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Gene expression profiling of preovulatory follicle in the buffalo cow: effects of increased IGF-I concentration on periovulatory events.

Rao JU, Shah KB, Puttaiah J, Rudraiah M - PLoS ONE (2011)

Bottom Line: Thus, further experiments were conducted to verify the effects of increased intrafollicular IGF-I levels on the expression of genes associated with the above mentioned processes.The results indicated that increased intrafollicular concentrations of IGF-I caused changes in expression of genes associated with steroidogenesis (StAR, SRF) and apoptosis (BCL-2, FKHR, PAWR).These results taken together suggest that onset of gonadotropin surge triggers activation of various biological pathways and that the effects of growth factors and peptides on gonadotropin actions could be examined during preovulatory follicle development.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore, India.

ABSTRACT
The preovulatory follicle in response to gonadotropin surge undergoes dramatic biochemical, and morphological changes orchestrated by expression changes in hundreds of genes. Employing well characterized bovine preovulatory follicle model, granulosa cells (GCs) and follicle wall were collected from the preovulatory follicle before, 1, 10 and 22 h post peak LH surge. Microarray analysis performed on GCs revealed that 450 and 111 genes were differentially expressed at 1 and 22 h post peak LH surge, respectively. For validation, qPCR and immunocytochemistry analyses were carried out for some of the differentially expressed genes. Expression analysis of many of these genes showed distinct expression patterns in GCs and the follicle wall. To study molecular functions and genetic networks, microarray data was analyzed using Ingenuity Pathway Analysis which revealed majority of the differentially expressed genes to cluster within processes like steroidogenesis, cell survival and cell differentiation. In the ovarian follicle, IGF-I is established to be an important regulator of the above mentioned molecular functions. Thus, further experiments were conducted to verify the effects of increased intrafollicular IGF-I levels on the expression of genes associated with the above mentioned processes. For this purpose, buffalo cows were administered with exogenous bGH to transiently increase circulating and intrafollicular concentrations of IGF-I. The results indicated that increased intrafollicular concentrations of IGF-I caused changes in expression of genes associated with steroidogenesis (StAR, SRF) and apoptosis (BCL-2, FKHR, PAWR). These results taken together suggest that onset of gonadotropin surge triggers activation of various biological pathways and that the effects of growth factors and peptides on gonadotropin actions could be examined during preovulatory follicle development.

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Schematic representation of temporal changes in differentially expressed genes at 1 h and 22 h post peak LH surge.(A) Venn diagram representing the details of differentially expressed genes identified after microarray analysis of granulosa cells collected from the ovulatory follicle at −2, 1 and 22 h post peak LH surge. Data analyzed with ≥2 fold change cut-off and statistics. The number of differentially expressed genes found common between −2 vs. 1 h (red circle) and −2 vs. 22 h (blue circle) post peak LH surge, as well as comparison of differentially expressed genes between 1 vs. 22 h (green circle) post peak LH surge are presented. (B) Schematic representation of differentially expressed genes at different time points post peak LH surge. Red hexagon represents genes that were up regulated both at 1 and 22 h as well as those genes that were down regulated at 1 h but were up regulated at 22 h time point. Green hexagon represents differentially expressed genes that were down regulated both at 1 and 22 h time points as well as those genes that were up regulated at 1 h but were down regulated at 22 h time point. Also provided is the number of genes (represented as open hexagon) that were differentially expressed at 1 h time point but not at 22 h. (C) The number of differentially expressed genes listed as alphabetical groups in Fig. 2B are further represented in tabular form providing details on the number of genes in each group, pattern of expression change at both time points post peak LH surge as well as classification based on their ontological distribution within each ‘biological processes’ terms having high scores indicative of processes associated with each group.
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pone-0020754-g002: Schematic representation of temporal changes in differentially expressed genes at 1 h and 22 h post peak LH surge.(A) Venn diagram representing the details of differentially expressed genes identified after microarray analysis of granulosa cells collected from the ovulatory follicle at −2, 1 and 22 h post peak LH surge. Data analyzed with ≥2 fold change cut-off and statistics. The number of differentially expressed genes found common between −2 vs. 1 h (red circle) and −2 vs. 22 h (blue circle) post peak LH surge, as well as comparison of differentially expressed genes between 1 vs. 22 h (green circle) post peak LH surge are presented. (B) Schematic representation of differentially expressed genes at different time points post peak LH surge. Red hexagon represents genes that were up regulated both at 1 and 22 h as well as those genes that were down regulated at 1 h but were up regulated at 22 h time point. Green hexagon represents differentially expressed genes that were down regulated both at 1 and 22 h time points as well as those genes that were up regulated at 1 h but were down regulated at 22 h time point. Also provided is the number of genes (represented as open hexagon) that were differentially expressed at 1 h time point but not at 22 h. (C) The number of differentially expressed genes listed as alphabetical groups in Fig. 2B are further represented in tabular form providing details on the number of genes in each group, pattern of expression change at both time points post peak LH surge as well as classification based on their ontological distribution within each ‘biological processes’ terms having high scores indicative of processes associated with each group.

