Research ArticleWhat Changed on the Folliculogenesis in the Process ofMouse Ovarian Aging
Wenlei Ye 1 Wei Shen 1 Wei Yan 1 Su Zhou 1 Jing Cheng 12 Guangxin Pan 13
Meng Wu 1 LingweiMa 1 Aiyue Luo 1 and ShixuanWang 1
1Department of Obstetrics and Gynecology Tongji Hospital Tongji Medical College Huazhong University of Science and TechnologyWuhan Hubei China2Department of Obstetrics and Gynecology Zhongnan Hospital of Wuhan University Wuhan University Wuhan Hubei China3Department of Obstetrics and Gynecology The Central Hospital of Wuhan Tongji Medical CollegeHuazhong University of Science and Technology Wuhan Hubei China
Correspondence should be addressed to Aiyue Luo aiyueluo163com and Shixuan Wang shixuanwangtjhtjmueducn
Received 16 November 2018 Revised 15 January 2019 Accepted 30 January 2019 Published 1 April 2019
Academic Editor Siddharth Pratap
Copyright copy 2019 Wenlei Ye et alThis is an open access article distributed under theCreative CommonsAttribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
There are about 1-2million follicles presented in the ovary at birth while only around 1000 primordial follicles are left atmenopauseThe ovarian function also decreases in parallel with aging Folliculogenesis is vital for ovarian function no matter the synthesis offemale hormones or ovulation yet the mechanisms for its changing with increasing age are not fully understood Early folliclegrowth up to the large preantral stage is independent of gonadotropins in rodents and relies on intraovarian factors To furtherunderstand the age-related molecular changes in the process of folliculogenesis we performed microarray gene expression profileanalysis using total RNA extracted fromyoung (9weeks old) and old (32weeks old)mouse ovarian secondary folliclesThe results ofour currentmicroarray study revealed that there were 371 (ge2-fold q-value le005) genes differentially expressed in which 174 geneswere upregulated and 197 genes were downregulated in old mouse ovarian secondary follicles compared to young mouse ovariansecondary follicles The gene ontology and KEGG pathway analysis of differentially expressed genes uncovered critical biologicalfunctions such as immune system process aging transcription DNA replication DNA repair protein stabilization and apoptoticprocess were affected in the process of agingThe considerable changes in gene expression profile may have an adverse influence onfollicle quality and folliculogenesis Our study provided information on the processes that may contribute to age-related decline inovarian function
1 Introduction
Ovarian aging results in the cessation of ovarian functionthat is anovulation and a decrease in gonadal steroidssecretion The anovulation causes loss of fertility andreduced hormone production results in multiple healthconsequences including vasomotor symptoms cardiovascu-lar disease osteoporosis cognitive dysfunction depressionmood disorders sexual dysfunction vaginal atrophy andeven mortality [1 2] The age at which natural menopauseoccurs may be a marker of ovarian aging which is consideredto be the multiple pacemakers [3]
Ovarian follicle is the basic unit of ovarian physiolog-ical function After puberty the periodic development ofthe ovarian follicles enables the ovary to produce female
hormones to maintain secondary sexual characteristics andovulation The reproductive aging process is considered tobe dominated by the gradual decrease of both the quantityand the quality of the oocytes residing within the folliclespresent in the ovarian cortex [4] Females have approximately1-2 million primordial follicles at birth [5 6] After birth thenumber of follicles decreases gradually with increasing agethrough atresia with some 300000 to 400000 primordialfollicles remaining at menarche [4 7] During the reproduc-tive years the number of primordial follicles declines untila critical threshold when only about 1000 left at the time ofmenopause [8ndash10]
The information on the hormonal changes observedgradual decline of the follicle pool and the reduced oocytequality during ovarian aging is quite a bit however the
HindawiBioMed Research InternationalVolume 2019 Article ID 3842312 10 pageshttpsdoiorg10115520193842312
2 BioMed Research International
molecular mechanisms behind that are still not fully under-stood Studies have shown that accumulation of reactiveoxygen species (ROS) and free radicals and the action of envi-ronmental factors such as radiation and chemotherapeuticdrugs used in cancer patients can cause DNA damage in theoocytes during long periods of dictyate arrest and withoutrepairing the extent of DNA damage may cause genomicabnormalities (chromosomal breakages andmutations) lead-ing to cell death and follicle atresia [11 12] Researches haverevealed that the expression of BRCA1 (breast cancer type 1)related DNA repair decreased in the process of ovarian agingin rat and buffalo primordial follicles [13 14] Laboratory andclinical studies also demonstrated that expression of BRCA1declines in single mouse and human oocytes and BRCA1mutation is associated with primary ovarian insufficiency[15ndash17]
A better understanding of follicle biology is essentialto help make ovarian aging process explicit Early folliclegrowth up to the large preantral stage is independent ofgonadotropins in rodents and relies on intraovarian fac-tors [6] Thus in our present study the secondary folliclesfrom young and old mice ovaries were used to investigatethe changes of expression profile during ovarian aging bygenome-wide microarray analysis
2 Materials and Methods
21 Isolation of Secondary Follicles from the Mouse OvaryThe experimental animals were maintained as per theguidelines of the Animal Care Committee of Tongji Hos-pital within the Tongji Medical College at the HuazhongUniversity of Science and Technology in China The 9-week old and 32-week old specific pathogen-free (SPF)female C57BL6J mice were obtained from Beijing VitalRiver Laboratory Animal Technology Co Ltd (BeijingChina) All mice were killed by decapitation and ovarieswere collected free of adhering tissue Under the stereomi-croscope follicles with diameter of 120-140 120583m an intactbasal membrane a central and spherical oocyte surroundedby granulosa cells were mechanically dissecting by 2 syringeneedles The ovarian secondary follicles were stored atminus80∘C
22 RNA Isolation and Microarray Analysis The total RNAof the ovarian secondary follicles was extracted with RNAisoplus reagent according to the manufacturerrsquos instructions(Takara Japan) Of the total of 6 samples 3 replicate sampleswere from young mouse ovarian secondary follicles and 3replicate samples were from old mouse ovarian secondaryfollicles All RNA samples were stored in DEPC in orderto prevent RNA degeneration GeneChip hybridization foreach sample was examined on Affymetrix 3rsquo IVT ExpressionArrays (MouseGenome 430 20 array) at Bioassay Laboratoryof CapitalBio Corporation The technical procedures andquality controls were performed at the CapitalBio Corpo-ration Hybridization assay procedures were as describedin the GeneChip Expression Analysis Technical Manual(httpwwwaffymetrixcom)
23MicroarrayData Analysis Theraw data frommicroarrayanalysis was normalized using robust multiarray average(RMA) algorithm The differentially expressed genes with afold change ge2 and a q-value le005 were identified usingSignificant Analysis of Microarray (SAM) software Forvisualization of differentially expressed genes unsupervisedhierarchical clustering was performed using HemI 1037software (httphemibiocuckooorgdownphp) [18] GeneOntology (GO) consisting of three items molecular func-tions biological processes and cellular components [19]and Kyoto Encyclopedia of Genes and Genomes (KEGG)a set of high-throughput genes and protein pathways [20]analyses of differentially expressed genes were performedusing the DAVID online tools (httpsdavidncifcrfgov)compared with the mouse whole genome [21] Whole MouseGenome was used as the reference group Statistical sig-nificance was calculated with a standard hypergeometricequation corrected by a Benjamini Yekutelli correction formultiple testing which takes into account the dependencyamong the GO categories The minimal length of consideredGO-paths was 2 Significance was set at corrected p-valuelt 005 The Search Tool for the Tetrieval of InteractingGenes (STRING) database (httpstring-dborg) an onlinesoftware that provides comprehensive interactions of listsof proteins and genes was used to build a PPI network ofthe differentially expressed genes [22] The cut-off criteriaof the minimum required interaction score were 07 forthe PPI network The visualizing of the PPI network wasconstructed using the Cytoscape software (version 361) [23]The Clustering with Overlapping Neighborhood Expansion(ClusterONE) plug-in for Cytoscape was used to detectprotein complexes in the PPI network [24] The gene regu-latory network modeling for selected differentially expressedgenes was performed using Cytoscape software (version361)
3 Results
31 Global Gene Expression Analysis of Secondary Folliclesfrom Mouse Ovaries To characterize the genes that areassociated with mouse ovarian aging we examined the geneexpression profile of secondary follicles from young andold mouse ovaries The expression values of all the sixsamples (three samples each from young and old mouseovaries) were normalized using the robustmultiarray average(RMA) method The results of our microarray data weremade available in the public domain NCBI-GEO repository(accession ID GSE121493) The box-whisker plot analysisof normalized data showed uniform distribution of theexpression levels in both intra- and intersample mannerindicating reliable hybridization (see Figure 1) Summarystatistics showed effectiveness of quantile normalization as50th percentile values were close to 49 After normal-ization of raw data for all three biological replicates theR package significance analysis of microarray (SAM) wasused to identify genes that are differentially expressed insecondary follicles from young and old mouse ovaries (foldchange ge2 or le05 and q-value le005) And the results
BioMed Research International 3
Old
minus1
Old
minus2
Old
minus3
Youn
gminus1
Youn
gminus2
Youn
gminus3
2
4
6
8
10
12
14
Sample
Log2
(int
ensit
y)
Figure 1 Box and whisker plot (Box plot) We performed the comparison of gene expression with a total of six samples from young (n=3)and old (n=3) mouse ovarian secondary follicles by using Affymetrix 3rsquo IVT Expression Arrays (Mouse Genome 430 20 array) at BioassayLaboratory of CapitalBio Corporation Robust multiarray average (RMA) algorithm was used to eliminate the variation in the arrays fromnoisy data Box plot was constructed to illustrate the distribution of normalized probe hybridization signal intensities (log ratios) for all sixarrays (young and old mouse ovarian secondary follicles) The probe distribution by 0-100 quantiles as whiskers the 25-75 quantiles asdifferent color boxes and the 50 quantile as horizontal line within the box indicated a similar range of signal intensities and confirmedperfect hybridization
revealed that 371 genes were differentially expressed betweenthe two groups while 174 genes were upregulated and197 genes were downregulated in the secondary folliclesfrom the old mouse ovaries compared to those from theyoung mouse ovaries Further unsupervised hierarchicalclustering analysis using the HemI 1037 software showeddistinct patterns of up- and downregulated genes in thesecondary follicles from young and old mouse ovaries (seeFigure 2)
32 Functional Annotation for the Differentially ExpressedGenes The identified differentially expressed genes in thesecondary follicles from the old mouse ovaries comparedto those from the young mouse ovaries were further ana-lyzed via gene ontology (GO) and KEGG pathway analysisusing the DAVID online tool As shown in Table 1 GOterm enrichment analysis showed that the upregulated geneswere significantly enriched in immune system process inthe biological processes category cytoplasm and nucleusin the cellular component category and RNA and DNAbinding in the molecular function category While listedin Table 2 the functional annotation for the downregu-lated genes revealed that the most significant categoriesof biological process were involved in transcription andits regulation cellular component was involved in nucleusand cytoplasm and molecular function was involved in
protein RNA and DNA binding Furthermore KEGG path-way analysis showed that most of the upregulated genestook part in virus related and Toll-like receptor signalingpathways whereas downregulated genes mainly participatedin PI3K-Akt signaling pathway and Adherens junction inTable 3
33 Protein-Protein Interaction and Gene Regulation NetworkAnalysis In total 187 nodes and 572 edges were mappedin the PPI network of identified differentially expressedgenes using STRINGwith the minimum required interactionscore gt 07 (Figure 3) The 10 nodes with highest degreewere regarded as hub genes STAT1 IFIT1 IFIT3 IRF7USP18 OASL2 IFIT2 UBC DDX58 IFIH1 There were 12modules generated by ClusterONE with p-value lt 005 Themost significant module with p-value lt 0001 contained 35nodes and 286 edges (Figure 4) The 10 genes listed aboveapart from UBC were included in the module In additionto the 9 genes other nodes in the module were RSAD2IFI47 TRIM30A PARP14 PARP9 IFI44 RTP4 GBP7ISG15 IRF9 GBP6 GBP3 IGTP HERC6 IRGM1 DHX58IIGP1 OAS2 DDX60 CXCL10 ZBP1 GBP2 EIF2AK2IRGM2 IFI35 and TLR3 And all genes in the modulewere upregulated As shown in the gene regulatory networkmodeling for selected genes many differentially expressedgenes such as BRCA1 STAT3 JUN AKT1 SEPRING1
4 BioMed Research International
Old-1 Old-2 Old-3 Young-1 Young-2 Young-3
1088
992
896
800
704
608
512
416
320
224
Figure 2 Heat map visualization for selected differentially expressed genes Heat map was produced by unsupervised hierarchical-clusteringanalysis frommicroarraydata for top 50upregulated genes and top 50downregulated genes in oldmouse ovarian secondary follicles comparedto young The relative expression levels of each gene are mentioned in different colors The red lines represent high expression while bluelines represent low expressionThe numbers in the right side corresponding to the different colors represent the relative expression levels ofeach gene
BioMed Research International 5
