Open Access

Transcriptome profiling of granulosa cells from bovine ovarian follicles during atresia

  • Nicholas Hatzirodos1,
  • Katja Hummitzsch1,
  • Helen F Irving-Rodgers1, 2,
  • Margaret L Harland1,
  • Stephanie E Morris1 and
  • Raymond J Rodgers1Email author
BMC Genomics201415:40

DOI: 10.1186/1471-2164-15-40

Received: 30 June 2013

Accepted: 2 January 2014

Published: 18 January 2014

Abstract

Background

The major function of the ovary is to produce oocytes for fertilisation. Oocytes mature in follicles surrounded by nurturing granulosa cells and all are enclosed by a basal lamina. During growth, granulosa cells replicate and a large fluid-filled cavity (the antrum) develops in the centre. Only follicles that have enlarged to over 10 mm can ovulate in cows. In mammals, the number of primordial follicles far exceeds the numbers that ever ovulate and atresia or regression of follicles is a mechanism to regulate the number of oocytes ovulated and to contribute to the timing of ovulation. To better understand the molecular basis of follicular atresia, we undertook transcriptome profiling of granulosa cells from healthy (n = 10) and atretic (n = 5) bovine follicles at early antral stages (< 5 mm).

Results

Principal Component Analysis (PCA) and hierarchical classification of the signal intensity plots for the arrays showed primary clustering into two groups, healthy and atretic. These analyses and size-frequency plots of coefficients of variation of signal intensities revealed that the healthy follicles were more heterogeneous. Examining the differentially-expressed genes the most significantly affected functions in atretic follicles were cell death, organ development, tissue development and embryonic development. The overall processes influenced by transcription factor gene TP53 were predicted to be activated, whereas those of MYC were inhibited on the basis of known interactions with the genes in our dataset. The top ranked canonical pathway contained signalling molecules common to various inflammatory/fibrotic pathways such as the transforming growth factor-β and tumour necrosis factor-α pathways. The two most significant networks also reflect this pattern of tissue remodelling/fibrosis gene expression. These networks also contain molecules which are present in the canonical pathways of hepatic fibrosis/hepatic stellate cell activation and transforming growth factor-β signalling and were up regulated.

Conclusions

Small healthy antral follicles, which have a number of growth outcomes, exhibit greater variability in gene expression, particularly in genes associated with cell division and other growth-related functions. Atresia, on the other hand, not only involves cell death but clearly is an active process similar to wound healing.

Keywords

Ovary Microarray analysis Bovine Granulosa cells Atresia Follicles

Background

The function of the ovary is to produce and release oocytes to be fertilised, leading to the production of offspring. Oocytes develop within ovarian follicles which in most mammals are formed during fetal life. These primordial follicles consist of an oocyte arrested in meiosis, and therefore not capable of mitosis. The oocyte is surrounded by a single layer of inactive pregranulosa cells [1]. These primordial follicles comprise the ‘ovarian reserve’ from which a number of follicles are activated each day to commence growth and maturation. During this process of folliculogenesis, the oocyte enlarges substantially, pregranulosa cells differentiate into granulosa cells and replicate, and a large fluid-filled antrum develops in the middle of the follicle [2]. The growth of antral follicles is largely under the influence of Follicle-Stimulating Hormone (FSH) [3]. During follicle growth granulosa cells produce increasingly more of the hormone oestradiol. After the surge release of Luteinising Hormone (LH) from the anterior pituitary gland which results in ovulation of the oocyte, the remaining granulosa cells of the follicle wall transform into luteal cells of the corpus luteum and produce progesterone [4]. Hence both the numbers and maturation of granulosa cells in any given follicle are important and both processes are regulated by gonadotrophic hormones from the anterior pituitary.

In mammals, the number of primordial follicles far exceeds the numbers that ovulate over a lifetime. For example in humans, millions of primordial follicles are formed in the fetal gonad but only about 500 will be ovulated [5]. Since the numbers of follicles at menopause is practically nil [5], the vast majority of follicles undergo atresia and regress. The incidence of follicular atresia [6] is a normal process of ovarian function and its occurrence across species appears to have increased, with the evolution of viviparity in which a reduced number of female gametes are required when compared to mass-spawning species. Atresia in any species can regulate the number of oocytes ovulated and contribute to the timing of ovulation in a reproductive cycle.

The process of atresia in follicles large enough to have developed an antral cavity is characterised initially by death of the mural granulosa cells with the presence of pyknotic nuclei followed by loss of these layers into the antrum [7]. The entire follicle wall then begins to breakdown at the basal lamina and inflammatory cells migrate from the surrounding stromal theca layers, phagocytosing remnants of the granulosa cells and eventually the oocyte. Atresia leads eventually to death of all the granulosa cells within a follicle. The cell death processes can involve apoptosis, necrosis, autophagy and cornification, and any of the major cell types of the follicle can be involved, depending upon the stage of follicular development when atresia occurs [8]. Atresia also involves active cellular processes including macrophage infiltration, phagocytosis, migration of fibroblasts from the theca and the production of collagen. Interestingly, these are some of the processes also observed in wound healing [9, 10].

We hypothesise that apart from cell death, other signalling and pathways will be associated with the process of atresia. Therefore, to advance our knowledge of atresia we undertook transcriptome profiling of granulosa cells from small antral follicles before and during atresia. There have been several studies published, which investigate granulosa gene expression in developing bovine antral follicles by microarray [1114]. Evans et al [11] studied granulosa from small follicles using self–generated arrays of approximately 1,300 genes. Other studies have focused on follicles at larger sizes, comparing follicles with differences in oestradiol production due to selectively accelerated development (dominance). Here we examined individual morphologically-classified healthy (n = 10) and atretic (n = 5) follicles at the small antral stage of less than 5 mm in diameter, prior to size deviation due to dominant selection. The Bovine Affy arrays we used contain more than 11,000 annotated genes, thereby expanding the power to reveal networks and pathways involved in follicle regression. The healthy follicles were further classified into two phenotypes based upon the shape of the basally-situated granulosa cells, as either columnar or rounded [15]. These follicle types also differ in the quality of their oocytes when cultured in vitro[16]. The atretic follicles were of the type called antral atretic [7, 8]. This is the classic type of atresia commonly observed across species in which the antrally-situated granulosa cells are the first to undergo cell death.

Results and discussion

In this study we have identified major differences in gene expression pathways and networks that develop in granulosa cells of small antral follicles during the process of atresia. To achieve this, granulosa cells from small healthy (3.1 ± SEM 0.2 mm diameter; n = 10) and atretic (4.2 ± 0.5 mm; n = 5) follicles were selected for the microarray gene expression analysis. To ensure that the granulosa cells isolated were not contaminated with any thecal cells, no follicles with more than a 1% level of expression of CYP17A1 found in thecal samples were included. CYP17A1 is expressed exclusively in thecal cells [17]. We also validated that our microarray analyses could detect differentially-expressed genes here by immunohistochemistry and elsewhere [18] by real time reverse transcription polymerase chain reaction (real time RT-PCR). Table 1 shows the selected genes and their signal intensities and fold differences between healthy and atretic follicles. CDH1, the gene for the cell-cell adhesion molecule E-cadherin, and NID2, the gene for nidogen 2, were both increased in atretic follicles. By immunohistochemistry, the levels of both E-cadherin (Figure 1A, B) and nidogen 2 (Figure 1C, D) were elevated in the membrana granulosa of atretic follicles. Collagen type I was also examined by immunohistochemistry on the basis that COL1A2 was elevated in atretic follicles. However, no collagen type 1 was detected in the membrana granulosa of healthy or atretic follicles but it was identified in the thecal layers at higher levels in atretic follicles. Collagen type I contains both α1 and α2 subunits and whilst COL1A2 was elevated COL1A1 was not. Thus expression of collagen type I could not be validated, but both CDH1 and NID2 were.
Table 1

Expression of selected genes from the microarray analysis used for validation by immunohistochemistry

Gene symbol

Log2mean signal intensity ± SD†

Fold increases in atretic follicles‡

Healthy follicles

Atretic follicles

(n = 10)

(n = 5)

CDH1

6.5 ± 0.7

8.9 ± 1.4

4.3

COL1A1

7.9 ± 0.3

7.0 ± 0.7

-1.5

COL1A2

6.6 ± 0.2

8.7 ± 1.9

5.3

NID2

5.0 ± 0.6

9.2 ± 1.4

18.4

† SD = standard deviation.

‡ fold change calculated on non-log transformed data.

Figure 1

Localisation of E-cadherin, perlecan, nidogen 2 and collagen type I in small healthy and atretic follicles. (A, B) E-cadherin (red) is restricted to the membrana granulosa and is strongly expressed in atretic follicles. Perlecan (green) marks the follicular and sub-endothelial basal lamina and is expressed in the cytoplasm of granulosa cells of healthy and atretic follicles. (C, D) Nidogen-2 (red) is localised to the basal lamina of both follicle types, but is only expressed in the granulosa layer of small atretic follicles. Collagen type I (green) is not localised to the granulosa cells. It is restricted to the thecal and medullar stroma. The star indicates the granulosa layer. Bar = 50 μm.

Statistical analysis of differentially-expressed genes

Small healthy follicles were classified as either columnar (n = 5) or rounded (n = 5) on the basis of the shape of the basally-situated granulosa cells [15, 16] as described in the Methods. PCA for the first three components (Figure 2) and hierarchical clustering (Figure 3) for the total number of probe sets (n = 24,182) of all arrays in this study were conducted. Neither of these unsupervised analytical methods separated the small healthy follicle arrays into the rounded and columnar groups, and in fact no genes were shown to be more than 2-fold differentially expressed between the two subgroups with a Benjamini-Hochberg False Discovery Rate (FDR) of P < 0.05 by ANOVA. Therefore, the small healthy follicle arrays were treated as a single group for further analyses (n = 10) and compared with the small atretic follicle group.
Figure 2

Unsupervised PCA of arrays for small healthy (n = 5 rounded phenotypes in yellow and n = 5 columnar phenotypes in blue) and small atretic (n = 5, in red) follicles. The graph is a scatter plot of the values for the first (X) and second (Y) principal components based on the correlation matrix of the total normalised array intensity data.

