{"paper_id":"e9d7719b-b5ad-4faa-91ff-7b56392852ae","body_text":"Zheng et al. Hereditas          (2024) 161:47  \nhttps://doi.org/10.1186/s41065-024-00348-6\nRESEARCH Open Access\n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which \npermits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the \noriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or \nother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line \nto the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory \nregulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this \nlicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom-\nmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.\nHereditas\nPredicting the therapeutic role and potential \nmechanisms of Indole-3-acetic acid \nin diminished ovarian reserve based on network \npharmacology and molecular docking\nJianxiu Zheng1,2†, Liyan Wang3,4†, Ahui Liu1,2†, Haofei Shen3, Bin Wang3, Yanbiao Jiang1,2, Panpan Jing3, \nDefeng Guan3, Liulin Yu3 and Xuehong Zhang3,4* \nAbstract \nBackground Indole-3-acetic acid (IAA), an indole analog produced by intestinal microorganisms metabolizing \ntryptophan, has anti-inflammatory and antioxidant properties and thus has potential applications in ovarian protec-\ntion, although the exact mechanism is unknown. The present study preliminarily investigated the pharmacological \nmechanism of IAA in alleviating diminished ovarian reserve (DOR) by network pharmacology and molecular docking.\nMethods Relevant target proteins of IAA were searched in SwissTargetPrediction, PharmMapper, TargetNet, BAT-\nMAN-TCM, and SuperPred databases. The potential targets of DOR were obtained from GeneCards, DisGenet, OMIM, \nand Drugbank databases. Both common targets were then imported into the String website to construct a PPI net-\nwork, and these targets were analyzed for GO and KEGG enrichment. Finally, we utilized molecular docking to validate \nthe possible binding conformations between IAA and the candidate targets. We used in vitro experiments to prelimi-\nnarily investigate the effects of IAA on DOR.\nResults We obtained 88 potential targets for IAA and DOR interaction. We received 16 pivotal targets by constructed \nprotein interaction screening. KEGG enrichment analysis mainly included the AGE-RAGE signaling pathway, IL-17 \nsignaling pathway, Chemical carcinogenesis—reactive oxygen species in diabetic complications, etc. GO functional \nanalysis showed that IAA treatment of DOR may involve biological processes such as response to external stimuli, \nhypoxia, gene expression, and regulation of enzyme activity. Molecular docking and in vitro experiments further \nrevealed the potential effects of IAA on MMP2, TNF-α, AKT1, HSP90AA1, and NF-κ B.\nConclusion We preliminarily revealed the potential protective effects of IAA against DOR through multiple tar-\ngets and pathways, which provides a new research strategy for the molecular mechanism of IAA to alleviate DOR \nin the future. However, further studies need to demonstrate whether IAA can be used as a compound to prevent \nand treat DOR.\nKeywords Indole-3-acetic acid (IAA), Diminished ovarian reserve, Network pharmacology, Molecular docking, \nMolecular dynamics simulation\n†Jianxiu Zheng, Liyan Wang and Ahui Liu contributed equally to this work.\n*Correspondence:\nXuehong Zhang\nzhangxueh@lzu.edu.cn\nFull list of author information is available at the end of the article\n\nPage 2 of 17Zheng et al. Hereditas          (2024) 161:47 \nIntroduction\nDiminished ovarian reserve (DOR) is defined as a \ndecrease in the number and quality of oocytes [1]. DOR is \na significant challenge in the current fertility field, mainly \ndue to its poor outcome in assisted reproduction, which \nis primarily characterized by low ovarian responsive -\nness to ovarian stimulation in vitro fertilization-embryo \ntransfer, low egg acquisition rate, low fertilization rate \nand an increased risk of early miscarriage and embryo \ntermination [2–4]. The pathogenesis of DOR is currently \nunknown, and it may be related to inherited genetic \nmutations, DNA methylation levels, mitochondrial dys -\nfunction, infection, and medical or environmental factors \n[5, 6]. The age of patients with DOR is gradually getting \nyounger. DOR has become a popular research area in the \nfield of reproductive health, but there is no optimal treat-\nment option for patients with DOR [7].\nGranulosa cells can provide oocyte nutrients and sign -\naling molecules through gap linkage and transzonal pro -\njection (TZP) and secrete paracrine signals to influence \noocyte maturation and meiosis [8, 9]. ROS positively \naffects oocyte development and ovulation, but oxidative \nstress occurs when there is an imbalance between ROS \nand antioxidant systems. Oxidative stress is one of the \ncauses of ovarian dysfunction, which can lead to follicular \natresia and abnormal meiosis of oocytes, shortened chro -\nmosomal telomeres, reduced embryonic developmental \npotential, and, ultimately, low fertility [10]. Maintaining \nthe balance of the body’s oxidative and antioxidant sys -\ntems, supplementation with antioxidants may attenuate \na series of injuries caused by excessive accumulation of \nROS and positively affect the quality of oocytes and ovar -\nian reserve function.\nIndole-3-acetic acid (IAA) is a plant growth regula -\ntor; however, it has been demonstrated that mammalian \nintestinal microorganisms metabolize tryptophan to pro -\nduce IAA, which can be absorbed into the bloodstream \nto reach various organs and tissues. IAA has anti-inflam -\nmatory and antioxidant effects in tissues and cells associ -\nated with nonalcoholic fatty liver disease [11], ankylosing \nspondylitis [12], and antioxidant dental pulp stem cells \n[13]. At the same time, our previous study found signifi -\ncantly lower levels of tryptophan and its indole metabo -\nlites IAA and IPA in the follicular fluid of infertile women \nwith decreased ovarian reserve function compared to \ninfertile women with normal ovarian reserve function \n[14]. However, the relevant role and biological mecha -\nnisms regarding IAA in ovarian reserve hypoplasia have \nnot been studied.