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Endometrial vezatin and its association with endometriosis risk

Overview of attention for article published in Human Reproduction, March 2016
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Title
Endometrial vezatin and its association with endometriosis risk
Published in
Human Reproduction, March 2016
DOI 10.1093/humrep/dew047
Pubmed ID
Authors

Sarah J. Holdsworth-Carson, Jenny N. Fung, Hien T.T. Luong, Yadav Sapkota, Lisa M. Bowdler, Leanne Wallace, Wan Tinn Teh, Joseph E. Powell, Jane E. Girling, Martin Healey, Grant W. Montgomery, Peter A.W. Rogers

Abstract

Do endometriosis risk-associated single nucleotide polymorphisms (SNPs) found at the 12q22 locus have effects on vezatin (VEZT) expression? The original genome-wide association study (GWAS) SNP (rs10859871), and other newly identified association signals, demonstrate strong evidence for cis-expression quantitative trait loci (eQTL) effects on VEZT expression. GWAS have identified several disease-risk loci (SNPs) associated with endometriosis. The SNP rs10859871 is located within the VEZT gene. VEZT expression is altered in the endometrium of endometriosis patients and is an excellent candidate for having a causal role in endometriosis. Most of the SNPs identified from GWAS are not located within the coding region of the genome. However, they are likely to have an effect on the regulation of gene expression. Genetic variants that affect levels of gene expression are called expression quantitative trait loci (eQTL). Samples for genotyping and VEZT variant screening were drawn from women recruited for genetic studies in Australia/New Zealand and women undergoing surgery in a tertiary care centre. Coding variants for VEZT were screened in blood from 100 unrelated individuals (endometriosis-dense families) from the QIMR Berghofer Medical Research Institute dataset. SNPs at the 12q22 locus were imputed and reanalysed for their association with endometriosis. Reanalysis of endometriosis risk-association was performed on a final combined Australian dataset of 2594 cases and 4496 controls. Gene expression was performed on 136 endometrial samples. eQTL analysis in whole blood was performed on 862 individuals from the Brisbane Systems Genetics Study. Endometrial tissue-specific eQTL analysis was performed on 122 samples (eutopic endometrium) collected following laparoscopic surgery. VEZT protein expression studies employed n = 56 (western blotting) and n = 42 (immunohistochemistry) endometrial samples. The women recruited for this study provided blood and/or endometrial tissue samples in a hospital setting. Genomic DNA was screened for common and coding variants. SNPs of interest in the 12q22 region were genotyped using Agena MassARRAY technology or Taqman SNP genotyping assay. Gene expression profiles from RNA extracted from blood and endometrial tissue samples were generated using Illumina whole-genome expression chips (Human HT-12 v4.0). Whole protein extracted from endometrium was used for VEZT western blots, and paraffin sections of endometrium were employed for VEZT immunohistochemistry semi-quantitative analysis. A total of 11 coding variants of VEZT (including one novel variant) were identified from an endometriosis-dense cohort. Polymorphic coding and imputed SNPs were combined with previous GWAS data to reanalyse the endometriosis risk association of the 12q22 region. The disease association signal at 12q22 was due to coding variants in VEZT or FGD6 (FYVE, RhoGEF and PH domain-containing 6) and SNPs with the strongest signals were either intronic or intergenic. We found strong evidence for VEZT cis-eQTLs with the sentinel SNP (rs10859871) in blood and endometrium, where the endometriosis risk allele (C) was associated with an increase in VEZT expression. We could not demonstrate this genotype-specific effect on VEZT protein expression in endometrium. However, we did observe a menstrual cycle stage specific increase in VEZT protein expression in endometrial glands, specific to the secretory phase (P = 2.0 × 10(-4)). In comparison to the blood sample datasets, the study numbers of endometrial tissues were substantially reduced. Protein studies failed to complement RNA results, also likely a reflection of the low study numbers in these experiments. In silico prediction tools used in this investigation are typically based on cell lines different to our tissues of interest, thus any functional annotations drawn from these approaches should be considered carefully. Therefore, functional studies on VEZT and related pathway components are still warranted to unequivocally implicate a causal role for VEZT in endometriosis pathophysiology. GWAS have proven to be very valuable tools for deciphering complex diseases. Endometriosis is a text-book example of a complex disease, involving genetic, lifestyle and environmental influences. Our focused investigation of the 12q22 region validates an association with increased endometriosis risk. Endometriosis risk SNPs (including rs10859871) located within this locus demonstrated evidence for cis-eQTLs on VEZT expression. By examining women who possess an enhanced genetic risk of developing endometriosis, we have identified an effect on VEZT expression and therefore a potential gene/gene pathway in endometriosis disease establishment and development. Funding for this work was provided by NHMRC Project Grants GNT1012245, GNT1026033, GNT1049472 and GNT1046880. G.W.M. is supported by the NHMRC Fellowship scheme (GNT1078399). S.J.H.-C. is supported by the J.N. Peters Bequest Fellowship. The authors declare no competing interests. N/A.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 18%
Researcher 6 11%
Student > Doctoral Student 5 9%
Student > Master 5 9%
Other 4 7%
Other 9 16%
Unknown 17 30%
Readers by discipline Count As %
Medicine and Dentistry 19 34%
Biochemistry, Genetics and Molecular Biology 8 14%
Agricultural and Biological Sciences 5 9%
Computer Science 1 2%
Environmental Science 1 2%
Other 2 4%
Unknown 20 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 May 2018.
All research outputs
#6,161,573
of 22,858,915 outputs
Outputs from Human Reproduction
#2,338
of 6,423 outputs
Outputs of similar age
#87,422
of 300,114 outputs
Outputs of similar age from Human Reproduction
#34
of 68 outputs
Altmetric has tracked 22,858,915 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 6,423 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 300,114 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.