Figure 3

Unsupervised hierarchical clustering across all probe sets (n = 24,182) for 15 arrays using the Euclidian dissimilarity algorithm with the average linkage method in Partek Genomics Suite. The heatmap represents the distribution of normalised signal intensity, grouping by pattern similarity for both probe set and array. (R = rounded and C = columnar phenotypes).

Before statistical analysis, PCA for all arrays revealed that the first principal component which accounted for 51% of the variation in the data, could separate the atretic and healthy follicle groups (Figure 2). Hierarchical classification of the signal intensity plot for these arrays similarly also showed primary clustering of the arrays into these two groups (Figure 3). The numbers of genes enriched 2-, 3-, and -4 fold by ANOVA in either healthy or atretic follicles are shown in Table 2. Follicular atresia was characterized by considerable change in the transcriptional processes of the granulosa cells as expected with over 22% of the total genes on the array being affected at least 2 fold or more (5,439 from 24,182). Four hundred and forty probesets were up regulated and 265 were down regulated more than 4 fold in atretic follicles relative to small healthy follicles. Using thresholds of 3- and 4-fold differential-expression levels with P < 0.05 and < 0.005 respectively, then 1,595 and 690 differentially-expressed probe sets were identified, respectively (Table 2). The larger dataset (1,595 probe sets) was tabulated with gene and fold change details added as Table 3 (up regulated in atretic) and Table 4 (down regulated) and in Additional file 1: Table S1.
Table 2

Number of probe sets differentially expressed in atretic follicles with respect to healthy follicles

False discovery rate

Fold change

Up regulated

Down regulated

Total

P < 0.05

>2

2573

2866

5439

>3

824

771

1595

 

>4

440

265

705

P < 0.005

>2

2397

2736

5133

>3

801

732

1533

 

>4

433

257

690

Determined by ANOVA and the step-up Benjamini Hochberg False Discovery Rate method for multiple corrections using Partek Genomics Suite Software.

Table 3

Genes which were up regulated in small atretic follicles with respect to healthy follicles

Gene symbol

Fold change

Gene symbol

Fold change

Gene symbol

Fold change

Cell Cycle and DNA replication

CDKN1C

40.2

RGCC

5.1

IFITM3

3.3

MICAL1

8.9

TOP1

5.1

DYNLT3

3.1

CDC37L1

5.9

RAD52

3.5

BAZ1A

3.0

TOPORS

5.7

KLC1

3.4

  

Cell Death

TNFRSF12A

10.4

*PHLDA1

4.7

TNFRSF25

3.4

CFLAR

6.0

PDCD4

3.9

TNFRSF1A

3.2

TNFRSF6B

5.9

FAIM

3.8

PDCD10

3.1

Cell Morphology

MAP1LC3C

16.7

APBB1IP

4.6

SNTB2

4.0

CAPG

11.1

MAP2

4.5

MTMR3

3.8

TAGLN

10.8

CNN1

4.4

LMNA

3.5

DBNDD2

9.2

AIF1L

4.4

ABLIM1

3.5

NDRG1

9.0

VCL

4.3

FHOD3

3.4

SYNE1

7.6

WASL

4.3

MARCKSL1

3.2

CLDN5

6.3

CDH1

4.3

MAP9

3.2

SVIL

6.1

ACTA2

4.3

MICAL2

3.1

MICALL2

6.1

TAGLN2

4.3

ELMO1

3.1

FILIP1L

5.6

FSCN1

4.2

ELMO2

3.1

ARPC1B

5.5

FNBP1L

4.1

*ARF5

3.1

FERMT2

5.4

TLN1

4.1

TPGS1

3.0

GSN

5.3

ACTN1

4.1

B9D2

3.0

TUBB6

5.1

DSTN

4.0

FBLIM1

3.0

CAV1

4.6

RILPL2

4.0

  

Cytokines, Hormones and Receptors

PDGFRA

26.5

IGF2R

6.2

CD302

3.7

SCG2

24.4

ELTD1

6.1

JAG1

3.6

CTGF

23.4

TSPO

5.7

IL10RA

3.6

DKK3

17.4

IGFBP6

5.1

PTPRK

3.6

XCL1

16.7

IGF2

5.0

PLXND1

3.6

SPP1

14.1

TGFBR2

5.0

CMTM3

3.5

OLR1

11.7

MIA3

4.6

IL17RA

3.5

MDK

9.9

IFI30

4.5

NR4A2

3.5

IL18

8.9

IGFBP5

4.3

ANGPTL4

3.4

PRICKLE1

8.2

EFNA4

4.2

EFNA5

3.3

BAMBI

7.6

NR2F1

4.1

GPRC5B

3.3

NDP

7.6

ADM

3.9

GPR155

3.3

GNG2

7.3

PLXNB2

3.8

ACVR1

3.1

PLAUR

6.7

*PVRL2

3.8

SCG5

3.1

BMP2

6.6

    

Directional Cell Growth

SLITRK2

4.3

ROCK2

3.8

ROCK1

3.3

Extracellular Matrix and Synthesis

*SPOCK2

23.8

TNFAIP6

7.1

COL18A1

4.0

*NID2

18.5

LOXL4

6.5

*FN1

3.9

*LEPREL1

12.8

LUM

6.1

FBN1

3.8

LAMB1

12.0

COL3A1

5.5

COL4A5

3.5

DCN

11.1

COL1A2

5.3

SPON2

3.5

MGP

9.5

PCOLCE2

4.6

SDC4

3.4

SDC2

9.0

COL4A1

4.5

COL12A1

3.4

FBLN2

8.7

AGRN

4.1

COL6A1

3.2

COL5A2

8.3

    

Intercellular and Cell to Matrix Adhesion

KRT18

62.4

CLDN1

10.2

MAGI3

4.6

KRT8

53.2

CD9

7.1

AMIGO2

4.5

PTX3

24.1

PCDH7

7.0

ANTXR2

3.7

THBS2

23.9

FLNB

6.6

SPARC

3.6

LGALS3

22.9

MAGI1

6.0

CADM1

3.3

*CD24

17.1

VMP1

5.3

JAM3

3.2

CYR61

14.1

CLDN11

4.9

CCDC80

3.0

CALD1

11.6

    

Ion Transport

KCNMB4

5.7

CACNB3

4.0

KCTD10

3.8

GPM6A

4.8

CLCA4

3.9

SEPP1

3.7

CLIC4

4.5

SCN5A

3.9

KCNJ2

3.7

Protein Trafficking

ANXA1

10.9

SCG3

4.0

PLEKHB2

3.6

GABARAPL1

6.2

CHMP5

3.7

SCAP

3.4

GIT2

4.4

MVP

3.7

PLEKHG2

3.3

SH3RF1

4.0

    

Proteolysis or Inhibition

TIMP1

68.9

CAST

6.7

CTSZ

3.5

PLAT

26.5

ADAMTS1

6.1

FBXO32

3.3

SERPINE1

19.3

C1S

5.9

TIMP2

3.3

PRSS23

10.9

CTSH

4.7

CTSK

3.2

CSTB

9.3

MMP11

4.4

CTSB

3.2

RELN

7.7

CTSS

4.4

KLHL36

3.2

MMP23B

7.7

CFD

3.8

SERPINB2

3.1

SERPINB8

7.4

PSMB1

3.5

LYZ

3.0

RNA Processing

PRPF38B

7.1

RBM25

3.9

SNRNP200

3.2

RBMS2

6.3

DDX5

3.7

HNRNPH3

3.2

RBM24

4.6

SYNCRIP

3.6

CPEB4

3.2

RBM17

4.5

DDX42

3.6

EBNA1BP2

3.1

TDRD7

4.3

INTS7

3.3

GEMIN8

3.1

REXO2

4.0

CWC22

3.2

  

Transcription Regulation

ANKRD1

48.9

TAX1BP3

4.8

LEF1

3.4

FOSL2

11.7

*MLL3

4.7

TGIF1

3.4

EGR1

11.3

MTPN

4.6

MPHOSPH8

3.4

ATF3

9.7

EPAS1

4.6

MORF4L2

3.3

BCL6

8.6

FOXP1

4.5

MAFK

3.3

ATRX

8.4

ING4

4.4

CSRNP1

3.3

ID1

7.4

GTF2F2

4.4

LARP7

3.3

USF1

6.8

SMAD3

4.3

GTF2E2

3.3

KLF6

6.5

STAT3

4.3

GTF2H4

3.3

RBFOX2

6.5

SERTAD1

4.3

BASP1

3.3

JUN

6.4

HHEX

4.2

LEO1

3.3

CEBPD

6.0

ZNF398

4.2

TCF3

3.3

ID3

5.8

HMBOX1

4.0

ZMIZ2

3.2

*TCF7L2

5.8

PDLIM1

3.9

NFKBIA

3.2

HDAC7

5.8

ZFP36L1

3.9

ZC3H8

3.2

NMI

5.7

JUNB

3.9

OSTF1

3.2

SNAI2

5.7

E4F1

3.9

CIR1

3.2

ZNF292

5.7

ZNF281

3.8

ZNHIT3

3.1

LITAF

5.6

FOS

3.7

GPS2

3.1

CITED2

5.6

FHL2

3.7

CSDC2

3.1

CTR9

5.5

CYLD

3.7

KHDRBS1

3.0

*CEBPD

5.4

TRANK1

3.6

ILF3

3.0

KANK2

5.1

FOXO1

3.6

ANKRD10

3.0

NFIL3

5.0

*CPEB2

3.6

  

JARID2

4.9

SCAND1

3.5

  

MXI1

4.9

HDAC5

3.5

  

Translation Regulation

MRPS28

3.6

EIF4G3

3.2

EIF2AK4

3.2

EIF2AK4

3.5

    

