6) 3 (7.0) 2 (9.1) 3 (5.1) Total 226 (100) 377 (100) 43 (100) 22 (100) 59 (100) ↑ = up-regulated proteins (p < 0.05 ; ↓ = down-regulated proteins (p < 0.05); NSE = no significant expression in comparison to control group. 17 Figure 3. Interactions between differentially expressed common proteins comparing diabetic and control groups (STRING database). Protein-protein interaction (PPI) enrichment p value < 1.0 e-16. Biological processes with significative e-value were selected: immune system process (red), response to stress (blue), cellular oxidant detoxification (purple) and antimicrobial humoral response (green). (a) Up-regulated proteins – A2M: Alpha-2-macroglobulin; ACTA1: Actin, alpha skeletal muscle; ACTA2: Actin, aortic smooth muscle; ACTB: Actin, cytoplasmic 1; ACTG1: Actin, cytoplasmic 2; EPX: Eosinophil peroxidase; GAPDH: Glyceraldehyde-3-phosphate dehydro-genase; HBA2: Hemoglobin subunit alpha 2; HBB: Hemoglobin subunit beta; HBD: Hemo-globin subunit delta; HBZ: Hemoglobin subunit zeta; HIST4H4: Histone cluster 4 H4; HIST2H3PS2: Histone cluster 2 H3 pseudogene 2; LCN2: Neutrophil gelatinase-associated lipocalin; LCP1: Plastin-2; LTF: Lactotransferrin; MPO: Myeloperoxidase; PFN1: Profilin-1; PKM: Pyruvate kinase PKM; PLS3: Plastin-3; POTEE: POTE ankyrin domain family member E; POTEJ: POTE ankyrin domain family member J; PRDX1: Peroxiredoxin-1; PRDX2: Peroxiredoxin-2; (b) Down- regulated proteins - ACTBL2: Beta-actin-like protein 2; ACTC1: Actin, alpha cardiac muscle 1; ACTG2: Actin, gamma-enteric smooth muscle; ALB: Serum albumin; ANXA1: Annexin A1; DEFA1: Defensin, alpha 1; DEFA3: Neutrophil defensin 3; DMD: Dystrophin; ENO1: Alpha-enolase; HBA1: Hemoglobin subunit alpha 1; HBE1: He-moglobin subunit epsilon; HBG1: Hemoglobin subunit gamma 1; HBG2: Hemoglobin subunit gamma 2; PIP: Prolactin induced protein; POTEF: POTE ankyrin domain family member F; POTEI: POTE ankyrin domain family member I; S100A8: Protein S100-A8; S100A9: Protein S100-A9; 18 4. Discussion The results of this study are the first to provide quantitative data on the differential expression of proteins in endodontic infections in patients with T2DM in comparison with systemically healthy patients. Qualitative data is also provided for the proteins found exclusively in each group, as well as information on their most relevant biological functions. By analysing protein expression and the corresponding biological pathways, our findings illuminate some of the main host mechanisms involved in AP in diabetic patients. Sixteen proteins related to immune/inflammatory function were found to have increased expression in the diabetic group. Thirteen immunoglobulins were found to be expressed at a high level (up to 5-fold higher), and these included 6 heavy chain and 7 light chain isotypes (lambda and kappa). Elevated levels of immunoglobulin light chains have been associated with inflammatory diseases (Brebner & Stockley 2013). Interestingly, increased immunoglobin light chains concentration has been shown to inhibit neutrophil apoptosis and stimulate mast cell degranulation, which may interfere with the normal resolution of inflammation, and thus contribute to a chronic inflammatory state (Cohen et al. 2003, Braber et al. 2012). Another protein that was up regulated in the diabetic sample group was NGAL. This protein is involved in mediating the immune response to bacterial infections, the inflammatory process and apoptosis (Wang et al. 2007). Increased NGAL expression is associated with more severe periodontal disease (Tsuchida et al. 2013, Tan et al. 2020) and with specific modulation of the inflammatory 19 response to periodontitis in diabetic patients (Kido et al. 2020). The increased levels of NGAL in patients in the diabetic group may therefore be related to their hyperglycaemic state. It may be argued that DM influences periapical status by increasing the production of AGEs, due to protein glycation occurring as a result of hyperglycaemia (Ahmed 2005, Ilea et al. 2018). AGEs induce NGAL expression in human oral cells, which modulates IL‐6 expression in neutrophils and promotes neutrophil migration, thus influencing the inflammatory condition in DM- associated periodontitis (Kido et al. 2020). Proteins S100A8 and S100A9 showed lower levels of expression in the diabetic group. Although these proteins commonly appear overexpressed during infectious or inflammatory processes (Wang et al. 2018), the downregulation of Protein S100 may indicate a deficient immune response (Pouwels et al. 2015), which is consistent with the exacerbation induced by infection that is widely reported in diabetic patients (Bender & Bender, 2003; Chakravarthy 2013). Additionally, S100A8 and S100A9 have been suggested as biomarkers for the detection of periodontal diseases, and lower levels of these proteins may imply a higher susceptibility to the development of periodontal inflammation (Dommisch et al. 2019). Regarding common proteins, it is important to note that those related to oxidative stress were up regulated in the diabetic group. These include Peroxiredoxin-1, Peroxiredoxin-2, Eosinophil peroxidase, Myeloperoxidase (MPO) and Glyceraldehyde-3-phosphate dehydrogenase. Increased levels of MPO have been reported in the dental pulp of diabetic rats (Catanzaro et al. 20 2006), in the gingival crevicular fluid of T2DM individuals with chronic periodontitis (Marinho et al. 2019) and in the progression of chronic inflammatory processes of T2DM patients with periodontitis (Peniche-Palma et al. 2019). MPO accumulation reveals the intensity of migration of polymorphonuclear leukocytes to the site of inflammation (Yamalik et al. 2000), and increased MPO may be related to the state of periodontal destruction and the enhancement of systemic conditions, including T2DM (Peniche-Palma et al. 2019). Among the exclusive proteins of both groups, there were more proteins associated with oxidative stress in the diabetic group than in the control. ROS are unstable molecules formed from oxygen that react easily with other molecules. ROS