Succinylation participates in the progress of Idiopathic pulmonary fibrosis through mitochondrial energy metabolism

Succinylation participates in the progress of Idiopathic pulmonary fibrosis through mitochondrial energy metabolism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Succinylation participates in the progress of Idiopathic pulmonary fibrosis through mitochondrial energy metabolism Yunmulan Zhao, Wenyu Hou, Liqing Yang, Lu Guo, Ping Wang, Lingyun Gao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3878025/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background A new pathogenic role for mitochondrial dysfunction has been associated with aging and correlated with the development of idiopathic pulmonary fibrosis (IPF). The latest study found that the lysine succinylation (Ksucc) is involved in many energy metabolism pathways and affects the metabolic process in mitochondria, making this modification highly valuable for studying IPF related to mitochondrial dysfunction. We speculate Ksucc participate in IPF progression through mitochondrial energy metabolism pathway. Methods We used liquid chromatography with tandem mass spectrometry (LC-MS/MS) to perform the first global profiling of Ksucc in lung tissues with IPF patients. The changes of candidate key proteins and Ksucc sites related to energy metabolism in IPF lung tissues were analyzed by using the clusters of orthologous groups of proteins (COG), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO). We then compared these proteins with those reported in the literature in normal lung tissues by parallel reaction monitoring (PRM). Results We identified 1964 Ksucc sites in 628 proteins. 675 Ksucc sites in 124 proteins closely related to mitochondrial metabolism. We compared these proteins with those reported in the literature in normal lung tissues to identify differences in 119 proteins and Ksucc sites in mitochondria. 43 Ksucc sites in 27 proteins were associated with energy metabolism. There were differences in the expression of 4 Ksucc sites in 4 proteins between normal and IPF lung tissues. Conclusion Our work expands the Ksucc database in IPF lung and suggested that mitochondrial energy metabolism is involved in the progression of IPF. Ksucc sites of proteins associated with mitochondrial energy metabolism can also serve as candidate molecules for future mechanism exploration and drug target selection in IPF. Idiopathic pulmonary fibrosis Ksucc mitochondrial dysfunction energy metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Idiopathic pulmonary fibrosis (IPF) is a lethal disease of unknown aetiology that largely presents in the elderly [ 1 ]. Although the pathogenesis of IPF is unclear, Susceptibility to IPF with aging has been linked to mitochondrial dysfunction [ 2 , 3 ]. Mitochondrial dysfunction in IPF mainly include metabolic changes, increasing production of reactive oxygen species, decreased mitochondrial biogenesis, and impaired mitochondrial macroautophagy [ 4 , 5 ]. Recent evidence suggests that metabolic dysregulation in mitochondria dysfunction are distinctive features of the IPF lungs and this is an important factor that causes cell senescence [ 6 ]. Mitochondria dysfunction can cause abnormal metabolism of lipids, amino acids, and sugars, and collaborates with TGF beta 1 (TGFβ1) to induce the activation of myofibroblasts and accelerate the formation of IPF [ 7 ]. Therefore, identifying key molecules involved in energy metabolism disorders in mitochondrial dysfunction could help to find therapeutic targets for IPF. Studies have shown that lysine succinylation (Ksucc) mainly occur in mitochondria, which are closely related to the energy metabolism in mitochondria, and participate in and regulate multiple metabolic signaling pathways including Tricarboxylic acid cycle (TCA cycle), amino acid metabolism and fatty acid metabolism [ 8 – 10 ]. Ksucc is an important post-translational modification, belonging to acid acylation modification (11). Its modification site is on the protein of the central metabolic pathway, and affects the performance of the modified protein by changing the protein structure [ 11 ]. Succinylated proteins clearly reflect a balance of Ksucc and de-succinylation [ 12 ]. The only recognized desuccinylase to act in all cells compartments is NAD + dependent Sirtuin [ 13 ]. Recent studies have found that the expression of Sirtuin is down-regulated in mice pulmonary fibrosis, and the removal of Ksucc by increasing the expression of Sirtuin has a protective effect on IPF [ 14 ]. Therefore, it is reasonable for us to reveal the mechanical effect of Ksucc on IPF through protein functional omics technology. We analyzed lung tissues from six IPF patients by Ksucc proteomics techniques as the characteristic changes of Ksucc in normal lung tissues have been reported [ 15 ]. We found that lung tissues with six IPF patients was extensively modified by Ksucc, with the modified proteins mainly distributed in the mitochondria. These succinylated modified proteins are closely related to the energy metabolism of mitochondria, including energy production and conversion, amino acid transport and fatty acid metabolism. Compared with normal lung tissues, the expression of 43 Ksucc sites in 27 proteins related to mitochondrial energy metabolism was significantly different in IPF lung tissues. Furtherly, there were differences in expression of 4 Ksucc sites in 4 proteins in normal lung tissues and IPF lung tissues by using PRM validation. Our work expands the Ksucc database in IPF lung and will thus help us study the function and regulation of mitochondrial energy metabolism in IPF based on Ksucc. Methods and methods Sample preparation Materials and reagents The trypsin was used for digestion. To enrich the succinylated modified peptides, tryptic peptides dissolved in NETN buffer. The peptides were subjected to a nanospray ionization (NSI) source followed by MS/MS with a Q Exactive TM Plus (Thermo) coupled on line to a ultra performance liquid chromatography (UPLC). The MaxQuant was used for database search. Patient tissue samples and protein extraction This study was approved by the Ethics Committee of Sichuan Provincial People's Hospital and Peking Union Medical College Hospital. IPF lung tissue samples ( n = 6) and Paracancerous lung tissue samples ( n = 3) were obtained from Lung transplant Center for respiratory and critical Care Medicine, Sichuan Provincial People's Hospital, Chengdu, China. The samples were frozen immediately and stored at − 80°C until protein extraction. The IPF diagnose was made by the guidance of the 2022 American Thoracic Society (ATS), European Respiratory Society (ERS), Japanese Respiratory Society (JRS), and Latin American Thoracic Society (ALAT) guideline. The sample was grinded with liquid nitrogen into cell powder and then transferred to a 5-mL centrifuge tube. After that, four volumes of lysis buffer (1%Triton X-100, 1% protease inhibitor cocktail, 3 µM TSA and 50 mM NAM) was added to the cell powder, followed by sonication three minutes on ice using a high intensity ultrasonic processor (Scientz). The remaining debris was removed by centrifugation at 12,000 g at 4°C for 10 min. Finally, the supernatant was collected and the protein concentration was determined with BCA kit according to the manufacturer’ s instructions. Trypsin digestion The sample was slowly added to the final concentration of 20% (m/v) TCA to precipitate protein, then vortexed to mix and incubated for 2 h at 4°C. The precipitate was collected by centrifugation at 4500 g for 5 min at 4°C. The precipitated protein was washed with 200 mM TEAB and ultrasonically dispersed. Trypsin was added at 1:50 trypsin-to-protein mass ratio for the first digestion overnight. The sample was reduced with 5 mM dithiothreitol for 60 min at 37°C and alkylated with 11 mM iodoacetamide for 45 min at room temperature in darkness. Finally, the peptides were desalted by Strata X SPE column. Affinity enrichment To enrich the lysine acetylation (Kac) peptides, the Ksu peptides, and tryptic peptides dissolved in NETN buffer (100 mM NaCl, 1 mM EDTA, 50 mM Tris-HCl, 0.5% NP-40, pH 8.0), the peptides were incubated with pre-washed antibody beads [PTM Biolabs, The Anti-acetyllysine antibody conjugated agarose beads (PTM-104), Anti-succinyllysine antibody conjugated agarose beads (PTM-402)] at 4°C overnight with gentle shaking. The beads were washed four times with NETN buffer and twice with ddH 2 O. The bound peptides were eluted from the beads with 0.1% TFA. The eluted fractions were combined and vacuum dried. The resulting peptides were cleaned with C18 ZipTips (Millipore) according to the manufacturer’s instructions, followed by LC-MS/MS analysis. Quantitative proteomic analysis by LC-MS/MS Materials and reagents The H 2 O was from Thermo company. The ACN was obtained from Fisher Chemical. The formic acid (FA) was procured from Fluka. LC-MS/MS analysis The tryptic peptides were dissolved in solvent A (0.1% FA, 2% ACN in water), directly loaded onto a home-made reversed-phase analytical column (25-cm length, 100 µm i.d.). Peptides were separated with a gradient from 6–22% solvent B (0.1% FA in ACN) over 40 min, 22–30% in 12 min and climbing to 80% in 4 min then holding at 80% for the last 4 min, all at a constant flow rate of 450 nL/min on a nanoElute UHPLC system (Bruker Daltonics). The peptides were subjected to Capillary source followed by the timsTOF Pro (Bruker Daltonics) mass spectrometry. The electrospray voltage applied was 1.60 kV. Precursors and fragments were analyzed at the TOF detector, with a MS/MS scan range from 100 to 1700 m/z. The timsTOF Pro was operated in parallel accumulation serial fragmentation (PASEF) mode. Precursors with charge states 0 to 5 were selected for fragmentation, and 10 PASEF-MS/MS scans were acquired per cycle. The dynamic exclusion was set to 24s. IPF lung tissue samples ( n = 3) and Paracancerous lung tissue samples ( n = 3) verified by the PRM validation experiment. The PRM-MS assay was performed on Q Exactive HF-X MS spectrometer and peptides were separated with the following gradient: 0–36 min, 9%-25%B, 36–54 min, 25%-35%B, 54–57 min, 35%-80%B, 57–60 min, 80%B, and all at a constant flow rate of 500 nl/minon a EASY-nLC 1200 UPLC system (ThermoFisher Scientific). The digested peptides for differential proteins and endogenous peptides identified from DDA-MS analysis with appropriated sequence length and MS/MS spectrum were programmed for the PRM assay. The full mass scan range was set as 495–1234 with a