Label-Free Quantitative Proteomic Study of The Effect of Dihydrotestosterone (DHT) On Rat Ovarian GCs

Label-Free Quantitative Proteomic Study of The Effect of Dihydrotestosterone (DHT) On Rat Ovarian GCs | 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 Label-Free Quantitative Proteomic Study of The Effect of Dihydrotestosterone (DHT) On Rat Ovarian GCs Tairen Chen, Mongjing Wu, Yuting Dong, Bin Kong, Yufang Cai, Changchun Hei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-781275/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 Dihydrotestosterone (DHT) is a main androgen in the human body. Previous reports have shown that DHT can affect the proliferation, apoptosis and estrogen and progesterone secretion of ovarian granulosa cells (GCs). An imbalance in DHT secretion leads to GC dysfunction and follicular development disorder. Therefore, exploring the influence of DHT on GCs is necessary. The purpose of this study was to analyze the effect of DHT on GCs through label-free quantitative proteomics (LFQP). After primary cultured rat GCs were treated with DHT (10-8 mol/L), the effect of DHT on GCs was analyzed by LFQP, and some of the differentially expressed proteins (DEPs) were verified by western blotting. A total of 6124.0 proteins were identified, of which 4496.0 were quantifiable. Compared with the control group, 28 proteins were upregulated and 10 were downregulated after DHT intervention. The subcellular localization of DEPs indicates that DHT is involved in the proliferation, migration, molding and metabolism of GCs. Gene Ontology (GO) revealed that DHT downregulated the oxygen transport capacity and oxygen-binding protein of GCs. Orthologous Groups of proteins (COG/KOG) showed that DHT had an important effect on the survival, growth and apoptosis of GCs. Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DHT promotes metabolism, amino acid degradation, chemical carcinogenesis, platelet activation and vasoconstriction in GCs. The western blot results were consistent with the proteomics results. Mark3 and Mre11a are DEPs that were upregulated, and Fth1 and Nqo1 were downregulated, which indicated that DHT could promote the proliferation of GCs. This study comprehensively analyzes the impact of DHT on GCs through LFQP and provides clues for further research. Obstetrics & Gynecology Proteomics Dihydrotestosterone ovary Granulosa cells Female fertility Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Dihydrotestosterone (DHT) is the reductive metabolite of testosterone (T), which is mainly transformed by T in tissues [1] Its presence in some tissues (such as the ovaries) is necessary for the development and function of the whole organ [2, 3]. The ovarian androgens are mainly androstenedione and T; androgen is the source of estrogen in the body, and granulosa cells (GCs) can aromatize T into estrogen [4]. Unlike T, DHT cannot be aromatized into estrogen [5], but it can increase the activity of aromatase in GCs [6]. During follicular development, ovarian membranous interstitial cells produce T under the action of luteinizing hormone (LH), which is then transformed into DHT by 5α reductase, is transferred to GCs, and plays a role through androgen receptors (ARs) in GCs, stromal cells and oocytes [7, 8]. The growth of GCs is the main factor determining follicular development and ovarian function. GCs also play an important role in steroid secretion. These steroids are essential to the function and normal development of many organs [9, 10]. The role of DHT in normal follicular development is mainly mediated by AR [11, 12]. AR is mainly located in GCs [13]. When AR deficiency leads to a decrease in the effect of DHT on follicles and GCs, mouse ovaries produce more atretic follicles, and the growth of follicles and GCs is slower [14-16]. When DHT was deleted by the androgen antagonist flutamide [17], the proliferation of adult porcine follicles increased, and apoptosis decreased [18-20]. In newborn pig ovaries, an excess or deficiency of androgen will lead to accelerated initial follicular recruitment, leading to premature ovarian failure, and androgen excess will reduce the percentage of oocytes, increase the number of primary follicles, and increase germ cell apoptosis [21]. The above data indicate that normal DHT utility plays an important role in maintaining the normal growth of GCs and follicles. There have been many reports about the effect of DHT on GCs. DHT can regulate the proliferation and apoptosis of GCs and steroid secretion in many ways, thus affecting the development of follicles. DHT can negatively regulate the proliferation of GCs, including inducing GC death due to overstress of the endoplasmic reticulum [22] and inducing chronic inflammation by stimulating the ovary [23]. Additionally, DHT can inhibit the proliferation of rat GCs stimulated by FSH by inhibiting the ERK signaling pathway [24] and inhibit the proliferation of GCs by blocking the cell cycle [25]. In contrast, other studies have found that DHT can regulate the proliferation of GCs in many ways and directly promote the growth and development of follicles and the proliferation of GCs in vitro [26]. DHT can promote the proliferation of porcine GCs by promoting mitosis [27], increase the proliferation of GCs in rhesus monkey follicles [28], and promote the proliferation of GCs induced by FSH [29]. DHT can also affect the hormone synthesis and secretion of GCs [30], inhibit the endogenous BMP signal in rat GCs in vitro and increase the production of progesterone by GCs [31]. DHT has a significant inhibitory effect on the estrogen secretion of ovarian GCs through the androgen receptor (AR) pathway [32]. An abnormal increase or decrease in DHT can result in ovarian dysfunction and many ovarian diseases. For example, an increase in DHT can induce the formation of polycystic ovary syndrome (PCOS). High DHT is also one of the diagnostic criteria of PCOS [33, 34]. DHT can block the growth inhibitor of malignant and nonmalignant ovarian epithelial cells [35, 36] and activate the G protein signal cascade [37], which leads to the occurrence of epithelial ovarian cancer. High DHT can also lead to chronic low-grade inflammation of the ovary, leading to ovarian dysfunction and fibrosis [23]. Knockout of AR reduced the effect of DHT on follicles and led to premature ovarian failure and a significant decrease in the estrous cycle and fertility in female mice [14, 16]. Although DHT has a very important and complex effect on the function of ovarian GCs and the occurrence of ovarian diseases, the mechanism of DHT on ovarian GCs is not very clear, so it is necessary to conduct further study. In this study, label-free quantitative proteomics (LFQP) [38] was used to study the effects of DHT on primary cultured GCs and analyze the changes in differentially expressed proteins (DEPs) and cellular signaling pathways. LFQP can be used to study the whole spectrum of protein changes under specific physiological conditions. It is a new protein quantitative technique and the main method used in this study. The corresponding proteins can be quantified by comparing the signal intensity of the corresponding peptides in two samples (DHT and control groups) by liquid chromatography-mass spectrometry (LC-MS). LFQP was used to understand the DEPs, their cellular localization and the signaling pathways regulated by DHT in GCs, and then we carried out bioinformatics analysis based on LEQP to reveal the effects of DHT on GCs. Finally, four DEPs, namely, Mark3 (microtubule affinity-regulating kinase 3), Fth1 (ferritin heavy chain), Nqo1 (NAD(P)H dehydrogenase [quinone] 1) and Mre11a (double-strand break repair protein MRE11), were selected for western blotting, and their effects on ovarian GCs regulated by DHT were analyzed. 2 Materials And Methods 2.1 Materials and animals 2.1.1 Experimental animals Female SD rats (grade SPF) at 21 days of age were provided by the Experimental Animal Center of Ningxia Medical University (Animal Certificate No.: SCXK (Ning) 2020-0001). This experiment was conducted in strict accordance with the guidelines of the Ethical Review of Experimental Animal Welfare issued by the State Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China and the State Standardization Administration of China. 2.1.2 The main materials and reagents are listed in the following table Materials and Reagents Company DHT (5α-Dihydrotestosterone-D3) Sigma, USA PMSG (pregnant mare serum gonadotropin) Prospec-Tany, USA DMEM/F-12 culture medium BI, USA Fetal bovine serum BI, USA Penicillin streptomycin BI, USA Whole protein extraction kit KeyGEN, China BCA kit KeyGEN, China PBS HyClone, USA Dithiothreitol Sigma, USA Iodoacetamide Sigma, USA Urea Sigma, USA Trypsin Promega, USA Formic acid Sigma, USA Acetonitrile Fisher Chemical, USA 8-12% SDS-PAGE Gel Keygen, China PVDF membrane Millipore, USA Rabbit pAb Mark3 Abclonal, China Rabbit pAb Mre11a Abclonal, China Rabbit pAb Fth1 Abclonal, China Rabbit pAb Nqo1 Abclonal, China Rabbit pAb β-Actin Abclonal, China HRP Goat Anti-Rabbit IgG Abclonal, China Luminous fluid Thermo, USA 2.1.3 Software used for analysis Analysis Software/method Version/URL Mass spectrometry data analysis MaxQuant v.1.5.2.8 http://www.maxquant.org/ GO comment InterProScan v.5.14-53.0 http://www.ebi.ac.uk/interpro/ KEGG comment KAAS KEGG Mapper v.2.0 http://www.genome.jp/kaas-bin/kaas_main V2.5 http://www.kegg.jp/kegg/mapper.html Enrichment analysis Perl module v.1.31 https://metacpan.org/pod/Text::NSP::Measures::2D::Fisher Subcellular localization Wolfpsort v.0.2 http://www.genscript.com/psot/wolfpsort.html 3 Experimental Method 3.1 Primary rat granulosa cell culture and DHT intervention The 21-day-old SD female rats were injected intraperitoneally with PMSG (10 IU/), and the animals were killed by cervical vertebra dislocation 36 hours later. The ovaries were removed, and the GCs were cultured in DMEM/F12 complete medium for 48 hours. The GCs were divided into the DHT group and control group. The DHT group was treated with DHT (10-8 mol/L) for 3 hours, and the control group was treated with the same amount of DMEM/F12 complete medium. 3.2 Protein sample preparation After DHT intervention, the culture medium was discarded, and the cells were washed with PBS three times. All the cells were scraped off with a cell scraper and centrifuged (1000 rpm/5 min). After removing the supernatant, 4 volumes of lytic buffer were added, the samples were ultrasonicated, centrifuged at 12,000 g at 4°C for 10 min, and the cell fragments were removed. The supernatant was transferred to a new centrifuge tube, the protein concentration was determined by a BCA kit, and then the protein was stored in a freezer at -80°C. Pearson’s correlation coefficient and principal component analysis (PCA) statistical methods were used to evaluate the consistency of repeated samples. 3.3 Liquid chromatography-mass spectrometry analysis The peptides obtained after trypsin cleavage were dissolved in liquid chromatographic mobile phase A (0.1% (v/w) formic acid aqueous solution) and then separated by a NanoElute ultrahigh-performance liquid phase system. The liquid phase gradient setting was as follows: 0-70 min, 6%~22% B; 70-84 min, 22%~32% B; 84-87 min, 32%~80% B; 87-90 min, 80% B. The flowrate was maintained at 300 nL/min. The peptides were separated by ultra-high-performance liquid chromatography, injected into a capillary ion source for ionization and then analyzed by tims-TOF Pro mass spectrometry. When the ion source voltage was set to 1.4 kV, the parent ion and its secondary fragments of the peptide were detected and analyzed by TOF. The scanning range of secondary mass spectrometry was set to 100-1700 ml z. The data acquisition mode used was parallel cumulative serial fragmentation (PASEF) mode. A first-order mass spectrum was collected after 10 cycles of PASEF mode to collect the second-order spectrum with the charge number of the parent ion in the range of 0-5. The dynamic exclusion time of tandem mass spectrometry scanning was set to 24 seconds to avoid repeated scanning of the parent ion. 3.4 Database search The secondary mass spectrometry data were retrieved by MaxQuant (v1.6.6.0). The search parameter settings were as follows: the database was Rattus_norvegicus_10116_PR (29,947 sequences), an anti-database was added to calculate the false positive rate (FDR), caused by random matching, and a common contamination database was added to the database to eliminate the influence of contaminated proteins in the identification results; the enzyme digestion mode was set to Trypsin/P; and the number of missing sites was set to 2. The mass error tolerance of the primary parent ion of the first search and main search was set to 40 ppm, and the mass error tolerance of the secondary fragment ion of 40 ppm was 0.02 Da. The alkylation of cysteine was set as a fixed modification, and variable modifications were set as methionine oxidation and N-terminal acetylation of the protein. The FDR of protein identification and PSM identification was set to 1%. 3.5 Bioinformatics analysis Gene Ontology (GO) analysis mainly includes three aspects: cell composition (CC), molecular function (MF), and biological process (BP). The UniProt-GOA database was searched for proteomic annotation, and the enrichment of DEPs in MF and BP was further analyzed. The proteins in eukaryotic cells were located on various intracellular elements, and we used wolfpsort software to analyze the subcellular localization of DEPs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to analyze and classify the regulated signaling pathways. Through database comparison analysis, the DEPs were classified by COG/KOG (Clusters of Orthologous Groups of proteins) function statistics. Eukaryotes are generally called the KOG database. 