Geeniekspressiooni andmete integreerimine teiste ‘oomika’ andmetega kirjeldamaks endomeetriumi retseptiivsuse bioloogilisi mehhanisme

Väitekirja elektrooniline versioon ei sisalda publikatsiooneMaailma Terviseorganisatsiooni statistika väidab, et umbes 10% püsisuhetes olevatest naistest on ühel või teisel põhjusel viljatud. Naise viljakust mõjutab välja palju erinevaid faktoreid ning setõttu on viljatuse põhjuste leidmine tihti väga keeruline. Viljatust põhjustavateks faktoriteks võivad olla üldine terviseseisund, erinevad haigused, geneetiline taust, väliskeskkonna ja eluviisiga seotud tegurid. Ühe näitena võib tuua embrüo pesastumist (implantatsioon) emaka limaskesta (endomeetriumi), mis võib toimuda ainult kindla lühikese perioodi vältel (implantatsiooni aken), kui endomeetrium on embrüo suhtes kõige vastuvõtlikum. Implantatsiooni akna periood on aga iga naise jaoks erinev, ning on määratud erinevate bioloogiliste protsesside poolt. Kunstliku viljastamise (IVF) läbiviimise jaoks on kriitiline teada täpset implantatsiooni akna aega, sellega seotud mehhanisme ja nende vastastikust mõju. Selleks, et uurida mehhanismide omavahelisi seoseid, panime paariviisiliselt kokku erinevaid geneetilise regulatsiooni andmekihte, milleks olid RNA, mikroRNA ja DNA metülatsiooni admed, ja mida koos nimetatakse ‘oomika’ andmekihtideks. Kokkuvõtvalt näitavad antud töö tulemused, et, võrreldes ühe ‘oomika’ andmekihi uurimisega, ‘oomika’ andmekihtide kombineerimine aitab paremini mõista endomeetriumi retseptiivsusega seotud bioloogilisi protsesse ning vältida valepositiivseid tulemusi. Antud tööga me rõhutame süsteemibioloogia ning paljude andmekihtide samaaegse kasutamise olulisust naise reproduktiivsuse bioloogiliste mehhanismide uurimisel.According to the World Health Organization, over 10% of females in a stable relationship are suffering from involuntary infertility/subfertility worldwide. Untangling the reasons for this is difficult because female reproduction is a sophisticated matter and can be affected by many factors such as health, accompanying diseases, genetic background, environment, and lifestyle. As a specific example, embryo implantation – its attachment to the uterine lining (endometrium) – occurs only during a relatively short period of time, called the window of implantation (WOI), when the endometrium is most receptive to an embryo. This is critical for a commonly used fertility treatment of in vitro fertilizaton (IVF) – and to make matters more complex, the WOI is not the same for everyone, but adjusted by an interlocking system of biological regulation mechanisms. Thus, to provide successful IVF, it is important to know these exact regulation mechanisms – and, since they interact with one another, to understand how they work together, not just individually. We used pairwise integration of data from different layers of genetic regulation, such as RNA, microRNA, and DNA methylation, called together the ‘omics’ layers, and showed the advantage of the data integration approach over the usage of just a single ‘omics’ layer. As a result, we obtained the lists of novel potential biomarkers that could regulate WOI, validated some previously known receptivity biomarkers, and showed that integration of different ‘omics’ layers helps to avoid false-positive results. With our work, we encourage other researchers in the female reproduction field to integrate several data layers for further studieshttps://www.ester.ee/record=b535138

High-throughput mRNA sequencing of stromal cells from endometriomas and endometrium

The aetiology of endometriosis is still unclear and to find mechanisms behind the disease development, it is important to study each cell type from endometrium and ectopic lesions independently. The objective of this study was to uncover complete mRNA profiles in uncultured stromal cells from paired samples of endometriomas and eutopic endometrium. High-throughput mRNA sequencing revealed over 1300 dysregulated genes in stromal cells from ectopic lesions, including several novel genes in the context of endometriosis. Functional annotation analysis of differentially expressed genes highlighted pathways related to cell adhesion, extracellular matrix–receptor interaction and complement and coagulation cascade. Most importantly, we found a simultaneous upregulation of complement system components and inhibitors, indicating major imbalances in complement regulation in ectopic stromal cells. We also performed in vitro experiments to evaluate the effect of endometriosis patients’ peritoneal fluid (PF) on complement system gene expression levels, but no significant impact of PF on C3, CD55 and CFH levels was observed. In conclusion, the use of isolated stromal cells enables to determine gene expression levels without the background interference of other cell types. In the future, a new standard design studying all cell types from endometriotic lesions separately should be applied to reveal novel mechanisms behind endometriosis pathogenesis