resolution of 120000. The MS/MS scans were acquired by the Orbitrap with a resolution of 15000. The collisional energy was 28%, and the AGC target was set to 1e5 with a maximum injection time of 220 ms for MS/MS. Database search The resulting MS/MS data were processed using MaxQuant search engine (v.1.6.15.0). Tandem mass spectra were searched against the human SwissProt database (20389 entries) concatenated with reverse decoy database. Trypsin/P was specified as cleavage enzyme allowing up to 4 missing cleavages. The mass tolerance for precursor ions was set as 20 ppm in First search and 20 ppm in Main search, and the mass tolerance for fragment ions was set as 0.02 Da. Carbamidomethyl on cysteine (Cys) was specified as fixed modification, and acetylation on protein N-terminal, oxidation on methionine (Met) and Ksucc on Lysine (Lys) were specified as variable modifications. False positive rate (FDR) was adjusted to < 1%. Statistical analysis Perseus software was applied to process label-free quantitative data. The two-tailed unpaired Student's t-test was performed to calculate p -value to determine statistical difference between IPF and normal groups. A fold change ≥ 1.2 or ≤ 0.8 were considered as a significant difference in protein and peptide features between the two groups. Skyline software was used to process the raw data of PRM detection, and the statistical analysis and graphs preparation were performed on GraphPad Prism 8 software (GraphPad Software, San Diego, CA, USA). The significance of the protein abundance change was calculated using test, and Welch correction was applied. A two-tailed test with p -value < 0.05 was considered significant. Bioinformatics analysis Gene ontology annotation Gene ontology (GO) is a major bioinformatics initiative to unify the representation of genes and gene product attributes across all species. The GO annotation proteome was derived from the UniProt-GOA database ( www.http://www.ebi.ac.uk/GOA/ ). First, the identified protein ID was converted to UniProt ID and was then mapped to GO IDs using the protein ID. If the identified proteins were not annotated by the UniProt-GOA database, the InterProScan software was be used to annotated the protein’s GO function based on a protein sequence alignment method. Then, the proteins were classified by GO annotation into three categories: biological process, cellular component and molecular function. Domain annotation The domain functional descriptions of the identified proteins were annotated by InterProScan (a sequence analysis application) based on a protein sequence alignment method, and the InterPro domain database was used. InterPro ( http://www.ebi.ac.uk/interpro/ ) is a database that integrates diverse information about protein families, domains, and functional sites and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in the characterization of individual protein sequences. Kyoto Encyclopedia of Genes and Genomes pathway annotation The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the protein pathways. First, the KEGG online service tool KAAS was used to annotate the protein’s KEGG database description. Then, the annotation result was mapped on the KEGG pathway database using the KEGG online service tool KEGG Mapper. Subcellular localization Wolfpsort, a subcellular localization predication software, was used to predict the subcellular localization, using an updated version of PSORT/PSORT II for the prediction of eukaryotic sequences. Motif analysis Soft motif-x was used to analyse the model sequences of amino acids in specific positions of modify-21-mers (10 amino acids upstream and downstream of the site) in all the protein sequences. All the database protein sequences were used as background database parameters, with other parameters set to the default. Enrichment of the GO analysis The proteins were classified by GO annotation into three categories: biological process, cellular compartment and molecular function. For each category, a two-tailed Fisher’s exact test was employed to test the enrichment of the differentially expressed proteins against all the identified proteins. Correction for multiple hypothesis testing was carried out using standard false discovery rate control methods. GO findings with a corrected p -value < 0.05 were considered significant. Enrichment of the pathway analysis KEGG database was used to identify enriched pathways by a two-tailed Fisher’s exact test to test the enrichment of the differentially expressed proteins against all the identified proteins. Correction for multiple hypothesis testing was carried out using standard false discovery rate control methods. Pathways with a corrected p -value < 0.05 were considered significant. These pathways were classified into hierarchical categories according to the KEGG website. Enrichment of the protein domain analysis For each category of proteins, the InterPro (a resource that provides a functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites) database was researched, and a two-tailed Fisher’s exact test was employed to test the enrichment of the differentially expressed proteins against all the identified proteins. Correction for multiple hypothesis testing was carried out using standard false discovery rate control methods, and domains with a corrected p -value < 0.05 were considered significant. Protein-protein interaction analysis The identified Kac proteins and Ksucc proteins were searched against FunRich- version 3.0 for protein-protein interactions. Only interactions between the proteins contained in the searched dataset were selected. The interaction network form FunRich was visualized in Cytoscape. Results MS data quality control After the database search, quality control evaluation is needed to ensure that the quality of the results meets the standards. The fragmented peptide length distribution showed that the majority of the peptides were in the range of 7–20 amino acids and had 2–3 charges, which fulfils the sample quality for mass detection. Quantification overview of the Ksucc sites and proteins in IPF lung We performed succinylation analysis with lungs obtained from six IPF patients. Before searching, we constructed a theoretical secondary spectrum database based on the protein sequences in the database, added an anti-database to calculate the FDR caused by random matching, and added a common pollution database to eliminate the influence of polluted protein in the identification results. Precursor and protein FDR were set to 1%, and the identification protein needed to contain at least one unique peptide segment. The following is an overview of the modified sites and protein numbers identified by the search results after data filtering. We found that 205842 secondary spectrograms were produced by mass spectrometry, among which 40008 spectrograms matched the theoretical secondary spectrograms. Based on the matching results, we identified 11306 peptide sequences, including 1964 modified sites (Supplementary Table 1), 1949 modified peptide sequences, and 628 proteins identified by specific peptide sequences (Table 1 ). Table 1 MS/MS spectrum database search analysis summary (Localization probability > 0.75) Name Number Total spectrums 205842 Matched spectrums 40008 Peptides 11306 Modified peptides 1949 Identified peptides 628 Identified sites 1964 Identification of Ksucc motifs To identify the possible specific sequence pattern associated with Ksucc sites, we used the MoMo analysis tool based on motif-x algorithm to analyze the sequence pattern of peptides with succinylated sites. The peptide sequences composed of 10 upstream and downstream amino acids (6 upstream and downstream amino acids for phosphorylation modification) of all identified modification sites were used as the analysis objects. When the number of peptides with a certain sequence pattern was greater than 20 and the statistical test P-value was less than 0.000001, this sequence pattern was considered as a motif of the modified peptide. Based on the results of MoMo analysis, the score of the frequency change of amino acids near the modification site was displayed in the form of thermogram, and three enriched conserved motifs were identified among the regions surrounding succinylated peptides (Fig. 1 A). Valine (V), aspartic acid (D), and alanine (A) residues were identified as the most common residues in the succinylated sequence, the number of succinylated peptides were 173, 144, and 145, respectively (Fig .1B). Then, we explored whether certain sequence motifs surrounding Ksucc sites inmitochondria-localized succinylated proteins were preferential substrates, and the results showed that the 246 succinylated peptides was significantly enriched among 1050 inmitochondria-localized succinylated peptides(Supplementary Table 2, 3). Secondary structural analysis of Ksuss proteins To further investigate the impact of Ksucc on protein structure in IPF lung tissues, we analyzed the sites of Ksucc and un-modified sites in the secondary structure. Using the NetSurf algorithm, we analyzed the secondary structure of the Ksucc sites in proteins. We found that 34.1% of the succinylated sites were located in regions with ordered secondary structure, with 27.68% of these sites located in alpha-helices and 6.42% located in beta-strands.The remaining 65.9% of the succinylated sites were distributed in disordered protein regions. Then, we compared the mean secondary structure probabilities between proteins with and without Ksucc sites in the motif, as identified in this study (Fig. 2 ). After using unpaired Wilcox tests, we found that modified lysine was more likely to occur on beta-strand structures( P = 0.0307) and alpha-helices ( P = 0.0022) than un-modified lysine. While the probability of un-modified lysine was more likely to occur on coils( P = 0.0096) than that of un-modified lysine. In addition, the surface accessibility of modified lysine and un-modified lysine of succinylated proteins was evaluated, and there was a significant difference was found. ( P = 1.03×10 − 5 ) Cellular localization and functional annotation of succinylated modified proteins The protein subcellular location provides information on the protein site, which gives insight into its function. To better understand the function of succinylated proteins in IPF lung tissues, a subcellular localization analysis was performed by using UniProt database notes. The data showed that the majority of the succinylated proteins were localized in mitochondria (44.75%) and cytoplasm (25.96%) (Fig. 3 A). The succinylated