257 Awardee Talk: Pursuit of Novel Factors that Regulate Ovarian Follicular Function

As follicles grow, theca cells (TC) and granulosa cells (GC) must proliferate with minimal differentiation while thecal vascularization increases so that follicles do not prematurely ovulate or luteinize before the oocyte is fully mature. In the early 2000s we used Affymetrix microarrays to discover several unique genes involved in ovarian follicular development. Thrombospondin and fibroblast growth factor (FGF) 2 receptor genes were stimulated by IGF1 in porcine GC. We compared GC gene expression in bovine cystic versus normal follicles and discovered several novel genes including Indian hedgehog protein (IHH), FGF9, brain ribonuclease (BRB), and G protein-coupled receptor 34 (GPR34), leading to identification of roles for these proteins in ovarian follicular development. During the past 10 years, follow-up TC microarray and mechanistic studies have identified FGF9 control of cell cycle proteins, tight junction proteins, and microRNA 221 (MIR221), and that the mitogenic and steroidogenic responses to the major trophic hormones of the ovary (including IGF1, LH and FSH) are altered by overexpression of MIR221 in GC. In addition, we discovered that: 1) FGF9 stimulates GC and TC mitosis while inhibiting steroidogenesis; 2) FGF9 induces E2 transcription factor (E2F)-1, E2F-8 and cyclin D1 (CCND1), and that both IGF1 and vascular endothelial growth factor-A (VEGFA) synergize with FGF9 to further induce E2F8 and CCND1 mRNA; 3) FGF9 induces the nuclear protein UHRF1; and 4) an E2F inhibitor blocks the stimulatory and inhibitory effects of FGF9 on GC proliferation and steroidogenesis, respectively, and down-regulates UHRF1 mRNA and up-regulates VEGFA mRNA. Thus, aberrant production of FGF9 and the factors it induces/inhibits may lead to vascular dysfunction and ovarian disorders such as ovarian cysts. With additional research, knowledge about these newly identified factors may be used to help the livestock industry improve reproductive efficiency via new treatments for estrous synchronization, superovulation and cystic ovaries.

Differences in human placental mesenchymal stromal cells may impair vascular function in FGR

Placentae from pregnancies with fetal growth restriction (FGR) exhibit poor oxygen and nutrient exchange, in part due to impaired placental vascular development. Placental mesenchymal stromal cells (pMSCs) reside in a perivascular niche, where they may influence blood vessel formation/ function. However, the role of pMSCs in vascular dysfunction in FGR is unclear. To elucidate the mechanisms by which pMSCs may impact placental vascularisation we compared the transcriptomes of human pMSCs isolated from FGR (<5th centile) (n=7) and gestation-matched control placentae (n=9) using Affymetrix microarrays. At the transcriptome level there were no statistically significant differences between normal and FGR pMSCs, however several genes linked to vascular function exhibited notable fold changes, and thus the dataset was used as a hypothesis-generating tool for possible dysfunction in FGR. Genes/proteins of interest were followed up by real-time PCR and by immunohistochemistry. Gene expression of ADAMTS1 and FBLN2 (fibulin-2) were significantly upregulated, whilst HAS2 (hyaluronan synthase-2) was significantly downregulated, in pMSCs from FGR placentae (n=8) relative to controls (n=7, p<0.05 for all). At the protein level, significant differences in the level of fibulin-2 and hyaluronan synthase-2, but not ADAMTS1 were confirmed between pMSCs from FGR and control pregnancies by western blot. All three proteins demonstrated perivascular expression in third-trimester placentae. Fibulin-2 maintains vessel elasticity, and its increased expression in FGR pMSCs could help explain the increased distensibility of FGR blood vessels. ADAMTS1 and hyaluronan synthase-2 regulate angiogenesis, and their differential expression by FGR pMSCs may contribute to the impaired angiogenesis in these placentae.