3.6 Western blot The concentration of the protein samples was determined by the BAC method. Eight-microliter protein samples were extracted and electrophoretically separated on an 8-12% SDS-PAGE gel and then transferred to PVDF membranes. After the membrane was transferred, it was sealed in 5% skim milk solution for 2 hours and incubated with rabbit pAb Mark3, Nqo1, Mre11a and Fth1 (all diluted at 1:1000) overnight at 4°C. After that, the corresponding secondary antibodies were incubated (diluted according to 1Suzhou 5000) for 1 hour, and then the luminescent solution was added for exposure. The relative protein gray value was analyzed by densitometry using ImageJ software. 3.7 Statistical analysis For the DEP screening, the relative quantitative value of each sample was taken as log2 (to make the data accord with the normal distribution), and the t -test method was used to calculate the P value, which was statistically significant when the P value was less than 0.05. A change in differential expression of more than 1.5 was regarded as the threshold of significant upregulation, and a change of less than 0.67 was regarded as the threshold of significant downregulation. For the bioinformatics analysis, Fisher’s exact test was used to detect the enrichment of DEPs in all identified proteins, and P < 0.05 was considered to be statistically significant. For the western blotting analysis, SPSS 22.0 statistical software was used for analysis, and a t -test was used to calculate the P value between groups. When P < 0.05, there was a significant difference between the two groups. 4 Result 4.1 Label-free quantitative proteomic analysis A total of 383,904 secondary spectra were obtained in this study. The number of available secondary spectra was 308,609, and the utilization rate of spectra was 80.4%. A total of 50,913 peptides were identified by spectral analysis, of which 46,646 were specific peptides. A total of 6124 proteins were identified, of which 4496 could be quantitatively analyzed (Table 1). Table 1. MS/MS spectrum database search analysis summary MS/MS spectrum database search analysis summary Total spectrum Matched spectrum Peptides Unique peptides Identified proteins Quantifiable proteins 383,904 308,609 50,913 46,646 6124 4496 4.1.1 Differentially expressed proteins A total of 38 differential proteins were detected, of which 28 were upregulated and 10 were downregulated (Table 2). In Fig. 1a, the horizontal axis is the logarithmic value of the protein relative quantitative value after log2 logarithmic conversion, and the vertical axis is the logarithmic value of the p-value after log10 logarithmic conversion. In Fig. 1b , the red pillar indicates the significantly upregulated proteins, and the blue pillar indicates the significantly downregulated proteins. Table 2. Basic information of DEPs Protein accession Protein description DHT/control ratio Regulation type Gene name Subcellular localization A0A0A0MY00 Short-/branched-chain-specific acyl-CoA dehydrogenase, mitochondrial 1.502 Up Acadsb mitochondria A0A0G2K0Q7 Myosin light chain kinase 1.627 Up Mylk cytoplasm A0A0G2KB92 Serine/threonine-protein kinase DCLK1 2.049 Up Dclk1 cytoplasm D3ZBN0 Histone H1.5 2.002 Up Hist1h1b nucleus D3ZKK3 Consortin, connexin sorting protein 1.52 Up Cnst cytoplasm D4A3K5 Histone H1.1 2.498 Up Hist1h1a nucleus D4A7G9 Similar to chromosome 1 open reading frame 50 1.557 Up RGD1564804 cytoplasm D4AAL4 Adhesion G protein-coupled receptor L3 1.72 Up Adgrl3 plasma membrane F1LSW7 60S ribosomal protein L14 1.881 Up Rpl14 mitochondria F1M0V4 THO complex 2 1.523 Up Thoc2 cytoplasm F1M836 Nonspecific serine/threonine protein kinase 1.64 Up Mark3 nucleus G3V781 Double-strand break repair protein 1.708 Up Mre11a cytoplasm G3V827 Cysteine conjugate-beta lyase 1, isoform CRA_a 1.661 Up Kyat1 mitochondria O88496 Vitamin K-dependent gamma-carboxylase 1.533 Up Ggcx nucleus P00406 Cytochrome c oxidase subunit 2 1.545 Up Mtco2 plasma membrane P05942 Protein S100-A4 1.96 Up S100a4 extracellular P07687 Epoxide hydrolase 1 1.875 Up Ephx1 endoplasmic reticulum P09034 Argininosuccinate synthase 1.655 Up Ass1 cytoplasm P15865 Histone H1.4 1.599 Up Hist1h1e nucleus P62083 40S ribosomal protein S7 1.895 Up Rps7 cytoplasm Q6PDU7 ATP synthase subunit g, mitochondrial 1.52 Up Atp5mg mitochondria Q6Q7Y5 Guanine nucleotide-binding protein subunit alpha-13 1.528 Up Gna13 extracellular Q6QLN3 Ribosome biogenesis protein NOP53 1.624 Up Nop53 nucleus Q8R4A1 ERO1-like protein alpha 1.824 Up Ero1a extracellular Q91ZW6 Trimethyllysine dioxygenase, mitochondrial 1.517 Up Tmlhe mitochondria Q9R1N3 Sodium bicarbonate cotransporter 3 1.963 Up Slc4a7 plasma membrane Q9R1T1 Barrier-to-autointegration factor 1.513 Up Banf1 extracellular Q9WVK7 Hydroxyacyl-coenzyme A dehydrogenase, mitochondrial 1.526 Up Hadh mitochondria Protein accession Protein description DHT/control ratio Regulation type Gene name Subcellular localization A0A0G2JSV6 Globin c2 0.513 Down Hba-a2 cytoplasm D4A352 Myelin regulatory factor 0.644 Down Myrf cytoplasm P02091 Hemoglobin subunit beta-1 0.542 Down Hbb cytoplasm P05982 NAD(P)H dehydrogenase [quinone] 1 0.606 Down Nqo1 cytoplasm P56741 Myosin-binding protein C, cardiac-type 0.527 Down Mybpc3 mitochondria P62859 40S ribosomal protein S28 0.656 Down Rps28 mitochondria P68370 Tubulin alpha-1A chain 0.492 Down Tuba1a cytoskeleton Q66HI5 Ferritin 0.599 Down Fth1 cytoplasm Q8R4R9 Protein phosphatase 1 regulatory subunit 14C 0.619 Down Ppp1r14c cytoplasm Q925G0 RNA-binding protein 3 0.611 Down Rbm3 nucleus According to the results in Table 2, through the search and analysis of the UniProt database, compared with the control group, the proteins related to ATP binding and ATP synthesis and metabolism in the DHT intervention group were significantly upregulated, in which Mylk, Dclk1, Mtco2, ATP5mg, ATP5mg, and Ass1 were upregulated. There were significant differences in the expression of proteins related to cell growth, apoptosis and migration, in which Hist1h1b, Thoc2, Mrak3, Gna13, Mre11a, and Nop53 were upregulated and Nqo1 was downregulated. The differential expression of proteins related to RNA binding was significant, including upregulation of Rps7 and Rp114; downregulation of Rps28 and Rbm3; upregulation of Hist1h1a, Hist1h1e, and Banf1; and downregulation of Myrf among proteins related to DNA binding. The protein Ppp1r14c related to protein phosphorylation was upregulated, and Cnst was downregulated. Adgrl3 and Gna13, which regulate G protein-coupled receptors, were upregulated. The proteins Ero1a, Tm1he, and Ggcx, which are related to oxygen binding and oxidoreductase activity, were upregulated, and Hba-a2 and Hbb were downregulated. Kyat1, which is involved in biosynthesis and amino acid biosynthesis metabolism, was upregulated, and Ephx1, which is involved in intracellular aromatic compound metabolism, was upregulated. Fth1, which is related to intracellular iron homeostasis and iron transport, was downregulated. Slc4a7, which is related to ion balance, was upregulated. 4.2 Bioinformatics analysis 4.2.1 GO analysis As shown in Fig. 2, these proteins are mainly involved in cell (23 upregulated [up] and 10 downregulated [down]), organelle (21 up, 7 down), membrane (12 up, 1 down), macromolecular complex (10 up, 5 down), membrane-enclosed lumen (10 up, 1 down), extracellular region (7 up, 4 down), cell junction (3 up), and other (1 up) in CC (Fig. 2a). For MF (Fig. 2b), they have a variety of activities, such as binding (23 up, 9 down), catalytic activity (14 up, 2 down), transporter activity (3 up, 2 down), structural molecule activity (2 up, 3 down), signal transducer activity (2 up), molecular transducer activity (1 up), electron carrier activity (1 up), nucleic acid binding transcript (1 down), molecular function regulator (1 down), and antioxidant activity (1 down). In addition, these proteins are involved in complex BPs (Fig. 2c), including cellular process (23 up, 7 down), metabolic process (18 up, 3 down), biological regulation (13 up, 6 down), single-organism process (18 up, 6 down), multicellular organismal process (11 up, 2 down), response to stimulus (10 up, 2 down), developmental process (10 up, 3 down), cellular component organization (9 up, 5 down), localization (6 up, 3 down), signaling (4 up), other (5 up), and biological adhesion (1 down). There were generally more upregulated proteins among CC, MF, and BP than downregulated proteins. 4.2.2 Subcellular localization of DEPs We performed statistics on the subcellular structure localization of DEPs. In Table 3, 8 proteins were upregulated in the cytoplasm, 6 proteins were downregulated in the cytoplasm, and 6 proteins were upregulated and only 1 protein was downregulated in the nucleus. In mitochondria, 6 proteins were upregulated and 2 proteins were downregulated. Four proteins in the extracellular matrix, three proteins in the plasma membrane, one protein in the endoplasmic reticulum and one protein in the cytoskeleton were upregulated. Fig. 2d shows the differential localization of DEPs in each subcellular structure after DHT intervention. Table 3. Subcellular localization of DEPs; the percentage is the percentage of DEPs in that subcellular localization among all identified DEPs UP-Subcell Number of proteins Percentage Cytoplasm 8 28.57% Nucleus 6 21.43% Mitochondria 6 21.43% Extracellular 4 14.29% Plasma membrane 3 10.71% Endoplasmic reticulum 1 3.57% DOWN-Subcell Number of proteins Percentage Cytoplasm 6 60% Mitochondria 2 20% Nucleus 1 10% Cytoskeleton 1 10% 4.2.3 COG/KOG function classification Through database comparison analysis, the DEPs were classified by COG/KOG function statistics. The upregulated proteins in COG/KOGA analysis in Fig. 3a were mainly concentrated in chromatin structure and dynamics (4); lipid transport and metabolism (3); energy production and conversion (2); amino acid transport and metabolism (2); translation ribosomal structure and biogenesis (2); replication recombination and repair (2); general function prediction only (4); signal transduction mechanisms (1); intracellular trafficking, secretion, and vesicular transport (1); transcription (1); posttranslational modification, protein turnover, and chaperones (1); inorganic ion transport and metabolism (1); and cell cycle control, cell division, and chromosome partitioning (1). The downregulation proteins are mainly involved in energy production and conversion (1), translation ribosomal structure and biogenesis (1), general function prediction only (1), function unknown (1), cytoskeleton (1) and inorganic ion transport and metabolism (1). 4.2.4 Functional enrichment analysis of MF/BP As shown in Table 4, the most obvious MFs of upregulated DEPs were chromatin DNA binding, vitamin binding and hydrogen ion transmembrane transporter, while the downregulated molecular functions were mainly oxygen transporter activity and oxygen binding, heme binding, tetrapyrrole binding, and iron ion binding. The most obvious BP functions of upregulated DEPs were protein-DNA complex assembly, nucleosome organization, chromatin assembly or disassembly, DNA packaging, DNA conformation change, response to corticosteroid, monovalent inorganic cation transport, response to steroid hormone, cellular ion homeostasis, ion transmembrane transport, and cation transport. The most obvious BPs of downregulated DEPs were oxygen transport, gas transport, and response to estradiol. Table 4. Functional enrichment analysis of DEPs and the number and degree (log2-fold enrichment) of regulated DEPs Functional enrichment Number of proteins log2-fold enrichment UP-Molecular function Chromatin DNA binding 4 3.9 Vitamin binding 2 3.4 Hydrogen ion transmembrane transporter 2 3.2 Protein C-terminus binding 2 2.4 Carbohydrate binding 2 2.2 Cofactor binding 4 1.9 Ion transmembrane transporter activity 4 1.8 Oxidoreductase activity 6 1.5 DOWN-Molecular function Oxygen transporter activity 2 6.8 Oxygen binding 2 6.7 Heme binding 2 5.2 Tetrapyrrole binding 2 5.1 Iron ion binding 4 5.1 Structural molecule activity 4 2.7 UP-Biological processes Protein-DNA complex assembly 3 3.8 Nucleosome organization 3 3.7 Chromatin assembly or disassembly 3 3.6 DNA packaging 3 3.55 DNA conformation change 4 3.25 Response to corticosteroid 3 3.2 Monovalent inorganic cation transport 3 2.8 Response to steroid hormone 3 2.5 Cellular ion homeostasis 3 2.4 Cation transmembrane transport 3 2.35 Ion transmembrane transport 4 2.25 Cation transport 4 2.2 Down-Biological processes Oxygen transport 2 6.8 Gas transport 2 6.8 Response to estradiol 2 4.2 4.2.5 Enrichment analysis of KEGG signaling pathways Table 5 shows the KEGG signaling pathways regulated by GCs after DHT intervention. Key regulatory protein indicates the key protein through which DHT acts on the regulated signaling pathway. The upregulated signaling pathways were mainly tryptophan metabolism; selenocompound metabolism; chemical carcinogenesis; fatty acid degradation; lysine degradation; vascular smooth muscle contraction; valine, leucine and isoleucine degradation; platelet activation; and the cGMP-PKG signaling pathway. The downregulated signaling pathways were mainly African trypanosomiasis and malaria. Fig. 3b shows the up- and downregulated signaling pathways, as well as the level of regulation. Table 5. Enrichment analysis of KEGG signaling pathways and enrichment levels (log2-fold enrichment), key regulatory proteins, and protein definitions KEGG pathway Key regulatory protein Protein definition log2-fold enrichment UP-regulated proteins Tryptophan metabolism CCBL fadB kynurenine-oxoglutarate transaminase, enoyl-CoA hydratase 4.4 Selenocompound metabolism CCBL kynurenine-oxoglutarate transaminase 4.4 Chemical carcinogenesis EPHX1 CCBL microsomal epoxide hydrolase, kynurenine-oxoglutarate transaminase 3.8 Fatty acid degradation HADH fadE hydroxyacyl-CoA dehydrogenase, acyl-CoA dehydrogenase 3.5 Lysine degradation TMLHE HADH trimethyllysine dioxygenase, 3-hydroxyacyl-CoA dehydrogenase 3.4 Vascular smooth muscle contraction GNA12 MYLK guanine nucleotide-binding protein subunit alpha-12 myosin-light-chain kinase 3.2 Valine, leucine and isoleucine degradation ACADSB short-chain 2-methylacyl-CoA dehydrogenase 3.2 Platelet activation GNA13 MYLK guanine nucleotide-binding protein subunit alpha-13 myosin-light-chain kinase 2.6 cGMP-PKG signaling pathway GNA12 MYLK guanine nucleotide-binding protein subunit alpha-12 myosin-light-chain kinase 2.5 Down-regulated proteins African trypanosomiasis HBA hemoglobin subunit alpha 6.3 Malaria HBA hemoglobin subunit alpha 5.8 4.3 Western blot We selected four proteins (Mre11a, Mark3, Fth1, and Noq1) that were more differentially expressed in the LFQP (more than 1.5 and less than 0.67 as the threshold for a significant difference) for western blotting experiments (Fig. 4). In GCs, Mark3 and Mre11a were significantly upregulated after DHT intervention, while Fth1 and Noq1 decreased significantly after DHT intervention. The results were consistent with that of the LFQP, which proved its accuracy. 