\nNetwork Pharmacology is a multidisciplinary research \nfield based on systems biology, genomics, proteomics, \nand other disciplines, which is a method to explore new \ndrug targets and molecular mechanisms by combining \ncomputer analysis simulation with in  vivo and in  vitro \nexperiments and integrating a large amount of informa -\ntion [15]. This research explores the potential targets \nand molecular mechanisms of IAA to improve ovarian \nreserve based on network pharmacology. The experimen-\ntal flow is shown in Fig. 1.\nMaterials and methods\nIAA target prediction\nThe SMILES numbers and 2D structures of IAA were \nobtained in PubChem (https:// pubch em. ncbi. nlm. nih. \ngov/) [16]. In SwissTargetPrediction (http:// swiss targe \ntpred iction. ch/) [17], PharmMapper (http:// www. lilab- \necust. cn/ pharm mapper) [18], TargetNet (http:// targe \ntnet. scbdd. com) [19], BATMAN-TCM (http:// bionet. \nncpsb. org. cn/ batman- tcm/ index. php) [20], SuperPred \n(http:// predi ction. chari te. de) [21]with “Homo sapiens” \nas the query condition to obtain IAA gene targets, and \nthen UniProt (https:// www. unipr ot. org) [22]was used to \nstandardize the IAA gene target names.\nDOR target acquisition\nUsing diminished ovarian reserve as the query condition, \nwe searched relevant target genes in GeneCards (https:// \nwww. genec ards. org/) [23], DisGenet (https:// www. disge \nnet. org/) [24], OMIM (https:// www. omim. org) [25], and \nDrugbank (https:// go. drugb ank. com) [26], selected tar -\nget genes with high degree of correlation with the dis -\nease, and then standardized the IAA gene target names \nthrough UniProt.\nMapping Venn diagrams and PPI protein interaction \nnetworks\nUse Venny 2.1.0 (https:// bioin fogp. cnb. csic. es/ tools/ \nvenny) to map the intersecting targets of IAA and DOR \nand obtain the intersecting gene targets. The above \nintersecting gene targets were imported into STRING \n(https:// cn. string- db. org) [27]network, Homo sapiens \nwas selected as the species, the minimum required inter -\naction threshold was set to medium confidence (medium \nconfidence 0.4), and the isolated targets with no connec -\ntion were removed. The intersecting gene targets with \nhigh correlation were imported into Cytoscape 3.10.1 to \nvisualize the protein interactions network diagram. The \nintersecting gene targets greater than or equal to the \nmedian of degree, closeness centrality, and betweenness \ncentrality were selected.\nGO and KEGG signaling pathway enrichment analysis\nThe key intersecting targets were imported into the \nDAVID(https:// david. ncifc rf. gov) [28]data platform to \nobtain gene ontology (GO) functional analysis and Kyoto \nEncyclopedia of Genes and Genomes (KEGG) signaling \n\nPage 3 of 17\nZheng et al. Hereditas          (2024) 161:47 \n \npathway enrichment analysis, and then the results were \nimported into the bioinformatics.com.cn(http:// www. \nbioin forma tics. com. cn) for data visualization.\nFunctional module analysis\nThe intersecting genes were subjected to func -\ntional module analysis using the MCODE plugin in \nCytoscape 3.10.1 software, and the highest weighted \nnodes were taken as significant nodes based on the cri -\nteria set by the module. Subsequently, GO and KEGG \nenrichment analyses were done on the target proteins \nobtained from module aggregation.\nFig. 1 Workflow chart\n\nPage 4 of 17Zheng et al. Hereditas          (2024) 161:47 \nMolecular docking analysis\nThe sdf file of the 2D structure of IAA (Compound \nCID: 802) was obtained from the PubChem (https:// \npubch em. ncbi. nlm. nih. gov) database, and the OpenBa-\nbel-3.1.1 software was used to convert the sdf format to \nmol2 format. The structures of the core target proteins \nin the PPI network diagram were obtained in the RCSB \nPDB(https:// www. rcsb. org) database and dewatered and \nremoved from unnecessary ligands using PyMOL (ver -\nsion 2.5) software. Processing, such as hydrogenation, \nstance parameter adjustment, etc., were performed and \nconverted to PDBQT format using Autodock Tools-1.5.7, \nGasteiger charges were calculated, and binding pock -\nets were identified. Molecular docking calculations were \nperformed using AutoDock Vina, and the docking results \nwere visualized using PyMOL and PLIP [29]. The stability \nof ligand-receptor binding was determined based on the \nAffinity (kcal/mol) value generated by docking.\nMolecular dynamics simulation\nAfter molecular docking, we chose AKT1, which has the \nstrongest binding activity to IAA, for molecular dynamics \nsimulations. Molecular dynamics simulations were per -\nformed using the GROMACS package (version 2022.3), \nwith the Amber99sb-ildn force field in Gromacs for \ntarget proteins and the GAFF force field for small mol -\necules to obtain the appropriate molecular parameters. \nThe simulation conditions were carried out at a static \ntemperature of 300 K and atmospheric pressure (1 Bar). \nAmber99sb-ildn was used as a force field, water mole -\ncules were used as a solvent (Tip3p water model), and the \ntotal charge of the simulation system was neutralized by \nadding an appropriate number of Na + ions. The steep -\nest descent method is first used to minimize the energy, \nand thereafter, 100,000 steps of isothermal isovolumetric \ntethered (NVT) and isothermal isobaric tethered (NPT) \nequilibrium are carried out with a coupling constant of \n0.1 ps and a duration of 100 ps, respectively. Finally, the \nfree molecular dynamics simulation was performed. The \nprocess consisted of 5,000,000 steps, the step length was \n2  fs, and the total duration was 100  ns. We calculated \nthe root mean square deviation (RMSD) and root mean \nsquare fuctuation (RMSF) values to assess the stability \nand fexibility of the complexes. The binding free energy \nvalues and interactions of ligands with proteins were cal -\nculated by the MM/GBSA method [30].