Transcriptome-based screening of ion channels and transporters in a migratory chondroprogenitor cell line isolated from late-stage osteoarthritic cartilage

Chondrogenic progenitor cells (CPCs) may be used as an alternative source of cells with potentially superior chondrogenic potential compared to mesenchymal stem cells (MSCs), and could be exploited for future regenerative therapies targeting articular cartilage in degenerative diseases such as osteoarthritis (OA). In this study, we hypothesised that CPCs derived from OA cartilage may be characterised by a distinct channelome. First, a global transcriptomic analysis using Affymetrix microarrays was performed. We studied the profiles of those ion channel and transporter families that may be relevant to chondroprogenitor cell physiology. Following validation of the microarray data, we examined the role of calcium-dependent potassium channels in CPCs and observed functional large conductance calcium-activated potassium channels (BK) involved in the maintenance of chondroprogenitor phenotype. In line with our very recent results, we found that the KCNMA1 gene was upregulated in CPCs and observed currents that could be attributed to the BK channel in both cell types. Through characterisation of their channelome we demonstrate that CPCs are a distinct cell population but are highly similar to MSCs in many respects. This work adds key mechanistic data to the in-depth characterisation of CPCs and their phenotype in the context of cartilage regeneration.

Coenzyme and cofactor metabolism belongs to biochemical processes significantly regulated in human granulosa cells collected after IVF during long-term primary in vitro culture

Granulosa cells (GCs) provide the microenvironment necessary for the development of the follicle and the maturation of the oocyte. GCs are associated with reproductive system function and the maintenance of pregnancy by participating in the synthesis of steroid hormones. Many authors point to new ways of using GCs in regenerative medicine and indicate the significant plasticity of this cell population, suggesting that GCs can undergo a transdifferentiation process. Employing primary in vitro cell cultures and high-throughput transcriptome analysis via Affymetrix microarrays, this study describes groups of genes associated with enzymatic reactions. 52 genes were identified belonging to four gene ontology biological process terms (GO BP): “coenzyme biosynthetic process”, “coenzyme metabolic process”, “cofactor biosynthetic process” and “cofactor metabolic process”. All identified genes showed reduction in the level of mRNA expression during long-term in vitro cultivation. Significanthe transcriptomic profile variability was exhibited for the genes (ELOVL5, ELOVL6 and GPAM) involved in enzymatic regulation of fatty acid metabolism.Running title: Enzymatic regulation in granulosa cells

Effects of pathogenic CNVs on biochemical markers: a study on the UK Biobank

ABSTRACTBackgroundPathogenic copy number variants (CNVs) increase risk for medical disorders, even among carriers free from neurodevelopmental disorders. The UK Biobank recruited half a million adults who provided samples for biochemical and haematology tests which have recently been released. We wanted to assess how the presence of pathogenic CNVs affects these biochemical test results.MethodsWe called all CNVs from the Affymetrix microarrays and selected a set of 54 CNVs implicated as pathogenic (including their reciprocal deletions/duplications) and present in five or more persons. We used linear regression analysis to establish their association with 28 biochemical and 23 haematology tests.ResultsWe analysed 421k participants who passed our CNV quality control filters and self-reported as white British or Irish descent. There were 268 associations between CNVs and biomarkers that were significant at a false discovery rate of 0.05. Deletions at 16p11.2 had the highest number of significant associations, but several rare CNVs had higher effect sizes indicating that the lack of significance was likely due to the reduced statistical power for rarer events. The distribution of values can be visualised on our interactive website: http://kirov.psycm.cf.ac.uk/.ConclusionsCarriers of many pathogenic CNVs have changes in biochemical and haematology tests, and many of those are associated with adverse health consequences. These changes did not always correlate with increases in diagnosed medical disorders in this population. Carriers should have regular blood tests in order to identify and treat adverse medical consequences early. Levels of cholesterol and related lipids were unexpectedly lower in carriers of CNVs associated with increased weight gain, most likely due to the use of statins by such people.

BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests

BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and re-processed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in anatomical structures (“TopAnat”). Along with the classical Gene Ontology enrichment test, this test provides a complementary way to interpret gene lists. Availability: https://www.bioconductor.org/packages/BgeeDB/

An end to end workflow for differential gene expression using Affymetrix microarrays

In this article, we walk through an end-to-end Affymetrix microarray differential expression workflow using Bioconductor packages. This workflow is directly applicable to current "Gene'' type arrays, e.g.the HuGene or MoGene arrays, but can easily be adapted to similar platforms. The data analyzed here is a typical clinical microarray data set that compares inflamed and non-inflamed colon tissue in two disease subtypes. For each disease, the differential gene expression between inflamed- and non-inflamed colon tissue was analyzed. We will start from the raw data CEL files, show how to import them into a Bioconductor ExpressionSet, perform quality control and normalization and finally differential gene expression (DE) analysis, followed by some enrichment analysis.

