A comparison of gene expression and DNA methylation patterns across tissues and species

Abstract

Previously published comparative functional genomic data sets from primates using frozen tissue samples, including many data sets from our own group, were often collected and analyzed using nonoptimal study designs and analysis approaches. In addition, when samples from multiple tissues were studied in a comparative framework, individuals and tissues were confounded. We designed a multitissue comparative study of gene expression and DNA methylation in primates that minimizes confounding effects by using a balanced design with respect to species, tissues, and individuals. We also developed a comparative analysis pipeline that minimizes biases attributable to sequence divergence. Thus, we present the most comprehensive catalog of similarities and differences in gene expression and DNA methylation levels between livers, kidneys, hearts, and lungs, in humans, chimpanzees, and rhesus macaques. We estimate that overall, interspecies and inter-tissue differences in gene expression levels can only modestly be accounted for by corresponding differences in promoter DNA methylation. However, the expression pattern of genes with conserved inter-tissue expression differences can be explained by corresponding interspecies methylation changes more often. Finally, we show that genes whose tissue-specific regulatory patterns are consistent with the action of natural selection are highly connected in both gene regulatory and protein-protein interaction networks.

A single-cell RNA expression map of human coronavirus entry factors

“…To examine the evolutionary conservation of SCARF expression across primates, we analyzed a recently published comparative transcriptome dataset (bulk RNA-seq) of heart, kidney, liver, and lung primary tissues from human (N=4), chimpanzee (N=4), and Rhesus macaque (N=4) individuals, comprising a total of 47 samples (Blake et al, 2020).…”

“…Specifically, we mined publicly available scRNA-seq datasets using consistent normalization procedures to integrate and compare the dynamics of SCARF expression in human pre-implantation embryos (Yan et al, 2013), at the maternal-fetal interface (Vento-Tormo et al, 2018), in male and female gonads (Sohni et al, 2019;Wagner et al, 2020) and 14 other adult tissues (Han et al, 2020), as well as nasal brushing from young and old healthy donors (Deprez et al, 2019;Garcıá et al, 2019;Vieira Braga et al, 2019). Additionally, we use bulk transcriptomics for four organs of interest (lung, kidney, liver, heart) from human, chimpanzee, and macaque (Blake et al, 2020) to examine the conservation of SCARF expression across primates. This study represents the most systematic survey of SCARF expression to date and provides a valuable resource, including a user-friendly web browser, for interpreting and prioritizing clinical, pathological, and biological studies of SARS-CoV-2 and COVID-19.…”

A comparison of gene expression and DNA methylation patterns across tissues and species

“…To identify differences in methylation levels between tissues and species we again employed a linear model framework (Methods). Focusing on methylation patterns across tissues within species, we identified between 7,026 to 41,280 differentially methylated regions between tissues, within species (T-DMRs), depending on the pairwise tissue comparisons we considered (Table 2; Additional File 1: Table S9A; [50]). Pairwise comparisons between hearts and lungs showed the lowest number of DMRs, regardless of species (7,026 in rhesus macaques, 8,524 in chimpanzees, 14,208 in humans), while comparisons involving heart and liver showed the largest number of DMRs (22,561 in humans, 28,767 in chimpanzee and 41,280 in rhesus macaques; Table 2).…”

“…DMRs were defined as regions containing at least three consecutive significant CpGs, an average methylation difference of 10% between conditions, and at least one CpG every 300 bp [49]. We used BEDTools (version 2.26.0) [98] to calculate the number of overlapping DMRs across tissues and/or species [50]. We required overlapping DMRs to have a minimum base pair overlap of at least 25%, unless otherwise stated.…”

“…Our custom pre-processing script is available upon request to the authors. Pairwise differentially methylated regions can be found at [50]. Data and R scripts used post-processing are available at https://github.com/Lauren-Blake/Reg_Evo_Primates and the results can be viewed at https://lauren-blake.github.io/Regulatory_Evol/analysis/.…”