\nCell line culture\nKGN cells were cultured in Procell’s KGN cell-specific \nmedium (DMED/F12) (Procell, China), which contains \n10% fetal bovine serum and 1% penicillin/streptomycin \nin a humidified incubator with 5% CO2.KGN cells were \nused from passages 3–20.\nCell proliferation assays\nAccording to the instructions, survival cell viability was \nassessed using the Cell Counting Kit-8 (CCK8; AbMole, \nUSA) kit. After drug treatment, 10 μl of CCK8 solution \nwas added to each well of the culture plate and placed \nin an incubator to culture the cells for 2  h, followed by \nabsorbance detection at 450 nm. This absorbance is pro -\nportional to cell viability and allows quantitative determi-\nnation of cell viability.\nWestern blotting\nCells were lysed in RIPA tissue lysate (Biosharp, China) \ncontaining PMSF, phosphorylated protein inhibitor (A/B) \n(Servicebio, China) on ice for 15 min, followed by centrif-\nugation at 12,000 rpm for 10 min. Protein samples were \nelectrophoresed on 8% or 10% polyacrylamide gels and \nthen transferred onto 0.22um PVDF (Immobilon Trans -\nfer Membrane) membranes, which were closed for 2  h \nat room temperature with 5% milk configured in Tween \n20 (TBST) with GAPDH (proteintech, 60,004–1-IG), \nHSP90AA1 (wanleibio, WL01763), AKT-1 (proteintech, \n10,176–2-AP), MMP2 (proteintech,10,373–2-AP), TNF-\nalpha (wanleibio, WL01581), and NF-κB (wanleibio, \nWL01917) were incubated overnight at 4 °C. After being \nwashed three times with TBST, the cells were incubated \nwith HRP-conjugated secondary antibody at room tem -\nperature for 2  h and then immersed in an ECL lumi -\nnescent solution (Xin Saimei, China) and exposed to a \nBIO-RAD instrument (Bio-Rad, USA). Finally, the bands \nwere analyzed for gray value using Image J.\nReal‑time quantitative PCR (RT‑qPCR)\nTotal RNA from KGN cells was extracted using M5 HiPer \nTotal RNA Extraction Reagent (TRIgent) (Mei5bio, \nChina) reagent. Extracted RNA was reverse transcribed \nusing FastKing One-Step De-genomic cDNA First Strand \nSynthesis Premix Reagent (KR118) (TIANGEN, China). \nReal-time quantitative PCR (RT-qPCR) was performed \nusing SuperReal Fluorescence Quantitative Premix Rea -\ngent Enhanced Kit (SYBR Green) (FP205) (TIANGEN, \nChina) and AB Applied Biosystems instrument (ABI, \nUSA). The primer sequences are shown in Table 1.\nStatistical analysis\nData are represented as means ± SD with the student’s \nt-test used to calculate statistical significance between \ngroups. P < 0.05 were considered significant.\nResults\nRetrieval of target genes for indole‑3‑acetic acid \nand diminished ovarian reserve\nAfter removing duplicates, 414 indole-3-acetic acid target \ngenes were retrieved by searching SwissTargetPrediction, \n\nPage 5 of 17\nZheng et al. Hereditas          (2024) 161:47 \n \nPharmMapper, TargetNet, BATMAN-TCM, and Super -\nPred databases (Supplementary file 1). After eliminating \nduplicates, 864 diminished  ovarian reserve gene tar -\ngets were retrieved by searching GeneCards, DisGenet, \nOMIM, and Drugbank databases (Supplementary file 2).\nObtaining intersecting gene targets of drugs and diseases \nand constructing PPI\n88 intersecting gene targets were obtained by Venny \n2.1.0 (Fig.  2A). These 88 intersecting gene targets were \nimported into the String database. The confidence value \nwas set to 0.4, and the remote targets were excluded from \nobtaining the PPI network graph (Fig.  2B). Subsequently, \nthe topological parameters of each node protein were \nanalyzed, and 16 central genes were obtained by screen -\ning based on the average values of Degree, Betweenness, \nand Closeness (35, 72.06897, and 0.605249, respectively) \n(Supplementary file 3). They included TNF, ALB, AKT1, \nIL1B, EGFR, HIF1A, PTGS2, PPARG, NFKB1, ICAM1, \nSRC, HSP90AA1, MMP2, SERPINE1, SIRT1, ACE \n(Fig.  2C). In addition to this, to more intuitively under -\nstand the pharmacological effects of IAA against DOR, \ndisease-intersecting gene target-drug maps were drawn \nby Cytoscape 3.10.1 (Fig.  2D). Green squares represent \ndrugs, red squares represent diseases, and blue circles \nrepresent intersecting target proteins.\nGO enrichment analysis\nThe 88 intersected genes were imported into the DAVAD \ndatabase for GO enrichment analysis. A total of 364 bio -\nlogical processes (BP), 52 cell components (CC), and 76 \nmolecular functions (MF) were obtained with P < 0.05 \nas the screening condition. The results of screening the \ntop 10 of the three entries according to the P value and \nthe number of enriched genes are shown in Fig.  3A. \nSubsequently, GO chord plots were plotted for the first \nfive enriched BPs (Fig.  3B). The BP results showed that \nthe effects of IAA on DOR were correlated with the \nmechanisms of response to xenobiotic stimulus, posi -\ntive regulation of gene expression, cellular response to \nlipopolysaccharide, response to hypoxia, negative regu -\nlation of apoptotic process, and mechanism of aging. In \naddition, 10 of the 16 core hub genes were also enriched \nin the first 5 BPs, including EGFR, NF-κB, PTGS2, SRC, \nHIF1A, PPARG, TNF-α, ALB, MMP2, and AKT1. The \nmolecular functions are mainly related to a series of bio -\nlogical functions such as enzyme binding, endopeptidase \nactivity, zinc ion binding, peptidase activity, and protein \nkinase activity. Meanwhile, cellular components such as \nthe extracellular region, macromolecular complex, cyto -\nplasm, and plasma membrane were also annotated using \nGO analysis.