Transport

APOD

37.5

SLC25A29

4.2

RBP1

3.4

STAR

22.5

STAU1

4.1

TCN2

3.4

SLC17A5

9.0

VPS13B

4.1

SLC25A17

3.4

FABP5

8.9

STEAP1

4.0

CYTH3

3.4

A2M

8.1

RABEP1

3.9

SYT11

3.2

NNAT

7.8

*ABCB1

3.8

FABP4

3.1

SLC40A1

7.2

CRABP2

3.7

AP1S2

3.1

VAT1

6.8

STARD3NL

3.7

NPC2

3.0

SLC39A8

5.2

ANKH

3.6

SNX9

3.0

Other Enzymes

*VNN1

15.1

PNMT

4.1

RAB31

3.2

*PDK4

9.7

HSP90AA1

4.0

PTPRN2

3.2

GFPT2

7.8

RAP2C

4.0

RIOK2

3.2

QSOX1

5.9

SETD7

4.0

RNF20

3.2

DDAH2

5.5

CHST2

4.0

LHFPL2

3.2

GLRX

5.4

SMS

3.8

PTPN21

3.2

PLSCR4

5.4

NT5E

3.8

RAB7A

3.1

ENPP5

4.9

MARCKS

3.7

DDAH1

3.1

PYGL

4.9

GDPD1

3.6

PPIE

3.1

CA2

4.8

DUSP7

3.6

ARF5

3.1

RPIA

4.7

EGLN1

3.5

RBKS

3.1

PELI1

4.7

ZNRF1

3.4

UST

3.1

MYCBP2

4.6

NAGK

3.4

ENO2

3.1

CPT1A

4.5

MGST2

3.4

RNF8

3.1

GEM

4.4

B4GALT5

3.4

PLCL2

3.0

PIGT

4.3

RAB22A

3.3

DPH5

3.0

DUSP1

4.3

ALDH1A1

3.3

IP6K2

3.0

PXDN

4.2

    

Other Signalling

*RND3

35.7

EFHD2

5.8

SHISA2

3.9

*DCLK1

19.5

JAK1

5.7

RALA

3.9

GAL

10.6

VAV2

5.6

CSNK2A1

3.7

POSTN

9.9

INSIG2

5.4

BMPER

3.7

ARHGEF11

9.9

WDR44

5.0

ARHGAP32

3.5

TP53INP1

8.9

STK17A

5.0

S100A11

3.3

LMTK2

8.1

PTPN5

4.6

UACA

3.2

SH3BP5

7.5

WHSC1L1

4.5

ERBB2

3.2

GIMAP8

7.5

IRAK1BP1

4.4

FYN

3.2

SH3KBP1

7.4

CFH

4.4

CORO1A

3.2

ARHGEF3

7.1

IFITM1

4.3

BCAR1

3.1

RSPO3

7.1

PIK3CA

4.3

DOCK5

3.1

CD200

7.0

DAB2

4.3

PIK3CD

3.1

PLIN2

6.6

CSNK1G2

4.2

STK38L

3.1

PKIB

6.5

MERTK

4.1

MAP2K3

3.1

FBXO33

6.2

RASA2

4.1

TYRO3

3.1

CD55

6.2

PIM1

4.0

CARD10

3.1

ERRFI1

6.1

    

CD99

6.0

    

DAPP1

5.8

    

Other

C8orf4

37.2

RCN2

4.4

CBLB

3.6

SATL1

15.2

KIAA1598

4.4

ASCC2

3.5

WDFY4

11.1

FAM126B

4.4

FAM32A

3.5

PLXDC2

9.1

ZNF317

4.2

OCIAD2

3.5

LUC7L3

8.9

C1orf35

4.2

SAFB2

3.5

ZNF462

7.5

RASAL2

4.2

ALKBH4

3.4

NTN4

7.3

AKAP8L

4.1

TSC22D3

3.4

KIAA0408

7.2

DENND3

4.1

IRGQ

3.4

RSRC2

7.1

C1QTNF6

4.1

AUTS2

3.4

C10orf10

7.0

CCDC85B

4.1

GATSL2

3.4

PHF3

6.4

ARHGEF2

4.0

COMMD4

3.4

SDE2

6.3

MAP1B

4.0

MLLT11

3.3

BTBD3

6.1

C3orf19

4.0

ZFYVE1

3.3

TMEM14A

5.9

CUL9

4.0

PPFIBP1

3.3

ODF2L

5.8

MAP7D1

3.9

FAM129B

3.3

MXRA8

5.8

MTA3

3.9

FLRT2

3.3

GLIPR2

5.6

LIMCH1

3.9

PRRC2C

3.2

NDRG4

5.6

KLHL24

3.9

GPN3

3.2

TMEM176A

5.4

CXorf26

3.9

MESDC1

3.2

MXRA5

5.3

SHF

3.9

ARMCX3

3.2

CRYAB

5.1

YPEL5

3.9

SNN

3.2

C7orf41

4.9

PQLC3

3.9

KIAA0922

3.2

ZNF608

4.9

TMTC2

3.9

TCP11L2

3.2

KLHL28

4.9

HECA

3.8

ANKRD11

3.2

PLEKHO1

4.8

C9orf3

3.8

C1orf212

3.1

TACC2

4.8

LRCH1

3.8

TMEM176B

3.1

GOLPH3L

4.8

DPH3

3.7

SYAP1

3.1

ERRFI1

4.7

CD83

3.7

METRNL

3.1

SRP14

4.7

CTTNBP2NL

3.7

FAM160A2

3.1

ZDHHC23

4.7

TES

3.7

MEF2BNB

3.1

EML1

4.7

GLTSCR2

3.7

C5orf30

3.1

ANXA2

4.7

CLU

3.6

ATG14

3.1

ZNF521

4.6

LHFP

3.6

BOD1L1

3.1

PDLIM4

4.6

PKIG

3.6

DEFB4A

3.0

KIAA0232

4.4

LSG1

3.6

C17orf101

3.0

C12orf75

4.4

    

Differentially regulated genes (> 3 fold, P < 0.05) were annotated based on the Entrez Gene database. Genes are listed in descending order of fold change within each functional category.

† Benjamini-Hochberg post-hoc test for multiple corrections following one way ANOVA.

* Indicates genes determined from the Partek analysis based on the Affymetrix annotations which were not assigned identities by IPA.

Table 4

Genes which were down regulated in small atretic follicles with respect to healthy follicles†

Gene symbol

Fold change

Gene symbol

Fold change

Gene symbol

Fold change

Cell Cycle

HAUS4

9.7

CASC5

4.1

FAM83D

3.4

KIFC1

7.2

*PTTG1

4.0

CDCA2

3.3

TOP2A

6.3

BUB1

3.9

KIF23

3.3

HJURP

5.6

SKA3

3.9

CENPF

3.3

CDCA8

4.6

TPX2

3.8

MCM10

3.3

MDC1

4.5

SGOL1

3.8

CDCA3

3.3

RAD51AP1

4.5

CDC20

3.8

OIP5

3.2

CENPE

4.5

GINS3

3.7

ASPM

3.2

ANAPC5

4.4

ASF1B

3.7

NCAPH

3.2

NUSAP1

4.3

CDC6

3.7

MAD2L1

3.1

MYO1D

4.3

ERCC6L

3.6

CKAP2

3.1

KLC2

4.3

CCNB1

3.6

*CENPA

3.1

BUB1B

4.2

PRCC

3.5

AURKB

3.0

CNNM2

4.2

CENPN

3.5

  

Cell Death

TRIB2

6.5

RIPK3

4.8

  

Cell Morphology

CKAP2L

4.7

SRGN

4.0

MAPT

3.3

PLP1

4.6

HMMR

3.8

PRC1

3.3

JAKMIP1

4.6

RSPH9

3.7

TNNC1

3.2

TMEM138

4.5

SYNPO

3.6

PHACTR1

3.1

ECT2

4.2

LEMD2

3.5

MYO5C

3.0

TMEM216

4.0

CDH26

3.5

  

Cytokines, Hormones and Receptors

FGFR2

8.4

INHA

3.8

CCR3

3.2

F2RL2

5.9

ADRA1A

3.7

CHRM4

3.2

VEGFA

5.8

IL7

3.6

P2RY10

3.2

RHO

5.8

CD5

3.6

NR5A1

3.2

IL17RE

5.5

GPR128

3.6

OPN1LW

3.2

LTA

4.8

GPR61

3.6

AHR

3.2

IL18R1

4.6

GPR77

3.5

IL20RA

3.2

FSHR

4.4

FGF10

3.4

HTR1D

3.2

HTR2A

4.4

CCL11

3.4

FGFBP1

3.1

AMH

4.2

OPCML

3.4

TSHB

3.1

GPRC5A

4.2

CCL25

3.4

ASGR1

3.1

OPRM1

4.2

CCL28

3.4

HEG1

3.1

INHBA

4.2

FIGF

3.4

IL1A

3.1

NTRK3

4.1

CD72

3.3

IGF1

3.1

FST

4.0

ABP1

3.3

CASR

3.0

FSHB

4.0

EDNRA

3.3

GMFG

3.0

IL21

4.0

ITGB3

3.2

HTR4

3.0

BMP15

3.8

NGFR

3.2

  

Directional Cell Growth

FAT1

3.6

    

Extracellular Matrix and Synthesis

LAMC2

6.4

COL6A6

3.7

COL10A1

3.5

EPYC

4.3

TRAM2

3.6

*AMELX//AMELY

3.3

Intercellular and Cell to Matrix Adhesion

MUC15

5.7

CLDN6

3.8

DSG1

3.1

PRELP

4.5

CD33

3.5

PECAM1

3.1

*MCAM

4.0

SMAGP

3.5

EPDR1

3.1

*GLYCAM1

3.9

*BOLA-DQ2

3.3

CLEC4E

3.0

SELL

3.9

CD96

3.2

  

Ion Transport

TTYH1

4.8

KCNE1

3.4

SLC25A34

3.1

FXYD7

4.3

P2RX5

3.2

CACNA1D

3.1

KCNJ15

3.5

TRPM6

3.2

*KCNIP2

3.0

Protein Trafficking

INSIG1

3.8

VPS52

3.2

  