Table 1 Functional enrichment analysis of upregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P ValueGOTERM BP DIRECT GO0035458simcellular response to interferon-beta 15 86 562E-21GOTERM BP DIRECT GO0009615simresponse to virus 18 103 673E-21GOTERM BP DIRECT GO0051607simdefense response to virus 21 121 115E-19GOTERM BP DIRECT GO0002376simimmune system process 24 138 101E-15GOTERM BP DIRECT GO0045087siminnate immune response 23 132 270E-14GOTERM BP DIRECT GO0071346simcellular response to interferon-gamma 9 52 180E-08GOTERM BP DIRECT GO0042832simdefense response to protozoan 6 34 160E-06GOTERM BP DIRECT GO0006952simdefense response 8 46 136E-05GOTERM BP DIRECT GO0032870simcellular response to hormone stimulus 6 34 157E-05GOTERM BP DIRECT GO0034097simresponse to cytokine 7 40 179E-05GOTERM CC DIRECT GO0020005simsymbiont-containing vacuole membrane 5 29 146E-07GOTERM CC DIRECT GO0005829simcytosol 28 161 700E-05GOTERM CC DIRECT GO0005737simcytoplasm 65 374 661E-04GOTERM CC DIRECT GO0005634simnucleus 58 333 275E-03GOTERM CC DIRECT GO0048471simperinuclear region of cytoplasm 12 69 823E-03GOTERM CC DIRECT GO0072562simbloodmicroparticle 5 29 134E-02GOTERM CC DIRECT GO0031225simanchored component of membrane 5 29 163E-02GOTERM CC DIRECT GO0009897simexternal side of plasma membrane 7 40 223E-02GOTERM MF DIRECT GO0003725simdouble-stranded RNA binding 9 52 262E-08GOTERM MF DIRECT GO0003690simdouble-stranded DNA binding 11 63 333E-08GOTERM MF DIRECT GO0003924simGTPase activity 11 63 181E-06GOTERM MF DIRECT GO0005525simGTP binding 12 69 676E-05GOTERM MF DIRECT GO0008134simtranscription factor binding 10 57 597E-04GOTERM MF DIRECT GO0003723simRNA binding 15 86 940E-04GOTERM MF DIRECT GO0003677simDNA binding 25 144 158E-03GOTERM MF DIRECT GO0001730sim21015840-51015840-oligoadenylate synthetase activity 3 17 248E-03GOTERM MF DIRECT GO0016817simhydrolase activity acting on acid anhydrides 3 17 248E-03
GOTERM MF DIRECT GO0001077simtranscriptional activator activity RNA polymerase IIcore promoter proximal region sequence-specific binding 8 46 265E-03
lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
TCF3 MAP3K7 and IRF7 took part in various pathways(Figure 5)
4 Discussion
Elucidating the mechanism of ovarian aging has significantmeanings to female health The gradual decrease of boththe quantity and the quality of the oocytes surrounded bythe granulosa cells in all stages of follicles dominates thereproductive aging [4] In previous study Govindaraj et alrevealed that gene expression patterns changed considerablyin the rat primordial follicles in the process of ovarianaging [25] Folliculogenesis in the ovary is a highly dynamicand periodic process regulated by both intra- and extra-oocyte factors [26] At each reproductive cycle activatedprimordial follicles join the growing pool transiting to pri-mary follicles [26] Through further development a primaryfollicle grows into a secondary follicle [26] And these stagesare gonadotropin independent but depend on the complex
bidirectional communication between the oocyte and thesomatic cells [26] In the subsequent stages of folliculogenesisthe presence of pituitary gonadotropins follicle-stimulatinghormone (FSH) and luteinizing hormone (LH) are required[26] So the secondary follicles from the mouse ovaries wereselected as research objective in our study
Gene expression profile of the secondary follicles from theyoung and old mouse ovaries was compared by microarrayanalysis to find what changed in the process of ovarian agingin the present studyThe results of our research found that 174genes were upregulated and 197 genes were downregulated inthe secondary follicles from the old mouse ovaries comparedto those from the young mouse ovaries
FurtherGOandKEGGpathway analyses were performedto study the function of the differentially expressed genesThe result of GO analysis showed that the upregulated geneswere mainly involved in biological process such as immunesystem process and defense response while downregulatedgenes were closely related to gene transcription and cell
6 BioMed Research International
Table 2 Functional enrichment analysis of downregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueGOTERM BP DIRECT GO0006355simregulation of transcription DNA-templated 42 213 702E-05GOTERM BP DIRECT GO0016311simdephosphorylation 8 41 984E-05GOTERM BP DIRECT GO0006351simtranscription DNA-templated 35 178 319E-04GOTERM BP DIRECT GO0045893simpositive regulation of transcription DNA-templated 16 81 556E-04GOTERM BP DIRECT GO0030335simpositive regulation of cell migration 9 46 887E-04GOTERM BP DIRECT GO0006470simprotein dephosphorylation 7 36 239E-03
GOTERM BP DIRECT GO0043154simnegative regulation of cysteine-type endopeptidaseactivity involved in apoptotic process 5 25 421E-03
GOTERM BP DIRECT GO0030177simpositive regulation of Wnt signaling pathway 4 20 561E-03GOTERM BP DIRECT GO0043065simpositive regulation of apoptotic process 10 51 585E-03GOTERM BP DIRECT GO0097194simexecution phase of apoptosis 3 15 692E-03GOTERM CC DIRECT GO0005634simnucleus 106 538 323E-13GOTERM CC DIRECT GO0005737simcytoplasm 100 508 449E-08GOTERM CC DIRECT GO0005654simnucleoplasm 44 223 926E-08GOTERM CC DIRECT GO0070062simextracellular exosome 50 254 319E-06GOTERM CC DIRECT GO0043234simprotein complex 19 96 326E-05GOTERM CC DIRECT GO0071013simcatalytic step 2 spliceosome 6 30 224E-03GOTERM CC DIRECT GO0005925simfocal adhesion 11 56 430E-03GOTERM CC DIRECT GO0005829simcytosol 29 147 576E-03GOTERM CC DIRECT GO0030529simintracellular ribonucleoprotein complex 9 46 117E-02GOTERM CC DIRECT GO0005911simcell-cell junction 7 36 126E-02GOTERM MF DIRECT GO0005515simprotein binding 77 391 699E-09GOTERM MF DIRECT GO0044822simpoly(A) RNA binding 30 152 252E-06GOTERM MF DIRECT GO0003723simRNA binding 20 102 371E-04GOTERM MF DIRECT GO0003677simDNA binding 35 178 423E-04GOTERM MF DIRECT GO0004725simprotein tyrosine phosphatase activity 7 36 462E-04GOTERM MF DIRECT GO0046982simprotein heterodimerization activity 15 76 777E-04GOTERM MF DIRECT GO0000166simnucleotide binding 35 178 978E-04GOTERM MF DIRECT GO0016791simphosphatase activity 7 36 129E-03GOTERM MF DIRECT GO0019903simprotein phosphatase binding 6 30 202E-03GOTERM MF DIRECT GO0005524simATP binding 28 142 262E-03lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
apoptosis However there was an unexpected phenomenonin the results of our functional enrichment Many upregu-lated genes were involved in response to virus and interferonin the biological process and took part in several virusrelated signal pathway This phenomenon revealed that theSPF mice used in our study might infect some virusesHowever the certain thing is that the immune related genescan be expressed in ovarian granulosa cells not only inimmune cells Several earlier studies indicated that virusescan induce innate immune response in granulosa cells andperturb ovarian function in mouse [27 28] And immuneresponse genes were overexpressed with increasing age asshowed by several microarray studies of aging [29] Recentlythe concept that innate immunity is an essential requisitein the ovulation process is forwarded [30] The importantrole of innate immune cells in decreasing the senescence
burden was also recognized [31] There was a probabilitythat innate immune related genes were upregulated in theprocess of aging and affected the progress of ovarian agingYet the actual role of innate immunity in the processof ovarian aging or folliculogenesis needs to be furtherresearched
We conducted protein-protein interaction network anal-ysis of differentially expressed genes The nodes regarded ashub genes weremostly involved in innate immune system Asin the gene regulatory network many differentially expressedgenes between young and old mouse ovarian secondaryfollicles such as BRCA1 STAT3 JUN AKT1 SEPRING1TCF3 MAP3K7 and IRF7 showed their genetic interactionsby various pathways Thus a number of pathways wereinteracted through many genes in the process of ovarianaging
BioMed Research International 7
Table 3 Pathway enrichment analysis of differentially expressed genes in old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueUp-regulatedKEGG PATHWAY mmu05164Influenza A 15 86 225E-11KEGG PATHWAY mmu05168Herpes simplex infection 15 86 313E-10KEGG PATHWAY mmu05162Measles 11 63 623E-08KEGG PATHWAY mmu05160Hepatitis C 11 63 623E-08KEGG PATHWAY mmu04380Osteoclast differentiation 8 46 440E-05KEGG PATHWAY mmu05161Hepatitis B 8 46 112E-04KEGG PATHWAY mmu04622RIG-I-like receptor signaling pathway 6 34 158E-04KEGG PATHWAY mmu04668TNF signaling pathway 7 40 169E-04KEGG PATHWAY mmu04620Toll-like receptor signaling pathway 6 34 993E-04KEGG PATHWAY mmu05133Pertussis 5 29 238E-03Down-regulatedKEGG PATHWAY mmu04151PI3K-Akt signaling pathway 14 71 414E-04KEGG PATHWAY mmu05200Pathways in cancer 14 71 131E-03KEGG PATHWAY mmu04550Signaling pathways regulating pluripotency of stem cells 8 41 169E-03KEGG PATHWAY mmu04520Adherens junction 6 30 203E-03KEGG PATHWAY mmu04015Rap1 signaling pathway 9 46 533E-03KEGG PATHWAY mmu04390Hippo signaling pathway 7 36 116E-02KEGG PATHWAY mmu05145Toxoplasmosis 6 30 136E-02KEGG PATHWAY mmu04510Focal adhesion 8 41 151E-02KEGG PATHWAY mmu04022cGMP-PKG signaling pathway 7 36 204E-02KEGG PATHWAY mmu03040Spliceosome 6 30 257E-02lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
Mrpl19
Ccl12 Ifi47Gbp6
Mrpl9 Mfap5
Mrpl44Efemp1
Soat1
Hsd17b12
Adh1
Adam10Cyp11a1Ugt1a2
Star Aplp2
Efna1
Fosb
Gbp7Prl2c2
Iigp1
Irgm1Stat1Mt2
Mst4
Nup133
Zwint
Ranbp9
Taok1Wdr26
G6pd2 Ly6e CtssTpm3 Upk3b Atp6v1aEpcam Tmed7
Ptpn12 Gnpda2Upk1b Atp6v1b2 Ly6a Mpeg1Krt19 Arf4
Atf3Gbp3
Zfp36
Parp14Plau
Gbp2
Irgm2Serping1
Lgals3bp
Ifitm3 Ublcp1Cxcl10 Igtp
Irf7
Ddx58C2 Rtp4 JunbIfit3
Eif2ak2
Npm1Dnajb6
Paip1Thoc1Hspa4Csnk2a1
Ncl Dnttip2
Taf1bAcly Hspa8
Snrpn
Cct3
Tra2b
Sox4
Unc45b Sik1Fzd2
Rpf1Eif4a1 Mtmr6
Fabp4
Mccc2
Ctnnb1 Mtmr2PtprfYwhaz
Ywhaq
Hsp90aa1 Tbc1d1
HnrnpuSrsf1
Slu7
Elavl2Acaa2
Sf3b2
Esf1Pik3r1 Mtm1
Ssb
EdnraRif1 Hsph1
Rrm1 Pcgf6Rbl2
Brca1Pknox1Mbd2 Pvrl3
Psme4Uba7
Irf9Isg15 Ifi44
Herc6Gphn
Crk
Ddx60Stat3
AxlPard3Klf4
BC006779
Jak1
Isg20
Oas1a
Rbbp4 H3f3a
Exo1Plec
Tsc22d3
Dcun1d1Uba3
Ubc
Rrm2b
Rpa2Usp1
Rad51cGlrx
Keap1
Casp3 Dsg2Fos Egr1Usp18
Mt1
Nr4a1
Parp3
Akt1
Cyr61
Rap1b
Trim30a
OsmrHmgb1
Jun
Adcy7 Stk3Ppp2r2b
Eif4eThbs1
Ifi35Oasl2Ifit1
Dhx58Oas2
Rsad2Parp9
Ifih1
Mef2aBcl10Map3k7
Ifit2Tcf3
Tlr3
C3
Socs3Rnf19b
Fbxw14
Zbp1
Psmb8
Figure 3 Protein-protein interaction (PPI) network complex Using the SearchTool for the retrieval of InteractingGenes (STRING) databaseonline database 187 out of 371 differentially expressed genes (DEGs) (upregulated genes are displayed in red and downregulated genes ingreen) were filtered into the DEGs protein-protein interaction (PPI) network complex
8 BioMed Research International
Parp9
Tlr3
Ifih1
Ifi44
Gbp7
Iigp1
Gbp3
Rtp4
Dhx58
Gbp2
Irf9
Ddx60
Oasl2
Cxcl10
Isg15
Trim30a
Rsad2
Zbp1
Irf7
Herc6Ifit2Ifit1
Ifit3
Stat1
Ifi35
Oas2
Igtp Irgm1
Ddx58
Ifi47
Irgm2Usp18
Eif2ak2
Gbp6
Parp14
Figure 4 Overlapping protein complexes in the protein interactionThe most significant module consisted of 35 nodes and 286 edges andall genes in this module were upregulated (in red)
5 Conclusions
In conclusion our data showed quite different gene expres-sion patterns of the secondary follicles between young and oldmouse ovaries The differentially expressed genes involved inthe process of ovarian aging are central to biological processessuch as immune system process aging transcription DNAreplication DNA repair protein stabilization and apoptoticprocess However many upregulated genes in the old mouseovarian secondary follicles were innate immune relatedgenes We proposed that innate immune system may play avital role in the process of ovarian aging Our results of alteredgenes and related transcriptional networks may be helpful forunderstanding the mechanism of the folliculogenesis in theprocess of ovarian aging in mice
There are however limitations in the present studyFindings of our present research were mainly based
on the bioinformatics analysis and further experimentsare needed to verify Furthermore these data were ac-quired with secondary follicles from mouse ovariesand needed to be confirmed with the samples from thehuman
Data Availability
The results of our microarray data were made available in thepublic domain NCBI-GEO repository
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this article
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
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[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
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Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
2 BioMed Research International
molecular mechanisms behind that are still not fully under-stood Studies have shown that accumulation of reactiveoxygen species (ROS) and free radicals and the action of envi-ronmental factors such as radiation and chemotherapeuticdrugs used in cancer patients can cause DNA damage in theoocytes during long periods of dictyate arrest and withoutrepairing the extent of DNA damage may cause genomicabnormalities (chromosomal breakages andmutations) lead-ing to cell death and follicle atresia [11 12] Researches haverevealed that the expression of BRCA1 (breast cancer type 1)related DNA repair decreased in the process of ovarian agingin rat and buffalo primordial follicles [13 14] Laboratory andclinical studies also demonstrated that expression of BRCA1declines in single mouse and human oocytes and BRCA1mutation is associated with primary ovarian insufficiency[15ndash17]
A better understanding of follicle biology is essentialto help make ovarian aging process explicit Early folliclegrowth up to the large preantral stage is independent ofgonadotropins in rodents and relies on intraovarian fac-tors [6] Thus in our present study the secondary folliclesfrom young and old mice ovaries were used to investigatethe changes of expression profile during ovarian aging bygenome-wide microarray analysis
2 Materials and Methods
21 Isolation of Secondary Follicles from the Mouse OvaryThe experimental animals were maintained as per theguidelines of the Animal Care Committee of Tongji Hos-pital within the Tongji Medical College at the HuazhongUniversity of Science and Technology in China The 9-week old and 32-week old specific pathogen-free (SPF)female C57BL6J mice were obtained from Beijing VitalRiver Laboratory Animal Technology Co Ltd (BeijingChina) All mice were killed by decapitation and ovarieswere collected free of adhering tissue Under the stereomi-croscope follicles with diameter of 120-140 120583m an intactbasal membrane a central and spherical oocyte surroundedby granulosa cells were mechanically dissecting by 2 syringeneedles The ovarian secondary follicles were stored atminus80∘C