\nKEGG enrichment analysis\nKEGG enrichment analysis of 88 therapeutic targets \nyielded 123 pathways (P < 0.05). After data screen -\ning, we identified the top 20 pathways of IAA for DOR \n(Fig. 3C), which were mainly enriched in the AGE-RAGE \nsignaling pathway in diabetic complications、IL-17 \nsignaling pathway、 Chemical carcinogenesis—reactive \noxygen species、Endocrine resistance、Relaxin signaling \npathway、Estrogen signaling pathway 、PI3K-Akt sign -\naling pathway、Insulin resistance、TNF signaling path -\nway 、MAPK signaling pathway、Apoptosis. In addition, \nto further understand the possible signaling pathways by \nwhich IAA affects DOR, we did a KEGG analysis of the \n16 core gene targets, whose top 10 enriched pathways \nalso included the AGE-RAGE signaling pathway in dia -\nbetic complications, the TNF signaling pathway, the IL- \n17 signaling pathway (Fig.  3D). Meanwhile, to visualize \nthe critical targets of action in the first five enrichment \npathways involved in the regulation of DOR by IAA, we \ntherefore constructed the Target-KEGG pathway net -\nwork (Fig. 3E-F).\nFunctional module‑based network analysis\nTo fully understand the biological function of common \ntargets of IAA mitigation DOR, we analyzed the two \nassociated gene clusters (Cluster 1 and Cluster 2) using \nthe “MCODE” module in Cytoscape 3.10.1 (Fig. 4A). The \nKEGG-enriched pathways of the two clusters were fur -\nther analyzed. It was found that the enriched pathways of \ncluster 1 (Fig.  4B) mainly included the AGE-RAGE sign -\naling pathway in diabetic complications、IL-17 signaling \nTable 1 Primer sequences\nGene name Forward Reverse\nGAPDH GGA AGC TTG TCA TCA ATG GAA ATC TGA TGA CCC TTT TGG CTC CC\nTNF-α GCT GCA CTT TGG AGT GAT CG ATG AGG TAC AGG CCC TCT GA\nAKT1 ATG AGG TAC AGG CCC TCT GA CCC GGT ACA CCA CGT TCT TCT \nHSP90AA1 GAA GGA ATT TGA GGG GAA GAC TTT A TGC CAT GTA ACC CAT TGT TGAG \nNF-κB TGT AAC TGC TGG ACC CAA GGAC CAA ATA GGC AAG GTC AGG GTG \nMMP2 AGT GGA TGA TGC CTT TGC TCG CAA GGT CCA TAG CTC ATC GTCAT \n\nPage 6 of 17Zheng et al. Hereditas          (2024) 161:47 \nFig. 2 Venn diagram and PPI network of potential targets. A Venn diagram showing the common genes between IAA and DOR. B PPI network \nof potential targets. C 13 hub genes of IAA against DOR were identified by network topological parameters analysis (Degree, Closeness, \nand Betweenness). D IAA-target genes-DOR network\n\nPage 7 of 17\nZheng et al. Hereditas          (2024) 161:47 \n \npathway、TNF signaling pathway、Relaxin signaling \npathway. The enriched pathways in module 2 (Fig.  4D) \nwere mainly Endocrine resistance、Thyroid hormone \nsignaling pathway、PI3K-Akt signaling pathway、VEGF \nsignaling pathway、FoxO signaling pathway (Supple -\nmentary file 4). The critical gene targets involved in the \nvarious pathways of gene cluster 1 were NK-κB1, AKT1, \nTNF, and IL-1β (Fig.  4C), whereas gene family 2 focused \non related genes such as FGFR, HSP90AA1, IGF1R, \nMAPK14, and SOD2 (Fig. 4E).\nDisease‑pathway‑target‑drug network map\nCytoscape 3.10.1 was used to construct a DOR-target \ngene-pathway-IAA network graph, which consisted of \nFig. 3 The GO and KEGG enrichment analysis for common targets. A GO enrichment analysis (The top 10 enriched terms of each part). B The \ntop 5 of the biological processes. C KEGG enrichment analysis of therapeutic targets (the top 20 enriched pathways). D KEGG enrichment analysis \nof 16 core gene targets (the top 10 enriched pathways). E–F Target-KEGG pathway network. The orange V-shaped nodes represent the pathways, \nand the green square-shaped nodes represent the targets\n\nPage 8 of 17Zheng et al. Hereditas          (2024) 161:47 \n110 nodes (where yellow nodes represent diminished \novarian reserve, red nodes represent pathways, green \nnodes represent mutually shared intersecting gene \ntargets, and dark green represents Indole-3-acetic acid) \nand 235 interaction links (Fig.  5). These targets are dis -\ntributed in different pathways, and their interactions \nFig. 4 A Function modules-based network analysis. cluster1 in red, and cluster2 in green. B KEGG enrichment analysis of cluster1 (the top 10 \nenriched pathway). C Target-KEGG pathway network(cluster1). The pink V-shaped nodes represent the pathways, and the green square-shaped \nnodes represent the targets. D KEGG enrichment analysis of cluster2 (the top 10 enriched pathway). E Target-KEGG pathway network(cluster2). The \npink V-shaped nodes represent the pathways, and the green square-shaped nodes represent the targets\n\nPage 9 of 17\nZheng et al. Hereditas          (2024) 161:47 \n \ntogether determine the mechanism of IAA treatment of \nDOR. The core pathway-specific information is shown in \nTable 2.