proteins in mitochondria were increased in IPF lung tissues compared with normal lung tissues (34.2%) [ 15 ]. To understand the functions of succinylated proteins in our data, a COG analysis was employed to categorize these proteins. The COG analysis was performed in terms of cellular processes and signaling, information storage and processing, metabolism, poorly characterized. For metabolism, the succinylated proteins mainly participated in energy production and conversion, lipid transport and amino acid transport [Fig. 3 B]. Functional enrichment of succinylated modified proteins To further elucidate the potential role of succinylated proteins in IPF, we analysed the data for enrichment at three levels: GO enrichment, KEGG pathway and protein domain. We analysed the data for enrichment in three GO annotation categories: cell component (CC), biological process (BP), and molecular function (MF). The succinylated proteins were enriched in the TCA cycle enzyme complex and mitochondrial matrix (CC) (Fig. 4 A), Acyl-CoA metabolism and NAD binding (MF) (Fig. 4 B), and TCA cycle metabolism and fatty acid beta − oxidation (BP) (Fig. 4 C). KEGG PATHWAY analysis is a method that represents the molecular interaction, reaction and relation networks of input proteins. The results showed that these succinylated proteins were enriched in pathways associated with amino acid degradation, TCA cycle and fatty acid degradation (Fig. 5 ). The cellular localization and function of proteins are often indicated by their domains.The protein domain enrichment analysis showed that these succinylated proteins were enriched in Acyl − CoA dehydrogenase and Thiolase (Fig. 6 ), which agreed with our findings from the GO enrichment analysis and KEGG pathway analysis. To sum up, at three levels (GO classification, KEGG pathway and protein domain) we find functional enrichment of succinylated modified proteins primarily enriched in TCA cycle, amino acid and fatty acid metabolism. Significant Differences in Mitochondrial Energy Metabolism Proteins in IPF lung tissues Compared to Normal Lung Tissues An article on the detection of succinylated proteins in normal lung tissues using result of the IPF lung tissues we identified with the data provided in the normal lung tissues to ascertain whether there are differences in mitochondrial energy metabolism proteins between the two groups. The results show that the majority of succinylated proteins in both groups are located in the mitochondria. Furthermore, upon comparing all mitochondrial proteins in the two groups, it was found that there are 163 proteins expressed in both IPF and normal lung tissues (Supplementary Table 4,5). Among these, 144 proteins have different expression sites (Supplementary Table 4), and 19 proteins have identical expression sites (Supplementary Table 5). Additionally, there are 119 proteins expressed in IPF but not in the normal lung tissues (Supplementary Table 6). Based on the functional annotation of COG, it was found that among the differentially expressed proteins, there are a total of 43 Ksucc sites in 27 proteins associated with energy metabolism(Supplementary Table 7).There are 9 succinylated proteins related to energy production and conversion (ADHFE1, GRHPR, CYC1, PC, PDHB, NDUFAB1, UQCRQ, NDUFA2, GPD2), 9 succinylated proteins related to lipid transport (HSD17B8, PLA2G4F, ACSF3, OXSM, ACSS1, ACAA1, ACOT7, ACADSB, NDUFAB1), and 10 succinylated proteins related to amino acid transport (KYAT, NIT1, GCDH, XPNPEP1, OAT, PRODH, IDH3G, NPEPPS, GPT, THNSL1). Differences in energy metabolism by PRM Validation In order to verify the reliability of these differentially expressive proteins, a more sensitive and specific targeted PRM approach was applied to characterize the alterations in the peptide forms between control groups and IPF groups. Among the differentially expressed proteins and sites, we conducted a risk assessment and subsequently chose sites and proteins with low risk for PRM analyses (Supplementary Table 4, Supplementary Table 6). To ensure the reliability of the experimental results, lung tissues from three normal cases and three IPF patients were selected for PRM analyses. We conducted PRM validation and analysis for 29 sites in 8 proteins (Supplementary Table 8). The validation results revealed notable differences in expression between the IPF groups and the normal groups. Among the 8 proteins, 6 sites in 4 proteins exhibited upregulation, while 1 site in 1 protein showed downregulation, reaching statistical significance ( P < 0.05). We conducted COG functional annotation on the five statistically significant proteins mentioned above and discovered that four proteins—KYAT3 (Lys-108), HSD17B8 (Lys-173), GRHPR (Lys-65), and IDH2 (Lys-80)—are associated with energy metabolism. These findings from the validation suggest a close correlation between the occurrence of IPF and energy metabolism, consistent with existing literature in cell research. Discussion Existing studies and our published article have confirmed that IPF exhibit significant aging characteristics, which supports IPF as an age-related disease [ 16 , 17 ]. Recent studies have found that hypersuccinylation of mitochondria is related to cellular senescence [ 18 ], so it is necessary to further elucidate the mechanism of IPF based on Ksucc omics technology. The primary innovations of this study are outlined as follows: 1. Revealing the involvement of mitochondrial energy metabolism in the progression of IPF using Ksucc technology; 2. Employing PRM validation technology to demonstrate that proteins related to energy metabolism in IPF are regulated by Ksucc. The pathobiology underlying IPF is still incomplete. It is accepted that aging is a major risk factor in the disease while growing evidence suggests that the mitochondria play an important role in the initiation and progression of IPF [ 19 , 20 ]. Mitochondria dysfunction and metabolic reprogramming had been identified in different IPF lung cells (alveolar epithelial cells, fibroblasts, and macrophages) promoting low resilience and increasing susceptibility to activation of profibrotic responses [ 21 – 23 ]. Mitochondria act as a central hub in the cell age and mitochondrial dysfunction is present in aged-related lung diseases [ 24 – 25 ]. Hundreds of proteins Ksucc sites are present in proteins of multiple tissues and species, and the significance is being actively investigated, and the few completed studies demonstrate that Ksucc alters rates of enzymes and pathways, especially mitochondrial metabolic pathways [ 26 , 27 ]. Thus, Ksucc provides an elegant and efficient mechanism to coordinate metabolism and signaling by utilizing metabolic intermediates as sensors to regulate metabolism. In this research, the results of the subcellular localization analysis revealed that succinylated proteins in IPF lung tissues are more abundant in mitochondria (44.75%), the cytoplasm (25.96%), and the extracellular (13.38%), which was different from the localization of succinylated proteins in normal lung tissues, in which they were that mainly found concentrated in mitochondria (34.2%), the membrane (19.6%), and the cytoplasm (13.2%). This difference indicates that the key protein modified by Ksucc in IPF lung tissues also occurs in mitochondria. The role of metabolic dysregulation in the pathogenesis of IPF has not been investigated in detail. The new research applied proteomic methods to the bronchoalveolar lavage (BAL) fluid of IPF patients, observing alterations in the synthesis and activity of fatty acids, cholesterol and other lipids that may play a role in cell energy storage, structure and signalling [ 28 ]. Proteomics coupled with systems biology studies, performed on BAL samples from patients with IPF, normal lung tissues other interstitial lung diseases (ILDs) and different phenotypes of IPF, has brought to light a number of interesting molecules that seem to be involved in the onset of fibrosis via metabolic dysfunction [ 29 ]. Currently, literature has reported proteins in normal lung tissues associated with Ksucc [ 15 ]. Therefore, in this study, we compared the proteins detected through Ksucc technology with the data provided in that literature. The results indicated that the majority of proteins with Ksucc in both groups were located within the mitochondria. Furthermore, combining COG functional annotation revealed significant differences in proteins related to energy metabolism. Existing literature has already demonstrated that mitochondrial energy metabolism is a primary factor in cellular aging. Considering our screening and comparative results, there is reason to believe that energy metabolism is closely associated with the progression of IPF. Mitochondria regulate a multitude of different metabolic and signaling pathways and also play an important role in programmed cell death [ 30 ]. Metabolomic abnormalities in mitochondrial dysfunction is of interest, as evidence suggests that metabolomic changes in amino acids, lipids, and glycolysis have seen in IPF lung tissues, especially amino acids and lipid metabolism [ 31 – 33 ]. These evidences indicate that modulating specific metabolites can provide clues for new therapeutic avenues. Mitochondrial Ksucc is widely involved in various energy substance synthesis and metabolism processes [ 34 ]. In this study, in order to observe the changes in Ksucc sites of selected mitochondrial energy metabolism proteins in different groups and ensure the rigor of experimental design, we used PRM technology to detect 3 cases of paracancerous lung tissues and 3 cases of IPF lung tissues, respectively, aiming to further elucidate whether the energy metabolism occurring in mitochondria is controlled by Ksucc. The results revealed that, among the validated proteins with Ksucc, 21 sites were upregulated, while 17 sites were downregulated in the IPF groups. Notably, KYAT3 (Lys-108), HSD17B8 (Lys-173), and GRHPR (Lys-65) exhibited significant upregulation in IPF lung tissues. In conclusion, our work uncovered the Ksucc profile changes in IPF lung tissues and validated that Ksucc may be involved in the process of IPF through energy metabolism pathways. In the future, more functional tests need to be conducted to unveil the molecular mechanisms. Abbreviations A=alanine; ALAT=Latin American Thoracic Society; ATS=American Thoracic Society; BAL=bronchoalveolar lavage; BP=biological process; CC=cell component; COG=clusters of orthologous groups of proteins; Cys=cysteine; D=aspartic acid; ERS=European Respiratory Society; FA=formic