scholarly journals Genetic Regulation of Transcription in the Endometrium in Health and Disease

2022 ◽  
Vol 3 ◽  
Author(s):  
Sally Mortlock ◽  
Brett McKinnon ◽  
Grant W. Montgomery

The endometrium is a complex and dynamic tissue essential for fertility and implicated in many reproductive disorders. The tissue consists of glandular epithelium and vascularised stroma and is unique because it is constantly shed and regrown with each menstrual cycle, generating up to 10 mm of new mucosa. Consequently, there are marked changes in cell composition and gene expression across the menstrual cycle. Recent evidence shows expression of many genes is influenced by genetic variation between individuals. We and others have reported evidence for genetic effects on hundreds of genes in endometrium. The genetic factors influencing endometrial gene expression are highly correlated with the genetic effects on expression in other reproductive (e.g., in uterus and ovary) and digestive tissues (e.g., salivary gland and stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. There is also increasing evidence for cell specific genetic effects for some genes. Sample size for studies in endometrium are modest and results from the larger studies of gene expression in blood report genetic effects for a much higher proportion of genes than currently reported for endometrium. There is also emerging evidence for the importance of genetic variation on RNA splicing. Gene mapping studies for common disease, including diseases associated with endometrium, show most variation maps to intergenic regulatory regions. It is likely that genetic risk factors for disease function through modifying the program of cell specific gene expression. The emerging evidence from our gene mapping studies coupled with tissue specific studies, and the GTEx, eQTLGen and EpiMap projects, show we need to expand our understanding of the complex regulation of gene expression. These data also help to link disease genetic risk factors to specific target genes. Combining our data on genetic regulation of gene expression in endometrium, and cell types within the endometrium with gene mapping data for endometriosis and related diseases is beginning to uncover the specific genes and pathways responsible for increased risk of these diseases.

Cells ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2611
Author(s):  
Jayron J. Habibe ◽  
Maria P. Clemente-Olivo ◽  
Carlie J. de Vries

Susceptibility to complex pathological conditions such as obesity, type 2 diabetes and cardiovascular disease is highly variable among individuals and arises from specific changes in gene expression in combination with external factors. The regulation of gene expression is determined by genetic variation (SNPs) and epigenetic marks that are influenced by environmental factors. Aging is a major risk factor for many multifactorial diseases and is increasingly associated with changes in DNA methylation, leading to differences in gene expression. Four and a half LIM domains 2 (FHL2) is a key regulator of intracellular signal transduction pathways and the FHL2 gene is consistently found as one of the top hyper-methylated genes upon aging. Remarkably, FHL2 expression increases with methylation. This was demonstrated in relevant metabolic tissues: white adipose tissue, pancreatic β-cells, and skeletal muscle. In this review, we provide an overview of the current knowledge on regulation of FHL2 by genetic variation and epigenetic DNA modification, and the potential consequences for age-related complex multifactorial diseases.


2020 ◽  
Vol 35 (2) ◽  
pp. 377-393 ◽  
Author(s):  
Sally Mortlock ◽  
Raden I Kendarsari ◽  
Jenny N Fung ◽  
Greg Gibson ◽  
Fei Yang ◽  
...  

Abstract STUDY QUESTION Are genetic effects on endometrial gene expression tissue specific and/or associated with reproductive traits and diseases? SUMMARY ANSWER Analyses of RNA-sequence data and individual genotype data from the endometrium identified novel and disease associated, genetic mechanisms regulating gene expression in the endometrium and showed evidence that these mechanisms are shared across biologically similar tissues. WHAT IS KNOWN ALREADY The endometrium is a complex tissue vital for female reproduction and is a hypothesized source of cells initiating endometriosis. Understanding genetic regulation specific to, and shared between, tissue types can aid the identification of genes involved in complex genetic diseases. STUDY DESIGN, SIZE, DURATION RNA-sequence and genotype data from 206 individuals was analysed and results were compared with large publicly available datasets. PARTICIPANTS/MATERIALS, SETTING, METHODS RNA-sequencing and genotype data from 206 endometrial samples was used to identify the influence of genetic variants on gene expression, via expression quantitative trait loci (eQTL) analysis and to compare these endometrial eQTLs with those in other tissues. To investigate the association between endometrial gene expression regulation and reproductive traits and diseases, we conducted a tissue enrichment analysis, transcriptome-wide association study (TWAS) and summary data-based Mendelian randomisation (SMR) analyses. Transcriptomic data was used to test differential gene expression between women with and without endometriosis. MAIN RESULTS AND THE ROLE OF CHANCE A tissue enrichment analysis with endometriosis genome-wide association study summary statistics showed that genes surrounding endometriosis risk loci were significantly enriched in reproductive tissues. A total of 444 sentinel cis-eQTLs (P < 2.57 × 10−9) and 30 trans-eQTLs (P < 4.65 × 10−13) were detected, including 327 novel cis-eQTLs in endometrium. A large proportion (85%) of endometrial eQTLs are present in other tissues. Genetic effects on endometrial gene expression were highly correlated with the genetic effects on reproductive (e.g. uterus, ovary) and digestive tissues (e.g. salivary gland, stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. The TWAS analysis indicated that gene expression at 39 loci is associated with endometriosis, including five known endometriosis risk loci. SMR analyses identified potential target genes pleiotropically or causally associated with reproductive traits and diseases including endometriosis. However, without taking account of genetic variants, a direct comparison between women with and without endometriosis showed no significant difference in endometrial gene expression. LARGE SCALE DATA The eQTL dataset generated in this study is available at http://reproductivegenomics.com.au/shiny/endo_eqtl_rna/. Additional datasets supporting the conclusions of this article are included within the article and the supplementary information files, or are available on reasonable request. LIMITATIONS, REASONS FOR CAUTION Data are derived from fresh tissue samples and expression levels are an average of expression from different cell types within the endometrium. Subtle cell-specifc expression changes may not be detected and differences in cell composition between samples and across the menstrual cycle will contribute to sample variability. Power to detect tissue specific eQTLs and differences between women with and without endometriosis was limited by the sample size in this study. The statistical approaches used in this study identify the likely gene targets for specific genetic risk factors, but not the functional mechanism by which changes in gene expression may influence disease risk. WIDER IMPLICATIONS OF THE FINDINGS Our results identify novel genetic variants that regulate gene expression in endometrium and the majority of these are shared across tissues. This allows analysis with large publicly available datasets to identify targets for female reproductive traits and diseases. Much larger studies will be required to identify genetic regulation of gene expression that will be specific to endometrium. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Health and Medical Research Council (NHMRC) under project grants GNT1026033, GNT1049472, GNT1046880, GNT1050208, GNT1105321, GNT1083405 and GNT1107258. G.W.M is supported by a NHMRC Fellowship (GNT1078399). J.Y is supported by an ARC Fellowship (FT180100186). There are no competing interests.