\nMolecular docking results\nTo explore the possible role of IAA in target proteins \nassociated with diminished ovarian reserve, we used \na molecular docking approach to investigate the bind -\ning affinity of the top 16 potential core gene targets in \nthe PPI network to IAA. It is generally accepted that the \nlower the binding energy, the more stable the receptor \nand ligand binding conformation. A less than −5 kCal/\nmol binding energy indicates a strong affinity between \nthe two. The following five target proteins were pre -\ndicted to be most likely to bind to IAA according to the \nbinding affinity from high to low (Table  3), including \nAKT1(4EJN), MMP2(8H78), HSP90AA1(4BQG), TNF-\nα(7TA6), and NF-κB(1SVC). This suggests the possibility \nTable 2 The KEGG results\nPathway number Pathway Count p‑value\nhsa05417 Lipid and atherosclerosis 20 4.5349E-14\nhsa05200 Pathways in cancer 26 4.43861E-12\nhsa04933 AGE-RAGE signaling pathway in diabetic complications 13 9.21905E-11\nhsa04657 IL-17 signaling pathway 12 7.83043E-10\nhsa05415 Diabetic cardiomyopathy 15 3.82524E-09\nhsa05418 Fluid shear stress and atherosclerosis 13 4.39125E-09\nhsa05208 Chemical carcinogenesis - reactive oxygen species 14 1.08079E-07\nhsa05215 Prostate cancer 10 2.32979E-07\nhsa01522 Endocrine resistance 10 2.54636E-07\nhsa04926 Relaxin signaling pathway 11 2.62398E-07\nhsa04625 C-type lectin receptor signaling pathway 10 4.2515E-07\nhsa04915 Estrogen signaling pathway 11 4.61036E-07\nhsa05142 Chagas disease 9 4.09918E-06\nhsa04151 PI3K-Akt signaling pathway 15 4.38456E-06\nhsa04936 Alcoholic liver disease 10 5.82325E-06\nhsa04931 Insulin resistance 9 6.29293E-06\nhsa04668 TNF signaling pathway 9 9.4085E-06\nhsa04932 Nonalcoholic fatty liver disease 10 1.18876E-05\nhsa04010 MAPK signaling pathway 13 1.84147E-05\nhsa04210 Apoptosis 9 3.41578E-05\nFig. 5 IAA-target genes-pathway-DOR network\n\nPage 10 of 17Zheng et al. Hereditas          (2024) 161:47 \nthat IAA affects DOR by regulating the activity of these \nproteins. Molecular docking visualization results are \nshown in Fig.  6(A-E). For example, IAA forms hydrogen \nbonds with MMP2 at amino acid residues THR-144 and \nTHR-146 and has hydrophobic interactions with THR-\n146 and LEU-138.\nMolecular dynamic simulation analyses\nRMSD is an index to assess the structural changes of \nproteins. As shown in Fig.  7A, the RMSD of the small \nmolecule protein complex fluctuated smoothly and main-\ntained in a small range after 20  ns, indicating that the \nbinding of AKE1 to IAA was relatively stable. RMSF is \nan indicator for assessing the dynamics of proteins. The \nresults showed that the RMSF values of amino acid resi -\ndues fluctuated less in the regions of 190–290 and 390–\n410  ps (Fig.  7B). In addition, the results of MM/GBSA \nshowed that the free energy of binding of IAA to AKT1 \nprotein was −28.5 ± 0.57 kcal/mol (Table 4).\nTable 3 The molecular docking parameters and results\nSerial \nnumber\nTargets PDB ID Box_center(x,y,z)/Å Affinity(kcal/\nmol)\n1 AKT1 4EJN -1.54,6.34,-12.65 -7.14\n2 MMP2 8H78 12.70,18.66,7.33 -6.47\n3 TNF-α 7TA6 0.25,14.84,20,45 -5.99\n4 HSP90AA1 4BQG 1.59,16.21,20.45 -5.42\n5 NF-κB 1SVC 0.257,14.84,20.45 -5.28\nFig. 6 Molecular docking of the top five hub targets with IAA. A The binding poses of MMP2 complexed with IAA. B The binding poses of NF-κB \ncomplexed with IAA. C The binding poses of AKT1 complexed with IAA. D The binding poses of HSP90AA1 complexed with IAA. E The binding \nposes of TNF-α complexed with IAA. (Blue Line—Hydrogen Bonding, Yellow line-hydrophobic interactions, Orange line—π-stacking)\n\nPage 11 of 17\nZheng et al. Hereditas          (2024) 161:47 \n \nEffect of IAA on the viability of KGN cells\nKGN cells were pretreated with 0, 20, 40, 80, 160, 240, \n320, and 640 μ M concentrations of IAA (Aladdin, China, \npurities:98%, CAS No.: 87–51-4, Item No.: I101074) for \n24 h and 48 h, followed by detection of KGN cell viabil -\nity by CCK8. At concentrations lower than 240 μ M for \n24 h and 48 h, IAA had no significant effect on the via -\nbility of KGN cells (Fig.  8 A-B). In addition, KGN cells \nwere treated with 0- 900 μ M of tert-Butyl hydroperoxide \nsolution (TBHP (MACKLIN, China, concentration,70% \nin H2O) for 4 h, as shown in Fig.  8C. Finally, we selected \na 600 μ M concentration of TBHP to model oxidative \nstress. We used IAA to pretreat KGN followed by TBHP \naction, and cell viability was increased in the 40 μ M IAA \nTable 4 Binding free energy calucations by MM/GBSA(kcal/mol)\nVDWAALS Van der Waals energy, ΔEEL Electrostatic energy, ΔEGB Polar solvation \nenergy, ΔESURF Non polar solvation energy, ΔGgas Molecular mechanics term \nenergy, ΔGsolv Solvation energy, ΔGMMGBSA Binding free energy\nEnergy AKT1‑\nindole‑3‑\nacetic acid\nVDWAALS -32.73±0.05\nΔE EL -26.65±0.50\nΔE GB -34.71±0.27\nΔE SURF -3.38±0.05\nΔG gas -59.38±0.50\nΔG solv -31.33±0.27\nΔG MMGBSA -28.05±0.57\nFig. 7 Molecular dynamics simulation between IAA and AKT1 protein. A RMSD.B RMSF\n\nPage 12 of 17Zheng et al. Hereditas          (2024) 161:47 \npretreated group compared to the model group (p < 0.01) \n(Fig. 8D).\nIAA intervention affects the expression level of each target\nThe gene and protein expression levels of HSP90AA1, \nMMP2, TNF, AKT1, and NFκ-B were examined in KGN \nafter treatment with IAA and TBHP . RT-qPCR results \nshowed that compared with the model group, the expres-\nsion levels of HSP90AA1 and AKT1 were increased in \nKGN after the administration of IAA (p < 0.05), whereas \nthe expression levels of TNF, and NFκ-B significantly \ndecreased (p < 0.05) (Fig.  9A). Western blot analysis \nshowed that the protein expression levels of these mol -\necules were also altered (Fig. 9B).