acid; FDR=False positive rate; GO=Gene ontology; ILDs=interstitial lung diseases; IPF=idiopathic pulmonary fibrosis; JRS=Japanese Respiratory Society; Kac=lysine acetylation; KEGG=Kyoto Encyclopedia of Genes and Genomes; Ksucc=lysine succinylation; LC-MS/MS=liquid chromatography with tandem mass spectrometry; Lys=Lysine; Met=methionine; MF=molecular function; NSI=nanospray ionization; PASEF=parallel accumulation serial fragmentation; PRM=parallel reaction monitoring; TCA cycle=Tricarboxylic acid cycle; TGFβ1=TGF beta 1; UPLC=ultra performance liquid chromatography; V= Valine Conclusion Our work expands the Ksucc database in IPF lung and suggested that mitochondrial energy metabolism is involved in the progression of IPF. Ksucc sites of proteins associated with mitochondrial energy metabolism can also serve as candidate molecules for future mechanism exploration and drug target selection in IPF. Declarations contributors Yunmulan Zhao and Wenyu Hou takes responsibility for the content of this manuscript, including the data and analysis. Wei Sun, Lingyun Gao contributed to the concept and design of study and provided funding support. Lu Guo provided clinical samples. Wang Ping contributed to the acquisition of data. Zuojun Xu contributed to the analysis of data and contributed to the drafting of the manuscript. Data sharing statement All data analysed in this study can be obtained by a reasonable request to corresponding authors Declaration of interests The authors declare that they have no competing interests. Consent for publication Not applicable Funding: This work was supported by the National Natural Science Foundation of China (No. 82070067), Sichuan Natural Science Foundation (No. 23NSFSC1556), Key R&D Plan of Sichuan Provincial Department of Science and Technology (No. 23ZDYF1850) and Beijing Natural Science Foundation (No. 7222132). Acknowledgments We thank all participants and investigator involved in Ksucc proteomic analysis. We also thank PTM Biolabs (Hangzhou, China) for its support. References Chanda D, Otoupalova E, Smith SR, Volckaert T, De Langhe SP, Thannickal VJ. Developmental pathways in the pathogenesis of lung fibrosis. Mol Aspects Med. 2019;65:56–69. Schuliga M, Pechkovsky DV, Read J, Waters DW, Blokland KEC, Reid AT, et al. Mitochondrial dysfunction contributes to the senescent phenotype of IPF lung fibroblasts. J Cell Mol Med. 2018;22(12):5847–61. Cala-Garcia JD, Medina-Rincon GJ, Sierra-Salas PA, Rojano J, Romero F. 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Nambiar S, Tan DBA, Clynick B, Bong SH, Rawlinson C, Gummer J, et al. Untargeted metabolomics of human plasma reveal lipid markers unique to chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. Proteom Clin Appl. 2021;15(2–3):e2000039. Nguyen TT, Wei S, Nguyen TH, Jo Y, Zhang Y, Park W, et al. Mitochondria-associated programmed cell death as a therapeutic target for age-related disease. Exp Mol Med. 2023;55(8):1595–619. Gaugg MT, Engler A, Bregy L, Nussbaumer-Ochsner Y, Eiffert L, Bruderer T, et al. Molecular breath analysis supports altered amino acid metabolism in idiopathic pulmonary fibrosis. Respirology. 2019;24(5):437–44. Xu Q, Cheng D, Li G, Liu Y, Li P, Sun W, et al. CircHIPK3 regulates pulmonary fibrosis by facilitating glycolysis in miR-30a-3p/FOXK2-dependent manner. Int J Biol Sci. 2021;17(9):2294–307. Ma R, Fan Y, Huang X, Wang J, Li S, Wang Y, et al. Lipid dysregulation associated with progression of silica-induced pulmonary fibrosis. Toxicol Sci. 2023;191(2):296–307. Takada S, Maekawa S, Furihata T, Kakutani N, Setoyama D, Ueda K, et al. Succinyl-CoA-based energy metabolism dysfunction in chronic heart failure. Proc Natl Acad Sci U S A. 2022;119(41):e2203628119. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryTable4.xlsx SupplementaryTable5.xlsx SupplementaryTable6.xlsx SupplementaryTable7.xlsx SupplementaryTable8.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3878025","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268870189,"identity":"811007ba-1932-4d89-bc66-76510cd9dde1","order_by":0,"name":"Yunmulan Zhao","email":"","orcid":"","institution":"Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunmulan","middleName":"","lastName":"Zhao","suffix":""},{"id":268870190,"identity":"ae92731c-2a32-4b7c-8ce1-bf6d6c098ec5","order_by":1,"name":"Wenyu Hou","email":"","orcid":"","institution":"Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenyu","middleName":"","lastName":"Hou","suffix":""},{"id":268870191,"identity":"0df7608b-76e4-4825-92c9-5d47e2b3c718","order_by":2,"name":"Liqing Yang","email":"","orcid":"","institution":"Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liqing","middleName":"","lastName":"Yang","suffix":""},{"id":268870192,"identity":"5f3a271f-d6b8-46b3-b764-a92659b4dec8","order_by":3,"name":"Lu Guo","email":"","orcid":"","institution":"Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Guo","suffix":""},{"id":268870193,"identity":"4608c4b3-c89c-4502-8b5e-6d4844cd85b0","order_by":4,"name":"Ping Wang","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Wang","suffix":""},{"id":268870194,"identity":"5fac9b9d-1043-4688-8475-e2983ba64ee9","order_by":5,"name":"Lingyun Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACPmZk3gcDGzuCWtiQtTDOKEhLJqwFmcPM8+EQYwNBLey8h1/ztt2x629vf/jZxuAAMwP74aMb8DuML82at+1Z8owzZ4ylcwzu8DHwpKXdwK+Fx8yYt+1wMsONHDbmHINnzAwSPGbEaZG/kf6M2cLgMGMDEVqMHwO12BncSDBjZiBSixnjnHOHEwyBfpHsMUhLZiPkF37+M8Yf3pQdtpc73v7ww48/Nnb87IeP4dUCskiKh4EhsQHOJaAcBJg//mBgsCdC4SgYBaNgFIxUAAC7JUUCU+CDAwAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Gao","suffix":""},{"id":268870195,"identity":"f5553e18-97f9-42be-9e2f-a7da1f9308ef","order_by":6,"name":"Zuojun Xu","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zuojun","middleName":"","lastName":"Xu","suffix":""},{"id":268870196,"identity":"7a7a70dd-8bbd-42a0-b961-5cd818ab8366","order_by":7,"name":"Wei Sun","email":"","orcid":"","institution":"Sichuan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-01-19 07:44:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3878025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3878025/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50170042,"identity":"edccf044-24e0-4212-b785-4587ac2e205f","added_by":"auto","created_at":"2024-01-25 15:35:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":459363,"visible":true,"origin":"","legend":"\u003cp\u003eMotif analysis of the proteins involved in IPF. A) Motif analysis of succinylated proteins. B) Succinylated proteins.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/fe9900495dc4f6091c49d691.png"},{"id":50170037,"identity":"2e7977a3-b513-49f8-ab09-11afd3783eca","added_by":"auto","created_at":"2024-01-25 15:35:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73371,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of un-modified lysine and modified lysine in protein secondary structures and protein surface accessibility.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/5a8b1b1f5f7df03bbd6dd450.png"},{"id":50170958,"identity":"89d4f0ef-c8f7-4631-8efd-1a93bd98b1e8","added_by":"auto","created_at":"2024-01-25 15:43:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":233769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional annotation of succinylated modified proteins.\u003c/strong\u003eA) Subcellular localization on succinylated sites proteins. B) COG/KOG function classify of the succinylated proteins in IPF lung tissues\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/aee6e8b54decdfc418eccb43.png"},{"id":50170043,"identity":"e1779e7e-91fe-4571-80cc-b72adf739acc","added_by":"auto","created_at":"2024-01-25 15:35:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":226443,"visible":true,"origin":"","legend":"\u003cp\u003eTop 20 enriched items for GO enrichment analysis of succinylated modified proteins in IPF lung tissues. A) cell component. B) molecular function. C) biological process.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/a8d88afcf9b69fb92b05d93a.png"},{"id":50170959,"identity":"ae8e4c63-8d30-4930-ad80-00d6f0486767","added_by":"auto","created_at":"2024-01-25 15:43:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":240481,"visible":true,"origin":"","legend":"\u003cp\u003eTop 20 enriched items for KEGG analysis of succinylated modified proteins\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/1ef90cdfc3432c3b82e087a8.png"},{"id":50170039,"identity":"d516da85-bea6-4e82-9db7-1ad4ffd9a4f9","added_by":"auto","created_at":"2024-01-25 15:35:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131716,"visible":true,"origin":"","legend":"\u003cp\u003eTop 20 enriched items for protein domain enrichment analysis of succinylated modified proteins\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/408246de95b8e2b06478d38c.png"},{"id":50170038,"identity":"64e4f1ca-6c0b-4be8-b9bf-fb93463e701d","added_by":"auto","created_at":"2024-01-25 15:35:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":55725,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of relative protein abundances measured by PRM mass spectrometry in succinylated samples between normal lung tissues and IPF lung tissues. Each group contains three samples. The asterisks indicate the level of significance. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01;\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/97a1980204dacac672dcf58a.png"},{"id":57502435,"identity":"6bf507f9-7053-4607-9584-429635aba06a","added_by":"auto","created_at":"2024-05-31 14:14:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2078615,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/1602f30e-dd67-4b28-906a-2f13cf476718.pdf"},{"id":50170050,"identity":"c41882a8-4380-4695-9050-330227952e53","added_by":"auto","created_at":"2024-01-25 15:35:02","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1536919,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/7c61976e9d869449757fe7ce.xlsx"},{"id":50171970,"identity":"d8760da8-a90b-4095-a157-13a55c2330c5","added_by":"auto","created_at":"2024-01-25 15:51:02","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":21710,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/94beed88075af33cf3bc66ca.xlsx"},{"id":50170964,"identity":"e3b56318-1773-438b-af13-bd77836b32f6","added_by":"auto","created_at":"2024-01-25 15:43:02","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":167949,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/a30e7536d769d095f486de68.xlsx"},{"id":50170044,"identity":"7050c0eb-b8c3-4ecb-8109-e1a6ac9bdb74","added_by":"auto","created_at":"2024-01-25 15:35:02","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":18197,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/58d9b9a6f13efefcefd9a3b5.xlsx"},{"id":50170960,"identity":"562ba952-ed1c-4a76-b8e6-031e66624681","added_by":"auto","created_at":"2024-01-25 15:43:02","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":9896,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/6bb3368b11d4ca1e0aefee68.xlsx"},{"id":50170962,"identity":"1566dec6-486e-45f1-a908-425d2017f7bc","added_by":"auto","created_at":"2024-01-25 15:43:02","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":13550,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/5999086a0e44fd7537107026.xlsx"},{"id":50170963,"identity":"c17ef869-076b-43cb-8b19-d92051c8f2b1","added_by":"auto","created_at":"2024-01-25 15:43:02","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":11671,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/3da8b682af1c7e055a7ef382.xlsx"},{"id":50170048,"identity":"492686ed-dfc1-40c4-bfe3-09f1587e91a1","added_by":"auto","created_at":"2024-01-25 15:35:02","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":19914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3878025/v1/ca312e267baa12ac3655076c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Succinylation participates in the progress of Idiopathic pulmonary fibrosis through mitochondrial energy metabolism","fulltext":[{"header":"Background","content":"\u003cp\u003eIdiopathic pulmonary fibrosis (IPF) is a lethal disease of unknown aetiology that largely presents in the elderly [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the pathogenesis of IPF is unclear, Susceptibility to IPF with aging has been linked to mitochondrial dysfunction [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Mitochondrial dysfunction in IPF mainly include metabolic changes, increasing production of reactive oxygen species, decreased mitochondrial biogenesis, and impaired mitochondrial macroautophagy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent evidence suggests that metabolic dysregulation in mitochondria dysfunction are distinctive features of the IPF lungs and this is an important factor that causes cell senescence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Mitochondria dysfunction can cause abnormal metabolism of lipids, amino acids, and sugars, and collaborates with TGF beta 1 (TGFβ1) to induce the activation of myofibroblasts and accelerate the formation of IPF [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, identifying key molecules involved in energy metabolism disorders in mitochondrial dysfunction could help to find therapeutic targets for IPF.\u003c/p\u003e \u003cp\u003eStudies have shown that lysine succinylation (Ksucc) mainly occur in mitochondria, which are closely related to the energy metabolism in mitochondria, and participate in and regulate multiple metabolic signaling pathways including Tricarboxylic acid cycle (TCA cycle), amino acid metabolism and fatty acid metabolism [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Ksucc is an important post-translational modification, belonging to acid acylation modification (11). Its modification site is on the protein of the central metabolic pathway, and affects the performance of the modified protein by changing the protein structure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Succinylated proteins clearly reflect a balance of Ksucc and de-succinylation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The only recognized desuccinylase to act in all cells compartments is NAD\u0026thinsp;+\u0026thinsp;dependent Sirtuin [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent studies have found that the expression of Sirtuin is down-regulated in mice pulmonary fibrosis, and the removal of Ksucc by increasing the expression of Sirtuin has a protective effect on IPF [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, it is reasonable for us to reveal the mechanical effect of Ksucc on IPF through protein functional omics technology.\u003c/p\u003e \u003cp\u003eWe analyzed lung tissues from six IPF patients by Ksucc proteomics techniques as the characteristic changes of Ksucc in normal lung tissues have been reported [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We found that lung tissues with six IPF patients was extensively modified by Ksucc, with the modified proteins mainly distributed in the mitochondria. These succinylated modified proteins are closely related to the energy metabolism of mitochondria, including energy production and conversion, amino acid transport and fatty acid metabolism. Compared with normal lung tissues, the expression of 43 Ksucc sites in 27 proteins related to mitochondrial energy metabolism was significantly different in IPF lung tissues. Furtherly, there were differences in expression of 4 Ksucc sites in 4 proteins in normal lung tissues and IPF lung tissues by using PRM validation. Our work expands the Ksucc database in IPF lung and will thus help us study the function and regulation of mitochondrial energy metabolism in IPF based on Ksucc.\u003c/p\u003e"},{"header":"Methods and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample preparation\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eMaterials and reagents\u003c/h2\u003e \u003cp\u003eThe trypsin was used for digestion. To enrich the succinylated modified peptides, tryptic peptides dissolved in NETN buffer. The peptides were subjected to a nanospray ionization (NSI) source followed by MS/MS with a Q Exactive TM Plus (Thermo) coupled on line to a ultra performance liquid chromatography (UPLC). The MaxQuant was used for database search.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePatient tissue samples and protein extraction\u003c/h2\u003e \u003cp\u003eThis study was approved by the Ethics Committee of Sichuan Provincial People's Hospital and Peking Union Medical College Hospital. IPF lung tissue samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) and Paracancerous lung tissue samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) were obtained from Lung transplant Center for respiratory and critical Care Medicine, Sichuan Provincial People's Hospital, Chengdu, China. The samples were frozen immediately and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until protein extraction. The IPF diagnose was made by the guidance of the 2022 American Thoracic Society (ATS), European Respiratory Society (ERS), Japanese Respiratory Society (JRS), and Latin American Thoracic Society (ALAT) guideline.\u003c/p\u003e \u003cp\u003eThe sample was grinded with liquid nitrogen into cell powder and then transferred to a 5-mL centrifuge tube. After that, four volumes of lysis buffer (1%Triton X-100, 1% protease inhibitor cocktail, 3 \u0026micro;M TSA and 50 mM NAM) was added to the cell powder, followed by sonication three minutes on ice using a high intensity ultrasonic processor (Scientz). The remaining debris was removed by centrifugation at 12,000 g at 4\u0026deg;C for 10 min. Finally, the supernatant was collected and the protein concentration was determined with BCA kit according to the manufacturer\u0026rsquo; s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTrypsin digestion\u003c/h2\u003e \u003cp\u003eThe sample was slowly added to the final concentration of 20% (m/v) TCA to precipitate protein, then vortexed to mix and incubated for 2 h at 4\u0026deg;C. The precipitate was collected by centrifugation at 4500 g for 5 min at 4\u0026deg;C. The precipitated protein was washed with 200 mM TEAB and ultrasonically dispersed. Trypsin was added at 1:50 trypsin-to-protein mass ratio for the first digestion overnight. The sample was reduced with 5 mM dithiothreitol for 60 min at 37\u0026deg;C and alkylated with 11 mM iodoacetamide for 45 min at room temperature in darkness. Finally, the peptides were desalted by Strata X SPE column.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAffinity enrichment\u003c/h2\u003e \u003cp\u003eTo enrich the lysine acetylation (Kac) peptides, the Ksu peptides, and tryptic peptides dissolved in NETN buffer (100 mM NaCl, 1 mM EDTA, 50 mM Tris-HCl, 0.5% NP-40, pH 8.0), the peptides were incubated with pre-washed antibody beads [PTM Biolabs, The Anti-acetyllysine antibody conjugated agarose beads (PTM-104), Anti-succinyllysine antibody conjugated agarose beads (PTM-402)] at 4\u0026deg;C overnight with gentle shaking. The beads were washed four times with NETN buffer and twice with ddH\u003csub\u003e2\u003c/sub\u003eO. The bound peptides were eluted from the beads with 0.1% TFA. The eluted fractions were combined and vacuum dried. The resulting peptides were cleaned with C18 ZipTips (Millipore) according to the manufacturer\u0026rsquo;s instructions, followed by LC-MS/MS analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative proteomic analysis by LC-MS/MS\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eMaterials and reagents\u003c/h2\u003e \u003cp\u003eThe H\u003csub\u003e2\u003c/sub\u003eO was from Thermo company. The ACN was obtained from Fisher Chemical. The formic acid (FA) was procured from Fluka.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLC-MS/MS analysis\u003c/h2\u003e \u003cp\u003eThe tryptic peptides were dissolved in solvent A (0.1% FA, 2% ACN in water), directly loaded onto a home-made reversed-phase analytical column (25-cm length, 100 \u0026micro;m i.d.). Peptides were separated with a gradient from 6\u0026ndash;22% solvent B (0.1% FA in ACN) over 40 min, 22\u0026ndash;30% in 12 min and climbing to 80% in 4 min then holding at 80% for the last 4 min, all at a constant flow rate of 450 nL/min on a nanoElute UHPLC system (Bruker Daltonics).\u003c/p\u003e \u003cp\u003eThe peptides were subjected to Capillary source followed by the timsTOF Pro (Bruker Daltonics) mass spectrometry. The electrospray voltage applied was 1.60 kV. Precursors and fragments were analyzed at the TOF detector, with a MS/MS scan range from 100 to 1700 m/z. The timsTOF Pro was operated in parallel accumulation serial fragmentation (PASEF) mode. Precursors with charge states 0 to 5 were selected for fragmentation, and 10 PASEF-MS/MS scans were acquired per cycle. The dynamic exclusion was set to 24s.