Proteolysis or Inhibition

*PTI

58.8

TROAP

3.5

DBC1

3.2

CPXM2

5.2

MMP7

3.4

TASP1

3.2

TFPI

5.1

ACE2

3.3

PGA5

3.1

PRSS22

4.4

CPB1

3.3

SERPINI2

3.1

CUL7

4.3

SPPL2B

3.2

MMP3

3.0

KLK4

4.1

SPAG5

3.2

TRIM8

3.0

USP28

3.8

    

RNA Processing

DCP1A

3.6

U2SURP

3.0

  

Transcription Regulation

ZFHX3

6.3

HAND1

3.8

BCORL1

3.2

HOXB2

4.6

NFE2L2

3.6

NCOA6

3.2

ZNF385A

4.5

SP100

3.6

EHMT2

3.2

CCNT1

4.5

UHRF1

3.5

CC2D1B

3.2

SRCAP

4.3

BCOR

3.5

IRF2BP1

3.2

LDB1

4.2

RFX5

3.4

NSD1

3.1

POLR3D

4.0

NOTCH4

3.4

TFAP2A

3.1

ELK1

4.0

ASB12

3.3

ZBTB4

3.1

VSX1

4.0

NFIA

3.3

POU1F1

3.1

VGLL1

3.8

HOXD9

3.2

ZNF274

3.0

Translation Regulation

YBX2

4.9

EEFSEC

3.3

EEF1A1

3.1

NARS

3.3

    

Transport

AQP1

7.0

SLC12A8

4.0

SLC8A1

3.4

TRPA1

6.1

SLC5A9

4.0

EPB42

3.4

*ATP10A

6.1

PDYN

4.0

SYNGR3

3.3

TNPO1

6.0

SLC24A1

4.0

SLC6A9

3.2

ALB

5.7

RHBG

4.0

AP4B1

3.2

SLC27A3

5.5

ATP13A2

3.8

GOSR1

3.2

SLCO2B1

5.1

KIF20A

3.6

ATP4A

3.2

APOB

5.0

STRA6

3.6

PLLP

3.2

MAL2

4.6

CLDN16

3.5

FLVCR2

3.1

SLC4A2

4.5

SLC7A1

3.5

SLC37A2

3.0

ATP2B4

4.4

GC

3.4

SLC13A2

3.0

ABCD1

4.2

ENSA

3.4

SLC38A11

3.0

SLC16A3

4.1

    

Other Enzymes

CYP19A1

19.8

CYP4F2

4.2

PNLIP

3.4

AOAH

15.1

PDE4D

4.1

PDE6G

3.3

*PDK4

9.7

GPT

4.1

TRNAU1AP

3.3

NOS2

8.8

PDP2

3.9

ALDH1L2

3.3

ISG20

8.4

DBT

3.9

PJA2

3.3

ALG3

7.8

LPO

3.9

WDR46

3.3

CHST8

7.0

ALOX12B

3.8

IPMK

3.2

HMGCS1

6.9

METTL7B

3.7

ST6GAL1

3.2

GCLC

6.9

*IGL@

3.7

ALG5

3.2

CA14

6.8

*IGHG

3.7

CA5B

3.2

UGT2B17

6.5

ETNK2

3.6

ACAD10

3.2

AKR1C3

5.9

PIPOX

3.6

DIO1

3.2

GYLTL1B

5.7

RNF168

3.6

ACSM1

3.1

SCD

5.6

LHPP

3.6

GSTM4

3.1

GPX3

5.4

CYP21A2

3.6

RSAD2

3.1

CYP2C19

5.2

NQO1

3.6

ACSM2A

3.1

GPX2

5.0

METTL21B

3.5

SH3GL2

3.1

HSD17B1

4.8

GCNT1

3.5

SEPT4

3.1

*FHL3

4.7

LPPR2

3.5

UBE2C

3.1

LPL

4.6

BCAT1

3.5

RBBP8

3.1

PLA2G1B

4.5

BBOX1

3.5

B3GNT3

3.1

PPP3CC

4.5

PNLIPRP2

3.5

DUSP14

3.0

PDE6A

4.4

METTL2A

3.4

GNA14

3.0

FMO2

4.4

P4HA2

3.4

CPS1

3.0

TYRP1

4.3

GALNT13

3.4

  

CMBL

4.2

DCT

3.4

  

Other Signalling

IHH

7.1

MAP4K1

4.0

LAT

3.3

HLA-A

6.2

RIC3

3.8

PILRA

3.3

TBKBP1

6.2

CD84

3.8

GPSM3

3.2

*HSPA1A

5.7

SHCBP1

3.6

DNAJB1

3.1

TESPA1

5.4

GUCA1A

3.6

KIR2DL5A

3.1

JAK3

5.1

MTUS1

3.4

PEX11B

3.1

GUCY2F

4.6

DOK2

3.4

UPK1A

3.1

SKAP1

4.5

RPS6KA4

3.3

LY6G6C

3.1

RASGRP4

4.2

RGS3

3.3

FIGNL1

3.0

BCL9

4.0

    

Other

CSN2

17.7

EMID1

4.3

GIMAP7

3.5

MZB1

10.1

CYLC1

4.3

GUCA2A

3.4

STAC3

6.8

CCDC159

4.3

PLEKHF2

3.4

CCDC3

6.3

C21orf62

4.2

IFI44L

3.4

C9orf152

5.9

C1orf170

4.2

ODZ3

3.4

AGR2

5.8

APOBEC4

4.2

MICA

3.3

STAC

5.6

VWA8

4.1

TMEM139

3.3

EFHD1

5.5

PEAR1

4.1

UBN2

3.3

MTHFSD

5.4

MPDZ

4.0

C6orf47

3.3

NXPH2

5.4

IFIT2

4.0

Btnl1

3.3

SPEF1

5.3

ELMOD3

4.0

CXorf64

3.3

CRISPLD2

5.2

PLEK

4.0

DENND2D

3.3

KLHL29

5.2

SOWAHA

3.9

CHAC1

3.3

MEX3C

5.0

GCA

3.8

BIRC5

3.2

CAMP

4.7

LY9

3.8

CAPSL

3.2

KIAA0101

4.6

CCDC132

3.7

*RSPH10B

3.2

HRG

4.6

CRP

3.6

ANKRD17

3.1

KLHL33

4.6

C16orf53

3.6

*RNFT1

3.1

CEP85

4.5

HYOU1

3.6

HYDIN

3.1

PTGFRN

4.5

RASGEF1A

3.6

ASPHD1

3.1

TMIGD2

4.5

PIP

3.6

CCDC97

3.0

C6

4.4

OLFML1

3.5

CCDC43

3.0

WDR76

4.4

FAM178A

3.5

*PA1

3.0

MOB3B

4.4

TAGLN3

3.2

MLKL

3.0

RBL2

3.8

TMED6

3.2

WDR87

3.0

† Differentially expressed genes (≥ 3 fold, P < 0.05) as annotated based on the Entrez Gene database and categorised by function using the Benjamini-Hochberg post-hoc test for multiple corrections following one way ANOVA.

*indicates genes determined from the Partek analysis based on the Affymetrix annotations which were not assigned identities by IPA.

Variability of gene expression between follicles

The PCA indicated that the healthy follicles were a more heterogeneous group than the atretic follicles and we examined this further. In other studies examination of the variably expressed genes has recently been used as a tool to identify differences in the pathways of different neurological diseases [19], therefore we applied a similar approach to our data. The coefficients of variation (SD/Mean × 100) for the healthy and the atretic follicles of each probe set were calculated and the size-frequency distribution plot for healthy and atretic follicles is shown in Figure 4. The healthy follicles (Figure 4A) show increasing gene variation with increasing fold difference for the subset of genes which are differentially regulated between healthy and atretic follicles, which is not seen in the atretic follicle group (Figure 4B). We investigated this variation further and identified the most highly variable genes in small healthy follicles. A group of 682 of the most variable probe sets in small healthy follicles, which had a coefficient of variation value of > 46.8%, was assembled and analysed by Ingenuity Pathway Analysis (IPA) and Gene Ontology (GO) enrichment analysis (Tables 5 and 6). Cell cycle regulation is the most common function associated with the highly variable gene dataset. Thirteen genes were associated with GO terms for this function and cyclin genes such as CCNB1, CCNB2 and CDK1 were represented in both analyses and in the top canonical pathways. The enrichment analysis produced a number of additional functionally-related gene groups associated with variable expression. These categories included; regulation of vascularity, extracellular matrix, energy metabolism, inflammation, cell migration and MAPK activity. Interestingly, there were 17 extracellular matrix genes found to be highly variable across our healthy follicle arrays, and several of them code for a number of collagen types (1α1, 1α2, 3α1, 4α3, 4α4 and 18). Energy metabolism was identified as an important process with an association of 13 genes from this variable group, particularly glucose metabolism via ISR2, IGFBP2, PDK4 and ASPSCR1. Molecules known to promote angiogenesis in the ovary such as VEGF and angiopoietin, and an inhibitor thrombospondin, were also associated with our variable dataset.
Figure 4

Frequency coefficients of variation (CV) distributions in selected datasets from healthy follicles (n = 10) in A and atretic (n = 5) follicles in B. ‘All genes’ include all the probe sets present on the array (n = 24,128). ‘2 fold’ or ‘3 fold’ or more represent all genes which were 2 fold or more (n = 5,475) or 3 fold or more (n = 1,596) differentially-regulated genes between healthy and atretic follicles using Partek Genomics Suite Software.