\nDiscussion\nDiminished ovarian reserve (DOR) has a complex etiol -\nogy and unknown pathogenesis, and its incidence has \nbeen on the rise in recent years. Currently, there is a \nlack of effective methods to treat DOR, so seeking effec -\ntive treatments for DOR has become one of the current \nresearch hotspots in the reproductive neighborhood \n[7]. Tryptophan is an essential amino acid in humans, \nobtained only through diet, and is widely involved in \nvarious physiological processes such as protein synthe -\nsis, inflammatory response, oxidative stress, and intesti -\nnal homeostasis. Tryptophan has three main metabolic \npathways in the human body: the kynurenine pathway, \nthe 5-hydroxytryptamine pathway, and the indole path -\nway, of which intestinal microorganisms metabolize \nthe indole pathway. IAA is an indole-ring containing \nmetabolite produced by intestinal microorganisms to \nmetabolize tryptophan, and it has anti-inflammatory \nand antioxidant properties. In rats, L. rhamnosus may \nexert anti-inflammatory effects by up-regulating trypto -\nphan metabolism and increasing the IAA content to treat \nacne vulgaris [31]. In cellular experiments, IAA attenu -\nated LPS-induced inflammatory responses and free radi -\ncal production in RAW264.7 macrophages by inducing \nheme oxygenase-1 (HO-1) and directly neutralizing free \nradicals [32].\nInterestingly, our previous study found that IAA levels \nin follicular fluid (FF) were significantly lower in patients \nwith DOR than in controls and suggested that IAA may \nbe a potential biological marker or overprotective agent \nfor DOR [14]. Overall, it is reasonable to assume that IAA \nmay play a potential role in improving ovarian function. \nHowever, the biological pathways and specific mecha -\nnisms of its action are still unclear. In this study, for the \nfirst time, the mechanism of action of IAA on DOR was \nrevealed using systematic network pharmacology and \nbioinformatics, 16 hub target genes were obtained, and \nfurther molecular docking was performed. According to \nnetwork pharmacology and molecular docking results, \nFig. 8 Effect of IAA on the viability of KGN cells. A-B The viability of KGN cells was assessed using the CCK8 assay after 24 h and 48 h of IAA \nat the corresponding concentrations. C The viability of KGN cells was assessed using CCK8 assay after TBHP treatment. D Viability of KGN cells \nafter IAA and TBHP treatment. (n = 6), *p < 0.05, **p < 0.01\n\nPage 13 of 17\nZheng et al. Hereditas          (2024) 161:47 \n \nTNF-α, NF-κ B, AKT1, MMP2, and HSP90AA1 play cru -\ncial roles in improving DOR by IAA.\nTumor necrosis factor (TNF-α) is an essential fac -\ntor that participates in and maintains the inflammatory \nresponse in  vivo, and TNF-α induces the expression of \ninflammatory genes to directly drive the inflammatory \nresponse or indirectly by causing cell death [33]. TNF-α \nis widely expressed in the reproductive system, includ -\ning ovarian granulosa and endometrial cells. Its expres -\nsion is subject to highly integrated endocrine, paracrine, \nand autocrine mechanisms. Its expression is regulated \nby highly integrated endocrine, paracrine, and autocrine \nmechanisms. It is crucial in maintaining granulosa cell \nsurvival, regular ovulation, follicular development, and \natresia [34, 35]. However, imbalanced TNF-α expression \nis closely associated with polycystic ovary syndrome, \ndiminished ovarian reserve function, ovarian senes -\ncence, and granulosa cell apoptosis [36–41]. A previous \nstudy showed that the expression of TNF-α and related \npro-inflammatory factors was significantly increased \nin the follicular fluid of patients with diminished ovar -\nian reserve (DOR) compared to those with usual ovar -\nian reserve (NOR) [39]. Existing studies have shown that \nintervention treatment with IAA significantly reduces \nthe expression level of TNF-α in NAFLD, dental pulp \nstem cells, and rat model of acne vulgaris [31], thereby \nattenuating the inflammatory response and oxidative \nstress levels [11, 42]. In the present study, IAA advance \nintervention significantly reduced the expression level of \nTNF-α in oxidative stress-impaired KGN cells.\nFig. 9 IAA intervention affects the expression level of each target. A mRNA expression level of TNF-α, HSP90AA1, AKT1, NF-κB, MMP2 in KGN \nafter IAA intervention. B The protein expression level of HSP90AA1, MMP2, AKT1, NF-κB, and TNF-α in KGN after IAA intervention. Data are expressed \nas mean ± scale (n = 3): *p < 0.05, **p < 0.01\n\nPage 14 of 17Zheng et al. Hereditas          (2024) 161:47 \nNF-κB can be activated by various pathological factors \nand is involved in regulating the expression of inflamma -\ntory factors and various enzymes involved in amplifying \nthe inflammatory response cascade (e.g., COX-2, iNOS, \netc.) [43]. Abnormal activation of the NF-κB signaling \npathway in  vitro experiments involves oxidative stress, \napoptosis, and other biological processes in granulosa \ncells, closely related to decreased ovarian function and \npremature ovarian aging [44, 45]. The intervention of \nIAA could reverse the expression of NF-κB in the mod -\neled KGN cells, and it was hypothesized that IAA might \nattenuate the ovarian inflammatory response by down-\nregulating the NF-κB signaling pathway, thereby protect -\ning the ovarian function.\nAKT1 is a serine/threonine protein kinase widely \nexpressed in human primordial follicles, follicles at all \nstages of growth, and granulosa cells. It involves bio -\nlogical processes such as primordial activation, second -\nary follicle development, and oocyte survival [46, 47]. \nIt has been reported that AKT1 plays an essential role \nin regulating ovarian growth and maturation and that \nAKT1 deficiency leads to premature ovarian failure in \nfemale mice [48]. Abnormal PI3K/AKT signaling path -\nway is closely related to pathological changes in the \novary. Reduced activity of the PI3K/AKT pathway pro -\nmotes the translocation of Bax to the mitochondria, fol -\nlowed by the release of cytochrome C, which triggers \napoptosis through the caspase pathway [49], resulting \nin impairment of ovarian function. Early intervention \nof IAA alleviated the H2O2-induced elevation of AKT \nexpression levels to a certain extent or may play a positive \nrole in mitigating DOR by influencing the expression of \nother molecules through AKT1. Meanwhile, the molec -\nular docking results showed that hydrogen bonds were \nformed between ASP274 amino acid residues on AKT1 \nand hydrophobic forces were generated by residues GLU-\n198, TYR-272, ASP274, and ASP292, which are located in \nthe catalytic structural domains of AKT1, and may have a \nspecific effect on the activity of the AKT1 kinase; there -\nfore, we hypothesized that IAA could improve the ovar -\nian function by regulating AKT levels to improve ovarian \nfunction, but further experimental verification is needed.