Mentions: Microarray analysis results (GEO # GSE11312), after subjecting to high stringency statistical analysis revealed 450 genes to be differentially expressed within 1 h of peak LH surge (≥2 fold change with Benjamini and Hochberg correction for false discovery rate), of these 220 and 228 genes were up and down regulated, respectively. The analysis of microarray data at 22 h post peak LH surge revealed 111 genes to be differentially expressed, of which 31 genes were up regulated, while 80 genes were down regulated (Fig. 2). A Venn diagram in Fig. 2A describes the number of genes differentially up or down regulated within a time point examined and the number of differentially expressed genes found common between time points (−2 vs. 1 h and −2 vs. 22 h post peak LH surge). The Venn diagram also provides the comparison of differentially expressed genes between 1 and 22 h post peak LH surge time points. Further, distribution of nearly 656 differentially expressed genes from −2 to 1 and 22 h post peak LH surge time points is represented in pictorial and tabular form in Fig. 2B&C. Most cluster of genes were observed to be modulated in a synchronous way at the two temporal points post peak LH surge i.e., higher percentage of the genes regulated at 1 h time point had an opposite regulation at 22 h time point.


Gene expression profiling of preovulatory follicle in the buffalo cow: effects of increased IGF-I concentration on periovulatory events.

Rao JU, Shah KB, Puttaiah J, Rudraiah M - PLoS ONE (2011)

Schematic representation of temporal changes in differentially expressed genes at 1 h and 22 h post peak LH surge.(A) Venn diagram representing the details of differentially expressed genes identified after microarray analysis of granulosa cells collected from the ovulatory follicle at −2, 1 and 22 h post peak LH surge. Data analyzed with ≥2 fold change cut-off and statistics. The number of differentially expressed genes found common between −2 vs. 1 h (red circle) and −2 vs. 22 h (blue circle) post peak LH surge, as well as comparison of differentially expressed genes between 1 vs. 22 h (green circle) post peak LH surge are presented. (B) Schematic representation of differentially expressed genes at different time points post peak LH surge. Red hexagon represents genes that were up regulated both at 1 and 22 h as well as those genes that were down regulated at 1 h but were up regulated at 22 h time point. Green hexagon represents differentially expressed genes that were down regulated both at 1 and 22 h time points as well as those genes that were up regulated at 1 h but were down regulated at 22 h time point. Also provided is the number of genes (represented as open hexagon) that were differentially expressed at 1 h time point but not at 22 h. (C) The number of differentially expressed genes listed as alphabetical groups in Fig. 2B are further represented in tabular form providing details on the number of genes in each group, pattern of expression change at both time points post peak LH surge as well as classification based on their ontological distribution within each ‘biological processes’ terms having high scores indicative of processes associated with each group.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3119055&req=5

