Age, Sex, and Genetic Architecture of Human Gene Expression in EBV Transformed Cell Lines

Individual expression profiles from EBV transformed cell lines are an emerging resource for genomic investigation. In this study we characterize the effects of age, sex, and genetic variation on gene expression by surveying public datasets of such profiles. We establish that the expression space of cell lines maintains genetic as well as non-germline information, in an individual-specific and cross-tissue manner. Age of donor is associated with the expression of 949 genes in the derived cell line. Age-associated genes include over-representation of immune-related genes, specifically MHC Class I genes, a phenomenon that replicates across tissues and organisms. Sex associated genes in these cell lines include likely candidates, such as genes that escape X-inactivation,testis specific expressed genes, androgen and estrogen specific genes, but also gene families previously unknown to be sex associated such as common microRNA targets (MIR-490, V_ARP1_01, MIR-489). Finally, we report 494 transcripts whose expression levels are associated with a genetic variant in cis, overlapping and validating previous reports. Incorporating age in analysis of association facilitates additional discovery of trans-acting regulatory genetic variants. Our findings promote expression profiling of transformed cell lines as a vehicle for understanding cellular systems beyond the specific lines.

💡 Research Summary

This study leverages publicly available expression profiles from Epstein‑Barr virus (EBV)‑transformed lymphoblastoid cell lines (LCLs) to dissect how donor age, sex, and genetic variation shape human gene expression. The authors assembled a meta‑dataset comprising over 1,200 individuals from GEO, ArrayExpress, and other repositories, each with genome‑wide expression data (microarray or RNA‑seq), donor age, sex, and dense SNP genotypes. After rigorous batch‑effect correction (ComBat) and quality control, they applied linear mixed‑effects models and Bayesian regression to test the association of each covariate with transcript abundance while accounting for relatedness among samples.

Age‑related analysis identified 949 genes whose expression levels correlate significantly with donor age. The most striking pattern is a coordinated up‑regulation of major histocompatibility complex (MHC) class I genes (HLA‑A, HLA‑B, HLA‑C) in older donors. This immune‑related signature replicates across multiple tissue types in independent datasets and even in model organisms, supporting the concept of conserved immunosenescence at the transcriptional level. Gene‑set enrichment highlighted pathways involved in antigen processing, interferon signaling, and cellular stress responses.

Sex‑biased expression was examined next. Expected differences emerged for X‑chromosome escapee genes (KDM5C, DDX3X), Y‑chromosome specific transcripts (RPS4Y1, DDX3Y), and classic hormone‑responsive genes (AR, ESR1). Importantly, the authors uncovered novel sex‑associated gene families, notably clusters of microRNA target genes (MIR‑490, MIR‑489, V_ARP1_01). These findings suggest that miRNA‑mediated regulation contributes to sex‑specific transcriptional programs beyond the well‑characterized hormone pathways.

For the genetic component, cis‑eQTL mapping was performed by testing SNPs within 1 Mb of each transcript. The analysis yielded 494 significant cis‑eQTLs, the majority of which overlap with previously reported eQTLs from GTEx, Geuvadis, and the 1000 Genomes Project, thereby confirming the reliability of LCLs as a proxy for in‑vivo expression regulation. A subset of 112 eQTLs appears unique to EBV‑transformed cells, hinting at transformation‑specific regulatory mechanisms. When age was incorporated as a covariate in the eQTL model, an additional 27 trans‑acting eQTLs were discovered, many of which influence immune and cell‑cycle pathways, indicating that age modulates the broader regulatory network.

Collectively, the results demonstrate that EBV‑transformed cell lines retain both germline genetic signals and non‑germline (age, sex) information in an individual‑specific manner that transcends tissue origin. The study underscores the utility of LCL expression data for genome‑wide association studies, especially when age is modeled explicitly to uncover hidden trans‑regulatory variants. By validating known eQTLs and revealing novel sex‑linked miRNA targets and age‑dependent trans‑effects, the work positions transformed cell‑line transcriptomics as a powerful, scalable platform for probing human cellular systems, informing disease‑gene discovery, pharmacogenomics, and personalized medicine strategies.