5 Discussion DHT, the reductive metabolite of T, is a more effective androgen in CGs than T and plays an important role in the development of CGs and the secretion of steroids, but there a comprehensive understanding of DHT is lacking. In this study, LFQP was used to analyze the effect of DHT on primary cultured CGs of rat ovaries. A total of 38 proteins were found to have changed, including 28 upregulated and 10 downregulated proteins. Among them, Mylk, Dclk1, Mtco2, ATP5mg, Hist1h1e, and Ass1 were all upregulated after DHT intervention, indicating that the functions of ATP binding and anabolism were significantly enhanced after DHT treatment of CGs. The upregulation of Hist1h1b, Thoc2, Mrak3, Gna13, Mre11a, and Nop53 and downregulation of Nqo1 suggest that DHT is related to the growth, apoptosis and migration of GCs, wherein Hist1h1b, Thoc2, and Mrak3 can promote cell proliferation; Mre11a, and Nqo1 participate in the negative regulation of apoptosis; Gna13 affects cell differentiation, migration and formation; and Nop53 participates in the regulation of apoptosis and the cell cycle. RGD1564804, Banf1, Hadh, and S100a4 were upregulated, and Myrf, Mybpc3, and Tuba1a were downregulated, indicating that DHT plays an important role in the binding function of the same protein in GCs. Rps7 and Rp114 were upregulated, and Rps28 and Rbm3 were downregulated, suggesting that DHT is involved in the regulation of RNA binding in GCs. Hist1h1a, Hist1h1e, and Banf1 were upregulated, and Myrf was downregulated, showing the effect of DHT on DNA binding in GCs through different pathways. Ppp1r14c was upregulated, and Cnst was downregulated, which revealed the effect of DHT on protein phosphorylation in GCs. Adgrl3 and Gna13 were upregulated, indicating that DHT could promote G protein-coupled receptors in GCs. Ero1a, Tm1he, and Ggcx were upregulated, and Hba-a2 and Hbb were downregulated, indicating that DHT affected oxygen binding and oxidoreductase activity in GCs. Kyat1 was upregulated, suggesting that DHT is involved in amino acid biosynthesis and metabolism in GCs. Ephx1 was upregulated, which emphasized that DHT could promote the metabolism of aromatic compounds in GCs. Fth1 was downregulated, suggesting that DHT is related to iron ion homeostasis and iron ion transport in GCs. The upregulation of Slc4a7 shows that DHT has a certain effect on the ion balance in GCs. 5.1 Gene Ontology (GO) functional enrichment analysis of MF and BP GO is a basic bioinformatics network that can define and describe the function of proteins. Bioinformatics analysis revealed the GO terms of regulated proteins based on MF, BP and CC. The main distribution of DEPs among MF, BP, and CC was predicted. Here, we focused on the analysis of MF and BP. According to the experimental data in Table 3, the oxygen transport and oxygen binding functions of MF and BP decreased significantly. It has been reported that hypoxia can cause tissue inflammation, apoptosis and cell necrosis [22]. Therefore, we speculate that the significant decrease in oxygen transport capacity and oxygen-binding protein of GCs caused by DHT intervention may be one of the mechanisms of ovarian tissue damage and infertility caused by DHT. 5.2 Subcellular localization of DEPs The nucleus is the regulatory center of cellular metabolism and heredity and controls the heredity, growth and development of cells. After GCs were treated with DHT, the DEPs in the nucleus were significantly upregulated, indicating that DHT had a significant effect on the growth and development of GCs. The main function of mitochondria is to provide energy for cells and to control cell growth and apoptosis. The extracellular matrix is involved in controlling cell growth, shape, migration and metabolic activity. After DHT treatment of GCs, the proteins in the mitochondria and extracellular matrix were upregulated, suggesting that DHT is involved in the proliferation, migration, molding, and metabolism of GCs. 5.3 COG/KOG function classification The Prokaryotic/Eukaryotic Orthologous Groups (COG/KOG) database covers the phylogenetic relationship of proteins encoded by the whole genome of prokaryotes and eukaryotes. The proteins that make up COG are assumed to be from an ancestral protein, including orthologs or paralogs. Eukaryotes are found in the KOG databases. Through database comparison and analysis, we classified DEPs into COG/KOG functions. Fig. 3a reveals in detail that DHT intervention in GCs has an important effect on the function, survival, growth and development and apoptosis of GCs. 5.4 KEGG signaling pathway analysis In this study, 11 different signaling pathways were detected in which chemical carcinogenesis was upregulated, indicating that DHT has a carcinogenic effect on GCs. The upregulation of tryptophan metabolism is more obvious. Tryptophan can participate in the renewal of plasma protein in animals and can promote the role of riboflavin but can also contribute to the synthesis of nicotinic acid and heme. The promoting effect of DHT on tryptophan metabolism after intervention with GCs suggests that DHT may contribute to the synthesis of heme. The upregulation of platelet activation channels means that DHT can promote platelet activation. The upregulation of the vascular smooth muscle contraction pathway suggests that DHT may have a certain effect on vasoconstriction. 5.5 Western blot and DEP analysis To further understand the specific effects of DHT on GCs and to evaluate the accuracy of the LFQP, we screened four differential proteins (Mark3, Nqo1, Mre11a, and Fth1) by western blot analysis according to the degree of differential expression of DEPs after intervention with DHT. After analysis, DEPs promoted cell proliferation after being regulated by DHT, and the occurrence of ovarian cancer and granulosa cell tumors was related to the proliferation of GCs. Mre11a (double-strand break repair protein) was upregulated by DHT (Fig. 4a). Mre11a is a component of the MRN complex, which promotes cell proliferation through homologous recombination to repair DNA double strand breaks and negatively regulate apoptosis. Mre11a can resist the replication pressure induced by anticancer genes and promote the growth of cancer cells [39]. DHT can induce the occurrence of ovarian cancer [35, 40, 41]. After DHT intervention, mre11a in GCs was significantly upregulated, which may be one of the mechanisms of ovarian cancer caused by DHT. Mark3 (microtubule affinity-regulating kinase 3) can inhibit the kinase activity of STK 3/MST 2 toward LATS 1 by antagonizing the phosphorylation of LATS 1 and DLG 5, which negatively regulates the HIPPO signaling pathway [42]. The role of the Hippo signaling pathway is mainly negative regulation of cell growth, and inhibition of the Hippo signaling pathway can promote cell proliferation. DHT obviously upregulated Mark3 (Fig. 4b) and enhanced the inhibitory effect of Mark3 on the Hippo signaling pathway. The nuclear localization of the YAP protein downstream of the Hippo signaling pathway increases correspondingly (activated: nonphosphorylated), and increased nuclear localization of YAP can promote the growth of ovary and granulosa cells, which leads to granulosa cell tumors and ovarian cancer [43-46]. Fth1 DHT significantly downregulated Fth1 (ferritin heavy chain) (Fig. 4c). The role of Fth1 is mainly to store iron in a soluble, nontoxic and readily available form, which is very important for the steady state of iron [47]. Previous studies have found that there is a significant decrease in Fth1 in the GCs of female patients with infertility [48]. FTH1 can negatively regulate cell proliferation, and the cell migration and proliferation ability is significantly enhance with inhibition of FTH1 [49], while ovarian cancer and granulosa cell tumors can also enhance cell proliferation [50, 51]. Therefore, the significant inhibition of Fth1 after DHT intervention in GCs may be related to the occurrence of granulosa cell tumors and ovarian cancer. Nqo1 After DHT intervention in GCs, Nqo1 (NAD(P)H dehydrogenase [quinone] 1) decreased significantly (Fig. 4d). Nqo1 is a quinone reductase, an intracellular enzyme that detoxifies quinones, is a key component of the antioxidant defense system and can protect cells from oxidative stress [52]. Previous studies have reported that NQO1 gene knockout accelerates cell proliferation and tumorigenesis [52, 53]. Therefore, the inhibitory effect of DHT on Nqo1 may promote the proliferation of GCs and the occurrence of ovarian cancer, which provides ideas for future research and the treatment of ovarian cancer and granulosa cell tumors. 6 Conclusion The effects of DHT on GCs were comprehensively studied by LFQP, and the possible effects of some DEPs regulated by DHT on ovarian diseases and female infertility were analyzed in detail. The purpose of this study was to provide ideas for further research on the effect of DHT on GCs, ovarian diseases and female infertility. Declarations Funding This study was supported by the National Natural Science Foundation of China (81160084, 81460230) Conflicts of interest The authors declare that they have no competing interests. Ethics approval This experiment strictly abides by the guidelines for ethical review of laboratory animal welfare issued by the General Administration of quality supervision, inspection and Quarantine of the people's Republic of China and the State Administration of standardization of the people's Republic of China. Availability of data and material The data sets supporting the results of this article are included within the article and its additional files. Authors' contributions Tairen Chen carried out most of the experiments, analyzed the data, and wrote a manuscript. Yuting Dong assisted in some experiments, editing and revising manuscripts. 