pone-0020754-g002: Schematic representation of temporal changes in differentially expressed genes at 1 h and 22 h post peak LH surge.(A) Venn diagram representing the details of differentially expressed genes identified after microarray analysis of granulosa cells collected from the ovulatory follicle at −2, 1 and 22 h post peak LH surge. Data analyzed with ≥2 fold change cut-off and statistics. The number of differentially expressed genes found common between −2 vs. 1 h (red circle) and −2 vs. 22 h (blue circle) post peak LH surge, as well as comparison of differentially expressed genes between 1 vs. 22 h (green circle) post peak LH surge are presented. (B) Schematic representation of differentially expressed genes at different time points post peak LH surge. Red hexagon represents genes that were up regulated both at 1 and 22 h as well as those genes that were down regulated at 1 h but were up regulated at 22 h time point. Green hexagon represents differentially expressed genes that were down regulated both at 1 and 22 h time points as well as those genes that were up regulated at 1 h but were down regulated at 22 h time point. Also provided is the number of genes (represented as open hexagon) that were differentially expressed at 1 h time point but not at 22 h. (C) The number of differentially expressed genes listed as alphabetical groups in Fig. 2B are further represented in tabular form providing details on the number of genes in each group, pattern of expression change at both time points post peak LH surge as well as classification based on their ontological distribution within each ‘biological processes’ terms having high scores indicative of processes associated with each group.
Mentions: Microarray analysis results (GEO # GSE11312), after subjecting to high stringency statistical analysis revealed 450 genes to be differentially expressed within 1 h of peak LH surge (≥2 fold change with Benjamini and Hochberg correction for false discovery rate), of these 220 and 228 genes were up and down regulated, respectively. The analysis of microarray data at 22 h post peak LH surge revealed 111 genes to be differentially expressed, of which 31 genes were up regulated, while 80 genes were down regulated (Fig. 2). A Venn diagram in Fig. 2A describes the number of genes differentially up or down regulated within a time point examined and the number of differentially expressed genes found common between time points (−2 vs. 1 h and −2 vs. 22 h post peak LH surge). The Venn diagram also provides the comparison of differentially expressed genes between 1 and 22 h post peak LH surge time points. Further, distribution of nearly 656 differentially expressed genes from −2 to 1 and 22 h post peak LH surge time points is represented in pictorial and tabular form in Fig. 2B&C. Most cluster of genes were observed to be modulated in a synchronous way at the two temporal points post peak LH surge i.e., higher percentage of the genes regulated at 1 h time point had an opposite regulation at 22 h time point.

Bottom Line: Thus, further experiments were conducted to verify the effects of increased intrafollicular IGF-I levels on the expression of genes associated with the above mentioned processes.The results indicated that increased intrafollicular concentrations of IGF-I caused changes in expression of genes associated with steroidogenesis (StAR, SRF) and apoptosis (BCL-2, FKHR, PAWR).These results taken together suggest that onset of gonadotropin surge triggers activation of various biological pathways and that the effects of growth factors and peptides on gonadotropin actions could be examined during preovulatory follicle development.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore, India.

ABSTRACT
The preovulatory follicle in response to gonadotropin surge undergoes dramatic biochemical, and morphological changes orchestrated by expression changes in hundreds of genes. Employing well characterized bovine preovulatory follicle model, granulosa cells (GCs) and follicle wall were collected from the preovulatory follicle before, 1, 10 and 22 h post peak LH surge. Microarray analysis performed on GCs revealed that 450 and 111 genes were differentially expressed at 1 and 22 h post peak LH surge, respectively. For validation, qPCR and immunocytochemistry analyses were carried out for some of the differentially expressed genes. Expression analysis of many of these genes showed distinct expression patterns in GCs and the follicle wall. To study molecular functions and genetic networks, microarray data was analyzed using Ingenuity Pathway Analysis which revealed majority of the differentially expressed genes to cluster within processes like steroidogenesis, cell survival and cell differentiation. In the ovarian follicle, IGF-I is established to be an important regulator of the above mentioned molecular functions. Thus, further experiments were conducted to verify the effects of increased intrafollicular IGF-I levels on the expression of genes associated with the above mentioned processes. For this purpose, buffalo cows were administered with exogenous bGH to transiently increase circulating and intrafollicular concentrations of IGF-I. The results indicated that increased intrafollicular concentrations of IGF-I caused changes in expression of genes associated with steroidogenesis (StAR, SRF) and apoptosis (BCL-2, FKHR, PAWR). These results taken together suggest that onset of gonadotropin surge triggers activation of various biological pathways and that the effects of growth factors and peptides on gonadotropin actions could be examined during preovulatory follicle development.

Show MeSH