22 RNA Isolation and Microarray Analysis The total RNAof the ovarian secondary follicles was extracted with RNAisoplus reagent according to the manufacturerrsquos instructions(Takara Japan) Of the total of 6 samples 3 replicate sampleswere from young mouse ovarian secondary follicles and 3replicate samples were from old mouse ovarian secondaryfollicles All RNA samples were stored in DEPC in orderto prevent RNA degeneration GeneChip hybridization foreach sample was examined on Affymetrix 3rsquo IVT ExpressionArrays (MouseGenome 430 20 array) at Bioassay Laboratoryof CapitalBio Corporation The technical procedures andquality controls were performed at the CapitalBio Corpo-ration Hybridization assay procedures were as describedin the GeneChip Expression Analysis Technical Manual(httpwwwaffymetrixcom)
23MicroarrayData Analysis Theraw data frommicroarrayanalysis was normalized using robust multiarray average(RMA) algorithm The differentially expressed genes with afold change ge2 and a q-value le005 were identified usingSignificant Analysis of Microarray (SAM) software Forvisualization of differentially expressed genes unsupervisedhierarchical clustering was performed using HemI 1037software (httphemibiocuckooorgdownphp) [18] GeneOntology (GO) consisting of three items molecular func-tions biological processes and cellular components [19]and Kyoto Encyclopedia of Genes and Genomes (KEGG)a set of high-throughput genes and protein pathways [20]analyses of differentially expressed genes were performedusing the DAVID online tools (httpsdavidncifcrfgov)compared with the mouse whole genome [21] Whole MouseGenome was used as the reference group Statistical sig-nificance was calculated with a standard hypergeometricequation corrected by a Benjamini Yekutelli correction formultiple testing which takes into account the dependencyamong the GO categories The minimal length of consideredGO-paths was 2 Significance was set at corrected p-valuelt 005 The Search Tool for the Tetrieval of InteractingGenes (STRING) database (httpstring-dborg) an onlinesoftware that provides comprehensive interactions of listsof proteins and genes was used to build a PPI network ofthe differentially expressed genes [22] The cut-off criteriaof the minimum required interaction score were 07 forthe PPI network The visualizing of the PPI network wasconstructed using the Cytoscape software (version 361) [23]The Clustering with Overlapping Neighborhood Expansion(ClusterONE) plug-in for Cytoscape was used to detectprotein complexes in the PPI network [24] The gene regu-latory network modeling for selected differentially expressedgenes was performed using Cytoscape software (version361)
3 Results
31 Global Gene Expression Analysis of Secondary Folliclesfrom Mouse Ovaries To characterize the genes that areassociated with mouse ovarian aging we examined the geneexpression profile of secondary follicles from young andold mouse ovaries The expression values of all the sixsamples (three samples each from young and old mouseovaries) were normalized using the robustmultiarray average(RMA) method The results of our microarray data weremade available in the public domain NCBI-GEO repository(accession ID GSE121493) The box-whisker plot analysisof normalized data showed uniform distribution of theexpression levels in both intra- and intersample mannerindicating reliable hybridization (see Figure 1) Summarystatistics showed effectiveness of quantile normalization as50th percentile values were close to 49 After normal-ization of raw data for all three biological replicates theR package significance analysis of microarray (SAM) wasused to identify genes that are differentially expressed insecondary follicles from young and old mouse ovaries (foldchange ge2 or le05 and q-value le005) And the results
BioMed Research International 3
Old
minus1
Old
minus2
Old
minus3
Youn
gminus1
Youn
gminus2
Youn
gminus3
2
4
6
8
10
12
14
Sample
Log2
(int
ensit
y)
Figure 1 Box and whisker plot (Box plot) We performed the comparison of gene expression with a total of six samples from young (n=3)and old (n=3) mouse ovarian secondary follicles by using Affymetrix 3rsquo IVT Expression Arrays (Mouse Genome 430 20 array) at BioassayLaboratory of CapitalBio Corporation Robust multiarray average (RMA) algorithm was used to eliminate the variation in the arrays fromnoisy data Box plot was constructed to illustrate the distribution of normalized probe hybridization signal intensities (log ratios) for all sixarrays (young and old mouse ovarian secondary follicles) The probe distribution by 0-100 quantiles as whiskers the 25-75 quantiles asdifferent color boxes and the 50 quantile as horizontal line within the box indicated a similar range of signal intensities and confirmedperfect hybridization
revealed that 371 genes were differentially expressed betweenthe two groups while 174 genes were upregulated and197 genes were downregulated in the secondary folliclesfrom the old mouse ovaries compared to those from theyoung mouse ovaries Further unsupervised hierarchicalclustering analysis using the HemI 1037 software showeddistinct patterns of up- and downregulated genes in thesecondary follicles from young and old mouse ovaries (seeFigure 2)
32 Functional Annotation for the Differentially ExpressedGenes The identified differentially expressed genes in thesecondary follicles from the old mouse ovaries comparedto those from the young mouse ovaries were further ana-lyzed via gene ontology (GO) and KEGG pathway analysisusing the DAVID online tool As shown in Table 1 GOterm enrichment analysis showed that the upregulated geneswere significantly enriched in immune system process inthe biological processes category cytoplasm and nucleusin the cellular component category and RNA and DNAbinding in the molecular function category While listedin Table 2 the functional annotation for the downregu-lated genes revealed that the most significant categoriesof biological process were involved in transcription andits regulation cellular component was involved in nucleusand cytoplasm and molecular function was involved in
protein RNA and DNA binding Furthermore KEGG path-way analysis showed that most of the upregulated genestook part in virus related and Toll-like receptor signalingpathways whereas downregulated genes mainly participatedin PI3K-Akt signaling pathway and Adherens junction inTable 3
33 Protein-Protein Interaction and Gene Regulation NetworkAnalysis In total 187 nodes and 572 edges were mappedin the PPI network of identified differentially expressedgenes using STRINGwith the minimum required interactionscore gt 07 (Figure 3) The 10 nodes with highest degreewere regarded as hub genes STAT1 IFIT1 IFIT3 IRF7USP18 OASL2 IFIT2 UBC DDX58 IFIH1 There were 12modules generated by ClusterONE with p-value lt 005 Themost significant module with p-value lt 0001 contained 35nodes and 286 edges (Figure 4) The 10 genes listed aboveapart from UBC were included in the module In additionto the 9 genes other nodes in the module were RSAD2IFI47 TRIM30A PARP14 PARP9 IFI44 RTP4 GBP7ISG15 IRF9 GBP6 GBP3 IGTP HERC6 IRGM1 DHX58IIGP1 OAS2 DDX60 CXCL10 ZBP1 GBP2 EIF2AK2IRGM2 IFI35 and TLR3 And all genes in the modulewere upregulated As shown in the gene regulatory networkmodeling for selected genes many differentially expressedgenes such as BRCA1 STAT3 JUN AKT1 SEPRING1
4 BioMed Research International
Old-1 Old-2 Old-3 Young-1 Young-2 Young-3
1088
992
896
800
704
608
512
416
320
224
Figure 2 Heat map visualization for selected differentially expressed genes Heat map was produced by unsupervised hierarchical-clusteringanalysis frommicroarraydata for top 50upregulated genes and top 50downregulated genes in oldmouse ovarian secondary follicles comparedto young The relative expression levels of each gene are mentioned in different colors The red lines represent high expression while bluelines represent low expressionThe numbers in the right side corresponding to the different colors represent the relative expression levels ofeach gene
BioMed Research International 5
Table 1 Functional enrichment analysis of upregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P ValueGOTERM BP DIRECT GO0035458simcellular response to interferon-beta 15 86 562E-21GOTERM BP DIRECT GO0009615simresponse to virus 18 103 673E-21GOTERM BP DIRECT GO0051607simdefense response to virus 21 121 115E-19GOTERM BP DIRECT GO0002376simimmune system process 24 138 101E-15GOTERM BP DIRECT GO0045087siminnate immune response 23 132 270E-14GOTERM BP DIRECT GO0071346simcellular response to interferon-gamma 9 52 180E-08GOTERM BP DIRECT GO0042832simdefense response to protozoan 6 34 160E-06GOTERM BP DIRECT GO0006952simdefense response 8 46 136E-05GOTERM BP DIRECT GO0032870simcellular response to hormone stimulus 6 34 157E-05GOTERM BP DIRECT GO0034097simresponse to cytokine 7 40 179E-05GOTERM CC DIRECT GO0020005simsymbiont-containing vacuole membrane 5 29 146E-07GOTERM CC DIRECT GO0005829simcytosol 28 161 700E-05GOTERM CC DIRECT GO0005737simcytoplasm 65 374 661E-04GOTERM CC DIRECT GO0005634simnucleus 58 333 275E-03GOTERM CC DIRECT GO0048471simperinuclear region of cytoplasm 12 69 823E-03GOTERM CC DIRECT GO0072562simbloodmicroparticle 5 29 134E-02GOTERM CC DIRECT GO0031225simanchored component of membrane 5 29 163E-02GOTERM CC DIRECT GO0009897simexternal side of plasma membrane 7 40 223E-02GOTERM MF DIRECT GO0003725simdouble-stranded RNA binding 9 52 262E-08GOTERM MF DIRECT GO0003690simdouble-stranded DNA binding 11 63 333E-08GOTERM MF DIRECT GO0003924simGTPase activity 11 63 181E-06GOTERM MF DIRECT GO0005525simGTP binding 12 69 676E-05GOTERM MF DIRECT GO0008134simtranscription factor binding 10 57 597E-04GOTERM MF DIRECT GO0003723simRNA binding 15 86 940E-04GOTERM MF DIRECT GO0003677simDNA binding 25 144 158E-03GOTERM MF DIRECT GO0001730sim21015840-51015840-oligoadenylate synthetase activity 3 17 248E-03GOTERM MF DIRECT GO0016817simhydrolase activity acting on acid anhydrides 3 17 248E-03
GOTERM MF DIRECT GO0001077simtranscriptional activator activity RNA polymerase IIcore promoter proximal region sequence-specific binding 8 46 265E-03
lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
TCF3 MAP3K7 and IRF7 took part in various pathways(Figure 5)
4 Discussion
Elucidating the mechanism of ovarian aging has significantmeanings to female health The gradual decrease of boththe quantity and the quality of the oocytes surrounded bythe granulosa cells in all stages of follicles dominates thereproductive aging [4] In previous study Govindaraj et alrevealed that gene expression patterns changed considerablyin the rat primordial follicles in the process of ovarianaging [25] Folliculogenesis in the ovary is a highly dynamicand periodic process regulated by both intra- and extra-oocyte factors [26] At each reproductive cycle activatedprimordial follicles join the growing pool transiting to pri-mary follicles [26] Through further development a primaryfollicle grows into a secondary follicle [26] And these stagesare gonadotropin independent but depend on the complex
bidirectional communication between the oocyte and thesomatic cells [26] In the subsequent stages of folliculogenesisthe presence of pituitary gonadotropins follicle-stimulatinghormone (FSH) and luteinizing hormone (LH) are required[26] So the secondary follicles from the mouse ovaries wereselected as research objective in our study
Gene expression profile of the secondary follicles from theyoung and old mouse ovaries was compared by microarrayanalysis to find what changed in the process of ovarian agingin the present studyThe results of our research found that 174genes were upregulated and 197 genes were downregulated inthe secondary follicles from the old mouse ovaries comparedto those from the young mouse ovaries
FurtherGOandKEGGpathway analyses were performedto study the function of the differentially expressed genesThe result of GO analysis showed that the upregulated geneswere mainly involved in biological process such as immunesystem process and defense response while downregulatedgenes were closely related to gene transcription and cell
6 BioMed Research International
Table 2 Functional enrichment analysis of downregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueGOTERM BP DIRECT GO0006355simregulation of transcription DNA-templated 42 213 702E-05GOTERM BP DIRECT GO0016311simdephosphorylation 8 41 984E-05GOTERM BP DIRECT GO0006351simtranscription DNA-templated 35 178 319E-04GOTERM BP DIRECT GO0045893simpositive regulation of transcription DNA-templated 16 81 556E-04GOTERM BP DIRECT GO0030335simpositive regulation of cell migration 9 46 887E-04GOTERM BP DIRECT GO0006470simprotein dephosphorylation 7 36 239E-03
GOTERM BP DIRECT GO0043154simnegative regulation of cysteine-type endopeptidaseactivity involved in apoptotic process 5 25 421E-03
GOTERM BP DIRECT GO0030177simpositive regulation of Wnt signaling pathway 4 20 561E-03GOTERM BP DIRECT GO0043065simpositive regulation of apoptotic process 10 51 585E-03GOTERM BP DIRECT GO0097194simexecution phase of apoptosis 3 15 692E-03GOTERM CC DIRECT GO0005634simnucleus 106 538 323E-13GOTERM CC DIRECT GO0005737simcytoplasm 100 508 449E-08GOTERM CC DIRECT GO0005654simnucleoplasm 44 223 926E-08GOTERM CC DIRECT GO0070062simextracellular exosome 50 254 319E-06GOTERM CC DIRECT GO0043234simprotein complex 19 96 326E-05GOTERM CC DIRECT GO0071013simcatalytic step 2 spliceosome 6 30 224E-03GOTERM CC DIRECT GO0005925simfocal adhesion 11 56 430E-03GOTERM CC DIRECT GO0005829simcytosol 29 147 576E-03GOTERM CC DIRECT GO0030529simintracellular ribonucleoprotein complex 9 46 117E-02GOTERM CC DIRECT GO0005911simcell-cell junction 7 36 126E-02GOTERM MF DIRECT GO0005515simprotein binding 77 391 699E-09GOTERM MF DIRECT GO0044822simpoly(A) RNA binding 30 152 252E-06GOTERM MF DIRECT GO0003723simRNA binding 20 102 371E-04GOTERM MF DIRECT GO0003677simDNA binding 35 178 423E-04GOTERM MF DIRECT GO0004725simprotein tyrosine phosphatase activity 7 36 462E-04GOTERM MF DIRECT GO0046982simprotein heterodimerization activity 15 76 777E-04GOTERM MF DIRECT GO0000166simnucleotide binding 35 178 978E-04GOTERM MF DIRECT GO0016791simphosphatase activity 7 36 129E-03GOTERM MF DIRECT GO0019903simprotein phosphatase binding 6 30 202E-03GOTERM MF DIRECT GO0005524simATP binding 28 142 262E-03lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