EarlyR genomic signature to predict pathological complete response following neoadjuvant anthracycline-taxane chemotherapy in estrogen-receptor positive (ER+) breast cancer.

579 Background: EarlyR is a prognostic signature computed from expression values of ESPL1, SPAG5, MKI67, PLK1 and PGR that stratifies ER+ breast cancer (BC) into EarlyRLow, EarlyRInt, and EarlyRHigh risk strata. Here, we show that EarlyR is also predictive of pathological complete response (pCR) following neoadjuvant anthracycline-taxane (AT) based chemotherapy. Methods: The ability of EarlyR gene signature to predict pCR was assessed in Affymetrix microarrays datasets (GSE25065, GSE25066, GSE20194, GSE20271; n = 541, pCR = 42 (7.8%) collectively labeled as Cohort A) derived from patients with ER+ breast cancer treated with neoadjuvant TFAC or FEC. For 2 of these datasets (GSE25065, GSE25066 (n = 291)) distant metastasis-free survival (DMFS) results were also available. The DMFS data in cohort A were compared to that of BC patients not treated with chemotherapy (denoted Cohort B, n = 1269) derived from ER+ samples from 7 GEO datasets (GSE3494, GSE7390, GSE12093, GSE6532, GSE2034, GSE11121, GSE17705). Results: In cohort A, EarlyR is a significant predictor of pCR (p = 1.0 x 10-6) (EarlyRLow, n = 273, pCR = 5, 1.8%; EarlyRInt, n = 11, pCR = 1, 9.1% and EarlyRHigh, n = 256, pCR = 36, 14.1%). Notably, 86% of the 42 cases with pCR have EarlyRHigh. Of the 291 patients with 8-year DMFS data from Cohort A, who had received chemotherapy, the survival of EarlyRHigh [0.79 (95%CI 0.70-0.89)] was nearly the same as that of EarlyRLow [0.82 (95%CI 0.74-0.90)]. In contrast, in cohort B, who were not treated with chemotherapy, 8-year DMFS is significantly (p = 5 x 10-15) lower in EarlyRHigh [0.57 (95%CI 0.51-0.64)] than in EarlyRLow[0.81 (95%CI 0.78-0.84)]. Conclusions: EarlyR is a strong predictor of pathological complete response in patients treated with TFAC or FEC. In addition, EarlyR also predicts poor DMFS outcomes for patients in EarlyRHigh not receiving chemotherapy. More importantly, neoadjuvant chemotherapy dramatically improved survival of patients in EarlyRHigh. These results document that EarlyR identifies a set of patients, EarlyRHigh, with a high risk of distant metastasis, who are also likely to respond favorably to chemotherapy.

A Tool to Build Up-To-Date Gene Annotations for Affymetrix Microarrays

Genome-wide expression profiling and genotyping is widely applied in functional genomics research, ranging from stem cell studies to cancer, in drug response studies, and in clinical diagnostics. The Affymetrix GeneChip microarrays represent the most popular platform for such assays. Nevertheless, due to rapid and continuous improvement of the knowledge about the genome, the definition of many of the genes and transcripts change, and new genes are discovered. Thus the original probe information is out-dated for a number of Affymetrix platforms, and needs to be re-defined. It has been demonstrated, that accurate probe set definition improves both coverage of the gene expression analysis and its statistical power. Therefore we developed a method that incorporates the most recent genome annotations into the annotation of the microarray probe sets, using tools from the next generation sequencing. Additionally our method allows to quickly build project specific gene annotation models, as well as for comparison of microarray to RNAseq data.

BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests

BgeeDB is a collection of functions to import into R re-annotated, quality-controlled and reprocessed expression data available in the Bgee database. This includes data from thousands of wild-type healthy samples of multiple animal species, generated with different gene expression technologies (RNA-seq, Affymetrix microarrays, expressed sequence tags, and in situ hybridizations). BgeeDB facilitates downstream analyses, such as gene expression analyses with other Bioconductor packages. Moreover, BgeeDB includes a new gene set enrichment test for preferred localization of expression of genes in anatomical structures (“TopAnat”). Along with the classical Gene Ontology enrichment test, this test provides a complementary way to interpret gene lists. Availability: http://www.bioconductor.org/packages/BgeeDB/