Table 5

The most variable genes in small healthy follicles mapping to networks and pathways in IPA

 

Genes mapped from dataset

 
 

Symbol

Biological process

 

Network 1 †(Score = 42)

PLSCR1, PLSCR4, heparanase, CTSL1, ATP2B1

apoptosis

 

UHRF1

cell cycle regulation, mitosis

 

TIMP1,

matrix degradation

 

EGFR , JUNB , FOSL2

differentiation/maturation of granulosa cell through AP-1

 

CYP19A1 , CYP11A1, INHBA , BAMBI

steroidogenesis, regulation of gonadotropin secretion/granulosa cell proliferation

 

SOD3

stress response

 

ADM

regulation of blood supply

 

CLDN11

maintenance of epithelial integrity

 

VARS

growth metabolism

 

AHNAK

actin cytoskeleton organisation, cell polarization

 

Network 2 †(Score = 42)

CCNB1 , ESPL1, H1FX, H2AFX, BUB1,NCAPG, NCAPG2, SEPT4, S100A4, G3BP1

cell cycle regulation, mitosis

 

PSMD4, PSMD13, UBQLN1, PDIA4, HSP70

protein recycling and folding

 

PEG3

apoptosis

 

PRC1

cell migration

 

Canonical pathways

Cell Cycle: G2/M DNA Damage Checkpoint Regulation, Mitotic roles of Polo-Like Kinases

P value††

   

Fisher’s

B-H FDR

1

CCNB1,CCNB2,CDK1,GADD45A, RPRM, TOP2A, YWHAG

cell cycle regulation, mitosis

3.2 × 10-4

5.8 × 10-2

2

3.9 × 10-4

5.8 × 10-2

3

ATM Signalling

 
 

JUN, SMC2, GADD45A, H2AFX, CCNB2, CDK1, CCNB1

response to DNA damage

8.2 × 10-4

8 × 10-2

†The network score is based on the hypergeometric distribution and is calculated with the right-tailed Fisher’s Exact Test. The score is the negative log of this P value.

†† Significance of association of genes with canonical pathways was determined by a right tailed Fisher’s Exact Test and the Benjamini-Hochberg False Discovery Rate (B-H FDR) for multiple comparisons. The variability of expression was determined by a frequency distribution of the coefficients of variation for probe sets across the arrays in the small healthy follicle group. The cut-off chosen was a coefficient of variation of > 46.8% (n = 10 arrays, n = 682 probe sets).

Gene symbols which are underlined indicate those genes which interact with a minimum of 4 other molecules within the dataset.

Table 6

GO enrichment analysis of the most variable genes in granulosa cells from small healthy follicles using GOEAST

Biological process

Molecular function

Cellular component

Symbol

Entrez gene name

Regulation of vascularity Angiogenesis (P = 0.009)

  

CYR61

cysteine-rich angiogenic inducer

COL18A1

collagen 18

THBS1

thrombospondin 1

CXCR4

chemokine receptor 4

ANGPT2

angiopoietin 2

VEGFA

vascular endothelial growth factor A

Energy metabolism Glycogen biosynthetic process (P = 0.044)

Insulin-like growth factor binding, two component sensor activity

microsome

ISR2

insulin receptor 2

EGFR

epidermal growth factor receptor

HSP90AA1

heat shock protein 90 kDa alpha (cytosolic), class A member 1

PDK4

pyruvate dehydrogenase kinase, isozyme 4

IGFBP2

insulin-like growth factor binding protein 2

CTGF

connective tissue growth factor

CYR61

cysteine-rich angiogenic inducer

MGST1

glutathione transferase 1

CALR

calreticulin

PTGES

prostaglandin E synthase

PLC4

phospholipase C, beta 4

OPTN

optineurin

ASPSCR1

alveolar soft part sarcoma chromosome region, candidate 1

Cell Cycle Mitosis (P = 2.3 × 10-5)

 

Condensed chromosome kinetochore, spindle

CCNA2

cyclin A2

CCNB1

cyclin B1

CCNB2

cyclin B2

UBE2

ubiquitin conjugating enzyme 2

CDK1

cyclin-dependent kinase 1

NUSAP

nucleolar and spindle associated protein 1

SKA1

spindle and kinetochore associated complex subunit 1

NUP85

nucleoporin 85 kDa

CENPN

centromere protein N

CENPO

centromere protein O

ZW10

kinetochore associated homolog

BUB1

budding inhibited by benzimidazoles 1 homolog (yeast)

STK

serine/threonine kinase

Extracellular matrix Extracellular matrix organisation (P = 0.01)

Heparin binding, extracellular matrix binding

Basement membrane

COL1A1

collagen type I, alpha 1

COL1A2

collagen type I, alpha 2

COL3A1

collagen type III, alpha 1

COL4A3

collagen type IV, alpha 3

COL4A4

collagen type IV, alpha 4

COL18

collagen type XVIII

LAMB1

laminin, beta 1

VCAN

versican

FMOD

fibromodulin

ADAMTS1

ADAM metallopeptidase with thrombospondin type 1 motif, 1

ADAMTS6

ADAM metallopeptidase with thrombospondin type 1 motif, 6

MMP2

matrix metallopeptidase 2

TIMP1

TIMP metallopeptidase inhibitor 1

OGN

osteoinductive factor/osteoglycin

OSF-2

osteoblast-specific factor 2

SPARCL1

SPARC-like 1 (hevin)

ASPN

asporin

Inflammation

 

Fibrinogen complex

THBS1

thrombospondin 1

NOS biosynthesis (P = 0.044)

CALR

calreticulin

Antigen processing (P = 0.026)

BOLA-A

major histocompatibility complex (MHC), class I, A

TGFβ receptor signalling pathway (P = 0.05)

BOLA-N

MHC class I antigen

Fibrinolysis (P = 0.09)

Cell migration

  

CXCR4

chemokine receptor 4

Axon guidance (P = 0.0008)

ABLIM1

actin binding LIM protein 1

COL18

collagen type XVIII

EFNA5

ephrin A5

THBS1

thrombospondin 1

EGFR

epidermal growth factor receptor

TDGF

teratocarcinoma-derived growth factor 1

F2RL1

coagulation factor II (thrombin) receptor-like 1

INSR

insulin receptor

MAPK activity (P = 0.002)

  

THBS1

thrombospondin 1

TDGF

teratocarcinoma-derived growth factor 1

   

INSR

insulin receptor

†The significance of association for the GO enrichment analysis was determined by the Benjamini-Yekuteli FDR method for multiple comparisons. The variability of expression was determined by a frequency distribution of the coefficients of variation for probe sets across the arrays in the small healthy follicle group. The cut-off chosen was a coefficient of variation of > 46.8% (n = 10 arrays, n = 682 probe sets).

The large variability of gene expression across healthy follicles is probably not unexpected since small growing follicles have a number of possible growth trajectories: 1. continued growth to become a dominant follicle, with the likelihood of a) ovulation or b) atresia, 2. continued growth as a subordinate follicle with atresia as the ultimate fate or 3. atresia at an earlier stage. Whether this variability reflects early commitment or predisposition of follicles to one of the three outcomes, or whether it reflects flexibility without a predetermined outcome is not clear at this stage. However, our identification of the pathways and genes involved is an important first step towards understanding the underlying mechanisms responsible for the growth and atresia of follicles.

Pathway, network and upstream regulator analyses of healthy versus atretic follicles

A set of 690 probe sets, which were more than 4-fold differentially regulated in signal intensity between atretic and healthy follicles with a FDR of P < 0.005, were subjected to pathway analyses in IPA. Of these, 456 were mapped to known identities in the Ingenuity Knowledge database and only 428 were eligible for network generation, due to the presence of replicate probe sets with specificity for the same gene on the chip. This group contained 288 probe sets which were up regulated in atretic with respect to healthy follicles and 140 which were down regulated. The most significantly affected functions associated with this dataset were found to be cell death, organ development, tissue development and embryonic development, which were all predicted to be negatively regulated in atretic follicles (Table 7). Upstream Regulator analysis revealed that the transcription factor genes TP53, FOXO4 and CEBPB are predicted to be activated, whereas those of RXRA, HNF1A and MYC are inhibited on the basis of known interactions with the genes in our dataset (Table 8). The most significant canonical pathways represented in our analysis are shown in Figure 5. The top ranked canonical pathway (Figure 6), contains signalling molecules from our dataset which are common to various inflammatory/fibrotic pathways such as the transforming growth factor-β (TGFβ) (Additional file 2: Figure S1) and tumour necrosis factor-α (TNFα) pathways. The two most significant networks (Figure 7A and 7B) also reflect this pattern of tissue remodelling/fibrosis gene expression. These networks also contain molecules which are present in the canonical pathways of hepatic fibrosis/hepatic stellate cell activation and TGFβ signalling and are up regulated in our analysis and should therefore positively stimulate these networks. These include genes such as THBS2, PLAT, BAMBI, TGFBR2, BMP2, SMAD3, FGFR2, PDGFRA and TIMP1.
Table 7

Biological functions determined in IPA for genes differentially regulated between atretic and small healthy follicles

Category

Functions annotation

P Value

Bias-corrected z-score

Genes

Fisher’s

(B-H) FDR

Cancer

tumorigenesis of organ

8.31E-04

1.57E-02

-2.907

CAV1, CDKN1C, COL18A1, CYP19A1, CYR61, FST, GADD45A, ING4, JUN, MMP11, NDRG1, SMAD3, STAR, TGFBR2, TIMP1, VEGFA

Cancer

hyperproliferation

5.94E-05

9.22E-03

-2.889

AXL, BCL6, CAV1, CDKN1C, CEBPD, CYP19A1, FGFR2, FST, GPX3, IGF2R, ING4, MERTK, MGP, MXI1, NOS2, PLAUR, PLP1, POSTN, SERPINE1, SMAD3, SPP1, STAR, STAT3, TGFBR2, TIMP1, TOP1, VEGFA

Cell Death

cell death of organ

1.32E-10

3.34E-08

-2.868

A2M, AMH, ATF3, AXL, BCL6, BMP2, C8orf4, CAMP, CAST, CCNT1, CD200, CDKN1C, CFH, CFLAR, CLIC4, CTGF, CYP19A1, CYP2C9, DCN, DUSP1, EGR1, GAL, GCLC, GLRX, GNG2, GSN, GTF2F2, ID3, IER3, IGF2, IL18, JUN, KRT8, LGALS3, LTA, MDK, MERTK, MTPN, MZB1, NOS2, NTRK3, OPRM1, PIK3CA, PIM1, PLAT, PLAUR, PTPN5, RND3, SERPINA3, SERPINE1, SH3BP5, SMAD3, SPP1, STAR, STAT3, THBS2, TIMP1, TNFRSF6B, TOP2A, TP53INP1, VEGFA

