Scientists characterize sex differences in the human transcriptome

We talk to CRG PhD student Manuel Muñoz Aguirre about the results of this study, and about the importance of considering sex as a biological variable in human genetics and genomics studies.

Sex, defined by the X and Y chromosomes, influences gene expression in most human tissues.

Sex, defined by the X and Y chromosomes, influences gene expression in most human tissues. Composition by Mario Ejarque, background images by Izuddin Helmi Adnan, Shaah Shahidh and Ivan Bandura on Unsplash

Sex matters, according to a recent study with participation of researchers from the Guigó laboratory at the Centre for Genomic Regulation (CRG).

They found that biological sex has a small but ubiquitous influence on gene expression in almost every type of human tissue. The genes found to be expressed at different levels in men and women are involved in many different biological processes, from birth weight and percentage of body fat to response to medication or cancer

The study was part of the Genotype-Tissue Expression (GTEx) Consortium, an ongoing international effort funded by the US National Institutes of Health (NIH) to study tissue-specific gene expression and regulation in a comprehensive manner.

We talk to CRG PhD student and first co-author Manuel Muñoz Aguirre about these latest results, and what they suggest about the importance of considering sex as a biological variable in human genetics and genomics studies.

Why did you decide to do this study and what did you find out?

The motivation to perform this study was that, although it is well known that biological sex (as defined by sex chromosomes) has an influence in human gene expression, we did not have a bird’s eye view on the extent of these differences. So we set to analyse the prevalence of sex-differentiated effects on the human transcriptome in a collection of 44 non-diseased tissues from the Genotype-Tissue Expression Project (GTEx).

It was known that biological sex has an influence in human gene expression, but we did not have a bird’s eye view on the extent of these differences.

We found sex differences with respect to gene expression in many tissues (37% of genes had a sex-biased expression in at least one tissue), but the differences in abundance are quite small and many of these genes expressed differently in the different sexes are tissue-specific. However, these genes play a role in a variety of biological functions such as drug metabolism, hormone and immune responses and cancer. We also provide evidence of gene regulators and epigenetic marks that might partially explain the sex differences that we observed.

We also wanted to look into whether genetic regulation is sex-differentiated and the link of these with diseases and complex traits. In this respect, there are much fewer differences in the genetic regulation of gene expression, and these can partially derive from changes in cellular abundances between sexes.

What is the novelty of the study? 

Due to the breadth of tissue types included in GTEx, we were able to characterize sex-differentiated gene expression tissue specificities that would have been missed otherwise. 

Besides creating a compendium of sex-differentiated effects, we also demonstrated the importance of considering sex as a biological variable: through sex-aware analyses we were able to identify some genetic links with complex traits that cannot be identified solely by a sex-combined approach. This speaks about the importance of considering sex-disaggregated data, for example, in medical studies. It’s important to mention, though, that even if we observe differences at the transcriptome level, most of the biology at the phenotypic level is shared between females and males.

“Our results speak about the importance of considering sex-disaggregated data, for example, in medical studies”

What are the implications for future studies?

Our work represents an extensive characterization of the baseline sex differences in the human transcriptome that could be used when analysing other gene expression studies. Although it is still too early to say, possible examples could be establishing different predicion models for diseases based on sex, the discovery of sex-specific biomarkers that can help describe the progression of disease, or response to drugs that affect mena and women differentially, among other aspects that could potentially translate to clinical practice. 

“Our work represents an extensive characterization of the baseline sex differences in the human transcriptome that could be taken into account in other gene expression studies”

One example of the potential impact of collecting information about sex is a recent work that has provided plausible explanations for mechanisms underlying sex biases observed in COVID-19 that could set a basis for developing sex-aware guidelines at the treatment level.

Here, we analyzed bulk RNA-Seq data, but newer technologies such as single cell omics will surely enable new ways to study the biology of sex, especially with regards to genetic effects. In addition, there is quite a lot of work yet to be done in unraveling the relationship between sex differences driven by biology and gendered environments, which we did not address in our study.

“We show there are discoveries that could be missed when sex is not taken into account”

What are the shortfalls of the study?

Many elements are at play when analyzing sex differences, such as the environment: it is well known that there are sex-differentiated behaviors, such as smoking, and other factors that need to be studied in order to fully characterize the direct and indirect effects of sex on human health, and in our study we did not have proper epidemological data to address this question. 

We were also limited by the inherent composition of the GTEx cohort: it is a snapshot of mostly older individuals, two-thirds of which are male and nearly 85% are from European descent. Apart from this lack of diversity, there is interindividual expression heterogeneity, and for some analyses, limitations of sample size.

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