2021 ◽  
Author(s):  
Marios Arvanitis ◽  
Karl Tayeb ◽  
Benjamin J Strober ◽  
Alexis Battle

Understanding the mechanisms that underlie genetic regulation of gene expression is crucial to explaining the diversity that governs complex traits. Large scale expression quantitative trait locus (eQTL) studies have been instrumental in identifying genetic variants that influence the expression of target genes. However, a large fraction of disease-associated genetic variants have not been clearly explained by current eQTL data, frustrating attempts to use these data to comprehensively characterize disease loci. One notable observation from recent studies is that cis-eQTL effects are often shared across different cell types and tissues. This would suggest that common genetic variants impacting steady-state, adult gene expression are largely tolerated, shared across tissues, and less relevant to disease. However, allelic heterogeneity and complex patterns of linkage disequilibrium (LD) within each locus may skew the quantification of sharing of genetic effects between tissues, impede our ability to identify causal variants, and hinder the identification of regulatory effects for disease-associated genetic variants. Indeed, recent research suggests that multiple causal variants are often present in many eQTL and complex trait associated loci. Here, we re-analyze tissue-specificity of genetic effects in the presence of LD and allelic heterogeneity, proposing a novel method, CAFEH, that improves the identification of causal regulatory variants across tissues and their relationship to disease loci.


2019 ◽  
Author(s):  
Samar S. M. Elsheikh ◽  
Emile R. Chimusa ◽  
Nicola J. Mulder ◽  
Alessandro Crimi ◽  

ABSTRACTNetworks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging and genetics allows a better understanding of the effects of the genetic variations on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer’s disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. Here, we show that the expression of PLAU and HFE genes increases the change over time respectively in betweenness centrality related to the fusiform gyrus and clustering coefficient of the cingulum bundle. We also show that the betweenness centrality metric highlights impact dementia-related changes in distinct brain regions. Ourfindings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer’s genetic risk factors in the estimation of regional brain connection alterations.


2019 ◽  
Author(s):  
Briana Mittleman ◽  
Sebastian Pott ◽  
Shane Warland ◽  
Tony Zeng ◽  
Mayher Kaur ◽  
...  

AbstractWith the exception of mRNA splicing, little is known about co-transcriptional or post-transcriptional regulatory mechanisms that link noncoding variation to variation in organismal traits. To begin addressing this gap, we used 3’ Seq to characterize alternative polyadenylation (APA) in the nuclear and total RNA fractions of 52 HapMap Yoruba lymphoblastoid cell lines, which we have studied extensively in the past. We identified thousands of polyadenylation sites that are differentially detected in nuclear mRNA and whole cell mRNA, and found that APA is an important mediator of genetic effects on gene regulation and complex traits. Specifically, we mapped 602 apaQTLs at 10% FDR, of which 152 were found only in the nuclear fraction. Nuclear-specific apaQTLs are highly enriched in introns and are also often associated with changes in steady-state expression levels, suggesting a widespread mechanism whereby genetic variants decrease mRNA expression levels by increasing usage of intronic PAS. We identified 24 apaQTLs associated with protein expression levels, but not mRNA expression, and found that eQTLs that are not associated with chromatin QTLs are enriched in apaQTLs. These findings support multiple independent pathways through which genetic effects on APA can impact gene regulation. Finally, we found that 19% of apaQTLs were also previously associated with disease. Thus, our work demonstrates that APA links genetic variation to variation in gene expression levels, protein expression levels, and disease risk, and reveals uncharted modes of genetic regulation.