Cell-To-Cell Signalling and Interaction

activation of blood cells

5.83E-04

1.21E-02

-2.800

AGRN, ANXA1, ANXA2, AXL, C6, CAMP, CD200, CFH, DUSP1, F2RL2, HDAC7, HLA-A, HRG, IGF2R, IL18, IL18R1, LTA, MERTK, NDRG1, NOS2, OPRM1, PDGFRA, PELI1, PLAT, PLP1, SMAD3, SPP1, STAT3, TNFRSF12A, TNFRSF6B, VEGFA, WASL

Cancer

hyperplasia

2.82E-04

7.20E-03

-2.753

AXL, CAV1, CDKN1C, CEBPD, CYP19A1, FGFR2, FST, GPX3, IGF2R, ING4, MGP, MXI1, NOS2, PLAUR, POSTN, SERPINE1, SPP1, STAR, STAT3, TGFBR2, TIMP1, TOP1, VEGFA

Tissue Development

development of organ

1.95E-09

3.70E-07

-2.665

ADAMTS1, ALB, AMH, ANKRD1, AQP1, ATF3, ATRX, AXL, BCL6, BMP2, CAMP, CAV1, CDKN1C, CFLAR, CITED2, CLDN1, CLDN11, COL18A1, COL1A2, COL3A1, CTGF, CTSH, CYP19A1, CYR61, DCN, EGR1, EPAS1, ERRFI1, FABP5, FGFR2, FOSL2, FOXP1, FSHB, FST, GAL, HHEX, HRG, ID1, ID3, IGF2, IGF2R, IGFBP5, IHH, INSIG2, JARID2, JUN, KLF6, KRT18, LAMC2, LDB1, LGALS3, LTA, MDK, MERTK, MGP, NDP, NOS2, NR2F1, NTRK3, PDGFRA, PIM1, PLAT, RHO, SERPINE1, SLC40A1, SLC4A2, SMAD3, SNAI2, STAT3, TACC2, TDRD7, TGFBR2, THBS2, TIMP1, TNFRSF12A, TYRP1, VCL, VEGFA, YBX2

All predicted to be decreased.

The predicted activation state is inferred from the bias-corrected z-score, (+ = increased, - = decreased). The bias-corrected z-score is computed based on the proportion of target genes present in the dataset which are directionally regulated as expected according to known associations with functions compiled from the literature.

The P value of overlap measures the statistical significance of overlap using Fisher’s exact t-test or the Benjamini-Hochberg False Discovery Rate for multiple comparisons (B-H FDR), between genes from the dataset and those known to be associated with a function.

Table 8

Upstream regulators predicted by target gene expression in the atretic versus healthy dataset

Upstream regulator

Predicted activation state

Bias-corrected z-Score

P value of overlap

Target molecules in dataset

TP53

activated

4.272

5.78E-11

ALB, ANXA1, ANXA2, ARHGEF2, ARPC1B, ATF3, CALD1, CAV1, CFLAR, CLIC4, CNN1, COL18A1, COL1A2, COL3A1, COL4A1, CTGF, CTSH, CYR61, DKK3, DUSP1, EGR1, FBLN2, FERMT2, GADD45A, GLRX, GPX3, GSN, HMGCS1, ID1, ID3, IER3, IFI30, IGF2, IGFBP5, JUN, KRT8, LGALS3, MAP4K1, MMP23B, MPDZ, NDRG1, NOS2, NR2F1, PCDH7, PDE6A, PGFRA, PHLDA1, PIM1, PLAUR, POSTN, RAD51AP1, SAT1, SERPINE1, SGK1, SNAI2, SPP1, STAU1, STK17A, TAGLN2, TGFBR2, THBS2, TOP2A, TP53INP1, VCL, VEGFA

FOXO4

activated

2.203

5.41E-05

BCL6, CTGF, GADD45A, HMGCS1, IER3, SERPINE1, SGK1, VEGFA

CEBPB

activated

2.142

1.59E-07

ALB, CEBPD, COL1A2, CPT1A, CSN2, CYP19A1, DAB2, DCN, GADD45A, GLIPR2, ID1, IFIT, M3, MGP, MMP11, NDRG4, NOS2, PDGFRA, PDK4, PLAUR, SAT1, SERPINE1, SGK1, SPP1, STAR, TMEM176A, TNFAIP6, TOP1

RXRA

inhibited

-2.100

3.52E-04

CEBPD, CPT1A, CYP2C9, FABP5, GCLC, GPT, IER3, LPL, MGP, MMP11, OLR1, PNMT, SAT1, SLC10A2, SPP1, STAR, VEGFA

HNF1A

inhibited

-2.168

4.07E-01

ALB, BCL6, C1S, CD55, COL3A1, IHH, SERPINE1, SLC10A2, SLC40A1, SLC4A2, UGT2B4

MYC

inhibited

-3.197

7.80E-07

ACTN1, ALB, AQP1, BCL6, CALD1, CAST, CAV1, CD9, CEBPD, CFLAR, CLIC4, COL1A2, COL3A1, COL4A1, CPT1A, CSTB, DSTN, DUSP1, FABP5, FBLN2, GADD45A, GCLC, GTF2F2, HLA-A, ID1, ID3, IER3, KLF6, LUM, NDRG1, PLAUR, PLP1, POLR3D, SGK1, SPP1, TAGLN2, TGFBR2, THBS2, TIMP1, TLN1, TSPO, VEGFA

MYCN

inhibited

-3.202

8.50E-03

ARPC1B, CAV1, CCNT1, CITED2, COL18A1, COL4A1, CTGF, DKK3, FGFR2, HLA-A, SDC2, SERPINE1, TAGLN

The predicted activation state is inferred from the bias-corrected z-score, (+ = activated, - = inhibited). The bias-corrected z-score is computed based on the proportion of target genes present in the dataset which are directionally regulated as expected according to known effects of the regulator on the target compiled from the literature. The P value of overlap measures the statistical significance of overlap using Fisher’s exact t-test, between genes from the dataset and those known to be acted upon by an upstream regulator.

Figure 5

Top canonical pathways mapped in Ingenuity Pathway Analysis (A) and GO terms classified under biological process (B) for a selected set of genes differentially regulated between healthy and atretic follicles. In A the bar chart on the left represents the percentage of genes that map to each canonical pathway, showing those which are up regulated (in red) and down regulated (in blue) in atretic with respect to healthy follicles. The line chart on the right ranks these pathways derived for the same dataset, from the highest to lowest degree of association based on the value of a right-tailed Fishers exact t test (black), and the Benjamini-Hochberg False Discovery rate test for multiple comparisons (red) (top to bottom in graph on right). In B the significance of association was determined by the Benjamini-Yuketeli test for multiple comparisons. The bar chart indicates the percentage of genes that map to a GO term which are differentially regulated (in blue). Only those significantly enriched GO terms associated with a subset of genes of the most specific function were presented, to avoid terms which were too general and of limited value.

Figure 6

The hepatic fibrosis/hepatic stellate cell activation canonical pathway in IPA. Interactions between molecules are shown as explained in the legend, with focus molecules (those from our dataset) highlighted in colour, based on up (red) or down regulation (green) and increasing colour intensity with degree of fold change.

Figure 7

Two statistically significant networks (A and B) produced by mapping differentially expressed genes between healthy and atretic follicles to molecules in the IPA database. The networks are generated in IPA using triangle connectivity based on focus genes (those present in our dataset) and built according to the number of interactions between a single prospective gene and others in the existing network, and the number of interactions the prospective gene has outside this network with other genes as determined by IPA [20]. Interactions between molecules, and the degree and direction of regulation are indicated similarly as in Figure 6 with up (red) or down regulation (green) and increasing colour intensity with degree of fold change.

GO enrichment analyses of this dataset determined associations by the categories of biological process, molecular function and cellular component with atresia in granulosa cells from small follicles (Figure 5 and Additional file 3: Table S2). These analyses revealed that blood vessel development and cell adhesion processes were affected through genes such as CTGF and VEGFA, and VNN1 and PIK3CA, respectively. The affected products of genes were localised in terms of this analysis to the extracellular matrix and the genes LAMC2, COL1A2, COL3A1 and COL18A1 were found in this group. Other functionally related genes of interest such as IGF2, IGFBP5 and IGFBP6 indicate that insulin growth factor activity appeared to be modulated during the atretic process.

Transcriptional processes of atresia

Apoptosis

Apoptosis is believed to be the major process of cell death occurring in the antral atretic follicle [7, 21]. There was a significant association in our study between atretic follicles and molecules which are influenced by the p53 transcription factor. p53 is an activator of apoptotic pathways in response to cell stress and functions by regulating cell cycle, DNA repair and metabolic genes [22]. Contrary to expectations, many genes which were up regulated in atretic follicles in this study are actually anti-apoptotic or protective against apoptosis such as CFLAR[23, 24], PIK3CA[25, 26] and VNN1[27]. None of the caspases or Fas genes which are known to be involved with apoptotic pathways were found to be differentially expressed to the same extent as those genes mentioned above. Previous studies in cattle and humans have focused on antral follicles of a larger size [11, 14, 28, 29], than those used in this experiment. It is possible that cell death mechanisms that operate in larger follicles are different from those at an earlier stage, where cells are under different hormonal control, LH in addition to FSH, and in the presence of focimatrix [28]. An alternative theory is that only those granulosa cells in the atretic follicle with elevated expression of anti-apoptotic genes are capable of surviving longer during the process of apoptosis. This theory would fit with the granulosa stem cell model as proposed previously [30]. This model proposes that different types of granulosa cells within the same follicle are derived from stem cells which grow and divide and eventually differentiate into antral, basal and cumulus granulosa cells in the antral follicle. Hence, at any one time not all granulosa cells would be equal and some could be more susceptible to apoptosis than others, as stem cells in muscle [31], neural [32] and bone marrow [33] are (reviewed in [34]).

