Large-scale RNA-seq series Ep. 6 – Sex Impacts Gene Expression, but not the way you think!

In 2020, the Genotype-Tissue Expression (GTEx) Consortium published seminal findings about how sex influences tissue-specific gene expression (Oliva et al., 2020). Using large scale RNA-seq they found that over one third of all human genes have differences in gene expression depending on sex in at least one type of tissue.

Here, we discuss the role of RNA-seq in these findings and how novel, higher-throughput technologies could help similar studies.

Summary:

In this study, researchers generated a staggering data set of 16,245 RNA-seq samples from 838 individuals across 44 types of healthy human tissue.

Using this resource, the researchers compared gene expression from males and females in different tissues and discovered that over one third of all genes exhibit sex-specific gene expression differences.

Differences in gene expression were small and mostly tissue specific, but were involved in diverse biological functions. These included hormone response, embryonic development, sexual reproduction, fat metabolism, cancer and the immune response, among others.

Surprisingly, most gene expression differences between males and females came from autosomal chromosomes. This suggests a major role for hormone-related transcription factors in the regulation of gene expression in both sexes.

How RNA-seq was used:

RNA-seq libraries were prepared using the Illumina TruSeq RNA library preparation, followed by poly-A selection. These libraries were non-stranded and sequenced with 76bp paired-end reads on Illumina HiSeq machines to give an average of 50 million reads.

Firstly, the authors fitted a linear model for each tissue to generate accurate comparisons between male and female samples. This accounted for different sample and donor characteristics and other variables, such as tissue cell type composition. This was important because of known differences in cell type abundances depending on sex.

Then, the authors performed differential expression analysis using voom, followed by merging results of all tissues and meta-analysis using multivariate adaptive shrinkage (Law et al., 2014; Urbut et al., 2019). This technique allowed the estimation and testing of the effects of multiple conditions (Urbut et al., 2019). It led to the finding that one third of all genes are differentially expressed between sexes in at least one tissue.

How the large number of samples contributed to the results:

The size of the resource generated by the GTEx consortium is unparalleled. The large sample size ensured the detection of small effects due to high statistical power. This was important because most differentially expressed genes between males and females had only small changes. These may have been missed if the sample size was smaller.

Despite this, the study had extensive sampling biases based on sex. Whilst roughly two thirds of the 838 individuals in the study were males, only one third were females. This uneven distribution of samples may have led to some effects being missed, especially where the number of samples from females was low. For example, the kidney cortex had only 18 samples from females versus 55 samples from males.

How novel higher-throughput transcriptomics could help in similar studies:

In this study, sequencing double the number of female samples would remove the male to female sex bias in sample numbers, but this would lead to an increased cost.

The experimental expense of a project of this magnitude are already extremely high. This cost may limit the scope of similar studies, especially for smaller independent research groups with less funding.

Novel RNA-seq methods such as Bulk RNA Barcoding and sequencing (BRB-seq) address these cost issues, and are suited to high sample numbers (Alpern et al., 2019).

This technology is based on barcoding the 3’ end of mRNA and subsequent multiplexing of samples into the same tube. It allows for a significant cost saving, whilst giving results comparable to the Illumina TruSeq method (Alpern et al., 2019).

To find out more about how RNA-seq or BRB-seq could help your project, please contact us at info@alitheagenomics.com.

References:

  • Alpern, D., Gardeux, V., Russeil, J., Mangeat, B., Meireles-Filho, A.C., Breysse, R., Hacker, D. and Deplancke, B., 2019. BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing. Genome biology, 20(1), pp.1-15.
  • Law, C.W., Chen, Y., Shi, W. and Smyth, G.K., 2014. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome biology, 15(2), pp.1-17.
  • Oliva, M., Muñoz-Aguirre, M., Kim-Hellmuth, S., Wucher, V., Gewirtz, A.D., Cotter, D.J., Parsana, P., Kasela, S., Balliu, B., Viñuela, A. and Castel, S.E., 2020. The impact of sex on gene expression across human tissues. Science, 369(6509), p.eaba3066.
  • Urbut, S.M., Wang, G., Carbonetto, P. and Stephens, M., 2019. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nature genetics, 51(1), pp.187-195.