apoptosis However there was an unexpected phenomenonin the results of our functional enrichment Many upregu-lated genes were involved in response to virus and interferonin the biological process and took part in several virusrelated signal pathway This phenomenon revealed that theSPF mice used in our study might infect some virusesHowever the certain thing is that the immune related genescan be expressed in ovarian granulosa cells not only inimmune cells Several earlier studies indicated that virusescan induce innate immune response in granulosa cells andperturb ovarian function in mouse [27 28] And immuneresponse genes were overexpressed with increasing age asshowed by several microarray studies of aging [29] Recentlythe concept that innate immunity is an essential requisitein the ovulation process is forwarded [30] The importantrole of innate immune cells in decreasing the senescence
burden was also recognized [31] There was a probabilitythat innate immune related genes were upregulated in theprocess of aging and affected the progress of ovarian agingYet the actual role of innate immunity in the processof ovarian aging or folliculogenesis needs to be furtherresearched
We conducted protein-protein interaction network anal-ysis of differentially expressed genes The nodes regarded ashub genes weremostly involved in innate immune system Asin the gene regulatory network many differentially expressedgenes between young and old mouse ovarian secondaryfollicles such as BRCA1 STAT3 JUN AKT1 SEPRING1TCF3 MAP3K7 and IRF7 showed their genetic interactionsby various pathways Thus a number of pathways wereinteracted through many genes in the process of ovarianaging
BioMed Research International 7
Table 3 Pathway enrichment analysis of differentially expressed genes in old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueUp-regulatedKEGG PATHWAY mmu05164Influenza A 15 86 225E-11KEGG PATHWAY mmu05168Herpes simplex infection 15 86 313E-10KEGG PATHWAY mmu05162Measles 11 63 623E-08KEGG PATHWAY mmu05160Hepatitis C 11 63 623E-08KEGG PATHWAY mmu04380Osteoclast differentiation 8 46 440E-05KEGG PATHWAY mmu05161Hepatitis B 8 46 112E-04KEGG PATHWAY mmu04622RIG-I-like receptor signaling pathway 6 34 158E-04KEGG PATHWAY mmu04668TNF signaling pathway 7 40 169E-04KEGG PATHWAY mmu04620Toll-like receptor signaling pathway 6 34 993E-04KEGG PATHWAY mmu05133Pertussis 5 29 238E-03Down-regulatedKEGG PATHWAY mmu04151PI3K-Akt signaling pathway 14 71 414E-04KEGG PATHWAY mmu05200Pathways in cancer 14 71 131E-03KEGG PATHWAY mmu04550Signaling pathways regulating pluripotency of stem cells 8 41 169E-03KEGG PATHWAY mmu04520Adherens junction 6 30 203E-03KEGG PATHWAY mmu04015Rap1 signaling pathway 9 46 533E-03KEGG PATHWAY mmu04390Hippo signaling pathway 7 36 116E-02KEGG PATHWAY mmu05145Toxoplasmosis 6 30 136E-02KEGG PATHWAY mmu04510Focal adhesion 8 41 151E-02KEGG PATHWAY mmu04022cGMP-PKG signaling pathway 7 36 204E-02KEGG PATHWAY mmu03040Spliceosome 6 30 257E-02lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
Mrpl19
Ccl12 Ifi47Gbp6
Mrpl9 Mfap5
Mrpl44Efemp1
Soat1
Hsd17b12
Adh1
Adam10Cyp11a1Ugt1a2
Star Aplp2
Efna1
Fosb
Gbp7Prl2c2
Iigp1
Irgm1Stat1Mt2
Mst4
Nup133
Zwint
Ranbp9
Taok1Wdr26
G6pd2 Ly6e CtssTpm3 Upk3b Atp6v1aEpcam Tmed7
Ptpn12 Gnpda2Upk1b Atp6v1b2 Ly6a Mpeg1Krt19 Arf4
Atf3Gbp3
Zfp36
Parp14Plau
Gbp2
Irgm2Serping1
Lgals3bp
Ifitm3 Ublcp1Cxcl10 Igtp
Irf7
Ddx58C2 Rtp4 JunbIfit3
Eif2ak2
Npm1Dnajb6
Paip1Thoc1Hspa4Csnk2a1
Ncl Dnttip2
Taf1bAcly Hspa8
Snrpn
Cct3
Tra2b
Sox4
Unc45b Sik1Fzd2
Rpf1Eif4a1 Mtmr6
Fabp4
Mccc2
Ctnnb1 Mtmr2PtprfYwhaz
Ywhaq
Hsp90aa1 Tbc1d1
HnrnpuSrsf1
Slu7
Elavl2Acaa2
Sf3b2
Esf1Pik3r1 Mtm1
Ssb
EdnraRif1 Hsph1
Rrm1 Pcgf6Rbl2
Brca1Pknox1Mbd2 Pvrl3
Psme4Uba7
Irf9Isg15 Ifi44
Herc6Gphn
Crk
Ddx60Stat3
AxlPard3Klf4
BC006779
Jak1
Isg20
Oas1a
Rbbp4 H3f3a
Exo1Plec
Tsc22d3
Dcun1d1Uba3
Ubc
Rrm2b
Rpa2Usp1
Rad51cGlrx
Keap1
Casp3 Dsg2Fos Egr1Usp18
Mt1
Nr4a1
Parp3
Akt1
Cyr61
Rap1b
Trim30a
OsmrHmgb1
Jun
Adcy7 Stk3Ppp2r2b
Eif4eThbs1
Ifi35Oasl2Ifit1
Dhx58Oas2
Rsad2Parp9
Ifih1
Mef2aBcl10Map3k7
Ifit2Tcf3
Tlr3
C3
Socs3Rnf19b
Fbxw14
Zbp1
Psmb8
Figure 3 Protein-protein interaction (PPI) network complex Using the SearchTool for the retrieval of InteractingGenes (STRING) databaseonline database 187 out of 371 differentially expressed genes (DEGs) (upregulated genes are displayed in red and downregulated genes ingreen) were filtered into the DEGs protein-protein interaction (PPI) network complex
8 BioMed Research International
Parp9
Tlr3
Ifih1
Ifi44
Gbp7
Iigp1
Gbp3
Rtp4
Dhx58
Gbp2
Irf9
Ddx60
Oasl2
Cxcl10
Isg15
Trim30a
Rsad2
Zbp1
Irf7
Herc6Ifit2Ifit1
Ifit3
Stat1
Ifi35
Oas2
Igtp Irgm1
Ddx58
Ifi47
Irgm2Usp18
Eif2ak2
Gbp6
Parp14
Figure 4 Overlapping protein complexes in the protein interactionThe most significant module consisted of 35 nodes and 286 edges andall genes in this module were upregulated (in red)
5 Conclusions
In conclusion our data showed quite different gene expres-sion patterns of the secondary follicles between young and oldmouse ovaries The differentially expressed genes involved inthe process of ovarian aging are central to biological processessuch as immune system process aging transcription DNAreplication DNA repair protein stabilization and apoptoticprocess However many upregulated genes in the old mouseovarian secondary follicles were innate immune relatedgenes We proposed that innate immune system may play avital role in the process of ovarian aging Our results of alteredgenes and related transcriptional networks may be helpful forunderstanding the mechanism of the folliculogenesis in theprocess of ovarian aging in mice
There are however limitations in the present studyFindings of our present research were mainly based
on the bioinformatics analysis and further experimentsare needed to verify Furthermore these data were ac-quired with secondary follicles from mouse ovariesand needed to be confirmed with the samples from thehuman
Data Availability
The results of our microarray data were made available in thepublic domain NCBI-GEO repository
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this article
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
[1] J C Stevenson ldquoA womanrsquos journey through the reproductivetransitional and postmenopausal periods of life Impact on car-diovascular and musculo-skeletal risk and the role of estrogenreplacementrdquoMaturitas vol 70 no 2 pp 197ndash205 2011
[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
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Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
BioMed Research International 3
Old
minus1
Old
minus2
Old
minus3
Youn
gminus1
Youn
gminus2
Youn
gminus3
2
4
6
8
10
12
14
Sample
Log2
(int
ensit
y)
Figure 1 Box and whisker plot (Box plot) We performed the comparison of gene expression with a total of six samples from young (n=3)and old (n=3) mouse ovarian secondary follicles by using Affymetrix 3rsquo IVT Expression Arrays (Mouse Genome 430 20 array) at BioassayLaboratory of CapitalBio Corporation Robust multiarray average (RMA) algorithm was used to eliminate the variation in the arrays fromnoisy data Box plot was constructed to illustrate the distribution of normalized probe hybridization signal intensities (log ratios) for all sixarrays (young and old mouse ovarian secondary follicles) The probe distribution by 0-100 quantiles as whiskers the 25-75 quantiles asdifferent color boxes and the 50 quantile as horizontal line within the box indicated a similar range of signal intensities and confirmedperfect hybridization
revealed that 371 genes were differentially expressed betweenthe two groups while 174 genes were upregulated and197 genes were downregulated in the secondary folliclesfrom the old mouse ovaries compared to those from theyoung mouse ovaries Further unsupervised hierarchicalclustering analysis using the HemI 1037 software showeddistinct patterns of up- and downregulated genes in thesecondary follicles from young and old mouse ovaries (seeFigure 2)
32 Functional Annotation for the Differentially ExpressedGenes The identified differentially expressed genes in thesecondary follicles from the old mouse ovaries comparedto those from the young mouse ovaries were further ana-lyzed via gene ontology (GO) and KEGG pathway analysisusing the DAVID online tool As shown in Table 1 GOterm enrichment analysis showed that the upregulated geneswere significantly enriched in immune system process inthe biological processes category cytoplasm and nucleusin the cellular component category and RNA and DNAbinding in the molecular function category While listedin Table 2 the functional annotation for the downregu-lated genes revealed that the most significant categoriesof biological process were involved in transcription andits regulation cellular component was involved in nucleusand cytoplasm and molecular function was involved in
protein RNA and DNA binding Furthermore KEGG path-way analysis showed that most of the upregulated genestook part in virus related and Toll-like receptor signalingpathways whereas downregulated genes mainly participatedin PI3K-Akt signaling pathway and Adherens junction inTable 3
33 Protein-Protein Interaction and Gene Regulation NetworkAnalysis In total 187 nodes and 572 edges were mappedin the PPI network of identified differentially expressedgenes using STRINGwith the minimum required interactionscore gt 07 (Figure 3) The 10 nodes with highest degreewere regarded as hub genes STAT1 IFIT1 IFIT3 IRF7USP18 OASL2 IFIT2 UBC DDX58 IFIH1 There were 12modules generated by ClusterONE with p-value lt 005 Themost significant module with p-value lt 0001 contained 35nodes and 286 edges (Figure 4) The 10 genes listed aboveapart from UBC were included in the module In additionto the 9 genes other nodes in the module were RSAD2IFI47 TRIM30A PARP14 PARP9 IFI44 RTP4 GBP7ISG15 IRF9 GBP6 GBP3 IGTP HERC6 IRGM1 DHX58IIGP1 OAS2 DDX60 CXCL10 ZBP1 GBP2 EIF2AK2IRGM2 IFI35 and TLR3 And all genes in the modulewere upregulated As shown in the gene regulatory networkmodeling for selected genes many differentially expressedgenes such as BRCA1 STAT3 JUN AKT1 SEPRING1
4 BioMed Research International
Old-1 Old-2 Old-3 Young-1 Young-2 Young-3
1088
992
896
800
704
608
512
416
320
224
Figure 2 Heat map visualization for selected differentially expressed genes Heat map was produced by unsupervised hierarchical-clusteringanalysis frommicroarraydata for top 50upregulated genes and top 50downregulated genes in oldmouse ovarian secondary follicles comparedto young The relative expression levels of each gene are mentioned in different colors The red lines represent high expression while bluelines represent low expressionThe numbers in the right side corresponding to the different colors represent the relative expression levels ofeach gene
BioMed Research International 5
Table 1 Functional enrichment analysis of upregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P ValueGOTERM BP DIRECT GO0035458simcellular response to interferon-beta 15 86 562E-21GOTERM BP DIRECT GO0009615simresponse to virus 18 103 673E-21GOTERM BP DIRECT GO0051607simdefense response to virus 21 121 115E-19GOTERM BP DIRECT GO0002376simimmune system process 24 138 101E-15GOTERM BP DIRECT GO0045087siminnate immune response 23 132 270E-14GOTERM BP DIRECT GO0071346simcellular response to interferon-gamma 9 52 180E-08GOTERM BP DIRECT GO0042832simdefense response to protozoan 6 34 160E-06GOTERM BP DIRECT GO0006952simdefense response 8 46 136E-05GOTERM BP DIRECT GO0032870simcellular response to hormone stimulus 6 34 157E-05GOTERM BP DIRECT GO0034097simresponse to cytokine 7 40 179E-05GOTERM CC DIRECT GO0020005simsymbiont-containing vacuole membrane 5 29 146E-07GOTERM CC DIRECT GO0005829simcytosol 28 161 700E-05GOTERM CC DIRECT GO0005737simcytoplasm 65 374 661E-04GOTERM CC DIRECT GO0005634simnucleus 58 333 275E-03GOTERM CC DIRECT GO0048471simperinuclear region of cytoplasm 12 69 823E-03GOTERM CC DIRECT GO0072562simbloodmicroparticle 5 29 134E-02GOTERM CC DIRECT GO0031225simanchored component of membrane 5 29 163E-02GOTERM CC DIRECT GO0009897simexternal side of plasma membrane 7 40 223E-02GOTERM MF DIRECT GO0003725simdouble-stranded RNA binding 9 52 262E-08GOTERM MF DIRECT GO0003690simdouble-stranded DNA binding 11 63 333E-08GOTERM MF DIRECT GO0003924simGTPase activity 11 63 181E-06GOTERM MF DIRECT GO0005525simGTP binding 12 69 676E-05GOTERM MF DIRECT GO0008134simtranscription factor binding 10 57 597E-04GOTERM MF DIRECT GO0003723simRNA binding 15 86 940E-04GOTERM MF DIRECT GO0003677simDNA binding 25 144 158E-03GOTERM MF DIRECT GO0001730sim21015840-51015840-oligoadenylate synthetase activity 3 17 248E-03GOTERM MF DIRECT GO0016817simhydrolase activity acting on acid anhydrides 3 17 248E-03
GOTERM MF DIRECT GO0001077simtranscriptional activator activity RNA polymerase IIcore promoter proximal region sequence-specific binding 8 46 265E-03
lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
TCF3 MAP3K7 and IRF7 took part in various pathways(Figure 5)
4 Discussion
Elucidating the mechanism of ovarian aging has significantmeanings to female health The gradual decrease of boththe quantity and the quality of the oocytes surrounded bythe granulosa cells in all stages of follicles dominates thereproductive aging [4] In previous study Govindaraj et alrevealed that gene expression patterns changed considerablyin the rat primordial follicles in the process of ovarianaging [25] Folliculogenesis in the ovary is a highly dynamicand periodic process regulated by both intra- and extra-oocyte factors [26] At each reproductive cycle activatedprimordial follicles join the growing pool transiting to pri-mary follicles [26] Through further development a primaryfollicle grows into a secondary follicle [26] And these stagesare gonadotropin independent but depend on the complex
bidirectional communication between the oocyte and thesomatic cells [26] In the subsequent stages of folliculogenesisthe presence of pituitary gonadotropins follicle-stimulatinghormone (FSH) and luteinizing hormone (LH) are required[26] So the secondary follicles from the mouse ovaries wereselected as research objective in our study
Gene expression profile of the secondary follicles from theyoung and old mouse ovaries was compared by microarrayanalysis to find what changed in the process of ovarian agingin the present studyThe results of our research found that 174genes were upregulated and 197 genes were downregulated inthe secondary follicles from the old mouse ovaries comparedto those from the young mouse ovaries