\u003c/p\u003e \u003cp\u003eIPF lung tissue samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) and Paracancerous lung tissue samples (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) verified by the PRM validation experiment. The PRM-MS assay was performed on Q Exactive HF-X MS spectrometer and peptides were separated with the following gradient: 0\u0026ndash;36 min, 9%-25%B, 36\u0026ndash;54 min, 25%-35%B, 54\u0026ndash;57 min, 35%-80%B, 57\u0026ndash;60 min, 80%B, and all at a constant flow rate of 500 nl/minon a EASY-nLC 1200 UPLC system (ThermoFisher Scientific). The digested peptides for differential proteins and endogenous peptides identified from DDA-MS analysis with appropriated sequence length and MS/MS spectrum were programmed for the PRM assay. The full mass scan range was set as 495\u0026ndash;1234 with a resolution of 120000. The MS/MS scans were acquired by the Orbitrap with a resolution of 15000. The collisional energy was 28%, and the AGC target was set to 1e5 with a maximum injection time of 220 ms for MS/MS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDatabase search\u003c/h2\u003e \u003cp\u003eThe resulting MS/MS data were processed using MaxQuant search engine (v.1.6.15.0). Tandem mass spectra were searched against the human SwissProt database (20389 entries) concatenated with reverse decoy database. Trypsin/P was specified as cleavage enzyme allowing up to 4 missing cleavages. The mass tolerance for precursor ions was set as 20 ppm in First search and 20 ppm in Main search, and the mass tolerance for fragment ions was set as 0.02 Da. Carbamidomethyl on cysteine (Cys) was specified as fixed modification, and acetylation on protein N-terminal, oxidation on methionine (Met) and Ksucc on Lysine (Lys) were specified as variable modifications. False positive rate (FDR) was adjusted to \u0026lt;\u0026thinsp;1%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePerseus software was applied to process label-free quantitative data. The two-tailed unpaired Student's t-test was performed to calculate \u003cem\u003ep\u003c/em\u003e-value to determine statistical difference between IPF and normal groups. A fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.2 or \u0026le;\u0026thinsp;0.8 were considered as a significant difference in protein and peptide features between the two groups.\u003c/p\u003e \u003cp\u003eSkyline software was used to process the raw data of PRM detection, and the statistical analysis and graphs preparation were performed on GraphPad Prism 8 software (GraphPad Software, San Diego, CA, USA). The significance of the protein abundance change was calculated using test, and Welch correction was applied. A two-tailed test with \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eGene ontology annotation\u003c/h2\u003e \u003cp\u003eGene ontology (GO) is a major bioinformatics initiative to unify the representation of genes and gene product attributes across all species. The GO annotation proteome was derived from the UniProt-GOA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.http://www.ebi.ac.uk/GOA/\" target=\"_blank\"\u003ewww.http://www.ebi.ac.uk/GOA/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.http://www.ebi.ac.uk/GOA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). First, the identified protein ID was converted to UniProt ID and was then mapped to GO IDs using the protein ID. If the identified proteins were not annotated by the UniProt-GOA database, the InterProScan software was be used to annotated the protein\u0026rsquo;s GO function based on a protein sequence alignment method. Then, the proteins were classified by GO annotation into three categories: biological process, cellular component and molecular function.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDomain annotation\u003c/h2\u003e \u003cp\u003eThe domain functional descriptions of the identified proteins were annotated by InterProScan (a sequence analysis application) based on a protein sequence alignment method, and the InterPro domain database was used. InterPro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/interpro/\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/interpro/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a database that integrates diverse information about protein families, domains, and functional sites and makes it freely available to the public via Web-based interfaces and services. Central to the database are diagnostic models, known as signatures, against which protein sequences can be searched to determine their potential function. InterPro has utility in the large-scale analysis of whole genomes and meta-genomes, as well as in the characterization of individual protein sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eKyoto Encyclopedia of Genes and Genomes pathway annotation\u003c/h2\u003e \u003cp\u003eThe Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the protein pathways. First, the KEGG online service tool KAAS was used to annotate the protein\u0026rsquo;s KEGG database description. Then, the annotation result was mapped on the KEGG pathway database using the KEGG online service tool KEGG Mapper.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubcellular localization\u003c/h2\u003e \u003cp\u003eWolfpsort, a subcellular localization predication software, was used to predict the subcellular localization, using an updated version of PSORT/PSORT II for the prediction of eukaryotic sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMotif analysis\u003c/h2\u003e \u003cp\u003eSoft motif-x was used to analyse the model sequences of amino acids in specific positions of modify-21-mers (10 amino acids upstream and downstream of the site) in all the protein sequences. All the database protein sequences were used as background database parameters, with other parameters set to the default.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment of the GO analysis\u003c/h2\u003e \u003cp\u003eThe proteins were classified by GO annotation into three categories: biological process, cellular compartment and molecular function. For each category, a two-tailed Fisher\u0026rsquo;s exact test was employed to test the enrichment of the differentially expressed proteins against all the identified proteins. Correction for multiple hypothesis testing was carried out using standard false discovery rate control methods. GO findings with a corrected \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment of the pathway analysis\u003c/h2\u003e \u003cp\u003eKEGG database was used to identify enriched pathways by a two-tailed Fisher\u0026rsquo;s exact test to test the enrichment of the differentially expressed proteins against all the identified proteins. Correction for multiple hypothesis testing was carried out using standard false discovery rate control methods. Pathways with a corrected \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. These pathways were classified into hierarchical categories according to the KEGG website.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment of the protein domain analysis\u003c/h2\u003e \u003cp\u003eFor each category of proteins, the InterPro (a resource that provides a functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites) database was researched, and a two-tailed Fisher\u0026rsquo;s exact test was employed to test the enrichment of the differentially expressed proteins against all the identified proteins. Correction for multiple hypothesis testing was carried out using standard false discovery rate control methods, and domains with a corrected \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction analysis\u003c/h2\u003e \u003cp\u003eThe identified Kac proteins and Ksucc proteins were searched against FunRich- version 3.0 for protein-protein interactions. Only interactions between the proteins contained in the searched dataset were selected. The interaction network form FunRich was visualized in Cytoscape.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMS data quality control\u003c/h2\u003e \u003cp\u003eAfter the database search, quality control evaluation is needed to ensure that the quality of the results meets the standards. The fragmented peptide length distribution showed that the majority of the peptides were in the range of 7\u0026ndash;20 amino acids and had 2\u0026ndash;3 charges, which fulfils the sample quality for mass detection.\u003c/p\u003e \u003cp\u003eQuantification overview of the Ksucc sites and proteins in IPF lung\u003c/p\u003e \u003cp\u003eWe performed succinylation analysis with lungs obtained from six IPF patients. Before searching, we constructed a theoretical secondary spectrum database based on the protein sequences in the database, added an anti-database to calculate the FDR caused by random matching, and added a common pollution database to eliminate the influence of polluted protein in the identification results. Precursor and protein FDR were set to 1%, and the identification protein needed to contain at least one unique peptide segment.\u003c/p\u003e \u003cp\u003eThe following is an overview of the modified sites and protein numbers identified by the search results after data filtering. We found that 205842 secondary spectrograms were produced by mass spectrometry, among which 40008 spectrograms matched the theoretical secondary spectrograms. Based on the matching results, we identified 11306 peptide sequences, including 1964 modified sites (Supplementary Table\u0026nbsp;1), 1949 modified peptide sequences, and 628 proteins identified by specific peptide sequences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMS/MS spectrum database search analysis summary (Localization probability\u0026thinsp;\u0026gt;\u0026thinsp;0.75)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal spectrums\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatched spectrums\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified peptides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentified peptides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentified sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eIdentification of Ksucc motifs\u003c/h2\u003e \u003cp\u003eTo identify the possible specific sequence pattern associated with Ksucc sites, we used the MoMo analysis tool based on motif-x algorithm to analyze the sequence pattern of peptides with succinylated sites.\u003c/p\u003e \u003cp\u003eThe peptide sequences composed of 10 upstream and downstream amino acids (6 upstream and downstream amino acids for phosphorylation modification) of all identified modification sites were used as the analysis objects. When the number of peptides with a certain sequence pattern was greater than 20 and the statistical test P-value was less than 0.000001, this sequence pattern was considered as a motif of the modified peptide. Based on the results of MoMo analysis, the score of the frequency change of amino acids near the modification site was displayed in the form of thermogram, and three enriched conserved motifs were identified among the regions surrounding succinylated peptides (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Valine (V), aspartic acid (D), and alanine (A) residues were identified as the most common residues in the succinylated sequence, the number of succinylated peptides were 173, 144, and 145, respectively (Fig .1B).