are produced by host defence cells through the oxidation of crucial cell signalling proteins, acting as a mediator of inflammation (Mittal et al. 2014). ROS production increases the intensity of the immune/inflammatory response to pathogens by activating proinflammatory pathways (Schmidt et al. 1996). Prolonged release of ROS has been shown to exacerbate tissue destruction in rats, which may be related to the severity of AP associated with DM (Azuma et al. 2017; Prieto et al. 2017). Clinical data has also shown a positive association between diabetes and the incidence and severity of AP (Segura- Egea et al. 2012; Segura-Egea et al. 2005), as the imbalance of redox control in both diseases may act synergistically, thus amplifying their bidirectional courses (Allen et al. 2009). Studies suggest that periapical lesions in diabetic patients are usually larger in size than those in healthy patients (Rudranaik et al. 2016), and take 21 longer to heal following endodontic treatment, particularly in subjects with high blood glucose levels (Arya et al. 2017; Ugur Aydin et al. 2021). This is likely due to the exacerbation of several processes by DM, including inflammation, oxidative stress and apoptosis. In this study, several proteins involved in the apoptotic process were identified exclusively in the diabetic group. Hyperglycaemia increases the generation and activity of AGEs (Mei et al. 2019), which in turn act directly to activate caspases, or indirectly on pathways that regulate apoptosis, such as oxidative stress or the expression of pro-apoptotic genes (Graves et al. 2006). Proteolytic proteins were also identified more frequently in the diabetic group, which could be related to the increase in proteolytic processes caused by a hyperglycaemic state (Flakoll et al. 1993). Among these proteins were Tripeptidyl-peptidase 2 and Dipeptidyl peptidase 3. While Tripeptidyl-peptidase 2 may be involved in part of the pathway leading to the degradation of bone matrix proteins (Page et al. 1993), Dipeptidyl peptidase 3 could represent a new potential osteoimmunological biomarker of pathology (Menale et al. 2019). When comparing the results of this study with those recently presented by Loureiro et al. (2021), it is notable that there are common proteins in diabetic patients with asymptomatic AP (this study) and in healthy patients with symptomatic AP (Loureiro et al., 2021). These proteins appear to be primarily related to the inflammatory response and oxidative processes. This is an important comparison, as both conditions are associated with an imbalance in the host's inflammatory response (Lima et al. 2013; Gomes & Herrera, 2018). 22 Proteins with high levels of expression were shown in both conditions (diabetic/symptomatic AP), such as Serine/threonine-protein kinase 31, Myeloperoxidase and Peroxiredoxin-1 and -2. Proteins with low levels of expression were also found in both conditions, including Protein S100-A9, Prolactin-inducible protein and Neutrophil defensin 1 and 3. Furthermore, Glyceraldehyde-3-phosphate dehydrogenase showed increased expression in the asymptomatic diabetic group, and decreased expression in the symptomatic healthy group. Regarding the use of medications, it should be noted that all patients in the diabetic group were on metformin, and those with arterial hypertension were taking an antihypertensive drug, such as captopril, losartan or olmesartan. During treatment planning and execution, it is important to account for medications used to control systemic conditions, as the execution of endodontic treatment in systemically uncontrolled patients can represent a risk to the patient's health. Uncontrolled diabetic patients are more likely to have crises of hyper- or hypoglycaemia and to experience excessive bleeding (Lalla & D'Ambrosio 2001), while patients with hypertension may suffer hypertensive crises or orthostatic hypotension (Southerland et al. 2016). Endodontic treatment is only indicated for uncontrolled patients in urgent cases, and elective procedures must be postponed until the systemic condition stabilises. The proteins identified in patients with DM were frequently associated with the presence of AGEs, as the protein glycation process induces the expression of specific proteins (NGAL) and oxidative and apoptotic processes. In this 23 context, proteomic analysis can be applied to study the proteins produced by the glycation process (Chiu et al. 2018; Thornalley et al. 2003). Although this is a technique that commercially requires rigorous analytical standards, liquid chromatography-mass spectrometer is a method with high sensitivity and specificity for detecting products formed by the glycation of proteins. This approach is also known as AGEomics (Rabbani & Thornalley 2020). Future studies on AGEomics should be performed to identify the types of AGEs that influence the immunoinflammatory modulation of periapical tissues, and could potentially affect the prognosis of AP in diabetic patients. 5. Conclusions In conclusion, this study presents novel data on the proteomic profile of root canal infections in patients with T2DM, and on the complex interplay of identified proteins that are particularly involved in the immune response, oxidative stress, apoptotic and proteolytic processes. Most common differentially expressed proteins are up regulated in the diabetic group, and are largely associated with defence mechanisms. These findings reveal biological pathways that provide the basis for understanding the clinical relationship between AP and T2DM. 6. References Ahmed N (2005) Advanced glycation end products--role in pathology of diabetic complications. Diabetes Research and Clinical Practice 67, 3-21. Alfenas CF, Mendes TAO, Ramos HJO et al. 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Int Endod J. 2022 Sep;55(9):910-922. doi: 10.1111/iej.13794. 33 Anexo 1– Guia para submissão na revista International Endodontic Journal 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 Anexo 2– Comitê de Ética em Pesquisa em Humanos 49 50 51