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Down-regulating NQO1 promotes cellular proliferation in K562 cells via elevating DNA synthesis. Life Sci. 2020;248:117467. https://doi.org/10.1016/j.lfs.2020.117467 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-781275","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":58163573,"identity":"401f0123-2de8-4a71-8866-a5e02a46a0a9","order_by":0,"name":"Tairen Chen","email":"","orcid":"https://orcid.org/0000-0001-6737-3221","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tairen","middleName":"","lastName":"Chen","suffix":""},{"id":58163574,"identity":"26aebcdf-9ba1-49e1-8a6f-46d745c862e6","order_by":1,"name":"Mongjing Wu","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mongjing","middleName":"","lastName":"Wu","suffix":""},{"id":58163575,"identity":"3ad4c7bd-a2bd-4ed3-9cab-457d8fba0c20","order_by":2,"name":"Yuting Dong","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Dong","suffix":""},{"id":58163576,"identity":"538c4d83-489d-44ed-a223-f3f2cd17da31","order_by":3,"name":"Bin Kong","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Kong","suffix":""},{"id":58163577,"identity":"0ff22ade-04a0-4c0f-99d6-4a75331d0025","order_by":4,"name":"Yufang Cai","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yufang","middleName":"","lastName":"Cai","suffix":""},{"id":58163578,"identity":"4f285132-4dd7-4cc7-b9e1-e56476855c11","order_by":5,"name":"Changchun Hei","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Changchun","middleName":"","lastName":"Hei","suffix":""},{"id":58163579,"identity":"c8a83a9f-862f-4732-a5cb-a4adb7f93d80","order_by":6,"name":"Kai Wu","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wu","suffix":""},{"id":58163580,"identity":"9c17cb11-a6bd-40fc-bd3e-691a9977299a","order_by":7,"name":"Chengjun Zhao","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chengjun","middleName":"","lastName":"Zhao","suffix":""},{"id":58163581,"identity":"8dbcac62-d9e1-48c0-a4bc-af5553db84c5","order_by":8,"name":"Qing Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYLCCBIZ/9fzMzIcfkKLlQIJkO1uaASn2HEgwOM+jIEGUWvn23mcSD3fcyTM+zMNgwFBjE01Qi8GZ42YSiWeeFZsd5j3wgOFYWm4DQS0SaWwSiW3MjNsO8yUYMDYcJqxFfv4ziJbNzTwGEkRpYbjBBtJyOHEDM7FaDM6kMVsktqUZSxwGBnICMX6Rbz/GePNnm40cf//hww8+1NgQ4TAGBhZEdCQQoRwEmD8QqXAUjIJRMApGKgAAz04+PmIQRSMAAAAASUVORK5CYII=","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Chang","suffix":""}],"badges":[],"createdAt":"2021-08-04 17:31:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-781275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-781275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14918437,"identity":"4932c460-8388-4b85-bf7b-e872aa5f765d","added_by":"auto","created_at":"2021-10-26 19:17:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95648,"visible":true,"origin":"","legend":"The volcanic distribution map of DEPs (a) and the number of DEPs (b), P＜0.05","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-781275/v1/595a7147baad3aa4e75090a2.png"},{"id":14918436,"identity":"bbb9962f-4b8f-4988-89ef-5141e0e75d7f","added_by":"auto","created_at":"2021-10-26 19:17:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":321585,"visible":true,"origin":"","legend":"GO analysis results. A, B, and C are CC, MF, and BP, respectively. D is the subcellular localization of DEPs","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-781275/v1/42290f90d3542e26db8c1e5c.png"},{"id":14918435,"identity":"7152c30d-559b-4239-967d-8ade34734f2f","added_by":"auto","created_at":"2021-10-26 19:17:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340470,"visible":true,"origin":"","legend":" is the COG/KOG function classification\nshows the enrichment analysis of the KEGG signaling pathways","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-781275/v1/37130687803c217c532df7e9.png"},{"id":14918438,"identity":"4b27f7d7-3d26-46a5-bf49-bdd35a95b90b","added_by":"auto","created_at":"2021-10-26 19:17:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":248186,"visible":true,"origin":"","legend":"Western blot results and gray value analysis of Mre11a (a), Mark3 (b), Fth1 (c), and Noq1 (d)","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-781275/v1/3febe94416c7c8296e66397b.png"},{"id":14918446,"identity":"f053ebfe-7612-4412-a483-eb69c138be16","added_by":"auto","created_at":"2021-10-26 19:17:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1366427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-781275/v1/f0bc0482-8e71-4445-b3db-1bc9814113b2.pdf"},{"id":14918445,"identity":"31798eba-3738-48d3-9ac5-bc366d8e1f4f","added_by":"auto","created_at":"2021-10-26 19:17:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1366427,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-781275/v1/bd90a5b1-0800-4015-aa1b-1a40953f71cf.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eLabel-Free Quantitative Proteomic Study of The Effect of Dihydrotestosterone (DHT) On Rat Ovarian GCs\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDihydrotestosterone (DHT) is the reductive metabolite of testosterone (T), which is mainly transformed by T in tissues [1]\u003c/p\u003e\n\u003cp\u003eIts presence in some tissues (such as the ovaries) is necessary for the development and function of the whole organ [2, 3].\u003c/p\u003e\n\u003cp\u003eThe ovarian androgens are mainly androstenedione and T; androgen is the source of estrogen in the body,\u0026nbsp;and\u0026nbsp;granulosa\u0026nbsp;cells\u0026nbsp;(GCs) can aromatize T into estrogen [4].\u0026nbsp;Unlike T, DHT\u0026nbsp;cannot\u0026nbsp;be aromatized into estrogen [5], but it can increase the activity of aromatase in GCs [6]. During follicular development, ovarian membranous interstitial cells produce T under the action of luteinizing hormone\u0026nbsp;(LH), which is then transformed into DHT by\u0026nbsp;5\u0026alpha;\u0026nbsp;reductase, is transferred to GCs, and\u0026nbsp;plays\u0026nbsp;a role through androgen\u0026nbsp;receptors\u0026nbsp;(ARs) in GCs, stromal cells and oocytes [7, 8].\u003c/p\u003e\n\u003cp\u003eThe growth of GCs is the main factor determining follicular development and ovarian function. GCs also\u0026nbsp;play\u0026nbsp;an important role in steroid secretion. These steroids are essential to the function and normal development of many organs [9, 10].\u003c/p\u003e\n\u003cp\u003eThe role of DHT in normal follicular development is mainly mediated by AR [11, 12]. AR is mainly located in GCs [13]. When AR deficiency leads to\u0026nbsp;a\u0026nbsp;decrease\u0026nbsp;in\u0026nbsp;the effect of DHT on follicles and GCs, mouse ovaries produce more atretic follicles, and the growth of follicles and GCs is slower [14-16]. When DHT was deleted by\u0026nbsp;the\u0026nbsp;androgen antagonist\u0026nbsp;flutamide [17], the proliferation of adult porcine follicles increased,\u0026nbsp;and apoptosis decreased [18-20]. In newborn pig ovaries, an excess or deficiency of androgen will lead to accelerated initial follicular recruitment,\u0026nbsp;leading to premature ovarian failure,\u0026nbsp;and\u0026nbsp;androgen excess will reduce the percentage of oocytes, increase the number of primary follicles, and increase germ cell apoptosis [21]. The above data indicate that normal DHT utility plays an important role in maintaining the normal growth of GCs and follicles.\u003c/p\u003e\n\u003cp\u003eThere have been many reports about the effect of DHT on GCs.\u0026nbsp;DHT can regulate the proliferation and apoptosis of GCs and steroid secretion in many ways, thus affecting the development of follicles.\u003c/p\u003e\n\u003cp\u003eDHT can negatively regulate the proliferation of GCs, including inducing\u0026nbsp;GC\u0026nbsp;death due to overstress of\u0026nbsp;the\u0026nbsp;endoplasmic reticulum [22] and inducing chronic inflammation by stimulating the ovary [23]. Additionally, DHT can\u0026nbsp;inhibit\u0026nbsp;the proliferation of rat GCs stimulated by FSH by inhibiting the ERK\u0026nbsp;signaling\u0026nbsp;pathway [24] and\u0026nbsp;inhibit\u0026nbsp;the proliferation of GCs by blocking the cell cycle [25].\u003c/p\u003e\n\u003cp\u003eIn contrast, other studies have found that DHT can regulate the proliferation of GCs in many ways and directly promote the growth and development of follicles and the proliferation of GCs in vitro [26]. DHT can promote the proliferation of porcine GCs by promoting mitosis [27], increase the proliferation of GCs in rhesus monkey follicles [28], and promote the proliferation of GCs induced by FSH [29].\u003c/p\u003e\n\u003cp\u003eDHT can also affect the hormone synthesis and secretion of GCs [30], inhibit the endogenous BMP signal in rat GCs in vitro and increase the production of progesterone by GCs [31]. DHT has a significant inhibitory effect on\u0026nbsp;the\u0026nbsp;estrogen\u0026nbsp;secretion of ovarian GCs through\u0026nbsp;the\u0026nbsp;androgen receptor (AR) pathway [32].\u003c/p\u003e\n\u003cp\u003eAn abnormal\u0026nbsp;increase or decrease\u0026nbsp;in\u0026nbsp;DHT can result in ovarian dysfunction and many ovarian diseases. For example,\u0026nbsp;an\u0026nbsp;increase\u0026nbsp;in\u0026nbsp;DHT can induce the formation of polycystic ovary syndrome (PCOS). High DHT is also one of the diagnostic criteria of PCOS [33, 34]. DHT can block the growth inhibitor of malignant and\u0026nbsp;nonmalignant\u0026nbsp;ovarian epithelial cells [35, 36]\u0026nbsp;and activate\u0026nbsp;the\u0026nbsp;G protein signal cascade [37], which leads to the occurrence of epithelial ovarian cancer. High DHT can also lead to chronic low-grade inflammation of the ovary, leading to ovarian dysfunction and fibrosis [23]. Knockout of AR reduced the effect of DHT on follicles and led to premature ovarian failure and a significant decrease in\u0026nbsp;the\u0026nbsp;estrous cycle and fertility in female mice [14, 16].\u003c/p\u003e\n\u003cp\u003eAlthough DHT has a very important and complex effect on the function of ovarian GCs and the occurrence of ovarian diseases, the mechanism of DHT on ovarian GCs is not very clear, so it is necessary to conduct further study. In this study,\u0026nbsp;label-free quantitative\u0026nbsp;proteomics (LFQP) [38]\u0026nbsp;was\u0026nbsp;used to study the effects of DHT on primary cultured GCs and analyze the changes\u0026nbsp;in\u0026nbsp;differentially expressed\u0026nbsp;proteins (DEPs)\u0026nbsp;and cellular\u0026nbsp;signaling\u0026nbsp;pathways. LFQP can be used to study the whole spectrum of protein changes under specific physiological conditions. It is a new protein quantitative technique and the main method used in this study. The corresponding proteins can be quantified by comparing the signal intensity of the corresponding peptides in two samples (DHT and control groups) by liquid chromatography-mass spectrometry (LC-MS). LFQP\u0026nbsp;was\u0026nbsp;used to understand the DEPs, their cellular localization and the\u0026nbsp;signaling\u0026nbsp;pathways regulated by DHT\u0026nbsp;in\u0026nbsp;GCs, and then we carried out bioinformatics analysis based on LEQP to reveal the effects of DHT on GCs.\u003c/p\u003e\n\u003cp\u003eFinally, four DEPs, namely, Mark3 (microtubule affinity-regulating kinase 3), Fth1 (ferritin heavy chain), Nqo1 (NAD(P)H dehydrogenase [quinone] 1) and Mre11a (double-strand break repair protein MRE11), were selected for western blotting, and their effects on ovarian GCs regulated by DHT were analyzed.\u003c/p\u003e"},{"header":"2 Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eMaterials and animals\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1.1 Experimental animals\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFemale\u0026nbsp;SD rats (grade SPF)\u0026nbsp;at 21\u0026nbsp;days of age were provided by the Experimental Animal Center of Ningxia Medical University (Animal Certificate No.: SCXK (Ning) 2020-0001). This experiment was conducted in strict accordance with the guidelines of\u0026nbsp;the\u0026nbsp;Ethical Review of Experimental Animal Welfare issued by the State Administration of\u0026nbsp;Quality\u0026nbsp;Supervision, Inspection and Quarantine of the\u0026nbsp;People\u0026rsquo;s\u0026nbsp;Republic of China and the State Standardization Administration of China.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1.2 The main materials and reagents are listed in the following table\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eMaterials and Reagents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eCompany\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eDHT (5\u0026alpha;-Dihydrotestosterone-D3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eSigma, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003ePMSG (pregnant mare serum gonadotropin)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eProspec-Tany, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eDMEM/F-12 culture medium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eBI, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eFetal bovine serum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eBI, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003ePenicillin streptomycin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eBI, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eWhole protein extraction kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eKeyGEN,\u0026nbsp;China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eBCA kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eKeyGEN,\u0026nbsp;China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003ePBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eHyClone, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eDithiothreitol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eSigma, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eIodoacetamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eSigma, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eSigma, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eTrypsin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003ePromega, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eFormic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eSigma, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eAcetonitrile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eFisher Chemical, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003e8-12% SDS-PAGE Gel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eKeygen, China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003ePVDF membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eMillipore, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eRabbit pAb Mark3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eAbclonal, China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eRabbit pAb Mre11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eAbclonal, China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eRabbit pAb Fth1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eAbclonal, China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eRabbit pAb Nqo1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eAbclonal, China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eRabbit pAb\u0026nbsp;\u0026beta;-Actin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eAbclonal, China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eHRP Goat Anti-Rabbit IgG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eAbclonal, China\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"70.70707070707071%\"\u003e\n \u003cp\u003eLuminous fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.292929292929294%\"\u003e\n \u003cp\u003eThermo, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1.3 Software used for analysis\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31824611032532%\"\u003e\n \u003cp\u003eAnalysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.44978783592645%\"\u003e\n \u003cp\u003eSoftware/method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.23196605374823%\"\u003e\n \u003cp\u003eVersion/URL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31824611032532%\"\u003e\n \u003cp\u003eMass spectrometry data analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.44978783592645%\"\u003e\n \u003cp\u003eMaxQuant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.23196605374823%\"\u003e\n \u003cp\u003ev.1.5.2.8 http://www.maxquant.org/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31824611032532%\"\u003e\n \u003cp\u003eGO comment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.44978783592645%\"\u003e\n \u003cp\u003eInterProScan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.23196605374823%\"\u003e\n \u003cp\u003ev.5.14-53.0 http://www.ebi.ac.uk/interpro/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31824611032532%\"\u003e\n \u003cp\u003eKEGG comment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.44978783592645%\"\u003e\n \u003cp\u003eKAAS\u003c/p\u003e\n \u003cp\u003eKEGG Mapper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.23196605374823%\"\u003e\n \u003cp\u003ev.2.0 http://www.genome.jp/kaas-bin/kaas_main\u003c/p\u003e\n \u003cp\u003eV2.5 http://www.kegg.jp/kegg/mapper.html\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31824611032532%\"\u003e\n \u003cp\u003eEnrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.44978783592645%\"\u003e\n \u003cp\u003ePerl module\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.23196605374823%\"\u003e\n \u003cp\u003ev.1.31 https://metacpan.org/pod/Text::NSP::Measures::2D::Fisher\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31824611032532%\"\u003e\n \u003cp\u003eSubcellular localization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.44978783592645%\"\u003e\n \u003cp\u003eWolfpsort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"48.23196605374823%\"\u003e\n \u003cp\u003ev.0.2\u0026nbsp;http://www.genscript.com/psot/wolfpsort.html\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"3 Experimental Method","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 Primary rat granulosa cell culture and DHT intervention\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 21-day-old SD female rats were injected intraperitoneally with PMSG (10 IU/), and the animals were killed by cervical vertebra dislocation 36 hours later. The ovaries were removed,\u0026nbsp;and the GCs were cultured in DMEM/F12 complete medium for 48 hours. The GCs were divided into the DHT group and control group.\u0026nbsp;The\u0026nbsp;DHT group was treated with DHT\u0026nbsp;(10-8\u0026nbsp;mol/L) for 3 hours, and the control group was treated with the same amount of DMEM/F12 complete medium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Protein sample preparation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter DHT intervention, the culture medium was discarded,\u0026nbsp;and\u0026nbsp;the cells were\u0026nbsp;washed with PBS three times. All the cells were scraped off with a cell scraper and centrifuged (1000 rpm/5 min). After removing the supernatant, 4\u0026nbsp;volumes\u0026nbsp;of lytic buffer\u0026nbsp;were\u0026nbsp;added, the samples were ultrasonicated, centrifuged at 12,000 g at 4\u0026deg;C for 10 min, and the cell fragments were removed. The\u0026nbsp;supernatant was transferred to a new centrifuge tube, the protein concentration was determined by\u0026nbsp;a\u0026nbsp;BCA kit, and then the protein was stored in\u0026nbsp;a\u0026nbsp;freezer at\u0026nbsp;-80\u0026deg;C. Pearson\u0026rsquo;s\u0026nbsp;correlation coefficient\u0026nbsp;and principal component analysis (PCA) statistical methods were used to evaluate the consistency of repeated samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 Liquid chromatography-mass spectrometry analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe peptides obtained after trypsin cleavage were dissolved in liquid chromatographic mobile phase A (0.1% (v/w) formic acid aqueous solution) and then separated by a NanoElute ultrahigh-performance liquid phase system. The liquid phase gradient setting was as follows: 0-70 min, 6%~22% B; 70-84 min, 22%~32% B; 84-87 min, 32%~80% B; 87-90 min, 80% B. The flowrate was maintained at 300 nL/min. The peptides were separated by ultra-high-performance liquid chromatography, injected into a capillary ion source for ionization and then analyzed by tims-TOF Pro mass spectrometry. When the ion source voltage was set to 1.4 kV, the parent ion and its secondary fragments of the peptide were detected and analyzed by TOF. The scanning range of secondary mass spectrometry was set to 100-1700 ml z. The data acquisition mode used was parallel cumulative serial fragmentation (PASEF) mode. A first-order mass spectrum was collected after 10 cycles of PASEF mode to collect the second-order spectrum with the charge number of the parent ion in the range of 0-5. The dynamic exclusion time of tandem mass spectrometry scanning was set to 24 seconds to avoid repeated scanning of the parent ion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4 Database search\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe secondary mass spectrometry data were retrieved by MaxQuant (v1.6.6.0). The search parameter settings were as follows: the database was Rattus_norvegicus_10116_PR (29,947 sequences), an anti-database was added to calculate the false positive rate (FDR), caused by random matching, and a common contamination database was added to the database to eliminate the influence of contaminated proteins in the identification results; the enzyme digestion mode was set to Trypsin/P; and the number of missing sites was set to 2. The mass error tolerance of the primary parent ion of the first search and main search was set to 40 ppm, and the mass error tolerance of the secondary fragment ion of 40 ppm was 0.02 Da. The alkylation of cysteine was set as a fixed modification, and variable modifications were set as methionine oxidation and N-terminal acetylation of the protein. The FDR of protein identification and PSM identification was set to 1%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.5 Bioinformatics analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) analysis mainly includes three aspects: cell composition (CC), molecular function (MF), and biological process (BP). The UniProt-GOA database was searched for proteomic annotation, and the enrichment of DEPs in MF and BP was further analyzed. The proteins in eukaryotic cells were located on various intracellular elements, and we used wolfpsort software to analyze the subcellular localization of DEPs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to analyze and classify the regulated signaling pathways. Through database comparison analysis, the DEPs were classified by COG/KOG (Clusters of Orthologous Groups of proteins) function statistics. Eukaryotes are generally called the KOG database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.6 Western blot\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concentration of the protein samples was determined by the BAC method. Eight-microliter protein samples were extracted and electrophoretically separated on an 8-12% SDS-PAGE gel and then transferred to PVDF membranes. After the membrane was transferred, it was sealed in 5% skim milk solution for 2 hours and incubated with rabbit pAb Mark3, Nqo1, Mre11a and Fth1 (all diluted at 1:1000) overnight at 4\u0026deg;C. After that, the corresponding secondary antibodies were incubated (diluted according to 1Suzhou 5000) for 1 hour, and then the luminescent solution was added for exposure. The relative protein gray value was analyzed by densitometry using ImageJ software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.7 Statistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the\u0026nbsp;DEP\u0026nbsp;screening,\u0026nbsp;the\u0026nbsp;relative quantitative value of each sample was taken as log2 (to make the data accord with the normal distribution),\u0026nbsp;and the\u0026nbsp;\u003cem\u003et\u003c/em\u003e-test method\u0026nbsp;was used\u0026nbsp;to calculate the \u003cem\u003eP\u003c/em\u003e value, which was statistically significant when the \u003cem\u003eP\u003c/em\u003e value was less than 0.05.\u0026nbsp;A\u0026nbsp;change\u0026nbsp;in\u0026nbsp;differential expression\u0026nbsp;of\u0026nbsp;more than 1.5 was regarded as the threshold of significant\u0026nbsp;upregulation, and\u0026nbsp;a change of\u0026nbsp;less than 0.67\u0026nbsp;was regarded\u0026nbsp;as the threshold of significant\u0026nbsp;downregulation. For the bioinformatics analysis, Fisher\u0026rsquo;s exact test\u0026nbsp;was used\u0026nbsp;to detect the enrichment of DEPs in all identified proteins,\u0026nbsp;and\u0026nbsp;\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05\u0026nbsp;was\u0026nbsp;considered to be statistically significant. For the western blotting analysis, SPSS 22.0 statistical software\u0026nbsp;was used\u0026nbsp;for analysis,\u0026nbsp;and a\u0026nbsp;\u003cem\u003et\u003c/em\u003e-test was used to calculate\u0026nbsp;the\u0026nbsp;\u003cem\u003eP\u003c/em\u003e value between groups. When \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, there was a significant difference between the two groups.\u003c/p\u003e"},{"header":"4 Result","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1 Label-free quantitative proteomic analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 383,904 secondary spectra were obtained in this study. The number of available secondary spectra was 308,609, and the utilization rate of spectra was 80.4%. A total of 50,913 peptides were identified by spectral analysis, of which 46,646 were specific peptides.\u0026nbsp;A total of\u0026nbsp;6124 proteins were identified, of which 4496 could be quantitatively analyzed\u0026nbsp;(Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eMS/MS spectrum database search analysis summary\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMS/MS spectrum database search analysis summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.983050847457626%\"\u003e\n \u003cp\u003eTotal spectrum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003eMatched spectrum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.288135593220339%\"\u003e\n \u003cp\u003ePeptides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.677966101694915%\"\u003e\n \u003cp\u003eUnique peptides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.2090395480226%\"\u003e\n \u003cp\u003eIdentified proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.175141242937855%\"\u003e\n \u003cp\u003eQuantifiable proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.983050847457626%\"\u003e\n \u003cp\u003e383,904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e308,609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.288135593220339%\"\u003e\n \u003cp\u003e50,913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.677966101694915%\"\u003e\n \u003cp\u003e46,646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.2090395480226%\"\u003e\n \u003cp\u003e6124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.175141242937855%\"\u003e\n \u003cp\u003e4496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.1.1 Differentially expressed proteins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 38 differential proteins were detected, of which 28 were\u0026nbsp;upregulated\u0026nbsp;and 10\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ewere downregulated (Table 2). In Fig. 1a, the horizontal axis is the logarithmic value of\u0026nbsp;the\u0026nbsp;protein relative quantitative value after\u0026nbsp;log2\u0026nbsp;logarithmic conversion, and the vertical axis is the logarithmic value of\u0026nbsp;the\u0026nbsp;p-value after\u0026nbsp;log10\u0026nbsp;logarithmic conversion. In\u0026nbsp;Fig. 1b\u003cem\u003e,\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003ethe red\u0026nbsp;pillar\u0026nbsp;indicates the significantly\u0026nbsp;upregulated\u0026nbsp;proteins, and the blue\u0026nbsp;pillar\u0026nbsp;indicates the significantly\u0026nbsp;downregulated\u0026nbsp;proteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Basic information of DEPs\u003c/p\u003e\n\u003ctable align=\"left\" border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein accession\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDHT/control ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubcellular localization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eA0A0A0MY00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eShort-/branched-chain-specific acyl-CoA dehydrogenase, mitochondrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eAcadsb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003emitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eA0A0G2K0Q7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eMyosin light chain kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eMylk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eA0A0G2KB92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eSerine/threonine-protein kinase DCLK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eDclk1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eD3ZBN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eHistone H1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eHist1h1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003enucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eD3ZKK3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eConsortin, connexin sorting protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eCnst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eD4A3K5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eHistone H1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e2.