\u003c/p\u003e \u003cp\u003eThen, we explored whether certain sequence motifs surrounding Ksucc sites inmitochondria-localized succinylated proteins were preferential substrates, and the results showed that the 246 succinylated peptides was significantly enriched among 1050 inmitochondria-localized succinylated peptides(Supplementary Table\u0026nbsp;2, 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eSecondary structural analysis of Ksuss proteins\u003c/h2\u003e \u003cp\u003eTo further investigate the impact of Ksucc on protein structure in IPF lung tissues, we analyzed the sites of Ksucc and un-modified sites in the secondary structure. Using the NetSurf algorithm, we analyzed the secondary structure of the Ksucc sites in proteins. We found that 34.1% of the succinylated sites were located in regions with ordered secondary structure, with 27.68% of these sites located in alpha-helices and 6.42% located in beta-strands.The remaining 65.9% of the succinylated sites were distributed in disordered protein regions. Then, we compared the mean secondary structure probabilities between proteins with and without Ksucc sites in the motif, as identified in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After using unpaired Wilcox tests, we found that modified lysine was more likely to occur on beta-strand structures(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0307) and alpha-helices (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0022) than un-modified lysine. While the probability of un-modified lysine was more likely to occur on coils(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0096) than that of un-modified lysine. In addition, the surface accessibility of modified lysine and un-modified lysine of succinylated proteins was evaluated, and there was a significant difference was found. (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.03\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eCellular localization and functional annotation of succinylated modified proteins\u003c/h2\u003e \u003cp\u003eThe protein subcellular location provides information on the protein site, which gives insight into its function. To better understand the function of succinylated proteins in IPF lung tissues, a subcellular localization analysis was performed by using UniProt database notes. The data showed that the majority of the succinylated proteins were localized in mitochondria (44.75%) and cytoplasm (25.96%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The succinylated proteins in mitochondria were increased in IPF lung tissues compared with normal lung tissues (34.2%) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo understand the functions of succinylated proteins in our data, a COG analysis was employed to categorize these proteins. The COG analysis was performed in terms of cellular processes and signaling, information storage and processing, metabolism, poorly characterized. For metabolism, the succinylated proteins mainly participated in energy production and conversion, lipid transport and amino acid transport [Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment of succinylated modified proteins\u003c/h2\u003e \u003cp\u003eTo further elucidate the potential role of succinylated proteins in IPF, we analysed the data for enrichment at three levels: GO enrichment, KEGG pathway and protein domain.\u003c/p\u003e \u003cp\u003eWe analysed the data for enrichment in three GO annotation categories: cell component (CC), biological process (BP), and molecular function (MF). The succinylated proteins were enriched in the TCA cycle enzyme complex and mitochondrial matrix (CC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), Acyl-CoA metabolism and NAD binding (MF) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), and TCA cycle metabolism and fatty acid beta\u0026thinsp;\u0026minus;\u0026thinsp;oxidation (BP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). KEGG PATHWAY analysis is a method that represents the molecular interaction, reaction and relation networks of input proteins. The results showed that these succinylated proteins were enriched in pathways associated with amino acid degradation, TCA cycle and fatty acid degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The cellular localization and function of proteins are often indicated by their domains.The protein domain enrichment analysis showed that these succinylated proteins were enriched in Acyl\u0026thinsp;\u0026minus;\u0026thinsp;CoA dehydrogenase and Thiolase (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which agreed with our findings from the GO enrichment analysis and KEGG pathway analysis.\u003c/p\u003e \u003cp\u003eTo sum up, at three levels (GO classification, KEGG pathway and protein domain) we find functional enrichment of succinylated modified proteins primarily enriched in TCA cycle, amino acid and fatty acid metabolism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSignificant Differences in Mitochondrial Energy Metabolism Proteins in IPF lung tissues Compared to Normal Lung Tissues\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAn article on the detection of succinylated proteins in normal lung tissues using result of the IPF lung tissues we identified with the data provided in the normal lung tissues to ascertain whether there are differences in mitochondrial energy metabolism proteins between the two groups. The results show that the majority of succinylated proteins in both groups are located in the mitochondria. Furthermore, upon comparing all mitochondrial proteins in the two groups, it was found that there are 163 proteins expressed in both IPF and normal lung tissues (Supplementary Table\u0026nbsp;4,5). Among these, 144 proteins have different expression sites (Supplementary Table\u0026nbsp;4), and 19 proteins have identical expression sites (Supplementary Table\u0026nbsp;5). Additionally, there are 119 proteins expressed in IPF but not in the normal lung tissues (Supplementary Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003eBased on the functional annotation of COG, it was found that among the differentially expressed proteins, there are a total of 43 Ksucc sites in 27 proteins associated with energy metabolism(Supplementary Table\u0026nbsp;7).There are 9 succinylated proteins related to energy production and conversion (ADHFE1, GRHPR, CYC1, PC, PDHB, NDUFAB1, UQCRQ, NDUFA2, GPD2), 9 succinylated proteins related to lipid transport (HSD17B8, PLA2G4F, ACSF3, OXSM, ACSS1, ACAA1, ACOT7, ACADSB, NDUFAB1), and 10 succinylated proteins related to amino acid transport (KYAT, NIT1, GCDH, XPNPEP1, OAT, PRODH, IDH3G, NPEPPS, GPT, THNSL1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in energy metabolism by PRM Validation\u003c/h2\u003e \u003cp\u003eIn order to verify the reliability of these differentially expressive proteins, a more sensitive and specific targeted PRM approach was applied to characterize the alterations in the peptide forms between control groups and IPF groups. Among the differentially expressed proteins and sites, we conducted a risk assessment and subsequently chose sites and proteins with low risk for PRM analyses (Supplementary Table\u0026nbsp;4, Supplementary Table\u0026nbsp;6). To ensure the reliability of the experimental results, lung tissues from three normal cases and three IPF patients were selected for PRM analyses. We conducted PRM validation and analysis for 29 sites in 8 proteins (Supplementary Table\u0026nbsp;8). The validation results revealed notable differences in expression between the IPF groups and the normal groups. Among the 8 proteins, 6 sites in 4 proteins exhibited upregulation, while 1 site in 1 protein showed downregulation, reaching statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We conducted COG functional annotation on the five statistically significant proteins mentioned above and discovered that four proteins\u0026mdash;KYAT3 (Lys-108), HSD17B8 (Lys-173), GRHPR (Lys-65), and IDH2 (Lys-80)\u0026mdash;are associated with energy metabolism. These findings from the validation suggest a close correlation between the occurrence of IPF and energy metabolism, consistent with existing literature in cell research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eExisting studies and our published article have confirmed that IPF exhibit significant aging characteristics, which supports IPF as an age-related disease [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Recent studies have found that hypersuccinylation of mitochondria is related to cellular senescence [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], so it is necessary to further elucidate the mechanism of IPF based on Ksucc omics technology. The primary innovations of this study are outlined as follows: 1. Revealing the involvement of mitochondrial energy metabolism in the progression of IPF using Ksucc technology; 2. Employing PRM validation technology to demonstrate that proteins related to energy metabolism in IPF are regulated by Ksucc.\u003c/p\u003e \u003cp\u003eThe pathobiology underlying IPF is still incomplete. It is accepted that aging is a major risk factor in the disease while growing evidence suggests that the mitochondria play an important role in the initiation and progression of IPF [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Mitochondria dysfunction and metabolic reprogramming had been identified in different IPF lung cells (alveolar epithelial cells, fibroblasts, and macrophages) promoting low resilience and increasing susceptibility to activation of profibrotic responses [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Mitochondria act as a central hub in the cell age and mitochondrial dysfunction is present in aged-related lung diseases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Hundreds of proteins Ksucc sites are present in proteins of multiple tissues and species, and the significance is being actively investigated, and the few completed studies demonstrate that Ksucc alters rates of enzymes and pathways, especially mitochondrial metabolic pathways [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, Ksucc provides an elegant and efficient mechanism to coordinate metabolism and signaling by utilizing metabolic intermediates as sensors to regulate metabolism. In this research, the results of the subcellular localization analysis revealed that succinylated proteins in IPF lung tissues are more abundant in mitochondria (44.75%), the cytoplasm (25.96%), and the extracellular (13.38%), which was different from the localization of succinylated proteins in normal lung tissues, in which they were that mainly found concentrated in mitochondria (34.2%), the membrane (19.6%), and the cytoplasm (13.2%). This difference indicates that the key protein modified by Ksucc in IPF lung tissues also occurs in mitochondria.