Intercellular junctions

We found increased expression of the claudin tight junction genes CLDN1, CLDN5 and CLDN11 and the E-cadherin gene, CDH1, in atretic follicles. The increase in E-cadherin expression was also verified by immunohistochemistry. Tight junctions act as a critical barrier to the passage of substances between the vasculature and the interior lumen of glands [35] and together with gap junctions help maintain the overall stability of the epithelial layer structure [36]. They are also important for establishing polarity within epithelial cells in general [37]. Properly formed tight junctions have not been observed in the membrana granulosa, though they do exist between Sertoli cells, the analogous cell type in the testis [38]. Gap junctions are present in the granulosa of several species with predominant expression of connexins 43 and 37 (reviewed by [39]). E-cadherin is another protein found at intercellular junctions which is important for cell recognition and adhesion [36], and has been demonstrated in human ovarian follicles [40]. Claudin 1 gene expression has previously been shown to be up regulated during apoptotic insult induced by tamoxifen in some breast tumour cells [41]. Again it is possible that those granulosa cells which have increased expression of these intercellular junction proteins may be able to modulate the effects of apoptotic mechanisms on the cell by stabilising the overall membrana granulosa structure.

Inflammation/TGFβ signalling/Tissue remodelling

There have been a number of studies examining TGFβ signalling molecules in follicle development, though not from the aspect of atresia. This is perhaps surprising given that follicular atresia necessitates a cyclic process of tissue remodelling, and the well-known involvement of the TGFβ superfamily in tissue repair. In both IPA and GO enrichment analyses, there were a number of genes found to be associated with inflammatory/TGFβ signalling fibrosis pathways or processes. When we examined the hepatic fibrosis signalling pathway in IPA, with respect to the molecules affected from our dataset, there was increased expression of TGFBR2 and the downstream SMAD genes and subsequently COL1A2 and COL3A1. The TGFβ receptor has been localised immunohistochemically to the granulosa cells of antral follicles of certain species [42, 43], though only in large antral follicles which maybe differentiating as they luteinise. A focus on those genes in our study which are differentially expressed in small follicles, and are expressed at a high level in either the healthy or atretic state, reveals that the inhibin-activin-follistatin axis was most important. INHA and INHBA, which encode activins and inhibin, and FST which produces follistatin, were all down regulated in atretic follicles in our study, which is in agreement with previously published work, as all are required for proper development of the follicle through the antral stage of development [44, 45]. BAMBI expression in our atretic follicles was increased relative to healthy follicles; the encoded protein is known to bind and inhibit activin and BMP2 (bone morphogenetic protein-2) [46] thus antagonising FSH-induced follicle growth.

Anti-mullerian hormone (AMH) expression was also lower in the atretic follicle. AMH has been shown to delay recruitment of primary follicles to the next stage of growth or possibly atresia [47]. Additionally, AMH has been shown to be up regulated in large dominant bovine follicles versus subdominant and it probably has a survival effect [48]. GDF-9 and BMP-15 are growth factors which have been studied in specific knockouts in mice or mutations in sheep and are known to be important for antral follicle growth (reviewed by [49]). BMP15 was up regulated 3 fold in small healthy follicles in our study, whereas GDF9 was relatively unaffected, the proteins encoded by these genes are secreted specifically by the oocyte [50, 51] which makes up a small proportion of the total RNA in our samples, so clearly BMP15 expression was activated but the status of GDF9 was not apparent. BMPR2 (bone morphogenetic protein receptor-2) encodes a receptor for GDF-9 and BMP-15 and is therefore also critical for follicle expansion. There was a slight increase in BMPR2 in the atretic follicle expression in our study, however, this was not significant. Expression of BMP2 was elevated more than 6 fold in atretic follicles and this member of the BMP family has been implicated in activation of FSH-induced follicle growth and in suppression of luteinisation in human cultured granulosa cells [46]. However, this result was demonstrated in cells obtained from preovulatory follicles and it is unclear whether BMP-2 plays the same role earlier in follicle development.

Angiogenesis

VEGFA was down regulated in the atretic follicles and MDK (midkine; neurite growth-promoting factor 2) and THBS2 were up regulated in our study. VEGF is a key pro-angiogenic factor and its expression is known to correlate with the size of healthy antral follicles [52] and is weakly expressed in atretic follicles [53]. Thrombospondins-1 and -2 are anti-angiogenic and are expressed highly during tissue remodelling following injury. Thrombospondin-2 in particular appears during the late proliferative phase and is expressed during the remodelling period in conjunction with MMP-2 [54]. Thrombospondin-1 has been shown to promote follicular atresia in rats [55], and together with thrombospondin-2 is expressed inversely with VEGF in a cyclical fashion during folliculogenesis in cows [52]. MDK is expressed in many tissues throughout embryonic development, and it has been shown to antagonise VEGF signalling in vitro and in vivo[56]. Atresia in small antral follicles predictably appears to involve a decrease in blood vessel formation which agrees with previous data.

Insulin metabolism

We found increased levels of expression of IGF2R, and the binding protein genes IGFBP5 and IGFBP6 in atretic follicles with respect to healthy follicles. A number of studies have determined the importance of insulin-like growth factor metabolism at antral stages of follicle development [5760]. IGF-2 acts similarly to IGF-1 to promote proliferation and growth of granulosa cells, but it is specifically bound by IGF-2 receptor which does not signal by the same pathways as the IGF-1 receptor, but rather acts as a clearance mechanism for IGF-2 [61]. The IGF binding proteins bind IGF-1 and -2, and thus locally regulate their bioavailability in the follicle [62]. IGFBP-5 has previously been shown to increase in atretic follicles in ruminants [63] perhaps due to increased expression and changing levels of degradative enzymes. Therefore it seems that antral atretic follicles have reduced IGF signalling contributing to lower proliferation and decreased metabolism by granulosa cells as has been previously published.

Extracellular matrix and matrix proteases

There are several matrix genes which were differentially regulated in our arrays in the atretic follicles including COL1A2, COL3A1, COL4A1, NID2, LAMB1 and LAMC2. Immunostaining confirmed that nidogen-2 protein was increased in atretic follicles. Collagens type I and IV were not detectable in the granulosa cells of small follicles at the small antral stage. Previous immunohistochemical studies [64, 65] found that the composition of the extracellular matrix did not change, apart from the presence of laminin α2 in atretic but not healthy follicles, and that laminin β1 expression was very weak or nearly absent in small antral atretic follicles [66]. Unfortunately, the laminin α2 gene probe set was not present on the array so this could not be confirmed by the present analysis. It is well known that collagens 1 and 3 are synthesised during the tissue remodelling phase following inflammation [67]. Lee and Dunbar showed an increasing accumulation of laminin β1/γ1 in between granulosa and theca cells in progressively atretic follicles in the pig [68], although a similar study by ourselves in bovine follicles did not indicate differences in laminin β1 in the follicular basement membrane and membrana granulosa [66]. Recently, laminin γ2 was found to be secreted by cultured epithelial cells in response to wounding and may act as a scaffold for cell migration [69]. The expression of these genes in our current study probably represents a step in the eventual regression of the atretic follicle.

Annexin A2 has been shown to be actively involved in endocytosis and formation of adherens junctions [70]. There is phagocytosis of necrotic cellular debris during atresia and we observed an up regulation of E-cadherin expression in the atretic follicular granulosa cells.

Conclusions

Small healthy antral follicles, which can undergo a number of growth options, exhibit greater variability in gene expression, particularly in genes associated with cell division and other growth-related functions. It is clear that atresia is associated with transcriptional processes such as the inhibition of blood vessel formation and the differential expression of matrix genes which may signal the surrounding stromal cells to initiate follicular remodelling. Therefore, atresia not only involves changes in expression of genes associated with cell death but it is clearly also an active process not dissimilar to that of wound healing.

Methods

Tissues

Ovaries were collected at a local abattoir in South Australia (from non-pregnant Bos taurus cows, within 20 min of slaughter and transported to the laboratory in Hank’s balanced-salt solution with Mg2+ and Ca2+ (HBSS+/+; Sigma-Aldrich, Castle Hill, NSW, Australia) on ice. The follicles were dissected from each ovary and the diameter measured. A small piece of the follicle wall, approximately 100 mm3, was removed and fixed in 2.5% glutaraldehyde in 0.1 M phosphate buffer (pH 7.25) for subsequent classification of follicle health status. The granulosa cells were removed from the remainder of the follicles by gentle rubbing with a glass Pasteur pipette, previously modified by heat sealing the tip into a rounded smooth surface. The HBSS-/- containing the granulosa cells was centrifuged at 500 ×g for 7 min at 4°C, the medium was removed by aspiration and the cells washed twice in phosphate-buffered saline (PBS), pH 7.4. Finally the cells were resuspended in RNAlater (Ambion Life Technologies Australia Pty Ltd., Mulgrave, VIC, Australia), and stored at -20°C for subsequent RNA isolation and microarray analysis. A total of 10 samples of small healthy follicles and 5 small atretic follicles, all < 5 mm and from different animals, were used in this study. Due to limiting RNA, three of the samples from the small healthy follicles were pools of two follicles each from the same animal, whereas the rest were all individual follicles.

Histological classification of follicles

Following fixation overnight, the follicle wall portions of each follicle were rinsed several times with 0.1 M PBS, pH 7.25, post-fixed in 2% (v/v) aqueous osmium tetroxide for 1 h at 4°C and embedded in epoxy resin as described previously [71]. For light microscopic examination, 0.5 μm thick epoxy sections were cut using a glass knife and a Richert-Jung Ultracut E ultramicrotome (Leica Microsystems Pty Ltd., VIC, Australia), stained with 1% (w/v) aqueous methylene blue and examined using an Olympus BX50 microscope (Olympus Australia Pty Ltd., Mt. Waverly, VIC, Australia). Healthy and atretic follicles were identified as described previously [7, 8] wherein healthy follicles had no dead or dying granulosa cells and atretic follicles had substantial numbers of dead and dying granulosa cells. This death in atretic follicles was characterized by a loss of layers closest to the antrum and numerous pyknotic nuclei in the remaining antrally-situated layers [72, 73]. The healthy follicle phenotype was sub-classified into two types, rounded or columnar, based on the shape of the basally-situated granulosa cells [15, 16]. Additional file 4: Figure S2 shows examples of each of these follicle types.