\nMMP2 is a ubiquitin metalloproteinase involved in \nvarious biological functions in the human body, such as \nvascular system remodeling, angiogenesis, tissue repair, \ntumor infiltration, inflammation, and atherosclerotic \nplaque rupture. Matrix metalloproteinases (MMP) play \nan essential role in the dynamic process of folliculogen -\nesis [50], while hormone levels regulate the type of MMP \nexpression. Studies have shown that MMP2 is the most \nabundant MMP in cat follicles [51], and secretion of \nMMP2 and MMP9 by granulosa cells to catabolize type \nVI collagen components of the extracellular matrix plays \na vital role in tissue remodeling during follicular growth \nand development especially in the formation and expan -\nsion of sinusoidal lumen in the ovarian mound complex \nand ovulation [51, 52]. A study showed that MMP2 is \nmore expressed in the ovarian mound and granulosa \ncells in the presence of reduced ovarian response and \ndecreased fertilization [53]. Similarly, another study \nshowed elevated levels of MMP2 expression in senescent \nporcine granulosa cells [54]. This suggests that MMP2 \nmay be a potential target for ovarian dysfunction, and in \nthis study, IAA early intervention alleviated some of the \nH2O2-induced elevated expression levels of MMP2.\nHeat shock protein 90 (Hsp90) is a well-characterized \nmolecular chaperone protein. Unlike the constitutive \nexpression of Hsp90β, Hsp90α (HSP90AA1) is the stress-\ninduced isoform [55], which binds to phospholipids to \nstabilize the cell membrane structure [56], resists cellu -\nlar stress and helps normal cells to fold proteins into the \ncorrect spatial structure during heat stress thereby exe -\ncuting the appropriate biological functions. Hsp90AA1 \ncan maintain cell survival during heat stress by inhibit -\ning apoptosis and increasing cell autophagy [57]. A study \nfound that a decrease in Hsp90AA1 decreases the rate of \nin vitro maturation, cleavage, and blastocyst formation in \nbovine oocytes, as well as increasing cleavage rates and \naffecting embryo quality [58]. Hsp90 plays a vital role in \nsperm development, and males of certain trans-heterozy-\ngous combinations with mutant Hsp90AA1 alleles have \nbeen described to exhibit sterility and show disruption \nof meiosis in Drosophila [59]. In mice, the Hsp90 protein \nassists tNASP (testicular histone-binding protein) fold -\ning into the correct spatial structure and performing the \nappropriate function [60–62]. In the present study, IAA \nearly intervention alleviated the H2O2-induced decrease \nin the expression level of HSP90AA1 compared to the \nmodel group.\nIn conclusion, there is no direct evidence from animal \nand clinical studies that IAA can improve DOR through \nthe above five targets; however, the molecular docking \nresults of the present study confirmed that IAA has good \nbinding ability to all five of the six targets mentioned \nabove, and molecular dynamics simulations further \nproved the stability of the binding between AKT1 and \nIAA. Combined with the results of the in  vitro experi -\nments and its physiological functions in the ovary, we \nhypothesized that IAA could benefit the ovary by affect -\ning these targets and pathways. These results can pro -\nvide a preliminary reference and basis for future primary \nresearch.\nWe used a network pharmacology approach to inves -\ntigate the molecular mechanisms by which IAA affects \nDOR. Eighty-eight common targets and 16 core target \nproteins were analyzed by GO enrichment, and these \n\nPage 15 of 17\nZheng et al. Hereditas          (2024) 161:47 \n \ngenes were mainly involved in biological processes such \nas inflammatory response, regulation of cell proliferation \nand apoptosis, hypoxia response, regulation of enzyme \nactivities, and signal transduction. In addition, KEGG \npathway analysis showed that the AGE-RAGE signaling \npathway in diabetic complications, IL-17 signaling path -\nway, 、Chemical carcinogenesis—reactive oxygen spe -\ncies were the three most enriched signaling pathways.\nThe interaction of advanced glycosylation end products \n(AGEs) and their receptor RAGE can activate various \nsignaling pathways, such as NF-κB, MAPK, and PI3K-\nAKT-mTOR, leading to inflammatory responses, oxida -\ntive stress, and other adverse effects, which are closely \nrelated to ovarian aging [63]. Qiao-Li Zhang et al. found \nthat, compared with mice on a regular diet, female mice \non a high-sugar diet showed disturbed estrous cycles, \nsignificantly reduced numbers of antral and sinus folli -\ncles, and inhibited primary follicular growth [64]. Simi -\nlarly, human IVM oocytes on a high-sugar diet showed a \nsignificantly lower rate of first-polar body extrusion and \nabnormal levels of DNA methylation [65]\nInflammatory responses play a critical role in physi -\nologic events such as the female menstrual cycle, embryo \nimplantation, pregnancy, and childbirth and are intri -\ncately related to folliculogenesis and ovulation. Dysregu -\nlation of these regulatory processes can lead to a decline \nin oocyte quality and affect fertility [66]. Ovarian aging \nis closely related to inflammation, and Carolina Lliberos \net  al. [67] found that