FurtherGOandKEGGpathway analyses were performedto study the function of the differentially expressed genesThe result of GO analysis showed that the upregulated geneswere mainly involved in biological process such as immunesystem process and defense response while downregulatedgenes were closely related to gene transcription and cell
6 BioMed Research International
Table 2 Functional enrichment analysis of downregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueGOTERM BP DIRECT GO0006355simregulation of transcription DNA-templated 42 213 702E-05GOTERM BP DIRECT GO0016311simdephosphorylation 8 41 984E-05GOTERM BP DIRECT GO0006351simtranscription DNA-templated 35 178 319E-04GOTERM BP DIRECT GO0045893simpositive regulation of transcription DNA-templated 16 81 556E-04GOTERM BP DIRECT GO0030335simpositive regulation of cell migration 9 46 887E-04GOTERM BP DIRECT GO0006470simprotein dephosphorylation 7 36 239E-03
GOTERM BP DIRECT GO0043154simnegative regulation of cysteine-type endopeptidaseactivity involved in apoptotic process 5 25 421E-03
GOTERM BP DIRECT GO0030177simpositive regulation of Wnt signaling pathway 4 20 561E-03GOTERM BP DIRECT GO0043065simpositive regulation of apoptotic process 10 51 585E-03GOTERM BP DIRECT GO0097194simexecution phase of apoptosis 3 15 692E-03GOTERM CC DIRECT GO0005634simnucleus 106 538 323E-13GOTERM CC DIRECT GO0005737simcytoplasm 100 508 449E-08GOTERM CC DIRECT GO0005654simnucleoplasm 44 223 926E-08GOTERM CC DIRECT GO0070062simextracellular exosome 50 254 319E-06GOTERM CC DIRECT GO0043234simprotein complex 19 96 326E-05GOTERM CC DIRECT GO0071013simcatalytic step 2 spliceosome 6 30 224E-03GOTERM CC DIRECT GO0005925simfocal adhesion 11 56 430E-03GOTERM CC DIRECT GO0005829simcytosol 29 147 576E-03GOTERM CC DIRECT GO0030529simintracellular ribonucleoprotein complex 9 46 117E-02GOTERM CC DIRECT GO0005911simcell-cell junction 7 36 126E-02GOTERM MF DIRECT GO0005515simprotein binding 77 391 699E-09GOTERM MF DIRECT GO0044822simpoly(A) RNA binding 30 152 252E-06GOTERM MF DIRECT GO0003723simRNA binding 20 102 371E-04GOTERM MF DIRECT GO0003677simDNA binding 35 178 423E-04GOTERM MF DIRECT GO0004725simprotein tyrosine phosphatase activity 7 36 462E-04GOTERM MF DIRECT GO0046982simprotein heterodimerization activity 15 76 777E-04GOTERM MF DIRECT GO0000166simnucleotide binding 35 178 978E-04GOTERM MF DIRECT GO0016791simphosphatase activity 7 36 129E-03GOTERM MF DIRECT GO0019903simprotein phosphatase binding 6 30 202E-03GOTERM MF DIRECT GO0005524simATP binding 28 142 262E-03lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
apoptosis However there was an unexpected phenomenonin the results of our functional enrichment Many upregu-lated genes were involved in response to virus and interferonin the biological process and took part in several virusrelated signal pathway This phenomenon revealed that theSPF mice used in our study might infect some virusesHowever the certain thing is that the immune related genescan be expressed in ovarian granulosa cells not only inimmune cells Several earlier studies indicated that virusescan induce innate immune response in granulosa cells andperturb ovarian function in mouse [27 28] And immuneresponse genes were overexpressed with increasing age asshowed by several microarray studies of aging [29] Recentlythe concept that innate immunity is an essential requisitein the ovulation process is forwarded [30] The importantrole of innate immune cells in decreasing the senescence
burden was also recognized [31] There was a probabilitythat innate immune related genes were upregulated in theprocess of aging and affected the progress of ovarian agingYet the actual role of innate immunity in the processof ovarian aging or folliculogenesis needs to be furtherresearched
We conducted protein-protein interaction network anal-ysis of differentially expressed genes The nodes regarded ashub genes weremostly involved in innate immune system Asin the gene regulatory network many differentially expressedgenes between young and old mouse ovarian secondaryfollicles such as BRCA1 STAT3 JUN AKT1 SEPRING1TCF3 MAP3K7 and IRF7 showed their genetic interactionsby various pathways Thus a number of pathways wereinteracted through many genes in the process of ovarianaging
BioMed Research International 7
Table 3 Pathway enrichment analysis of differentially expressed genes in old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueUp-regulatedKEGG PATHWAY mmu05164Influenza A 15 86 225E-11KEGG PATHWAY mmu05168Herpes simplex infection 15 86 313E-10KEGG PATHWAY mmu05162Measles 11 63 623E-08KEGG PATHWAY mmu05160Hepatitis C 11 63 623E-08KEGG PATHWAY mmu04380Osteoclast differentiation 8 46 440E-05KEGG PATHWAY mmu05161Hepatitis B 8 46 112E-04KEGG PATHWAY mmu04622RIG-I-like receptor signaling pathway 6 34 158E-04KEGG PATHWAY mmu04668TNF signaling pathway 7 40 169E-04KEGG PATHWAY mmu04620Toll-like receptor signaling pathway 6 34 993E-04KEGG PATHWAY mmu05133Pertussis 5 29 238E-03Down-regulatedKEGG PATHWAY mmu04151PI3K-Akt signaling pathway 14 71 414E-04KEGG PATHWAY mmu05200Pathways in cancer 14 71 131E-03KEGG PATHWAY mmu04550Signaling pathways regulating pluripotency of stem cells 8 41 169E-03KEGG PATHWAY mmu04520Adherens junction 6 30 203E-03KEGG PATHWAY mmu04015Rap1 signaling pathway 9 46 533E-03KEGG PATHWAY mmu04390Hippo signaling pathway 7 36 116E-02KEGG PATHWAY mmu05145Toxoplasmosis 6 30 136E-02KEGG PATHWAY mmu04510Focal adhesion 8 41 151E-02KEGG PATHWAY mmu04022cGMP-PKG signaling pathway 7 36 204E-02KEGG PATHWAY mmu03040Spliceosome 6 30 257E-02lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
Mrpl19
Ccl12 Ifi47Gbp6
Mrpl9 Mfap5
Mrpl44Efemp1
Soat1
Hsd17b12
Adh1
Adam10Cyp11a1Ugt1a2
Star Aplp2
Efna1
Fosb
Gbp7Prl2c2
Iigp1
Irgm1Stat1Mt2
Mst4
Nup133
Zwint
Ranbp9
Taok1Wdr26
G6pd2 Ly6e CtssTpm3 Upk3b Atp6v1aEpcam Tmed7
Ptpn12 Gnpda2Upk1b Atp6v1b2 Ly6a Mpeg1Krt19 Arf4
Atf3Gbp3
Zfp36
Parp14Plau
Gbp2
Irgm2Serping1
Lgals3bp
Ifitm3 Ublcp1Cxcl10 Igtp
Irf7
Ddx58C2 Rtp4 JunbIfit3
Eif2ak2
Npm1Dnajb6
Paip1Thoc1Hspa4Csnk2a1
Ncl Dnttip2
Taf1bAcly Hspa8
Snrpn
Cct3
Tra2b
Sox4
Unc45b Sik1Fzd2
Rpf1Eif4a1 Mtmr6
Fabp4
Mccc2
Ctnnb1 Mtmr2PtprfYwhaz
Ywhaq
Hsp90aa1 Tbc1d1
HnrnpuSrsf1
Slu7
Elavl2Acaa2
Sf3b2
Esf1Pik3r1 Mtm1
Ssb
EdnraRif1 Hsph1
Rrm1 Pcgf6Rbl2
Brca1Pknox1Mbd2 Pvrl3
Psme4Uba7
Irf9Isg15 Ifi44
Herc6Gphn
Crk
Ddx60Stat3
AxlPard3Klf4
BC006779
Jak1
Isg20
Oas1a
Rbbp4 H3f3a
Exo1Plec
Tsc22d3
Dcun1d1Uba3
Ubc
Rrm2b
Rpa2Usp1
Rad51cGlrx
Keap1
Casp3 Dsg2Fos Egr1Usp18
Mt1
Nr4a1
Parp3
Akt1
Cyr61
Rap1b
Trim30a
OsmrHmgb1
Jun
Adcy7 Stk3Ppp2r2b
Eif4eThbs1
Ifi35Oasl2Ifit1
Dhx58Oas2
Rsad2Parp9
Ifih1
Mef2aBcl10Map3k7
Ifit2Tcf3
Tlr3
C3
Socs3Rnf19b
Fbxw14
Zbp1
Psmb8
Figure 3 Protein-protein interaction (PPI) network complex Using the SearchTool for the retrieval of InteractingGenes (STRING) databaseonline database 187 out of 371 differentially expressed genes (DEGs) (upregulated genes are displayed in red and downregulated genes ingreen) were filtered into the DEGs protein-protein interaction (PPI) network complex
8 BioMed Research International
Parp9
Tlr3
Ifih1
Ifi44
Gbp7
Iigp1
Gbp3
Rtp4
Dhx58
Gbp2
Irf9
Ddx60
Oasl2
Cxcl10
Isg15
Trim30a
Rsad2
Zbp1
Irf7
Herc6Ifit2Ifit1
Ifit3
Stat1
Ifi35
Oas2
Igtp Irgm1
Ddx58
Ifi47
Irgm2Usp18
Eif2ak2
Gbp6
Parp14
Figure 4 Overlapping protein complexes in the protein interactionThe most significant module consisted of 35 nodes and 286 edges andall genes in this module were upregulated (in red)
5 Conclusions
In conclusion our data showed quite different gene expres-sion patterns of the secondary follicles between young and oldmouse ovaries The differentially expressed genes involved inthe process of ovarian aging are central to biological processessuch as immune system process aging transcription DNAreplication DNA repair protein stabilization and apoptoticprocess However many upregulated genes in the old mouseovarian secondary follicles were innate immune relatedgenes We proposed that innate immune system may play avital role in the process of ovarian aging Our results of alteredgenes and related transcriptional networks may be helpful forunderstanding the mechanism of the folliculogenesis in theprocess of ovarian aging in mice
There are however limitations in the present studyFindings of our present research were mainly based
on the bioinformatics analysis and further experimentsare needed to verify Furthermore these data were ac-quired with secondary follicles from mouse ovariesand needed to be confirmed with the samples from thehuman
Data Availability
The results of our microarray data were made available in thepublic domain NCBI-GEO repository
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this article
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
[1] J C Stevenson ldquoA womanrsquos journey through the reproductivetransitional and postmenopausal periods of life Impact on car-diovascular and musculo-skeletal risk and the role of estrogenreplacementrdquoMaturitas vol 70 no 2 pp 197ndash205 2011
[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
4 BioMed Research International
Old-1 Old-2 Old-3 Young-1 Young-2 Young-3
1088
992
896
800
704
608
512
416
320
224
Figure 2 Heat map visualization for selected differentially expressed genes Heat map was produced by unsupervised hierarchical-clusteringanalysis frommicroarraydata for top 50upregulated genes and top 50downregulated genes in oldmouse ovarian secondary follicles comparedto young The relative expression levels of each gene are mentioned in different colors The red lines represent high expression while bluelines represent low expressionThe numbers in the right side corresponding to the different colors represent the relative expression levels ofeach gene
BioMed Research International 5
Table 1 Functional enrichment analysis of upregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P ValueGOTERM BP DIRECT GO0035458simcellular response to interferon-beta 15 86 562E-21GOTERM BP DIRECT GO0009615simresponse to virus 18 103 673E-21GOTERM BP DIRECT GO0051607simdefense response to virus 21 121 115E-19GOTERM BP DIRECT GO0002376simimmune system process 24 138 101E-15GOTERM BP DIRECT GO0045087siminnate immune response 23 132 270E-14GOTERM BP DIRECT GO0071346simcellular response to interferon-gamma 9 52 180E-08GOTERM BP DIRECT GO0042832simdefense response to protozoan 6 34 160E-06GOTERM BP DIRECT GO0006952simdefense response 8 46 136E-05GOTERM BP DIRECT GO0032870simcellular response to hormone stimulus 6 34 157E-05GOTERM BP DIRECT GO0034097simresponse to cytokine 7 40 179E-05GOTERM CC DIRECT GO0020005simsymbiont-containing vacuole membrane 5 29 146E-07GOTERM CC DIRECT GO0005829simcytosol 28 161 700E-05GOTERM CC DIRECT GO0005737simcytoplasm 65 374 661E-04GOTERM CC DIRECT GO0005634simnucleus 58 333 275E-03GOTERM CC DIRECT GO0048471simperinuclear region of cytoplasm 12 69 823E-03GOTERM CC DIRECT GO0072562simbloodmicroparticle 5 29 134E-02GOTERM CC DIRECT GO0031225simanchored component of membrane 5 29 163E-02GOTERM CC DIRECT GO0009897simexternal side of plasma membrane 7 40 223E-02GOTERM MF DIRECT GO0003725simdouble-stranded RNA binding 9 52 262E-08GOTERM MF DIRECT GO0003690simdouble-stranded DNA binding 11 63 333E-08GOTERM MF DIRECT GO0003924simGTPase activity 11 63 181E-06GOTERM MF DIRECT GO0005525simGTP binding 12 69 676E-05GOTERM MF DIRECT GO0008134simtranscription factor binding 10 57 597E-04GOTERM MF DIRECT GO0003723simRNA binding 15 86 940E-04GOTERM MF DIRECT GO0003677simDNA binding 25 144 158E-03GOTERM MF DIRECT GO0001730sim21015840-51015840-oligoadenylate synthetase activity 3 17 248E-03GOTERM MF DIRECT GO0016817simhydrolase activity acting on acid anhydrides 3 17 248E-03
GOTERM MF DIRECT GO0001077simtranscriptional activator activity RNA polymerase IIcore promoter proximal region sequence-specific binding 8 46 265E-03
lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
TCF3 MAP3K7 and IRF7 took part in various pathways(Figure 5)
4 Discussion
Elucidating the mechanism of ovarian aging has significantmeanings to female health The gradual decrease of boththe quantity and the quality of the oocytes surrounded bythe granulosa cells in all stages of follicles dominates thereproductive aging [4] In previous study Govindaraj et alrevealed that gene expression patterns changed considerablyin the rat primordial follicles in the process of ovarianaging [25] Folliculogenesis in the ovary is a highly dynamicand periodic process regulated by both intra- and extra-oocyte factors [26] At each reproductive cycle activatedprimordial follicles join the growing pool transiting to pri-mary follicles [26] Through further development a primaryfollicle grows into a secondary follicle [26] And these stagesare gonadotropin independent but depend on the complex
bidirectional communication between the oocyte and thesomatic cells [26] In the subsequent stages of folliculogenesisthe presence of pituitary gonadotropins follicle-stimulatinghormone (FSH) and luteinizing hormone (LH) are required[26] So the secondary follicles from the mouse ovaries wereselected as research objective in our study
Gene expression profile of the secondary follicles from theyoung and old mouse ovaries was compared by microarrayanalysis to find what changed in the process of ovarian agingin the present studyThe results of our research found that 174genes were upregulated and 197 genes were downregulated inthe secondary follicles from the old mouse ovaries comparedto those from the young mouse ovaries
FurtherGOandKEGGpathway analyses were performedto study the function of the differentially expressed genesThe result of GO analysis showed that the upregulated geneswere mainly involved in biological process such as immunesystem process and defense response while downregulatedgenes were closely related to gene transcription and cell
6 BioMed Research International
Table 2 Functional enrichment analysis of downregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueGOTERM BP DIRECT GO0006355simregulation of transcription DNA-templated 42 213 702E-05GOTERM BP DIRECT GO0016311simdephosphorylation 8 41 984E-05GOTERM BP DIRECT GO0006351simtranscription DNA-templated 35 178 319E-04GOTERM BP DIRECT GO0045893simpositive regulation of transcription DNA-templated 16 81 556E-04GOTERM BP DIRECT GO0030335simpositive regulation of cell migration 9 46 887E-04GOTERM BP DIRECT GO0006470simprotein dephosphorylation 7 36 239E-03
GOTERM BP DIRECT GO0043154simnegative regulation of cysteine-type endopeptidaseactivity involved in apoptotic process 5 25 421E-03
