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Differential messenger RNA expression in Granulosa Cells from polycystic ovary syndrome with Normoandrogen and Hyperandrogen: Identification of gene sets through bioinformatic Filtering analysis

ABSTRACT: PCOS is a widespread disease that primarily caused in-pregnancy in pregnant-age women. Normoandrogen (NA) and Hyperandrogen (HA) PCOS are distinguished under distinct level of testosterone, while markers and expression patterns for both subtypes were not adequately studied. Text-mining analysis stated the correlation for PCOS with granusola cells and thus we performed microarray analysis on granusola cells from HA PCOS, NA PCOS and normal tissue from individuals, and afterwards downloaded RNA-seq and microarray data from NCBI GEO database on granusola cells from PCOS and normal ovary. Applying our newly developed method using Monte Carlo algorithm and text mining method on combined data, we identified several potential markers for HA and NA PCOS. RT-PCR and Western blotting results validated the expression pattern of these markers for HA and NA PCOS as bioinformatical prediction. Further gene ontology (GO) and text-mining analysis discovered that (1) NA PCOS was highly correlated with estrogen-driven tumors including ovarian, breast and endometrial cancer as compared with normal granusola cells. NA PCOS specific genes IL6R and CD274 were bioinformatically predicted and experimentally validated and thus might be the key factor behind the mechanism responsible for the tumor-like pattern for NA PCOS; (2) HA PCOS showed high correlation with keratinization, epidermis development, GTPase activity, glomerular basement membrane development and apoptosis corresponding to the fact HA PCOS patients express characteristic of hirsutism, diabetes and obesity. Meanwhile HA PCOS was sustantially lowerly related with estrogen-driven tumors as compared with normal granusola cells.

ORGANISM(S): Homo sapiens

PROVIDER: GSE137684 | GEO | 2019/09/19

REPOSITORIES: GEO

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Project description:Influenza B virus (FLUBV) is a major respiratory pathogen of humans. Seasonal influenza vaccines include either one or both FLUBV lineage strains, Victoria, and Yamagata. Vaccine mismatch occurs frequently, particularly in countries where vaccines contain only one of the lineages. We have previously described the safety and efficacy of modified live attenuated FLUBV vaccines based on either virus with rearranged genomes (FluB-RAM and FluB-RANS) or carrying a PB1 segment with a combination of temperature sensitive mutations and a C-terminal HA tag (FluB-att). We compared side by side the immunological responses in female and male DBA/2J mice vaccinated with either one of these vaccines and those with isogenic backgrounds encoding a chimeric HA segment carrying an N-terminal peptide encoding the IgA inducing peptide (IGIP). Recombinant viruses with or without the IGIP modification were genetically stable over multiple passages in eggs. In mice, introduction of IGIP improved attenuation of the vaccine candidates, particularly for the FluB-RAM/IGIP compared with the non-IGIP FluB-RAM counterpart. In a prime-boost regime, mice were completely protected against lethal challenge with a homologous FLUBV strain. Recombinant viruses induced antibodies against HA considered of protective value. Antibodies against NA and NP were readily detected. Compared to male mice and regardless of the vaccine used, female mice showed a clear trend towards enhanced humoral and cross-reactive IgG and IgA anti-HA responses as well as against NA and NP. The presence of IGIP in the vaccine resulted in an overall trend towards reduced anti-HA responses but enhanced anti-NA and anti-NP responses, particularly of the IgA isotype. Mucosal and serological responses two weeks after challenge showed similar trends with clear differences observed based on sex, vaccine backbone, and whether the vaccine carried the IGIP modification. These findings are significant for the development of universal influenza vaccines.

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Differential messenger RNA expression in Granulosa Cells from polycystic ovary syndrome with Normoandrogen and Hyperandrogen: Identification of gene sets through bioinformatic Filtering analysis

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