\u003c/p\u003e \u003cp\u003eThe role of metabolic dysregulation in the pathogenesis of IPF has not been investigated in detail. The new research applied proteomic methods to the bronchoalveolar lavage (BAL) fluid of IPF patients, observing alterations in the synthesis and activity of fatty acids, cholesterol and other lipids that may play a role in cell energy storage, structure and signalling [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Proteomics coupled with systems biology studies, performed on BAL samples from patients with IPF, normal lung tissues other interstitial lung diseases (ILDs) and different phenotypes of IPF, has brought to light a number of interesting molecules that seem to be involved in the onset of fibrosis via metabolic dysfunction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Currently, literature has reported proteins in normal lung tissues associated with Ksucc [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, in this study, we compared the proteins detected through Ksucc technology with the data provided in that literature. The results indicated that the majority of proteins with Ksucc in both groups were located within the mitochondria. Furthermore, combining COG functional annotation revealed significant differences in proteins related to energy metabolism. Existing literature has already demonstrated that mitochondrial energy metabolism is a primary factor in cellular aging. Considering our screening and comparative results, there is reason to believe that energy metabolism is closely associated with the progression of IPF.\u003c/p\u003e \u003cp\u003eMitochondria regulate a multitude of different metabolic and signaling pathways and also play an important role in programmed cell death [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Metabolomic abnormalities in mitochondrial dysfunction is of interest, as evidence suggests that metabolomic changes in amino acids, lipids, and glycolysis have seen in IPF lung tissues, especially amino acids and lipid metabolism [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These evidences indicate that modulating specific metabolites can provide clues for new therapeutic avenues. Mitochondrial Ksucc is widely involved in various energy substance synthesis and metabolism processes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, in order to observe the changes in Ksucc sites of selected mitochondrial energy metabolism proteins in different groups and ensure the rigor of experimental design, we used PRM technology to detect 3 cases of paracancerous lung tissues and 3 cases of IPF lung tissues, respectively, aiming to further elucidate whether the energy metabolism occurring in mitochondria is controlled by Ksucc. The results revealed that, among the validated proteins with Ksucc, 21 sites were upregulated, while 17 sites were downregulated in the IPF groups. Notably, KYAT3 (Lys-108), HSD17B8 (Lys-173), and GRHPR (Lys-65) exhibited significant upregulation in IPF lung tissues.\u003c/p\u003e \u003cp\u003eIn conclusion, our work uncovered the Ksucc profile changes in IPF lung tissues and validated that Ksucc may be involved in the process of IPF through energy metabolism pathways. In the future, more functional tests need to be conducted to unveil the molecular mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eA=alanine; ALAT=Latin American Thoracic Society; ATS=American Thoracic Society; BAL=bronchoalveolar lavage; BP=biological process; CC=cell component; COG=clusters of orthologous groups of proteins; Cys=cysteine; D=aspartic acid; ERS=European Respiratory Society; FA=formic acid; FDR=False positive rate; GO=Gene ontology; ILDs=interstitial lung diseases; IPF=idiopathic pulmonary fibrosis; JRS=Japanese Respiratory Society; Kac=lysine acetylation; KEGG=Kyoto Encyclopedia of Genes and Genomes; Ksucc=lysine succinylation; LC-MS/MS=liquid chromatography with tandem mass spectrometry; Lys=Lysine; Met=methionine; MF=molecular function; NSI=nanospray ionization; PASEF=parallel accumulation serial fragmentation; PRM=parallel reaction monitoring; TCA cycle=Tricarboxylic acid cycle; TGF\u0026beta;1=TGF beta 1; UPLC=ultra performance liquid chromatography; V= Valine\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur work expands the Ksucc database in IPF lung and suggested that mitochondrial energy metabolism is involved in the progression of IPF. Ksucc sites of proteins associated with mitochondrial energy metabolism can also serve as candidate molecules for future mechanism exploration and drug target selection in IPF.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003econtributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYunmulan Zhao and Wenyu Hou takes responsibility for the content of this manuscript, including the data and analysis. Wei Sun, Lingyun Gao contributed to the concept and design of study and provided funding support. Lu Guo provided clinical samples. Wang Ping contributed to the acquisition of data. Zuojun Xu contributed to the analysis of data and contributed to the drafting of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysed in this study can be obtained by a reasonable request to corresponding authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82070067), Sichuan Natural Science Foundation (No. 23NSFSC1556), Key R\u0026amp;D Plan of Sichuan Provincial Department of Science and Technology (No. 23ZDYF1850) and Beijing Natural Science Foundation (No. 7222132).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants and investigator involved in Ksucc proteomic analysis. We also thank PTM Biolabs (Hangzhou, China) for its support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChanda D, Otoupalova E, Smith SR, Volckaert T, De Langhe SP, Thannickal VJ. Developmental pathways in the pathogenesis of lung fibrosis. Mol Aspects Med. 2019;65:56\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuliga M, Pechkovsky DV, Read J, Waters DW, Blokland KEC, Reid AT, et al. Mitochondrial dysfunction contributes to the senescent phenotype of IPF lung fibroblasts. J Cell Mol Med. 2018;22(12):5847\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCala-Garcia JD, Medina-Rincon GJ, Sierra-Salas PA, Rojano J, Romero F. The Role of Mitochondrial Dysfunction in Idiopathic Pulmonary Fibrosis: New Perspectives for a Challenging Disease. 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Int J Biol Sci. 2021;17(9):2294\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa R, Fan Y, Huang X, Wang J, Li S, Wang Y, et al. Lipid dysregulation associated with progression of silica-induced pulmonary fibrosis. Toxicol Sci. 2023;191(2):296\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakada S, Maekawa S, Furihata T, Kakutani N, Setoyama D, Ueda K, et al. Succinyl-CoA-based energy metabolism dysfunction in chronic heart failure. Proc Natl Acad Sci U S A. 2022;119(41):e2203628119.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic pulmonary fibrosis, Ksucc, mitochondrial dysfunction, energy metabolism","lastPublishedDoi":"10.21203/rs.3.rs-3878025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3878025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e A new pathogenic role for mitochondrial dysfunction has been associated with aging and correlated with the development of idiopathic pulmonary fibrosis (IPF). The latest study found that the lysine succinylation (Ksucc) is involved in many energy metabolism pathways and affects the metabolic process in mitochondria, making this modification highly valuable for studying IPF related to mitochondrial dysfunction. We speculate Ksucc participate in IPF progression through mitochondrial energy metabolism pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e We used liquid chromatography with tandem mass spectrometry (LC-MS/MS) to perform the first global profiling of Ksucc in lung tissues with IPF patients. The changes of candidate key proteins and Ksucc sites related to energy metabolism in IPF lung tissues were analyzed by using the clusters of orthologous groups of proteins (COG), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO). We then compared these proteins with those reported in the literature in normal lung tissues by parallel reaction monitoring (PRM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eWe identified 1964 Ksucc sites in 628 proteins. 675 Ksucc sites in 124 proteins closely related to mitochondrial metabolism. We compared these proteins with those reported in the literature in normal lung tissues to identify differences in 119 proteins and Ksucc sites in mitochondria. 43 Ksucc sites in 27 proteins were associated with energy metabolism. There were differences in the expression of 4 Ksucc sites in 4 proteins between normal and IPF lung tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Our work expands the Ksucc database in IPF lung and suggested that mitochondrial energy metabolism is involved in the progression of IPF. Ksucc sites of proteins associated with mitochondrial energy metabolism can also serve as candidate molecules for future mechanism exploration and drug target selection in IPF.\u003c/p\u003e","manuscriptTitle":"Succinylation participates in the progress of Idiopathic pulmonary fibrosis through mitochondrial energy metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 15:34:57","doi":"10.21203/rs.3.rs-3878025/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a61a6fc5-6d77-4bee-bdb8-c7044bc5ccb8","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-31T14:06:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 15:34:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3878025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3878025","identity":"rs-3878025","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}