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eHist1h1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003enucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eD4A7G9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eSimilar to chromosome 1 open reading frame 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eRGD1564804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eD4AAL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eAdhesion G protein-coupled receptor L3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eAdgrl3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eplasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eF1LSW7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003e60S ribosomal protein L14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eRpl14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003emitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eF1M0V4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eTHO complex 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eThoc2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eF1M836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eNonspecific serine/threonine protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eMark3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003enucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eG3V781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eDouble-strand break repair protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eMre11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eG3V827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eCysteine conjugate-beta lyase 1, isoform CRA_a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eKyat1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003emitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eO88496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eVitamin K-dependent gamma-carboxylase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eGgcx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003enucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP00406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eCytochrome c oxidase subunit 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eMtco2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eplasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP05942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eProtein S100-A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eS100a4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eextracellular\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP07687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eEpoxide hydrolase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eEphx1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eendoplasmic reticulum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP09034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eArgininosuccinate synthase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eAss1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP15865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eHistone H1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eHist1h1e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003enucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP62083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003e40S ribosomal protein S7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eRps7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ6PDU7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eATP synthase subunit g, mitochondrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eAtp5mg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003emitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ6Q7Y5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eGuanine nucleotide-binding protein subunit alpha-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eGna13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eextracellular\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ6QLN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eRibosome biogenesis protein NOP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eNop53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003enucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ8R4A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eERO1-like protein alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eEro1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eextracellular\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ91ZW6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eTrimethyllysine dioxygenase, mitochondrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eTmlhe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003emitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ9R1N3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eSodium bicarbonate cotransporter 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eSlc4a7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eplasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ9R1T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eBarrier-to-autointegration factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eBanf1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eextracellular\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ9WVK7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eHydroxyacyl-coenzyme A dehydrogenase, mitochondrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eHadh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003emitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein accession\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDHT/control ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulation type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubcellular localization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eA0A0G2JSV6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eGlobin c2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eHba-a2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eD4A352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eMyelin regulatory factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eMyrf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP02091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eHemoglobin subunit beta-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eHbb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP05982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eNAD(P)H dehydrogenase [quinone] 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eNqo1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP56741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eMyosin-binding protein C, cardiac-type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eMybpc3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003emitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP62859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003e40S ribosomal protein S28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eRps28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003emitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eP68370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eTubulin alpha-1A chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eTuba1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoskeleton\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ66HI5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eFerritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eFth1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ8R4R9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eProtein phosphatase 1 regulatory subunit 14C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003ePpp1r14c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003ecytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eQ925G0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.23711340206186%\"\u003e\n \u003cp\u003eRNA-binding protein 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.24742268041237%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003eRbm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003enucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAccording to the results in Table 2, through the search and analysis of the UniProt database, compared with the control group, the proteins related to ATP binding and ATP synthesis and metabolism in the DHT intervention group were significantly upregulated, in which Mylk, Dclk1, Mtco2, ATP5mg, ATP5mg, and Ass1 were upregulated. There were significant differences in the expression of proteins related to cell growth, apoptosis and migration, in which Hist1h1b, Thoc2, Mrak3, Gna13, Mre11a, and Nop53 were upregulated and Nqo1 was downregulated. The differential expression of proteins related to RNA binding was significant, including upregulation of Rps7 and Rp114; downregulation of Rps28 and Rbm3; upregulation of Hist1h1a, Hist1h1e, and Banf1; and downregulation of Myrf among proteins related to DNA binding. The protein Ppp1r14c related to protein phosphorylation was upregulated, and Cnst was downregulated. Adgrl3 and Gna13, which regulate G protein-coupled receptors, were upregulated. The proteins Ero1a, Tm1he, and Ggcx, which are related to oxygen binding and oxidoreductase activity, were upregulated, and Hba-a2 and Hbb were downregulated. Kyat1, which is involved in biosynthesis and amino acid biosynthesis metabolism, was upregulated, and Ephx1, which is involved in intracellular aromatic compound metabolism, was upregulated. Fth1, which is related to intracellular iron homeostasis and iron transport, was downregulated. Slc4a7, which is related to ion balance, was upregulated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.2 Bioinformatics analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2.1 GO analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 2, these proteins are mainly involved in cell (23 upregulated [up] and 10 downregulated [down]), organelle (21 up, 7 down), membrane (12 up, 1 down), macromolecular complex (10 up, 5 down), membrane-enclosed lumen (10 up, 1 down), extracellular region (7 up, 4 down), cell junction (3 up), and other (1 up) in CC (Fig. 2a). For MF (Fig. 2b), they have a variety of activities, such as binding (23 up, 9 down), catalytic activity (14 up, 2 down), transporter activity (3 up, 2 down), structural molecule activity (2 up, 3 down), signal transducer activity (2 up), molecular transducer activity (1 up), electron carrier activity (1 up), nucleic acid binding transcript (1 down), molecular function regulator (1 down), and antioxidant activity (1 down). In addition, these proteins are involved in complex BPs (Fig. 2c), including cellular process (23 up, 7 down), metabolic process (18 up, 3 down), biological regulation (13 up, 6 down), single-organism process (18 up, 6 down), multicellular organismal process (11 up, 2 down), response to stimulus (10 up, 2 down), developmental process (10 up, 3 down), cellular component organization (9 up, 5 down), localization (6 up, 3 down), signaling (4 up), other (5 up), and biological adhesion (1 down). There were generally more upregulated proteins among CC, MF, and BP than downregulated proteins.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2.2 Subcellular localization of DEPs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe\u0026nbsp;performed\u0026nbsp;statistics on the subcellular structure localization of DEPs. In Table 3, 8 proteins were\u0026nbsp;upregulated\u0026nbsp;in the cytoplasm, 6 proteins were\u0026nbsp;downregulated\u0026nbsp;in the cytoplasm, and 6 proteins were\u0026nbsp;upregulated\u0026nbsp;and only 1 protein was\u0026nbsp;downregulated\u0026nbsp;in the nucleus. In mitochondria, 6 proteins were\u0026nbsp;upregulated\u0026nbsp;and 2 proteins were\u0026nbsp;downregulated. Four proteins in\u0026nbsp;the\u0026nbsp;extracellular matrix, three proteins in\u0026nbsp;the\u0026nbsp;plasma membrane, one protein in\u0026nbsp;the\u0026nbsp;endoplasmic reticulum and one protein in\u0026nbsp;the\u0026nbsp;cytoskeleton were\u0026nbsp;upregulated.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eFig. 2d shows the differential localization of DEPs in each subcellular structure after DHT intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eSubcellular localization of DEPs; the percentage is the percentage of DEPs in that subcellular localization among all identified DEPs\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" id=\"isPasted\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUP-Subcell\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of proteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e28.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eNucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e21.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eMitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e21.