Large-scale meta-analysis highlights the hypothalamic–pituitary–gonadal axis in the genetic regulation of menstrual cycle length

The normal menstrual cycle requires a delicate interplay between the hypothalamus, pituitary and ovary. Therefore, its length is an important indicator of female reproductive health. Menstrual cycle length has been shown to be partially controlled by genetic factors, especially in the follicle-stimulating hormone beta-subunit (FSHB) locus. A genome-wide association study meta-analysis of menstrual cycle length in 44 871 women of European ancestry confirmed the previously observed association with the FSHB locus and identified four additional novel signals in, or near, the GNRH1, PGR, NR5A2 and INS-IGF2 genes. These findings not only confirm the role of the hypothalamic–pituitary–gonadal axis in the genetic regulation of menstrual cycle length but also highlight potential novel local regulatory mechanisms, such as those mediated by IGF2

C-reactive protein upregulates the whole blood expression of CD59 - an integrative analysis

Elevated C-reactive protein (CRP) concentrations in the blood are associated with acute and chronic infections and inflammation. Nevertheless, the functional role of increased CRP in multiple bacterial and viral infections as well as in chronic inflammatory diseases remains unclear. Here, we studied the relationship between CRP and gene expression levels in the blood in 491 individuals from the Estonian Biobank cohort, to elucidate the role of CRP in these inflammatory mechanisms. As a result, we identified a set of 1,614 genes associated with changes in CRP levels with a high proportion of interferon-stimulated genes. Further, we performed likelihood-based causality model selection and Mendelian randomization analysis to discover causal links between CRP and the expression of CRP-associated genes. Strikingly, our computational analysis and cell culture stimulation assays revealed increased CRP levels to drive the expression of complement regulatory protein CD59, suggesting CRP to have a critical role in protecting blood cells from the adverse effects of the immune defence system. Our results show the benefit of integrative analysis approaches in hypothesis-free uncovering of causal relationships between traits

Gene supported by the causal model with Δ_AIC ≥ 10 using the strongest <i>cis</i>-eQTL.

<p>Gene supported by the causal model with Δ_AIC ≥ 10 using the strongest <i>cis</i>-eQTL.</p

C-reactive protein upregulates the whole blood expression of CD59 - an integrative analysis.

Elevated C-reactive protein (CRP) concentrations in the blood are associated with acute and chronic infections and inflammation. Nevertheless, the functional role of increased CRP in multiple bacterial and viral infections as well as in chronic inflammatory diseases remains unclear. Here, we studied the relationship between CRP and gene expression levels in the blood in 491 individuals from the Estonian Biobank cohort, to elucidate the role of CRP in these inflammatory mechanisms. As a result, we identified a set of 1,614 genes associated with changes in CRP levels with a high proportion of interferon-stimulated genes. Further, we performed likelihood-based causality model selection and Mendelian randomization analysis to discover causal links between CRP and the expression of CRP-associated genes. Strikingly, our computational analysis and cell culture stimulation assays revealed increased CRP levels to drive the expression of complement regulatory protein CD59, suggesting CRP to have a critical role in protecting blood cells from the adverse effects of the immune defence system. Our results show the benefit of integrative analysis approaches in hypothesis-free uncovering of causal relationships between traits

Genes supported by the colliding model with Δ_AIC ≥ 10 for at least one of the independent <i>cis</i>-eQTLs.

<p>Genes supported by the colliding model with Δ_AIC ≥ 10 for at least one of the independent <i>cis</i>-eQTLs.</p

Top 10 CRP-associated genes.

<p>CRP-gene expression association effect sizes (Beta) with 95% confidence intervals (CI) and p-values adjusted for 5% FDR (Adjusted p-value) are shown.</p

Pairwise modelling pipeline of whole genome sequencing (WGS), RNA sequencing (RNA-seq) and C-reactive protein (CRP) data.

<p>(A) First, we identified genes whose expression levels (denoted by E) were significantly associated with CRP. Second, we used these genes to perform a <i>cis</i>-eQTL analysis and extract SNPs (denoted by G) that act on the expression of those genes. Third, for each triplet (G, E, CRP), we used maximum likelihood to select the best supported model out of a limited number of possible models–given that G is correlated with E, E is correlated with CRP and assuming directed acyclic graphs. The dashed edge in model IV indicates that either E acts on CRP or vice versa–these two models are Markov equivalent so we cannot differentiate between them. Fourth, we ensured that the best candidate models fulfilled necessary partial correlation criteria. Fifth, we subjected the best candidates to MR analysis where the instruments were chosen from published GWAS summary statistics. Finally, we validated the findings using cell culture stimulation assays. (B) Venn diagram of available sample sizes.</p