RNA isolation

Total RNA was extracted from the granulosa cells using RNeasy mini RNA extraction kits (Qiagen, Hilden, Germany) and RNAqueous-Micro kit™ (Ambion®/Life Technologies Australia Pty Ltd., Mulgrave, VIC, Australia). The concentration of the RNA was determined by spectrophotometric measurement at 260 nm. For each granulosa cell preparation, 5 μg of RNA was treated with DNA-free™ (Ambion Life Technologies). The quality of the RNA was assessed by electrophoresis using an Agilent 2100 Bioanalyser (Agilent Technologies, Santa Clara, CA, USA) and only that with a RNA integrity number exceeding eight was accepted for analysis.

Real time reverse transcription polymerase chain reaction (real time RT-PCR)

Synthesis of cDNA and real time RT-PCR using plasmid standards were performed as previously [74] and briefly described below. Total RNA (500 ng) was reverse transcribed with SuperScript® III Transcriptase (Life Technologies) using random hexamer primers (Geneworks, Thebarton, SA, Australia) according to the manufacturer’s instructions. Primer Express software (Life Technologies) was used to design primers to the bovine sequences of 18S ribosomal RNA and CYP17A1 (Table 9). An ABI Prism 7000 Sequence Detection System (Life Technologies) was used for real time reverse transcription RT-PCR detection with SYBR Green (Eppendorf, Hamburg, Germany) and 10 pmoles of forward and reverse primers in a 20 μl reaction. Primer sequences and PCR conditions are shown in Table 9. Plasmid standards were generated by cloning amplified products into pCR2.1-TOPO vector (Life Technologies), then transformed into E.coli strain XL1 Blue (Agilent Technologies) and DNA was extracted and purified. These DNA standards were quantitated by absorbance at 260 nm and serially diluted over three logs then amplified together with the diluted sample cDNA in the real time reaction to determine quantities of RNA expressed as fg RNA/ng 18S ribosomal RNA.
Table 9

Primers and conditions used for quantitative RT-PCR

Target

Primer sequences, 5′-3′

GenBank accession number

PCR reaction conditions

CYP17A1

forward accatcagagaagtgctccgaa

NM_174304

2 min 50°C, 10 min 95°C, 40 × cycles of 15 s 95°C and 60 s 60°C

reverse ccacaacgtctgtgcctttgt

18S

forward agaaacggctaccacatccaa

DQ2224

2 min 50°C, 10 min 95°C, 40 × cycles of 15 s 95°C and 60 s 60°C

 

reverse cctgtattgttatttttcgt

  

Microarray profiling

Following confirmation of the quality of RNA and cDNA synthesis, hybridisations to GeneChip Bovine Genome Arrays (Affymetrix, CA, USA) and scanning were performed according to Affymetrix protocols at the Australian Genome Research Facility (AGRF, Walter & Eliza Hall Institute of Medical Research, Parkville, VIC, Australia) and the Adelaide Microarray Centre (AMC, University of Adelaide, SA, Australia). Between 2 to 5 μg from the small healthy follicles and 250 ng of RNA from small atretic follicles was used per probe preparation with the Affymetrix Genechip 3′ IVT Express kit. Both types of samples followed a similar labelling and hybridisation procedure as detailed below. First-strand cDNA synthesis was performed using a T7-linked oligo-dT primer, followed by second strand synthesis. In vitro transcription reactions were performed in batches to generate biotinylated cRNA targets, which were subsequently chemically fragmented at 95°C for 35 min. Ten micrograms of the fragmented, biotinylated cRNA was hybridised at 45°C for 16 h to Affymetrix GeneChip Bovine Genome Arrays, which contain 24,128 probe sets representing over 23,000 transcripts and variants, including 19,000 UniGene clusters. The arrays were then washed and stained with streptavidin-phycoerythrin (final concentration 10 μg/ml). Signal amplification was achieved by using a biotinylated anti-streptavidin antibody. The array was then scanned according to the manufacturer’s instructions. The scanned images were inspected for the presence of any defect on the array.

Data normalisation and analyses

To minimize discrepancies due to variables such as sample preparation, hybridisation conditions, staining, or array lot, the raw expression data were normalised using RMA background correction (Robust Multi-array Average [75]) with quantile normalisation, log base 2 transformation and mean probe set summarisation with adjustment for GC content which were performed in Partek Genomics Suite Software version 6.5 (Partek Incorporated, St Louis, MO, USA). All samples sent for analysis passed all quality controls. The 15 arrays were analysed as part of a larger set of CEL files (which additionally included samples of granulosa cell RNA from 4 large follicles as discussed elsewhere [18]) uploaded to the Partek GS software program. Before statistical analysis, the data were first subjected to PCA [76] and hierarchical clustering analysis to compare the gene expression patterns of the arrays in terms of our classification. Hierarchical clustering was performed using the Euclidian algorithm for dissimilarity with average linkage. The expression data were analysed by ANOVA using method of moments estimation [77] with post-hoc step-up FDR test for multiple comparisons. The fold change in expression for each gene was based on the non log-transformed values after correction and normalisation. These differentially expressed genes were further annotated and classified based on the Gene Ontology (GO) consortium annotations from the GO Bos taurus database (2010/02/24) [78] using GOEAST (Gene Ontology Enrichment Analysis Software Toolkit; [79]). Expression data were also exported to Excel and used to generate size-frequency distributions of the coefficient of variation for each probe set for the two sets of follicles, healthy and atretic. The microarray CEL files, normalised data and experimental information have been deposited in the Gene Expression Omnibus (GEO) database under series record GSE39589.

Pathway analyses of differentially-expressed genes were conducted using IPA software (Redwood City, CA, USA). Network eligible molecules derived from these datasets were overlaid onto a global molecular network developed from information contained in the Ingenuity Knowledge Base. Networks of these molecules were then generated algorithmically based on their connectivity (Ingenuity Systems, 2005). The network score is based on the hypergeometric distribution and is calculated with the right-tailed Fisher’s Exact Test. The score is the negative log of this P value. Canonical pathway analysis identified the pathways from the IPA library of canonical pathways that were most significant to the dataset in terms of the ratio of the number of molecules that mapped to the pathway from the dataset and a right-tailed Fisher’s exact t-test to determine the probability that the molecules mapped to the pathway by chance alone. We also used IPA Upstream regulator analysis to identify upstream transcriptional regulators. Upstream regulators were predicted using a Fisher’s exact t-test to determine the probability that genes from the dataset correspond with targets which are known to be activated or inhibited by those molecules based on current knowledge in the Ingenuity database.

Immunohistochemistry

Follicles from bovine ovaries were collected and embedded in O.C.T. compound (ProSciTech, Thuringowa, QLD, Australia) and frozen at -80°C. Follicle sections (7 μm) were cut using a CM1800 Leica cryostat (Adeal Pty Ltd., Altona North, Vic, Australia), collected on Superfrost™ glass slides (HD Scientific Supplies, Wetherill Park, Australia), and stored at -20°C until use. Antigen localisation was undertaken on 9 small healthy and 7 small atretic follicles, using an indirect immunofluorescence method as previously described [80]. Frozen follicle sections were dried under vacuum for 5 min, fixed for 5 min and rinsed three times for 5 min in hypertonic PBS (10 mM sodium/potassium phosphate with 0.274 M NaCl, 5 mM KCl, pH 7.2) before treatment with blocking solution [10% normal donkey serum (Sigma-Aldrich) in antibody diluent containing 0.55 M NaCl and 10 mM sodium phosphate (pH 7.1)] for 30 min at room temperature. The sections were incubated with primary antibodies overnight at room temperature. Additional file 5: Table S3 lists the antibodies used for immunofluorescence and relevant fixation conditions. Sections were also treated with the nuclear stain 4′,6′-diamidino-2-phenylindole dihydrochloride (DAPI) solution (Molecular Probes, Eugene, OR, USA) to identify cell nuclei. Coverslips were attached with mounting medium for fluorescence (Dako Corporation, Carpinteria, CA, USA) and photographed with an Olympus BX51TRF microscope with an epifluorescence attachment (Olympus Australia Pty Ltd., Mt Waverley, VIC, Australia) and a Spot RT digital camera (Diagnostic Instruments Inc Pty Ltd., Victora Park, WA, Australia). Negative controls included no primary antisera and non-immune mouse, rabbit or rat serum. No staining of ovarian structures was observed with these controls.

Abbreviations

FDR: 

Benjamini-Hochberg False Discovery Rate

FSH: 

Follicle-stimulating hormone

GO: 

Gene ontology

GOEAST: 

Gene Ontology Enrichment Analysis Software Toolkit

GEO: 

Gene Expression Omnibus

HBBS: 

Hank’s balanced-salt solution

IPA: 

Ingenuity Pathway Analysis

LH: 

Luteinising hormone

PCA: 

Principal Component Analysis

RMA: 

Robust multi-array average

RT-PCR: 

Reverse transcription polymerase chain reaction

TGFβ: 

Transforming growth factor beta

TNFα: 

Tumour necrosis factor alpha.

Declarations

Acknowledgements

We thank Drs Richard D’Andrea, Thomas Ohnesorg, Andrew Sakko, Yi Peng, Anand Venkatraman, David Adelson, Cristin Print, William Rainey and Angela Chang and for their discussions and earlier contributions to this study and T&R Pastoral for donation of ovaries. This work was supported by the National Health and Medical Research Council of Australia, the Australian Research Council and the University of Adelaide.

Authors’ Affiliations

(1)
Research Centre for Reproductive Health, Discipline of Obstetrics and Gynaecology, School of Paediatrics and Reproductive Health, Robinson Institute, University of Adelaide
(2)
School of Medical Science, Griffith University

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