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eExtracellular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e14.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003ePlasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e10.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eEndoplasmic reticulum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e3.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDOWN-Subcell\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of proteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eCytoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eMitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eNucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"37.994350282485875%\"\u003e\n \u003cp\u003eCytoskeleton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.531073446327685%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.47457627118644%\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.2.3 COG/KOG function classification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThrough database comparison analysis, the DEPs were classified by COG/KOG function statistics. The upregulated proteins in COG/KOGA analysis in Fig. 3a were mainly concentrated in chromatin structure and dynamics (4); lipid transport and metabolism (3); energy production and conversion (2); amino acid transport and metabolism (2); translation ribosomal structure and biogenesis (2); replication recombination and repair (2); general function prediction only (4); signal transduction mechanisms (1); intracellular trafficking, secretion, and vesicular transport (1); transcription (1); \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; posttranslational modification, protein turnover, and chaperones (1); inorganic ion transport and metabolism (1); and cell cycle control, cell division, and chromosome partitioning (1).\u003c/p\u003e\n\u003cp\u003eThe downregulation proteins are mainly involved in energy production and conversion (1), translation ribosomal structure and biogenesis (1), general function prediction only (1), function unknown (1), cytoskeleton (1) and inorganic ion transport and metabolism (1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2.4 Functional enrichment analysis of MF/BP\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 4, the most obvious MFs of upregulated DEPs were chromatin DNA binding, vitamin binding and hydrogen ion transmembrane transporter, while the downregulated molecular functions were mainly oxygen transporter activity and oxygen binding, heme binding, tetrapyrrole binding, and iron ion binding. The most obvious BP functions of upregulated DEPs were protein-DNA complex assembly, nucleosome organization, chromatin assembly or disassembly, DNA packaging, DNA conformation change, response to corticosteroid, monovalent inorganic cation transport, response to steroid hormone, cellular ion homeostasis, ion transmembrane transport, and cation transport. The most obvious BPs of downregulated DEPs were oxygen transport, gas transport, and response to estradiol.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eTable 4.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eFunctional enrichment analysis of DEPs and the number and\u0026nbsp;degree (log2-fold enrichment) of\u0026nbsp;regulated DEPs\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional enrichment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of proteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003elog2-fold enrichment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUP-Molecular function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eChromatin DNA binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eVitamin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eHydrogen ion transmembrane transporter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eProtein C-terminus binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eCarbohydrate binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eCofactor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eIon transmembrane transporter activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eOxidoreductase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDOWN-Molecular function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eOxygen transporter activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eOxygen binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eHeme binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eTetrapyrrole binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eIron ion binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eStructural molecule activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUP-Biological processes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eProtein-DNA complex assembly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eNucleosome organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eChromatin assembly or disassembly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eDNA packaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eDNA conformation change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eResponse to corticosteroid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eMonovalent inorganic cation transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eResponse to steroid hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eCellular ion homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eCation transmembrane transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eIon transmembrane transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eCation transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-Biological processes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eOxygen transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eGas transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.186440677966104%\"\u003e\n \u003cp\u003eResponse to estradiol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.480225988700564%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2.5 Enrichment analysis of KEGG signaling pathways\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e shows the KEGG signaling pathways regulated by GCs after DHT intervention. Key regulatory protein indicates the key protein through which DHT acts on the regulated signaling pathway. The upregulated signaling pathways were mainly tryptophan metabolism; selenocompound metabolism; chemical carcinogenesis; fatty acid degradation; lysine degradation; vascular smooth muscle contraction; valine, leucine and isoleucine degradation; platelet activation; and the cGMP-PKG signaling pathway. The downregulated signaling pathways were mainly African trypanosomiasis and malaria. Fig. 3b shows the up- and downregulated signaling pathways, as well as the level of regulation.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eTable 5.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eEnrichment\u0026nbsp;analysis\u0026nbsp;of KEGG\u0026nbsp;signaling pathways\u0026nbsp;and enrichment\u0026nbsp;levels\u0026nbsp;(log2-fold\u0026nbsp;enrichment),\u0026nbsp;key\u0026nbsp;regulatory\u0026nbsp;proteins, and\u0026nbsp;protein\u0026nbsp;definitions\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKEGG pathway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey regulatory protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein definition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003elog2-fold enrichment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUP-regulated proteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eTryptophan metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eCCBL\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003efadB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003ekynurenine-oxoglutarate transaminase,\u003c/p\u003e\n \u003cp\u003eenoyl-CoA hydratase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eSelenocompound metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eCCBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003ekynurenine-oxoglutarate transaminase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eChemical carcinogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eEPHX1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCCBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003emicrosomal epoxide hydrolase,\u003c/p\u003e\n \u003cp\u003ekynurenine-oxoglutarate transaminase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eFatty acid degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eHADH\u003c/p\u003e\n \u003cp\u003efadE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003col\u003e\n \u003cli\u003ehydroxyacyl-CoA dehydrogenase,\u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003eacyl-CoA dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eLysine degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eTMLHE\u003c/p\u003e\n \u003cp\u003eHADH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003etrimethyllysine dioxygenase,\u003c/p\u003e\n \u003cp\u003e3-hydroxyacyl-CoA dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eVascular smooth muscle contraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGNA12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMYLK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eguanine nucleotide-binding protein subunit alpha-12\u003c/p\u003e\n \u003cp\u003emyosin-light-chain kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eValine, leucine and isoleucine degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eACADSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eshort-chain 2-methylacyl-CoA dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003ePlatelet activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGNA13\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMYLK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eguanine nucleotide-binding protein subunit alpha-13\u003c/p\u003e\n \u003cp\u003emyosin-light-chain kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003ecGMP-PKG signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGNA12\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMYLK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eguanine nucleotide-binding protein subunit alpha-12\u003c/p\u003e\n \u003cp\u003emyosin-light-chain kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDown-regulated proteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eAfrican trypanosomiasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eHBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003ehemoglobin subunit alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eMalaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eHBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003ehemoglobin subunit alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.3 Western blot\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected four proteins (Mre11a, Mark3, Fth1, and Noq1) that were more differentially expressed in the LFQP (more than 1.5 and less than 0.67 as the threshold for a significant difference) for western blotting experiments (Fig. 4). In GCs, Mark3 and Mre11a were significantly upregulated after DHT intervention, while Fth1 and Noq1 decreased significantly after DHT intervention. The results were consistent with that of the LFQP, which proved its accuracy.\u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eDHT, the reductive metabolite of T, is a more effective androgen in CGs than T and plays an important role in the development of CGs and the secretion of steroids, but there a comprehensive understanding of\u0026nbsp;DHT is\u0026nbsp;lacking. In this study, LFQP was used to analyze the effect of DHT on primary cultured CGs of rat\u0026nbsp;ovaries. A total of 38 proteins were found to have changed, including 28\u0026nbsp;upregulated\u0026nbsp;and 10\u0026nbsp;downregulated proteins. Among them, Mylk, Dclk1, Mtco2, ATP5mg, Hist1h1e,\u0026nbsp;and\u0026nbsp;Ass1\u0026nbsp;were\u0026nbsp;all\u0026nbsp;upregulated\u0026nbsp;after\u0026nbsp;DHT\u0026nbsp;intervention, indicating that the functions of ATP binding and anabolism were significantly enhanced after DHT treatment of CGs. The upregulation of Hist1h1b, Thoc2, Mrak3, Gna13, Mre11a,\u0026nbsp;and\u0026nbsp;Nop53 and\u0026nbsp;downregulation\u0026nbsp;of Nqo1 suggest that DHT is related to the growth, apoptosis and migration of GCs, wherein Hist1h1b,\u0026nbsp;Thoc2,\u0026nbsp;and\u0026nbsp;Mrak3 can promote cell proliferation; Mre11a,\u0026nbsp;and\u0026nbsp;Nqo1\u0026nbsp;participate\u0026nbsp;in the negative regulation of apoptosis; Gna13 affects cell differentiation, migration and formation; and Nop53 participates in the regulation of apoptosis and\u0026nbsp;the\u0026nbsp;cell cycle.