A sex-specific evolutionary interaction between ADCY9 and CETP
Abstract
Pharmacogenomic studies have revealed associations between rs1967309 in the adenylyl cyclase type 9 (ADCY9) gene and clinical responses to the cholesteryl ester transfer protein (CETP) modulator dalcetrapib, however, the mechanism behind this interaction is still unknown. Here, we characterized selective signals at the locus associated with the pharmacogenomic response in human populations and we show that rs1967309 region exhibits signatures of positive selection in several human populations. Furthermore, we identified a variant in CETP, rs158477, which is in long-range linkage disequilibrium with rs1967309 in the Peruvian population. The signal is mainly seen in males, a sex-specific result that is replicated in the LIMAA cohort of over 3,400 Peruvians. Analyses of RNA-seq data further suggest an epistatic interaction on CETP expression levels between the two SNPs in multiple tissues, which also differs between males and females. We also detected interaction effects of the two SNPs with sex on cardiovascular phenotypes in the UK Biobank, in line with the sex-specific genotype associations found in Peruvians at these loci. We propose that ADCY9 and CETP coevolved during recent human evolution due to sex-specific selection, which points towards a biological link between dalcetrapib’s pharmacogene ADCY9 and its therapeutic target CETP.
Data availability
- 1000 Genomes Project, GEUVADIS is freely available.- The Native American genetic dataset was shared to us upon request to the authors of the initial paper and through a data access agreement with Universidad de Antioquia (Prof. Omar Triana Chavez). We contacted bedoya.g@gmail.com and a.ruizlin@ucl.ac.uk to get access to the dataset and we completed a data access application form and signed a data access approval once approved. Applications for access to these data can be submitted at any time. These are considered on a rolling basis and a decision was given within 1 month of receipt. PhD student applicants must include their supervisors as a co-applicant and provide their full contact details. A publication list must be provided for the applicant, co-applicants and PhD supervisors where PhD students have applied to provide proof of competence in handling datasets of this size and nature. - UK Biobank was accessed through data access approval under the project number #15357 and #20168. Information to apply for data access can be found here: https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access - CARTaGENE biobank was accessed through data access approval under the project number #406713. Information to apply for data access can be found here: https://www.cartagene.qc.ca/en/researchers/access-request - GTEx v8 dataset was accessed through dbGaP under project number #19088 LIMAA dataset was accessed through dbGaP under the project number #26882. Information to apply for data access through dbGAP can be found here: https://dbgap.ncbi.nlm.nih.gov- RNA-sequencing of ADCY9-knocked-down HepG2 cell line data has been deposited under GSE174640 (embargo): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174640For reviewers/editors: Enter token ktaxgqkkrxupjkl into the box- Source data files and code for all main figures are available here: https://github.com/HussinLab/adcy9_cetp_Gamache_2021
-
RNA-sequencing of ADCY9-knocked-down HepG2 cell line (embargo)NCBI Gene Expression Omnibus, GSE174640.
-
LIMAAdbGaP project #26882.
-
Native American genetic dataset (NAGD)rom authors bedoya.g@gmail.com and a.ruizlin@ucl.ac.uk.
Article and author information
Author details
Funding
Institut de Cardiologie de Montréal
- Isabel Gamache
- Marc-André Legault
- Jean-Christophe Grenier
- Rocio Sanchez
- Eric Rhéaume
- Holly Trochet
- Jean-Claude Tardif
- Marie-Pierre Dubé
- Julie Hussin
Université de Montréal
- Isabel Gamache
Canadian Institutes of Health Research
- Marc-André Legault
Canada Research Chairs
- Jean-Claude Tardif
- Marie-Pierre Dubé
Fonds de Recherche du Québec - Santé
- Julie Hussin
Institut de Valorisation des Données
- Isabel Gamache
- Jean-Christophe Grenier
- Julie Hussin
Health collaboration acceleration fund from the Ministère de l'Économie et de l'Innovation of the Government of Quebec
- Jean-Claude Tardif
- Marie-Pierre Dubé
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The analyses done in this study were approved by the different cohorts used. Participants in these cohorts gave their general consent for their data to be used for research purposes. All individual-level data was anonymized and no efforts were made by the authors to deanonymize or recontact any of the participants from the cohorts, in keeping with our agreements with the UK Biobank, CARTaGENE, dbGAP, 1000Genomes and the Native American Genetics dataset (Universidad de Antioquia).
Copyright
© 2021, Gamache et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,172
- views
-
- 109
- downloads
-
- 8
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
Plasmid construction is central to life science research, and sequence verification is arguably its costliest step. Long-read sequencing has emerged as a competitor to Sanger sequencing, with the principal benefit that whole plasmids can be sequenced in a single run. Nevertheless, the current cost of nanopore sequencing is still prohibitive for routine sequencing during plasmid construction. We develop a computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides researchers to mix multiple plasmids and subsequently computationally de-mixes the resultant sequences. SAVEMONEY defines optimal mixtures in a pre-survey step, and following sequencing, executes a post-analysis workflow involving sequence classification, alignment, and consensus determination. By using Bayesian analysis with prior probability of expected plasmid construction error rate, high-confidence sequences can be obtained for each plasmid in the mixture. Plasmids differing by as little as two bases can be mixed as a single sample for nanopore sequencing, and routine multiplexing of even six plasmids per 180 reads can still maintain high accuracy of consensus sequencing. SAVEMONEY should further democratize whole-plasmid sequencing by nanopore and related technologies, driving down the effective cost of whole-plasmid sequencing to lower than that of a single Sanger sequencing run.
-
- Biochemistry and Chemical Biology
- Computational and Systems Biology
Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.