GOTERM BP DIRECT GO0030177simpositive regulation of Wnt signaling pathway 4 20 561E-03GOTERM BP DIRECT GO0043065simpositive regulation of apoptotic process 10 51 585E-03GOTERM BP DIRECT GO0097194simexecution phase of apoptosis 3 15 692E-03GOTERM CC DIRECT GO0005634simnucleus 106 538 323E-13GOTERM CC DIRECT GO0005737simcytoplasm 100 508 449E-08GOTERM CC DIRECT GO0005654simnucleoplasm 44 223 926E-08GOTERM CC DIRECT GO0070062simextracellular exosome 50 254 319E-06GOTERM CC DIRECT GO0043234simprotein complex 19 96 326E-05GOTERM CC DIRECT GO0071013simcatalytic step 2 spliceosome 6 30 224E-03GOTERM CC DIRECT GO0005925simfocal adhesion 11 56 430E-03GOTERM CC DIRECT GO0005829simcytosol 29 147 576E-03GOTERM CC DIRECT GO0030529simintracellular ribonucleoprotein complex 9 46 117E-02GOTERM CC DIRECT GO0005911simcell-cell junction 7 36 126E-02GOTERM MF DIRECT GO0005515simprotein binding 77 391 699E-09GOTERM MF DIRECT GO0044822simpoly(A) RNA binding 30 152 252E-06GOTERM MF DIRECT GO0003723simRNA binding 20 102 371E-04GOTERM MF DIRECT GO0003677simDNA binding 35 178 423E-04GOTERM MF DIRECT GO0004725simprotein tyrosine phosphatase activity 7 36 462E-04GOTERM MF DIRECT GO0046982simprotein heterodimerization activity 15 76 777E-04GOTERM MF DIRECT GO0000166simnucleotide binding 35 178 978E-04GOTERM MF DIRECT GO0016791simphosphatase activity 7 36 129E-03GOTERM MF DIRECT GO0019903simprotein phosphatase binding 6 30 202E-03GOTERM MF DIRECT GO0005524simATP binding 28 142 262E-03lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
apoptosis However there was an unexpected phenomenonin the results of our functional enrichment Many upregu-lated genes were involved in response to virus and interferonin the biological process and took part in several virusrelated signal pathway This phenomenon revealed that theSPF mice used in our study might infect some virusesHowever the certain thing is that the immune related genescan be expressed in ovarian granulosa cells not only inimmune cells Several earlier studies indicated that virusescan induce innate immune response in granulosa cells andperturb ovarian function in mouse [27 28] And immuneresponse genes were overexpressed with increasing age asshowed by several microarray studies of aging [29] Recentlythe concept that innate immunity is an essential requisitein the ovulation process is forwarded [30] The importantrole of innate immune cells in decreasing the senescence
burden was also recognized [31] There was a probabilitythat innate immune related genes were upregulated in theprocess of aging and affected the progress of ovarian agingYet the actual role of innate immunity in the processof ovarian aging or folliculogenesis needs to be furtherresearched
We conducted protein-protein interaction network anal-ysis of differentially expressed genes The nodes regarded ashub genes weremostly involved in innate immune system Asin the gene regulatory network many differentially expressedgenes between young and old mouse ovarian secondaryfollicles such as BRCA1 STAT3 JUN AKT1 SEPRING1TCF3 MAP3K7 and IRF7 showed their genetic interactionsby various pathways Thus a number of pathways wereinteracted through many genes in the process of ovarianaging
BioMed Research International 7
Table 3 Pathway enrichment analysis of differentially expressed genes in old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueUp-regulatedKEGG PATHWAY mmu05164Influenza A 15 86 225E-11KEGG PATHWAY mmu05168Herpes simplex infection 15 86 313E-10KEGG PATHWAY mmu05162Measles 11 63 623E-08KEGG PATHWAY mmu05160Hepatitis C 11 63 623E-08KEGG PATHWAY mmu04380Osteoclast differentiation 8 46 440E-05KEGG PATHWAY mmu05161Hepatitis B 8 46 112E-04KEGG PATHWAY mmu04622RIG-I-like receptor signaling pathway 6 34 158E-04KEGG PATHWAY mmu04668TNF signaling pathway 7 40 169E-04KEGG PATHWAY mmu04620Toll-like receptor signaling pathway 6 34 993E-04KEGG PATHWAY mmu05133Pertussis 5 29 238E-03Down-regulatedKEGG PATHWAY mmu04151PI3K-Akt signaling pathway 14 71 414E-04KEGG PATHWAY mmu05200Pathways in cancer 14 71 131E-03KEGG PATHWAY mmu04550Signaling pathways regulating pluripotency of stem cells 8 41 169E-03KEGG PATHWAY mmu04520Adherens junction 6 30 203E-03KEGG PATHWAY mmu04015Rap1 signaling pathway 9 46 533E-03KEGG PATHWAY mmu04390Hippo signaling pathway 7 36 116E-02KEGG PATHWAY mmu05145Toxoplasmosis 6 30 136E-02KEGG PATHWAY mmu04510Focal adhesion 8 41 151E-02KEGG PATHWAY mmu04022cGMP-PKG signaling pathway 7 36 204E-02KEGG PATHWAY mmu03040Spliceosome 6 30 257E-02lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
Mrpl19
Ccl12 Ifi47Gbp6
Mrpl9 Mfap5
Mrpl44Efemp1
Soat1
Hsd17b12
Adh1
Adam10Cyp11a1Ugt1a2
Star Aplp2
Efna1
Fosb
Gbp7Prl2c2
Iigp1
Irgm1Stat1Mt2
Mst4
Nup133
Zwint
Ranbp9
Taok1Wdr26
G6pd2 Ly6e CtssTpm3 Upk3b Atp6v1aEpcam Tmed7
Ptpn12 Gnpda2Upk1b Atp6v1b2 Ly6a Mpeg1Krt19 Arf4
Atf3Gbp3
Zfp36
Parp14Plau
Gbp2
Irgm2Serping1
Lgals3bp
Ifitm3 Ublcp1Cxcl10 Igtp
Irf7
Ddx58C2 Rtp4 JunbIfit3
Eif2ak2
Npm1Dnajb6
Paip1Thoc1Hspa4Csnk2a1
Ncl Dnttip2
Taf1bAcly Hspa8
Snrpn
Cct3
Tra2b
Sox4
Unc45b Sik1Fzd2
Rpf1Eif4a1 Mtmr6
Fabp4
Mccc2
Ctnnb1 Mtmr2PtprfYwhaz
Ywhaq
Hsp90aa1 Tbc1d1
HnrnpuSrsf1
Slu7
Elavl2Acaa2
Sf3b2
Esf1Pik3r1 Mtm1
Ssb
EdnraRif1 Hsph1
Rrm1 Pcgf6Rbl2
Brca1Pknox1Mbd2 Pvrl3
Psme4Uba7
Irf9Isg15 Ifi44
Herc6Gphn
Crk
Ddx60Stat3
AxlPard3Klf4
BC006779
Jak1
Isg20
Oas1a
Rbbp4 H3f3a
Exo1Plec
Tsc22d3
Dcun1d1Uba3
Ubc
Rrm2b
Rpa2Usp1
Rad51cGlrx
Keap1
Casp3 Dsg2Fos Egr1Usp18
Mt1
Nr4a1
Parp3
Akt1
Cyr61
Rap1b
Trim30a
OsmrHmgb1
Jun
Adcy7 Stk3Ppp2r2b
Eif4eThbs1
Ifi35Oasl2Ifit1
Dhx58Oas2
Rsad2Parp9
Ifih1
Mef2aBcl10Map3k7
Ifit2Tcf3
Tlr3
C3
Socs3Rnf19b
Fbxw14
Zbp1
Psmb8
Figure 3 Protein-protein interaction (PPI) network complex Using the SearchTool for the retrieval of InteractingGenes (STRING) databaseonline database 187 out of 371 differentially expressed genes (DEGs) (upregulated genes are displayed in red and downregulated genes ingreen) were filtered into the DEGs protein-protein interaction (PPI) network complex
8 BioMed Research International
Parp9
Tlr3
Ifih1
Ifi44
Gbp7
Iigp1
Gbp3
Rtp4
Dhx58
Gbp2
Irf9
Ddx60
Oasl2
Cxcl10
Isg15
Trim30a
Rsad2
Zbp1
Irf7
Herc6Ifit2Ifit1
Ifit3
Stat1
Ifi35
Oas2
Igtp Irgm1
Ddx58
Ifi47
Irgm2Usp18
Eif2ak2
Gbp6
Parp14
Figure 4 Overlapping protein complexes in the protein interactionThe most significant module consisted of 35 nodes and 286 edges andall genes in this module were upregulated (in red)
5 Conclusions
In conclusion our data showed quite different gene expres-sion patterns of the secondary follicles between young and oldmouse ovaries The differentially expressed genes involved inthe process of ovarian aging are central to biological processessuch as immune system process aging transcription DNAreplication DNA repair protein stabilization and apoptoticprocess However many upregulated genes in the old mouseovarian secondary follicles were innate immune relatedgenes We proposed that innate immune system may play avital role in the process of ovarian aging Our results of alteredgenes and related transcriptional networks may be helpful forunderstanding the mechanism of the folliculogenesis in theprocess of ovarian aging in mice
There are however limitations in the present studyFindings of our present research were mainly based
on the bioinformatics analysis and further experimentsare needed to verify Furthermore these data were ac-quired with secondary follicles from mouse ovariesand needed to be confirmed with the samples from thehuman
Data Availability
The results of our microarray data were made available in thepublic domain NCBI-GEO repository
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this article
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
[1] J C Stevenson ldquoA womanrsquos journey through the reproductivetransitional and postmenopausal periods of life Impact on car-diovascular and musculo-skeletal risk and the role of estrogenreplacementrdquoMaturitas vol 70 no 2 pp 197ndash205 2011
[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
BioMed Research International 5
Table 1 Functional enrichment analysis of upregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P ValueGOTERM BP DIRECT GO0035458simcellular response to interferon-beta 15 86 562E-21GOTERM BP DIRECT GO0009615simresponse to virus 18 103 673E-21GOTERM BP DIRECT GO0051607simdefense response to virus 21 121 115E-19GOTERM BP DIRECT GO0002376simimmune system process 24 138 101E-15GOTERM BP DIRECT GO0045087siminnate immune response 23 132 270E-14GOTERM BP DIRECT GO0071346simcellular response to interferon-gamma 9 52 180E-08GOTERM BP DIRECT GO0042832simdefense response to protozoan 6 34 160E-06GOTERM BP DIRECT GO0006952simdefense response 8 46 136E-05GOTERM BP DIRECT GO0032870simcellular response to hormone stimulus 6 34 157E-05GOTERM BP DIRECT GO0034097simresponse to cytokine 7 40 179E-05GOTERM CC DIRECT GO0020005simsymbiont-containing vacuole membrane 5 29 146E-07GOTERM CC DIRECT GO0005829simcytosol 28 161 700E-05GOTERM CC DIRECT GO0005737simcytoplasm 65 374 661E-04GOTERM CC DIRECT GO0005634simnucleus 58 333 275E-03GOTERM CC DIRECT GO0048471simperinuclear region of cytoplasm 12 69 823E-03GOTERM CC DIRECT GO0072562simbloodmicroparticle 5 29 134E-02GOTERM CC DIRECT GO0031225simanchored component of membrane 5 29 163E-02GOTERM CC DIRECT GO0009897simexternal side of plasma membrane 7 40 223E-02GOTERM MF DIRECT GO0003725simdouble-stranded RNA binding 9 52 262E-08GOTERM MF DIRECT GO0003690simdouble-stranded DNA binding 11 63 333E-08GOTERM MF DIRECT GO0003924simGTPase activity 11 63 181E-06GOTERM MF DIRECT GO0005525simGTP binding 12 69 676E-05GOTERM MF DIRECT GO0008134simtranscription factor binding 10 57 597E-04GOTERM MF DIRECT GO0003723simRNA binding 15 86 940E-04GOTERM MF DIRECT GO0003677simDNA binding 25 144 158E-03GOTERM MF DIRECT GO0001730sim21015840-51015840-oligoadenylate synthetase activity 3 17 248E-03GOTERM MF DIRECT GO0016817simhydrolase activity acting on acid anhydrides 3 17 248E-03
GOTERM MF DIRECT GO0001077simtranscriptional activator activity RNA polymerase IIcore promoter proximal region sequence-specific binding 8 46 265E-03
lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
TCF3 MAP3K7 and IRF7 took part in various pathways(Figure 5)
4 Discussion
Elucidating the mechanism of ovarian aging has significantmeanings to female health The gradual decrease of boththe quantity and the quality of the oocytes surrounded bythe granulosa cells in all stages of follicles dominates thereproductive aging [4] In previous study Govindaraj et alrevealed that gene expression patterns changed considerablyin the rat primordial follicles in the process of ovarianaging [25] Folliculogenesis in the ovary is a highly dynamicand periodic process regulated by both intra- and extra-oocyte factors [26] At each reproductive cycle activatedprimordial follicles join the growing pool transiting to pri-mary follicles [26] Through further development a primaryfollicle grows into a secondary follicle [26] And these stagesare gonadotropin independent but depend on the complex
bidirectional communication between the oocyte and thesomatic cells [26] In the subsequent stages of folliculogenesisthe presence of pituitary gonadotropins follicle-stimulatinghormone (FSH) and luteinizing hormone (LH) are required[26] So the secondary follicles from the mouse ovaries wereselected as research objective in our study
Gene expression profile of the secondary follicles from theyoung and old mouse ovaries was compared by microarrayanalysis to find what changed in the process of ovarian agingin the present studyThe results of our research found that 174genes were upregulated and 197 genes were downregulated inthe secondary follicles from the old mouse ovaries comparedto those from the young mouse ovaries
FurtherGOandKEGGpathway analyses were performedto study the function of the differentially expressed genesThe result of GO analysis showed that the upregulated geneswere mainly involved in biological process such as immunesystem process and defense response while downregulatedgenes were closely related to gene transcription and cell
6 BioMed Research International
Table 2 Functional enrichment analysis of downregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueGOTERM BP DIRECT GO0006355simregulation of transcription DNA-templated 42 213 702E-05GOTERM BP DIRECT GO0016311simdephosphorylation 8 41 984E-05GOTERM BP DIRECT GO0006351simtranscription DNA-templated 35 178 319E-04GOTERM BP DIRECT GO0045893simpositive regulation of transcription DNA-templated 16 81 556E-04GOTERM BP DIRECT GO0030335simpositive regulation of cell migration 9 46 887E-04GOTERM BP DIRECT GO0006470simprotein dephosphorylation 7 36 239E-03
GOTERM BP DIRECT GO0043154simnegative regulation of cysteine-type endopeptidaseactivity involved in apoptotic process 5 25 421E-03
GOTERM BP DIRECT GO0030177simpositive regulation of Wnt signaling pathway 4 20 561E-03GOTERM BP DIRECT GO0043065simpositive regulation of apoptotic process 10 51 585E-03GOTERM BP DIRECT GO0097194simexecution phase of apoptosis 3 15 692E-03GOTERM CC DIRECT GO0005634simnucleus 106 538 323E-13GOTERM CC DIRECT GO0005737simcytoplasm 100 508 449E-08GOTERM CC DIRECT GO0005654simnucleoplasm 44 223 926E-08GOTERM CC DIRECT GO0070062simextracellular exosome 50 254 319E-06GOTERM CC DIRECT GO0043234simprotein complex 19 96 326E-05GOTERM CC DIRECT GO0071013simcatalytic step 2 spliceosome 6 30 224E-03GOTERM CC DIRECT GO0005925simfocal adhesion 11 56 430E-03GOTERM CC DIRECT GO0005829simcytosol 29 147 576E-03GOTERM CC DIRECT GO0030529simintracellular ribonucleoprotein complex 9 46 117E-02GOTERM CC DIRECT GO0005911simcell-cell junction 7 36 126E-02GOTERM MF DIRECT GO0005515simprotein binding 77 391 699E-09GOTERM MF DIRECT GO0044822simpoly(A) RNA binding 30 152 252E-06GOTERM MF DIRECT GO0003723simRNA binding 20 102 371E-04GOTERM MF DIRECT GO0003677simDNA binding 35 178 423E-04GOTERM MF DIRECT GO0004725simprotein tyrosine phosphatase activity 7 36 462E-04GOTERM MF DIRECT GO0046982simprotein heterodimerization activity 15 76 777E-04GOTERM MF DIRECT GO0000166simnucleotide binding 35 178 978E-04GOTERM MF DIRECT GO0016791simphosphatase activity 7 36 129E-03GOTERM MF DIRECT GO0019903simprotein phosphatase binding 6 30 202E-03GOTERM MF DIRECT GO0005524simATP binding 28 142 262E-03lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