\u003c/p\u003e\n\u003cp\u003eRGD1564804, Banf1, Hadh, and S100a4 were upregulated, and Myrf, Mybpc3, and Tuba1a were downregulated, indicating that DHT plays an important role in the binding function of the same protein in GCs. Rps7 and Rp114 were upregulated, and Rps28 and Rbm3 were downregulated, suggesting that DHT is involved in the regulation of RNA binding in GCs. Hist1h1a, Hist1h1e, and Banf1 were upregulated, and Myrf was downregulated, showing the effect of DHT on DNA binding in GCs through different pathways. Ppp1r14c was upregulated, and Cnst was downregulated, which revealed the effect of DHT on protein phosphorylation in GCs. Adgrl3 and Gna13 were upregulated, indicating that DHT could promote G protein-coupled receptors in GCs. Ero1a, Tm1he, and Ggcx were upregulated, and Hba-a2 and Hbb were downregulated, indicating that DHT affected oxygen binding and oxidoreductase activity in GCs. Kyat1 was upregulated, suggesting that DHT is involved in amino acid biosynthesis and metabolism in GCs. Ephx1 was upregulated, which emphasized that DHT could promote the metabolism of aromatic compounds in GCs. Fth1 was downregulated, suggesting that DHT is related to iron ion homeostasis and iron ion transport in GCs. The upregulation of Slc4a7 shows that DHT has a certain effect on the ion balance in GCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5.1 Gene Ontology (GO) functional enrichment analysis of MF\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;and\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;BP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO is a basic bioinformatics network that can define and describe the function of proteins. Bioinformatics analysis revealed the GO\u0026nbsp;terms\u0026nbsp;of regulated proteins based on MF, BP and CC. The main distribution of DEPs among MF, BP,\u0026nbsp;and\u0026nbsp;CC\u0026nbsp;was\u0026nbsp;predicted.\u003c/p\u003e\n\u003cp\u003eHere, we focused on the analysis of MF and BP. According to the experimental data in Table 3, the oxygen transport and oxygen binding functions of MF and BP decreased significantly. It has been reported that hypoxia can cause tissue inflammation, apoptosis and cell necrosis [22]. Therefore, we speculate that the significant decrease in oxygen transport capacity and oxygen-binding protein of GCs caused by DHT intervention may be one of the mechanisms of ovarian tissue damage and infertility caused by DHT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5.2 Subcellular localization of DEPs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nucleus is the regulatory center of cellular metabolism and heredity and controls the heredity, growth and development of cells. After GCs\u0026nbsp;were\u0026nbsp;treated with DHT, the DEPs in the nucleus\u0026nbsp;were\u0026nbsp;significantly\u0026nbsp;upregulated, indicating that DHT had a significant effect on the growth and development of GCs.\u003c/p\u003e\n\u003cp\u003eThe main function of mitochondria is to provide energy for cells and to control cell growth and apoptosis. The extracellular matrix is involved in controlling cell growth, shape, migration and metabolic activity. After DHT treatment of GCs, the proteins in the mitochondria and extracellular matrix were upregulated, suggesting that DHT is involved in the proliferation, migration, molding, and metabolism of GCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5.3 COG/KOG function classification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;Prokaryotic/Eukaryotic\u0026nbsp;Orthologous Groups\u0026nbsp;(COG/KOG) database covers the phylogenetic relationship of proteins encoded by the whole genome of prokaryotes and eukaryotes.\u003c/p\u003e\n\u003cp\u003eThe proteins that make up COG are assumed to be from an ancestral protein, including orthologs or paralogs. Eukaryotes are found in the KOG databases. Through database comparison and analysis, we\u0026nbsp;classified\u0026nbsp;DEPs into COG/KOG functions.\u0026nbsp;Fig.\u0026nbsp;3a\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ereveals in detail that DHT intervention in GCs has an important effect on the function, survival, growth and development and apoptosis of GCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5.4 KEGG signaling pathway analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 11 different signaling pathways were detected in which chemical carcinogenesis was upregulated, indicating that DHT has a carcinogenic effect on GCs. The upregulation of tryptophan metabolism is more obvious. Tryptophan can participate in the renewal of plasma protein in animals and can promote the role of riboflavin but can also contribute to the synthesis of nicotinic acid and heme. The promoting effect of DHT on tryptophan metabolism after intervention with GCs suggests that DHT may contribute to the synthesis of heme. The upregulation of platelet activation channels means that DHT can promote platelet activation. The upregulation of the vascular smooth muscle contraction pathway suggests that DHT may have a certain effect on vasoconstriction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5.5 Western blot and DEP analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo\u0026nbsp;further understand the specific effects of DHT on GCs and to evaluate the accuracy of the LFQP, we screened four differential proteins (Mark3, Nqo1, Mre11a, and Fth1) by western blot\u0026nbsp;analysis\u0026nbsp;according to the degree of differential expression of DEPs after intervention\u0026nbsp;with\u0026nbsp;DHT. After analysis, DEPs\u0026nbsp;promoted\u0026nbsp;cell proliferation after being regulated by DHT, and the occurrence of ovarian cancer and granulosa cell\u0026nbsp;tumors was\u0026nbsp;related to the proliferation of GCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMre11a\u003c/em\u003e\u003c/strong\u003e (double-strand break repair protein) was upregulated by DHT\u0026nbsp;(Fig.\u0026nbsp;4a). Mre11a is a component of the MRN complex, which promotes cell proliferation through homologous recombination to repair DNA double strand breaks and\u0026nbsp;negatively regulate\u0026nbsp;apoptosis.\u0026nbsp;Mre11a can resist the replication pressure induced by anticancer genes and promote the growth of cancer cells [39]. DHT can induce the occurrence of ovarian cancer [35, 40, 41]. After\u0026nbsp;DHT\u0026nbsp;intervention, mre11a\u0026nbsp;in\u0026nbsp;GCs\u0026nbsp;was\u0026nbsp;significantly\u0026nbsp;upregulated, which may be one of the mechanisms of ovarian cancer caused by DHT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMark3\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e(microtubule affinity-regulating kinase 3) can inhibit the kinase activity of STK 3/MST 2 toward LATS 1 by antagonizing the phosphorylation of LATS 1 and DLG 5, which negatively regulates the HIPPO\u0026nbsp;signaling\u0026nbsp;pathway\u0026nbsp;[42]. The role of\u0026nbsp;the\u0026nbsp;Hippo\u0026nbsp;signaling\u0026nbsp;pathway is mainly negative regulation of cell growth,\u0026nbsp;and\u0026nbsp;inhibition of\u0026nbsp;the\u0026nbsp;Hippo\u0026nbsp;signaling\u0026nbsp;pathway can promote cell proliferation. DHT obviously\u0026nbsp;upregulated\u0026nbsp;Mark3\u0026nbsp;(Fig.\u0026nbsp;4b)\u0026nbsp;and\u0026nbsp;enhanced the inhibitory effect\u0026nbsp;of Mark3 on\u0026nbsp;the\u0026nbsp;Hippo signaling pathway. The nuclear localization of\u0026nbsp;the\u0026nbsp;YAP protein downstream of\u0026nbsp;the\u0026nbsp;Hippo\u0026nbsp;signaling\u0026nbsp;pathway increases correspondingly (activated:\u0026nbsp;nonphosphorylated), and\u0026nbsp;increased nuclear localization of YAP can promote the growth of ovary and granulosa cells, which leads to granulosa cell\u0026nbsp;tumors\u0026nbsp;and ovarian cancer [43-46].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFth1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eDHT significantly\u0026nbsp;downregulated\u0026nbsp;Fth1 (ferritin\u0026nbsp;heavy chain)\u0026nbsp;(Fig.\u0026nbsp;4c). The role of Fth1 is mainly to store iron in a soluble,\u0026nbsp;nontoxic\u0026nbsp;and readily available form, which is very important for the steady state of iron [47]. Previous studies have found that there is a significant decrease in Fth1 in the GCs of female patients with infertility [48]. FTH1 can negatively regulate cell proliferation, and the cell migration and proliferation ability is significantly enhance with\u0026nbsp;inhibition of\u0026nbsp;FTH1 [49], while ovarian cancer and granulosa cell\u0026nbsp;tumors\u0026nbsp;can also enhance cell proliferation [50, 51]. Therefore, the significant inhibition of Fth1 after DHT intervention in GCs may be related to the occurrence of granulosa cell\u0026nbsp;tumors\u0026nbsp;and ovarian cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNqo1\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eAfter DHT intervention in GCs, Nqo1 (NAD(P)H dehydrogenase [quinone] 1) decreased significantly (Fig. 4d). Nqo1 is a quinone reductase, an intracellular enzyme that detoxifies quinones, is a key component of the antioxidant defense system and can protect cells from oxidative stress [52]. Previous studies have reported that NQO1 gene knockout accelerates cell proliferation and tumorigenesis [52, 53]. Therefore, the inhibitory effect of DHT on Nqo1 may promote the proliferation of GCs and the occurrence of ovarian cancer, which provides ideas for future research and the treatment of ovarian cancer and granulosa cell tumors.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThe effects of DHT on GCs were comprehensively studied by LFQP, and the possible effects of some DEPs regulated by DHT on ovarian diseases and female infertility were analyzed in detail. The purpose of this study was to provide ideas for further research on the effect of DHT on GCs, ovarian diseases and female infertility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (81160084, 81460230)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis experiment strictly abides by the guidelines for ethical review of laboratory animal welfare issued by the General Administration of quality supervision, inspection and Quarantine of the people\u0026apos;s Republic of China and the State Administration of standardization of the people\u0026apos;s Republic of China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets supporting the results of this article are included within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTairen Chen carried out most of the experiments, analyzed the data, and wrote a manuscript. Yuting Dong assisted in some experiments, editing and revising manuscripts. Mengjing Wu participated in some experiments. Qing Chang and Changchun Hei designed the experiment, supervised the research of all aspects of the experiment, and guided the interpretation of the data and the revision of the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1. \u0026nbsp;Swerdloff RS, Dudley RE, Page ST, Wang C, Salameh WA. Dihydrotestosterone: biochemistry, physiology, and clinical implications of elevated blood levels. Endocr Rev. 2017;38:220\u0026ndash;54. https://doi.org/10.1210/er.2016-1067\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp;Franks S, Hardy K. Androgen action in the ovary. 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Life Sci. 2020;248:117467. https://doi.org/10.1016/j.lfs.2020.117467\u003c/p\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":"Proteomics, Dihydrotestosterone, ovary, Granulosa cells, Female fertility","lastPublishedDoi":"10.21203/rs.3.rs-781275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-781275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDihydrotestosterone (DHT) is a main androgen in the human body. Previous reports have shown that DHT can affect the proliferation, apoptosis and estrogen and progesterone secretion of ovarian granulosa cells (GCs). An imbalance in DHT secretion leads to GC dysfunction and follicular development disorder.\u003c/p\u003e\u003cp\u003eTherefore, exploring the influence of DHT on GCs is necessary. The purpose of this study was to analyze the effect of DHT on GCs through label-free quantitative proteomics (LFQP). After primary cultured rat GCs were treated with DHT (10-8 mol/L), the effect of DHT on GCs was analyzed by LFQP, and some of the differentially expressed proteins (DEPs) were verified by western blotting.\u003c/p\u003e\u003cp\u003eA total of 6124.0 proteins were identified, of which 4496.0 were quantifiable. Compared with the control group, 28 proteins were upregulated and 10 were downregulated after DHT intervention. The subcellular localization of DEPs indicates that DHT is involved in the proliferation, migration, molding and metabolism of GCs. Gene Ontology (GO) revealed that DHT downregulated the oxygen transport capacity and oxygen-binding protein of GCs. Orthologous Groups of proteins (COG/KOG) showed that DHT had an important effect on the survival, growth and apoptosis of GCs. Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DHT promotes metabolism, amino acid degradation, chemical carcinogenesis, platelet activation and vasoconstriction in GCs. The western blot results were consistent with the proteomics results. Mark3 and Mre11a are DEPs that were upregulated, and Fth1 and Nqo1 were downregulated, which indicated that DHT could promote the proliferation of GCs.\u003c/p\u003e\u003cp\u003eThis study comprehensively analyzes the impact of DHT on GCs through LFQP and provides clues for further research.\u003c/p\u003e","manuscriptTitle":"Label-Free Quantitative Proteomic Study of The Effect of Dihydrotestosterone (DHT) On Rat Ovarian GCs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-10-26 19:17:30","doi":"10.21203/rs.3.rs-781275/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":"c6de969e-56a7-4d2c-b455-be061afb4602","owner":[],"postedDate":"October 26th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8077102,"name":"Obstetrics \u0026 Gynecology"}],"tags":[],"updatedAt":"2021-10-26T19:17:32+00:00","versionOfRecord":[],"versionCreatedAt":"2021-10-26 19:17:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-781275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-781275","identity":"rs-781275","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}