Blood ◽  
2016 ◽  
Vol 127 (15) ◽  
pp. 1923-1929 ◽  
Author(s):  
Wenndy Hernandez ◽  
Eric R. Gamazon ◽  
Erin Smithberger ◽  
Travis J. O’Brien ◽  
Arthur F. Harralson ◽  
...  

Key Points Our study has identified common genetic risk factors for VTE among AAs. These risk factors are associated with decreased thrombomodulin gene expression, suggesting a mechanistic link.


Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 397-397
Author(s):  
Alessia Bogni ◽  
Cheng Cheng ◽  
Wei Liu ◽  
Wenjian Yang ◽  
Deborah French ◽  
...  

Abstract In children with acute lymphoblastic leukemia (ALL), failure due to therapy-related myeloid leukemia (t-ML) is a devastating complication. Using a target gene approach, only a few host genetic risk factors for t-ML have been defined. Microarray analysis of gene expression allows for a more genome-wide approach to identify possible genetic risk factors for t-ML. We assessed gene expression profiles (12625 gene probe sets) using oligonucleotide-based arrays in diagnostic ALL blasts from 228 children treated on St. Jude ALL protocols (Total XIII) that included etoposide; 13 of these children developed t-ML. A group of 83 probe sets were significantly related to the time-dependent risk of t-ML, with principal component analysis plot (right panel) separating patients who developed t-ML from the others. Hierarchical clustering of the 83 probe sets grouped patients into 3 clusters (n=163, n=52, n=13), with the cumulative incidence of t-ML being significantly higher in the last cluster (p < 0.0001, left panel) compared to those of the other gene-expression-defined clusters. Figure Figure A permutation test indicated that probe sets selected by chance are unlikely to obtain the observed distinct clusters (p=0.045). Distinguishing genes included transcription-related oncogenes (v-Myb, Pax-5), cyclins (CCNG1, CCNG2 and CCND1) and Histone H4. Common transcription factor recognition elements among similarly up- or down-regulated genes included several involved in hematopoietic differentiation or leukemogenesis (Maz, PU.1, FOXO4). This approach has identified several genes whose expression differentiates patients at risk of t-ML, and provides targets for assessing the germline predisposition to leukemogenesis.


2010 ◽  
Vol 68 (4) ◽  
pp. 292-297 ◽  
Author(s):  
Nahid Waleh ◽  
Ryan Hodnick ◽  
Nami Jhaveri ◽  
Suzanne McConaghy ◽  
John Dagle ◽  
...  

2017 ◽  
Vol 9 (2) ◽  
pp. 69-76 ◽  
Author(s):  
Jenny N. Fung ◽  
Yadav Sapkota ◽  
Dale R. Nyholt ◽  
Grant W. Montgomery

Advances in genetics and genomics are driving progress in understanding genetic risk factors for endometriosis. Genome-wide association scans (GWAS) in endometriosis have identified 11 genomic regions associated with increased risk of disease. Many of the regions contain interesting candidate genes, but the risk alleles may not always act through the obvious candidates. Functional evidence to identify the causal gene(s) will require multiple steps including better mapping precision, genetic studies on gene expression and epigenetic marks, chromatin looping and functional studies. Evidence from gene expression studies in endometrium and chromatin looping experiments implicate CDC42 on chromosome 1, CDKN2B-AS1 on chromosome 9 and VEZT on chromosome 12 as likely causal genes in these regions. Confirming the causal gene(s) in these and other regions will identify the important pathways increasing risk for endometriosis and identify novel targets for interventions to improve diagnosis and treatment.


Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1062
Author(s):  
Alice W. Yu ◽  
J. David Peery ◽  
Hyejung Won

Schizophrenia is a polygenic disorder with many genomic regions contributing to schizophrenia risk. The majority of genetic variants associated with schizophrenia lie in the non-coding genome and are thought to contribute to transcriptional regulation. Extensive transcriptomic dysregulation has been detected from postmortem brain samples of schizophrenia-affected individuals. However, the relationship between schizophrenia genetic risk factors and transcriptomic features has yet to be explored. Herein, we examined whether varying gene expression features, including differentially expressed genes (DEGs), co-expression networks, and central hubness of genes, contribute to the heritability of schizophrenia. We leveraged quantitative trait loci and chromatin interaction profiles to identify schizophrenia risk variants assigned to the genes that represent different transcriptomic features. We then performed stratified linkage disequilibrium score regression analysis on these variants to estimate schizophrenia heritability enrichment for different gene expression features. Notably, DEGs and co-expression networks showed nominal heritability enrichment. This nominal association can be partly explained by cellular heterogeneity, as DEGs were associated with the genetic risk of schizophrenia in a cell type-specific manner. Moreover, DEGs were enriched for target genes of schizophrenia-associated transcription factors, suggesting that the transcriptomic signatures of schizophrenia are the result of transcriptional regulatory cascades elicited by genetic risk factors.


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