apoptosis However there was an unexpected phenomenonin the results of our functional enrichment Many upregu-lated genes were involved in response to virus and interferonin the biological process and took part in several virusrelated signal pathway This phenomenon revealed that theSPF mice used in our study might infect some virusesHowever the certain thing is that the immune related genescan be expressed in ovarian granulosa cells not only inimmune cells Several earlier studies indicated that virusescan induce innate immune response in granulosa cells andperturb ovarian function in mouse [27 28] And immuneresponse genes were overexpressed with increasing age asshowed by several microarray studies of aging [29] Recentlythe concept that innate immunity is an essential requisitein the ovulation process is forwarded [30] The importantrole of innate immune cells in decreasing the senescence
burden was also recognized [31] There was a probabilitythat innate immune related genes were upregulated in theprocess of aging and affected the progress of ovarian agingYet the actual role of innate immunity in the processof ovarian aging or folliculogenesis needs to be furtherresearched
We conducted protein-protein interaction network anal-ysis of differentially expressed genes The nodes regarded ashub genes weremostly involved in innate immune system Asin the gene regulatory network many differentially expressedgenes between young and old mouse ovarian secondaryfollicles such as BRCA1 STAT3 JUN AKT1 SEPRING1TCF3 MAP3K7 and IRF7 showed their genetic interactionsby various pathways Thus a number of pathways wereinteracted through many genes in the process of ovarianaging
BioMed Research International 7
Table 3 Pathway enrichment analysis of differentially expressed genes in old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueUp-regulatedKEGG PATHWAY mmu05164Influenza A 15 86 225E-11KEGG PATHWAY mmu05168Herpes simplex infection 15 86 313E-10KEGG PATHWAY mmu05162Measles 11 63 623E-08KEGG PATHWAY mmu05160Hepatitis C 11 63 623E-08KEGG PATHWAY mmu04380Osteoclast differentiation 8 46 440E-05KEGG PATHWAY mmu05161Hepatitis B 8 46 112E-04KEGG PATHWAY mmu04622RIG-I-like receptor signaling pathway 6 34 158E-04KEGG PATHWAY mmu04668TNF signaling pathway 7 40 169E-04KEGG PATHWAY mmu04620Toll-like receptor signaling pathway 6 34 993E-04KEGG PATHWAY mmu05133Pertussis 5 29 238E-03Down-regulatedKEGG PATHWAY mmu04151PI3K-Akt signaling pathway 14 71 414E-04KEGG PATHWAY mmu05200Pathways in cancer 14 71 131E-03KEGG PATHWAY mmu04550Signaling pathways regulating pluripotency of stem cells 8 41 169E-03KEGG PATHWAY mmu04520Adherens junction 6 30 203E-03KEGG PATHWAY mmu04015Rap1 signaling pathway 9 46 533E-03KEGG PATHWAY mmu04390Hippo signaling pathway 7 36 116E-02KEGG PATHWAY mmu05145Toxoplasmosis 6 30 136E-02KEGG PATHWAY mmu04510Focal adhesion 8 41 151E-02KEGG PATHWAY mmu04022cGMP-PKG signaling pathway 7 36 204E-02KEGG PATHWAY mmu03040Spliceosome 6 30 257E-02lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
Mrpl19
Ccl12 Ifi47Gbp6
Mrpl9 Mfap5
Mrpl44Efemp1
Soat1
Hsd17b12
Adh1
Adam10Cyp11a1Ugt1a2
Star Aplp2
Efna1
Fosb
Gbp7Prl2c2
Iigp1
Irgm1Stat1Mt2
Mst4
Nup133
Zwint
Ranbp9
Taok1Wdr26
G6pd2 Ly6e CtssTpm3 Upk3b Atp6v1aEpcam Tmed7
Ptpn12 Gnpda2Upk1b Atp6v1b2 Ly6a Mpeg1Krt19 Arf4
Atf3Gbp3
Zfp36
Parp14Plau
Gbp2
Irgm2Serping1
Lgals3bp
Ifitm3 Ublcp1Cxcl10 Igtp
Irf7
Ddx58C2 Rtp4 JunbIfit3
Eif2ak2
Npm1Dnajb6
Paip1Thoc1Hspa4Csnk2a1
Ncl Dnttip2
Taf1bAcly Hspa8
Snrpn
Cct3
Tra2b
Sox4
Unc45b Sik1Fzd2
Rpf1Eif4a1 Mtmr6
Fabp4
Mccc2
Ctnnb1 Mtmr2PtprfYwhaz
Ywhaq
Hsp90aa1 Tbc1d1
HnrnpuSrsf1
Slu7
Elavl2Acaa2
Sf3b2
Esf1Pik3r1 Mtm1
Ssb
EdnraRif1 Hsph1
Rrm1 Pcgf6Rbl2
Brca1Pknox1Mbd2 Pvrl3
Psme4Uba7
Irf9Isg15 Ifi44
Herc6Gphn
Crk
Ddx60Stat3
AxlPard3Klf4
BC006779
Jak1
Isg20
Oas1a
Rbbp4 H3f3a
Exo1Plec
Tsc22d3
Dcun1d1Uba3
Ubc
Rrm2b
Rpa2Usp1
Rad51cGlrx
Keap1
Casp3 Dsg2Fos Egr1Usp18
Mt1
Nr4a1
Parp3
Akt1
Cyr61
Rap1b
Trim30a
OsmrHmgb1
Jun
Adcy7 Stk3Ppp2r2b
Eif4eThbs1
Ifi35Oasl2Ifit1
Dhx58Oas2
Rsad2Parp9
Ifih1
Mef2aBcl10Map3k7
Ifit2Tcf3
Tlr3
C3
Socs3Rnf19b
Fbxw14
Zbp1
Psmb8
Figure 3 Protein-protein interaction (PPI) network complex Using the SearchTool for the retrieval of InteractingGenes (STRING) databaseonline database 187 out of 371 differentially expressed genes (DEGs) (upregulated genes are displayed in red and downregulated genes ingreen) were filtered into the DEGs protein-protein interaction (PPI) network complex
8 BioMed Research International
Parp9
Tlr3
Ifih1
Ifi44
Gbp7
Iigp1
Gbp3
Rtp4
Dhx58
Gbp2
Irf9
Ddx60
Oasl2
Cxcl10
Isg15
Trim30a
Rsad2
Zbp1
Irf7
Herc6Ifit2Ifit1
Ifit3
Stat1
Ifi35
Oas2
Igtp Irgm1
Ddx58
Ifi47
Irgm2Usp18
Eif2ak2
Gbp6
Parp14
Figure 4 Overlapping protein complexes in the protein interactionThe most significant module consisted of 35 nodes and 286 edges andall genes in this module were upregulated (in red)
5 Conclusions
In conclusion our data showed quite different gene expres-sion patterns of the secondary follicles between young and oldmouse ovaries The differentially expressed genes involved inthe process of ovarian aging are central to biological processessuch as immune system process aging transcription DNAreplication DNA repair protein stabilization and apoptoticprocess However many upregulated genes in the old mouseovarian secondary follicles were innate immune relatedgenes We proposed that innate immune system may play avital role in the process of ovarian aging Our results of alteredgenes and related transcriptional networks may be helpful forunderstanding the mechanism of the folliculogenesis in theprocess of ovarian aging in mice
There are however limitations in the present studyFindings of our present research were mainly based
on the bioinformatics analysis and further experimentsare needed to verify Furthermore these data were ac-quired with secondary follicles from mouse ovariesand needed to be confirmed with the samples from thehuman
Data Availability
The results of our microarray data were made available in thepublic domain NCBI-GEO repository
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this article
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
[1] J C Stevenson ldquoA womanrsquos journey through the reproductivetransitional and postmenopausal periods of life Impact on car-diovascular and musculo-skeletal risk and the role of estrogenreplacementrdquoMaturitas vol 70 no 2 pp 197ndash205 2011
[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
6 BioMed Research International
Table 2 Functional enrichment analysis of downregulated genes in the old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueGOTERM BP DIRECT GO0006355simregulation of transcription DNA-templated 42 213 702E-05GOTERM BP DIRECT GO0016311simdephosphorylation 8 41 984E-05GOTERM BP DIRECT GO0006351simtranscription DNA-templated 35 178 319E-04GOTERM BP DIRECT GO0045893simpositive regulation of transcription DNA-templated 16 81 556E-04GOTERM BP DIRECT GO0030335simpositive regulation of cell migration 9 46 887E-04GOTERM BP DIRECT GO0006470simprotein dephosphorylation 7 36 239E-03
GOTERM BP DIRECT GO0043154simnegative regulation of cysteine-type endopeptidaseactivity involved in apoptotic process 5 25 421E-03
GOTERM BP DIRECT GO0030177simpositive regulation of Wnt signaling pathway 4 20 561E-03GOTERM BP DIRECT GO0043065simpositive regulation of apoptotic process 10 51 585E-03GOTERM BP DIRECT GO0097194simexecution phase of apoptosis 3 15 692E-03GOTERM CC DIRECT GO0005634simnucleus 106 538 323E-13GOTERM CC DIRECT GO0005737simcytoplasm 100 508 449E-08GOTERM CC DIRECT GO0005654simnucleoplasm 44 223 926E-08GOTERM CC DIRECT GO0070062simextracellular exosome 50 254 319E-06GOTERM CC DIRECT GO0043234simprotein complex 19 96 326E-05GOTERM CC DIRECT GO0071013simcatalytic step 2 spliceosome 6 30 224E-03GOTERM CC DIRECT GO0005925simfocal adhesion 11 56 430E-03GOTERM CC DIRECT GO0005829simcytosol 29 147 576E-03GOTERM CC DIRECT GO0030529simintracellular ribonucleoprotein complex 9 46 117E-02GOTERM CC DIRECT GO0005911simcell-cell junction 7 36 126E-02GOTERM MF DIRECT GO0005515simprotein binding 77 391 699E-09GOTERM MF DIRECT GO0044822simpoly(A) RNA binding 30 152 252E-06GOTERM MF DIRECT GO0003723simRNA binding 20 102 371E-04GOTERM MF DIRECT GO0003677simDNA binding 35 178 423E-04GOTERM MF DIRECT GO0004725simprotein tyrosine phosphatase activity 7 36 462E-04GOTERM MF DIRECT GO0046982simprotein heterodimerization activity 15 76 777E-04GOTERM MF DIRECT GO0000166simnucleotide binding 35 178 978E-04GOTERM MF DIRECT GO0016791simphosphatase activity 7 36 129E-03GOTERM MF DIRECT GO0019903simprotein phosphatase binding 6 30 202E-03GOTERM MF DIRECT GO0005524simATP binding 28 142 262E-03lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
apoptosis However there was an unexpected phenomenonin the results of our functional enrichment Many upregu-lated genes were involved in response to virus and interferonin the biological process and took part in several virusrelated signal pathway This phenomenon revealed that theSPF mice used in our study might infect some virusesHowever the certain thing is that the immune related genescan be expressed in ovarian granulosa cells not only inimmune cells Several earlier studies indicated that virusescan induce innate immune response in granulosa cells andperturb ovarian function in mouse [27 28] And immuneresponse genes were overexpressed with increasing age asshowed by several microarray studies of aging [29] Recentlythe concept that innate immunity is an essential requisitein the ovulation process is forwarded [30] The importantrole of innate immune cells in decreasing the senescence
burden was also recognized [31] There was a probabilitythat innate immune related genes were upregulated in theprocess of aging and affected the progress of ovarian agingYet the actual role of innate immunity in the processof ovarian aging or folliculogenesis needs to be furtherresearched
We conducted protein-protein interaction network anal-ysis of differentially expressed genes The nodes regarded ashub genes weremostly involved in innate immune system Asin the gene regulatory network many differentially expressedgenes between young and old mouse ovarian secondaryfollicles such as BRCA1 STAT3 JUN AKT1 SEPRING1TCF3 MAP3K7 and IRF7 showed their genetic interactionsby various pathways Thus a number of pathways wereinteracted through many genes in the process of ovarianaging
BioMed Research International 7
Table 3 Pathway enrichment analysis of differentially expressed genes in old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueUp-regulatedKEGG PATHWAY mmu05164Influenza A 15 86 225E-11KEGG PATHWAY mmu05168Herpes simplex infection 15 86 313E-10KEGG PATHWAY mmu05162Measles 11 63 623E-08KEGG PATHWAY mmu05160Hepatitis C 11 63 623E-08KEGG PATHWAY mmu04380Osteoclast differentiation 8 46 440E-05KEGG PATHWAY mmu05161Hepatitis B 8 46 112E-04KEGG PATHWAY mmu04622RIG-I-like receptor signaling pathway 6 34 158E-04KEGG PATHWAY mmu04668TNF signaling pathway 7 40 169E-04KEGG PATHWAY mmu04620Toll-like receptor signaling pathway 6 34 993E-04KEGG PATHWAY mmu05133Pertussis 5 29 238E-03Down-regulatedKEGG PATHWAY mmu04151PI3K-Akt signaling pathway 14 71 414E-04KEGG PATHWAY mmu05200Pathways in cancer 14 71 131E-03KEGG PATHWAY mmu04550Signaling pathways regulating pluripotency of stem cells 8 41 169E-03KEGG PATHWAY mmu04520Adherens junction 6 30 203E-03KEGG PATHWAY mmu04015Rap1 signaling pathway 9 46 533E-03KEGG PATHWAY mmu04390Hippo signaling pathway 7 36 116E-02KEGG PATHWAY mmu05145Toxoplasmosis 6 30 136E-02KEGG PATHWAY mmu04510Focal adhesion 8 41 151E-02KEGG PATHWAY mmu04022cGMP-PKG signaling pathway 7 36 204E-02KEGG PATHWAY mmu03040Spliceosome 6 30 257E-02lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
Mrpl19
Ccl12 Ifi47Gbp6
Mrpl9 Mfap5
Mrpl44Efemp1
Soat1
Hsd17b12
Adh1
Adam10Cyp11a1Ugt1a2
Star Aplp2
Efna1
Fosb
Gbp7Prl2c2
Iigp1
Irgm1Stat1Mt2
Mst4
Nup133
Zwint
Ranbp9
Taok1Wdr26
G6pd2 Ly6e CtssTpm3 Upk3b Atp6v1aEpcam Tmed7
Ptpn12 Gnpda2Upk1b Atp6v1b2 Ly6a Mpeg1Krt19 Arf4
Atf3Gbp3
Zfp36
Parp14Plau
Gbp2
Irgm2Serping1
Lgals3bp
Ifitm3 Ublcp1Cxcl10 Igtp
Irf7
Ddx58C2 Rtp4 JunbIfit3
Eif2ak2
Npm1Dnajb6
Paip1Thoc1Hspa4Csnk2a1
Ncl Dnttip2
Taf1bAcly Hspa8
Snrpn
Cct3
Tra2b
Sox4
Unc45b Sik1Fzd2
Rpf1Eif4a1 Mtmr6
Fabp4
Mccc2
Ctnnb1 Mtmr2PtprfYwhaz
Ywhaq
Hsp90aa1 Tbc1d1
HnrnpuSrsf1
Slu7
Elavl2Acaa2
Sf3b2
Esf1Pik3r1 Mtm1
Ssb
EdnraRif1 Hsph1
Rrm1 Pcgf6Rbl2
Brca1Pknox1Mbd2 Pvrl3
Psme4Uba7
Irf9Isg15 Ifi44
Herc6Gphn
Crk
Ddx60Stat3
AxlPard3Klf4
BC006779
Jak1
Isg20
Oas1a
Rbbp4 H3f3a
Exo1Plec
Tsc22d3
Dcun1d1Uba3
Ubc
Rrm2b
Rpa2Usp1
Rad51cGlrx
Keap1
Casp3 Dsg2Fos Egr1Usp18
Mt1
Nr4a1
Parp3
Akt1
Cyr61
Rap1b
Trim30a
OsmrHmgb1
Jun
Adcy7 Stk3Ppp2r2b
Eif4eThbs1
Ifi35Oasl2Ifit1
Dhx58Oas2
Rsad2Parp9
Ifih1
Mef2aBcl10Map3k7
Ifit2Tcf3
Tlr3
C3
Socs3Rnf19b
Fbxw14
Zbp1
Psmb8
Figure 3 Protein-protein interaction (PPI) network complex Using the SearchTool for the retrieval of InteractingGenes (STRING) databaseonline database 187 out of 371 differentially expressed genes (DEGs) (upregulated genes are displayed in red and downregulated genes ingreen) were filtered into the DEGs protein-protein interaction (PPI) network complex
8 BioMed Research International
Parp9
Tlr3
Ifih1
Ifi44
Gbp7
Iigp1
Gbp3
Rtp4
Dhx58
Gbp2
Irf9
Ddx60
Oasl2
Cxcl10
Isg15
Trim30a
Rsad2
Zbp1
Irf7
Herc6Ifit2Ifit1
Ifit3
Stat1
Ifi35
Oas2
Igtp Irgm1
Ddx58
Ifi47
Irgm2Usp18
Eif2ak2
Gbp6
Parp14
Figure 4 Overlapping protein complexes in the protein interactionThe most significant module consisted of 35 nodes and 286 edges andall genes in this module were upregulated (in red)
5 Conclusions
In conclusion our data showed quite different gene expres-sion patterns of the secondary follicles between young and oldmouse ovaries The differentially expressed genes involved inthe process of ovarian aging are central to biological processessuch as immune system process aging transcription DNAreplication DNA repair protein stabilization and apoptoticprocess However many upregulated genes in the old mouseovarian secondary follicles were innate immune relatedgenes We proposed that innate immune system may play avital role in the process of ovarian aging Our results of alteredgenes and related transcriptional networks may be helpful forunderstanding the mechanism of the folliculogenesis in theprocess of ovarian aging in mice
There are however limitations in the present studyFindings of our present research were mainly based
on the bioinformatics analysis and further experimentsare needed to verify Furthermore these data were ac-quired with secondary follicles from mouse ovariesand needed to be confirmed with the samples from thehuman
Data Availability
The results of our microarray data were made available in thepublic domain NCBI-GEO repository