serum concentrations of several \npro-inflammatory cytokines (TNF-α, IL-6, inflammatory \nvesicle genes ASC and NLRP3) and mRNA levels in the \novary increased significantly with age in mice. Also, the \nlevels of pro-inflammatory factors such as IL-1β, IL-6, \nand IL-21 were increased, and the level of anti-inflam -\nmatory factor IL-4 was decreased in the follicular fluid \nof POI patients compared to healthy controls [68]. IL-17 \nhas been reported to stimulate the release of inflam -\nmatory factors such as IL-6, TNF-α, IL-β, IFN-γ, etc., \nthrough multiple cascade effects of the MAPK pathway \n(including p38, ERK, and INK pathways) and the NF-κB \nsignaling pathway. In addition, it was found that IL-17A/\nIL-6 axis expression was up-regulated in a cyclophospha -\nmide (CTX)-induced model of premature ovarian failure \nin rats, significantly regulating the activities of REK1/2 \nand MEK1/2 [69].\nIn addition to the above 2 pathways, we also paid par -\nticular attention to the role of Chemical carcinogene -\nsis—reactive oxygen species signaling in IAA anti-DOR. \nNormal levels of ROS in ovarian tissues play an essential \nregulatory role in normal follicular development, steroid \nhormone synthesis, and angiogenesis. However, exces -\nsive accumulation of ROS causes DNA damage in gran -\nulosa cells, reduction of base oxidation and gene repair, \nmitochondrial dysfunction, abnormal meiosis in oocytes, \nand shortening of telomerase, which leads to apoptosis of \nGC cells, follicular atresia, and reduction of oocyte qual -\nity, which in turn leads to ovarian aging and diminished \novarian reserve function. This leads to GC cell apoptosis, \nfollicular atresia, and oocyte quality reduction, leading \nto ovarian senescence and diminished ovarian reserve \nfunction [70]. Meanwhile, high levels of ROS can mediate \nmultiple signaling pathways, such as phosphatidylinositol \n3-kinase/protein kinase B (PI3K/Akt), mitogen-activated \nprotein kinase (MAPK), and Kelch-like ECH-associated \nprotein 1 (Keap1)-like protein, nuclear factor erythroid \n2-related factor 2 (Nrf2)-antioxidant response elements \n(ARE), NF-κB signaling pathway, and FOXO axis, which \nare involved in biological processes such as ovarian oxi -\ndative stress injury, granulosa cell apoptosis, follicular \natresia, and diminished ovarian reserve function [43]. \nExcessive ROS accumulation leads to ovarian dysfunc -\ntion; thus, it is crucial to maintain a dynamic balance \nbetween ROS and antioxidant systems by reducing ROS \nproduction and increasing the activity of endogenous \nantioxidant systems on the one hand, and supplement -\ning with exogenous antioxidants to enhance the ability to \nscavenge excess oxygen radicals produced on the other. \nPrevious studies have shown that IAA has been found \nto have anti-inflammatory and antioxidant effects in tis -\nsues such as the liver and kidney, and combined with the \nabove analyses, we initially hypothesized that IAA may \nbe able to treat DOR by exerting anti-inflammatory and \nantioxidant effects.\nConclusions\nIn conclusion, through network pharmacology and \nmolecular docking, we have elucidated the possible \nmolecular mechanisms of IAA to treat diminished ovar -\nian reserve and identified some of its target actions that \nmay benefit DOR. However, these data support the idea \nthat additional in  vivo and in  vitro experiments are still \nneeded to further elucidate its function and mechanism \nof action.\nSupplementary Information\nThe online version contains supplementary material available at https:// doi. \norg/ 10. 1186/ s41065- 024- 00348-6.\nSupplementary Material 1.\nSupplementary Material 2.\nSupplementary Material 3.\nSupplementary Material 4.\nSupplementary Material 5.\nAcknowledgements\nThe authors would like to thank all authors of references.\n\nPage 16 of 17Zheng et al. Hereditas          (2024) 161:47 \nAuthors’ contributions\nJXZ, AHL, and LYW were involved in data analysis and writing of the original \nmanuscript. BW, HFS and LLY were involved in literature collection and chart-\ning. YBJ, PPJ, and DFG were involved in language editing. XHZ was involved \nin the design of the study and critically revised the original manuscript. All \nauthors critically reviewed and approved the final manuscript.\nFunding\nThis study was supported by grants from Hospital Fund of the First Hospital of \nLanzhou University (ldyyyn2020-42); Lanzhou Chengguan District Science and \nTechnology Plan Project (2023–11-3); Major Cultivation Project for Research \nand Innovation Platforms in Universities of Gansu Province (2024CXPT-16); \nGansu Provincial Department of Education 2024 Innovation Fund Project for \nCollege Teachers (2024B-014); Gansu Province Health Care Industry Research \nProgram Projects (GSWSHL2022-07).\nData availability\nNo datasets were generated or analysed during the current study.\nDeclarations\nEthics approval and consent to participate\nNot applicable.\nConsent for publication\nNot applicable.\nCompeting interests\nThe authors declare no competing interests.\nAuthor details\n1 Lanzhou University, Chengguan District, No. 222 Tian Shui South Road, Lan-\nzhou, Gansu 730000, People’s Republic of China. 2 The First School of Clinical \nMedicine, Lanzhou University, Chengguan District, No. 1, Dong Gang Xi Road, \nLanzhou, Gansu 730000, People’s Republic of China. 3 The First Hospital of Lan-\nzhou University, Chengguan District, No. 1 Dong Gang Xi Road, Lanzhou, \nGansu 730000, People’s Republic of China. 4 Key Laboratory for Reproductive \nMedicine and Embryo, Gansu Province, Lanzhou, People’s Republic of China. \nReceived: 9 September 2024   Accepted: 10 November 2024\nReferences\n 1. 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