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this article
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
[1] J C Stevenson ldquoA womanrsquos journey through the reproductivetransitional and postmenopausal periods of life Impact on car-diovascular and musculo-skeletal risk and the role of estrogenreplacementrdquoMaturitas vol 70 no 2 pp 197ndash205 2011
[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
BioMed Research International 7
Table 3 Pathway enrichment analysis of differentially expressed genes in old mouse ovarian secondary follicles compared to young mouse
Category Term Countlowast P valueUp-regulatedKEGG PATHWAY mmu05164Influenza A 15 86 225E-11KEGG PATHWAY mmu05168Herpes simplex infection 15 86 313E-10KEGG PATHWAY mmu05162Measles 11 63 623E-08KEGG PATHWAY mmu05160Hepatitis C 11 63 623E-08KEGG PATHWAY mmu04380Osteoclast differentiation 8 46 440E-05KEGG PATHWAY mmu05161Hepatitis B 8 46 112E-04KEGG PATHWAY mmu04622RIG-I-like receptor signaling pathway 6 34 158E-04KEGG PATHWAY mmu04668TNF signaling pathway 7 40 169E-04KEGG PATHWAY mmu04620Toll-like receptor signaling pathway 6 34 993E-04KEGG PATHWAY mmu05133Pertussis 5 29 238E-03Down-regulatedKEGG PATHWAY mmu04151PI3K-Akt signaling pathway 14 71 414E-04KEGG PATHWAY mmu05200Pathways in cancer 14 71 131E-03KEGG PATHWAY mmu04550Signaling pathways regulating pluripotency of stem cells 8 41 169E-03KEGG PATHWAY mmu04520Adherens junction 6 30 203E-03KEGG PATHWAY mmu04015Rap1 signaling pathway 9 46 533E-03KEGG PATHWAY mmu04390Hippo signaling pathway 7 36 116E-02KEGG PATHWAY mmu05145Toxoplasmosis 6 30 136E-02KEGG PATHWAY mmu04510Focal adhesion 8 41 151E-02KEGG PATHWAY mmu04022cGMP-PKG signaling pathway 7 36 204E-02KEGG PATHWAY mmu03040Spliceosome 6 30 257E-02lowastNumber of enriched genes in each term The top ten terms based on P value were chosen in each category
Mrpl19
Ccl12 Ifi47Gbp6
Mrpl9 Mfap5
Mrpl44Efemp1
Soat1
Hsd17b12
Adh1
Adam10Cyp11a1Ugt1a2
Star Aplp2
Efna1
Fosb
Gbp7Prl2c2
Iigp1
Irgm1Stat1Mt2
Mst4
Nup133
Zwint
Ranbp9
Taok1Wdr26
G6pd2 Ly6e CtssTpm3 Upk3b Atp6v1aEpcam Tmed7
Ptpn12 Gnpda2Upk1b Atp6v1b2 Ly6a Mpeg1Krt19 Arf4
Atf3Gbp3
Zfp36
Parp14Plau
Gbp2
Irgm2Serping1
Lgals3bp
Ifitm3 Ublcp1Cxcl10 Igtp
Irf7
Ddx58C2 Rtp4 JunbIfit3
Eif2ak2
Npm1Dnajb6
Paip1Thoc1Hspa4Csnk2a1
Ncl Dnttip2
Taf1bAcly Hspa8
Snrpn
Cct3
Tra2b
Sox4
Unc45b Sik1Fzd2
Rpf1Eif4a1 Mtmr6
Fabp4
Mccc2
Ctnnb1 Mtmr2PtprfYwhaz
Ywhaq
Hsp90aa1 Tbc1d1
HnrnpuSrsf1
Slu7
Elavl2Acaa2
Sf3b2
Esf1Pik3r1 Mtm1
Ssb
EdnraRif1 Hsph1
Rrm1 Pcgf6Rbl2
Brca1Pknox1Mbd2 Pvrl3
Psme4Uba7
Irf9Isg15 Ifi44
Herc6Gphn
Crk
Ddx60Stat3
AxlPard3Klf4
BC006779
Jak1
Isg20
Oas1a
Rbbp4 H3f3a
Exo1Plec
Tsc22d3
Dcun1d1Uba3
Ubc
Rrm2b
Rpa2Usp1
Rad51cGlrx
Keap1
Casp3 Dsg2Fos Egr1Usp18
Mt1
Nr4a1
Parp3
Akt1
Cyr61
Rap1b
Trim30a
OsmrHmgb1
Jun
Adcy7 Stk3Ppp2r2b
Eif4eThbs1
Ifi35Oasl2Ifit1
Dhx58Oas2
Rsad2Parp9
Ifih1
Mef2aBcl10Map3k7
Ifit2Tcf3
Tlr3
C3
Socs3Rnf19b
Fbxw14
Zbp1
Psmb8
Figure 3 Protein-protein interaction (PPI) network complex Using the SearchTool for the retrieval of InteractingGenes (STRING) databaseonline database 187 out of 371 differentially expressed genes (DEGs) (upregulated genes are displayed in red and downregulated genes ingreen) were filtered into the DEGs protein-protein interaction (PPI) network complex
8 BioMed Research International
Parp9
Tlr3
Ifih1
Ifi44
Gbp7
Iigp1
Gbp3
Rtp4
Dhx58
Gbp2
Irf9
Ddx60
Oasl2
Cxcl10
Isg15
Trim30a
Rsad2
Zbp1
Irf7
Herc6Ifit2Ifit1
Ifit3
Stat1
Ifi35
Oas2
Igtp Irgm1
Ddx58
Ifi47
Irgm2Usp18
Eif2ak2
Gbp6
Parp14
Figure 4 Overlapping protein complexes in the protein interactionThe most significant module consisted of 35 nodes and 286 edges andall genes in this module were upregulated (in red)
5 Conclusions
In conclusion our data showed quite different gene expres-sion patterns of the secondary follicles between young and oldmouse ovaries The differentially expressed genes involved inthe process of ovarian aging are central to biological processessuch as immune system process aging transcription DNAreplication DNA repair protein stabilization and apoptoticprocess However many upregulated genes in the old mouseovarian secondary follicles were innate immune relatedgenes We proposed that innate immune system may play avital role in the process of ovarian aging Our results of alteredgenes and related transcriptional networks may be helpful forunderstanding the mechanism of the folliculogenesis in theprocess of ovarian aging in mice
There are however limitations in the present studyFindings of our present research were mainly based
on the bioinformatics analysis and further experimentsare needed to verify Furthermore these data were ac-quired with secondary follicles from mouse ovariesand needed to be confirmed with the samples from thehuman
Data Availability
The results of our microarray data were made available in thepublic domain NCBI-GEO repository
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this article
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
[1] J C Stevenson ldquoA womanrsquos journey through the reproductivetransitional and postmenopausal periods of life Impact on car-diovascular and musculo-skeletal risk and the role of estrogenreplacementrdquoMaturitas vol 70 no 2 pp 197ndash205 2011
[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
8 BioMed Research International
Parp9
Tlr3
Ifih1
Ifi44
Gbp7
Iigp1
Gbp3
Rtp4
Dhx58
Gbp2
Irf9
Ddx60
Oasl2
Cxcl10
Isg15
Trim30a
Rsad2
Zbp1
Irf7
Herc6Ifit2Ifit1
Ifit3
Stat1
Ifi35
Oas2
Igtp Irgm1
Ddx58
Ifi47
Irgm2Usp18
Eif2ak2
Gbp6
Parp14
Figure 4 Overlapping protein complexes in the protein interactionThe most significant module consisted of 35 nodes and 286 edges andall genes in this module were upregulated (in red)
5 Conclusions
In conclusion our data showed quite different gene expres-sion patterns of the secondary follicles between young and oldmouse ovaries The differentially expressed genes involved inthe process of ovarian aging are central to biological processessuch as immune system process aging transcription DNAreplication DNA repair protein stabilization and apoptoticprocess However many upregulated genes in the old mouseovarian secondary follicles were innate immune relatedgenes We proposed that innate immune system may play avital role in the process of ovarian aging Our results of alteredgenes and related transcriptional networks may be helpful forunderstanding the mechanism of the folliculogenesis in theprocess of ovarian aging in mice
There are however limitations in the present studyFindings of our present research were mainly based
on the bioinformatics analysis and further experimentsare needed to verify Furthermore these data were ac-quired with secondary follicles from mouse ovariesand needed to be confirmed with the samples from thehuman
Data Availability
The results of our microarray data were made available in thepublic domain NCBI-GEO repository
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this article
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
[1] J C Stevenson ldquoA womanrsquos journey through the reproductivetransitional and postmenopausal periods of life Impact on car-diovascular and musculo-skeletal risk and the role of estrogenreplacementrdquoMaturitas vol 70 no 2 pp 197ndash205 2011
[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
BioMed Research International 9
KDM5C
TRIM30AKLF4
DNTTIP2
TGIF2CCNL2 GTF2I
PCGF6ZFP800
TCERG1KMT2A
IFI205
ZFP451
ZFP62TAF1B PHF11D
NFYA KEAP1
HBP1
ATF3 RBM39
FOXP1PARP14
POU6F2
IRF9
ZFP260KLF2
transcription
HELZ2CTNNB1
EGR1
INSM1
ZKSCAN3
NFKBIZ
ESF1 BTG2
RNF14
ZMYM2
C2
C3DHX58
AXL
RNF19B
HERC6
TLR3
IFIT3
IFIH1
immunesystemprocess
IRGM1
RSAD2
IFITM3
IFIT1
USP1
PARP9
DNArepair
EXO1
NPM1
RAD51C
IFIT2
S100A8
PSME4 BCL10
RBBP4
OASL2
DNAreplication
RRM2BNFIA
RRM1IIGP1
LGALS9
PSMB8
OAS2
ZBP1
ISG20
DDX58
EIF2AK2
STAT3
RBL2
RPA2
BRCA1
IRF7cell
cycle
UBA3
SH3RF1AKT1
STK26
apoptoticprocess
CASP3
ZFP385B
CCT3
PPP2R2B
XAF1
NR4A1STAT1
CSNK2A1
DAPK3MAP3K7
MEF2A
THOC1
aging
MBD2
DMD
APOD
IFI27L2AFOS
SOX4SERPINF1
JUNB
PIK3R1 SERPING1
STK3
TCF3
JUNTAOK1
proteinstabilization
HSPA8
Figure 5 Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 361)Gene regulatory network modeling of selected genes such as BRCA1 RPA2 STAT3 JUN AKT1 STK3 SOX4 TCF3 S100A8 and IFIT2showed their genetic interactions by various pathways Circles indicate genes red color indicates upregulation and green color indicatesdownregulation
Acknowledgments
This work was supported by the National Natural ScienceFoundation of China (Grant Numbers 81701438 81300453and 81370469)
References
[1] J C Stevenson ldquoA womanrsquos journey through the reproductivetransitional and postmenopausal periods of life Impact on car-diovascular and musculo-skeletal risk and the role of estrogenreplacementrdquoMaturitas vol 70 no 2 pp 197ndash205 2011
[2] M L Traub and N Santoro ldquoReproductive aging and itsconsequences for general healthrdquo Annals of the New YorkAcademy of Sciences vol 1204 pp 179ndash187 2010
[3] E B Gold ldquoThe timing of the age at which natural menopauseoccursrdquo Obstetrics amp Gynecology Clinics of North America vol38 no 3 pp 425ndash440 2011
[4] E R Te Velde ldquoThe variability of female reproductive ageingrdquoHuman Reproduction Update vol 8 no 2 pp 141ndash154 2002
[5] E Markstrom E C Svensson R Shao B Svanberg and H Bil-lig ldquoSurvival factors regulating ovarian apoptosismdashdependenceon follicle differentiationrdquo Reproduction vol 123 no 1 pp 23ndash30 2002
[6] I Huhtaniemi O Hovatta A La Marca et al ldquoAdvances in themolecular pathophysiology genetics and treatment of primaryovarian insufficiencyrdquo Trends in Endocrinology Metabolism 29pp 400ndash419 2018
[7] E Block ldquoA quantitative morphological investigation of thefollicular system in newborn female infantsrdquo Journal of ActaAnatomica vol 17 no 3 pp 201ndash206 1953
[8] S J Richardson V Senikas and J F Nelson ldquoFolliculardepletion during themenopausal transition Evidence for accel-erated loss and ultimate exhaustionrdquo The Journal of ClinicalEndocrinology amp Metabolism vol 65 no 6 pp 1231ndash1237 1987
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
10 BioMed Research International
[9] M J Faddy and R G Gosden ldquoAmodel conforming the declinein follicle numbers to the age of menopause in womenrdquoHumanReproduction vol 11 no 7 pp 1484ndash1486 1996
[10] M J Faddy ldquoFollicle dynamics during ovarian ageingrdquoMolecu-lar and Cellular Endocrinology vol 163 no 1-2 pp 43ndash48 2000
[11] J K Collins and K T Jones ldquoDNA damage responses inmammalian oocytesrdquoReproduction vol 152 no 1 pp R15ndashR222016
[12] D R Meldrum R F Casper A Diez-Juan C Simon A DDomar and R Frydman ldquoAging and the environment affectgamete and embryo potential can we intervenerdquo Fertility andSterility vol 105 no 3 pp 548ndash559 2016
[13] V Govindaraj and A J Rao ldquoOvarian aging possible molec-ular mechanisms with special emphasis on DNA repair geneBRCA1rdquo Womens Health International vol 02 no 01 p 1122016
[14] V Govindaraj R Keralapura Basavaraju and A J RaoldquoChanges in the expression of DNA double strand break repairgenes in primordial follicles from immature and aged ratsrdquoReproductive BioMedicine Online vol 30 no 3 pp 303ndash3102015
[15] I Rzepka-Gorska B Tarnowski A Chudecka-Głaz B GorskiD Zielinska and A Tołoczko-Grabarek ldquoPremature meno-pause in patients with BRCA1 gene mutationrdquo Breast CancerResearch and Treatment vol 100 no 1 pp 59ndash63 2006
[16] K Oktay J Y Kim D Barad and S N Babayev ldquoAssociationof BRCA1 mutations with occult primary ovarian insufficiencyA possible explanation for the link between infertility andbreastovarian cancer risksrdquo Journal of Clinical Oncology vol28 no 2 pp 240ndash244 2010
[17] S Titus F Li R Stobezki et al ldquoImpairment of BRCA1-relatedDNA double-strand break repair leads to ovarian aging in miceand humansrdquo Science Translational Medicine vol 5 no 172Article ID 172ra21 2013
[18] W K Deng Y BWang Z X LiuH Cheng andY Xue ldquoHemIa toolkit for illustrating heatmapsrdquo PLoS ONE vol 9 no 11Article ID e111988 2014
[19] C GeneOntology ldquoGene ontology consortium going forwardrdquoNucleic Acids Research vol 43 no 1 pp D1049ndashD1056 2015
[20] M Kanehisa Y Sato M Kawashima M Furumichi and MTanabe ldquoKEGG as a reference resource for gene and proteinannotationrdquo Nucleic Acids Research vol 44 no 1 pp D457ndashD462 2016
[21] D W Huang B T Sherman Q Tan et al ldquoDAVID Bioin-formatics Resources expanded annotation database and novelalgorithms to better extract biology from large gene listsrdquoNucleic Acids Research vol 35 supplement 2 pp W169ndashW1752007
[22] D Szklarczyk J H Morris H Cook et al ldquoThe STRINGdatabase in 2017 quality-controlled protein-protein associationnetworks made broadly accessiblerdquoNucleic Acids Research vol45 no 1 pp D362ndashD368 2017
[23] G Su J H Morris B Demchak and G D Bader ldquoBiologicalnetwork exploration with cytoscape 3rdquo Current Protocols inBioinformatics vol 47 pp 8131ndash81324 2014
[24] T Nepusz H Yu and A Paccanaro ldquoDetecting overlappingprotein complexes in protein-protein interaction networksrdquoNature Methods vol 9 no 5 pp 471-472 2012
[25] V Govindaraj H Krishnagiri P Chakraborty M Vasudevanand A J Rao ldquoAge-related changes in gene expression patternsof immature and aged rat primordial folliclesrdquo Systems Biologyin Reproductive Medicine vol 63 no 1 pp 37ndash48 2017
[26] N Rimon-Dahari L Yerushalmi-Heinemann L Alyagor andN Dekel ldquoOvarian folliculogenesisrdquo Results and Problems inCell Differentiation vol 58 pp 167ndash190 2016
[27] K Yan D Feng J Liang et al ldquoCytosolic DNA sensor-initiatedinnate immune responses in mouse ovarian granulosa cellsrdquoReproduction vol 153 no 6 pp 821ndash834 2017
[28] Q Wang H Wu L Cheng et al ldquoMumps virus induces innateimmune responses in mouse ovarian granulosa cells throughthe activation of Toll-like receptor 2 and retinoic acid-induciblegene IrdquoMolecular and Cellular Endocrinology vol 436 pp 183ndash194 2016
[29] J P de Magalhaes J Curado andG M Church ldquoMeta-analysisof age-related gene expression profiles identifies common signa-tures of agingrdquo Bioinformatics vol 25 no 7 pp 875ndash881 2009
[30] K Spanel-Borowski ldquoOvulation as danger signaling event ofinnate immunityrdquo Molecular and Cellular Endocrinology vol333 no 1 pp 1ndash7 2011
[31] C von Kobbe ldquoCellular senescence a view throughout organis-mal liferdquo Cellular and Molecular Life Sciences vol 75 no 19 pp3553ndash3567 2018
