システム生物学：セックスとジェンダーの相互作用の分析

The Challenge

It is an interdisciplinary field of study that focuses on complex interactions within biological systems (Tavassoly, Goldfarb, & Iyengar, 2018). Systems biology lets us understand how interactions among multiple components form functional networks at the organism level. For example, Tareen et al. (2019) used systems biology to identify a molecular switch that tissues use to rely either on glucose or on fatty acids as fuel (Tareen et al., 2019). Systems biology also develops predictive computational models of disease that will transform disease taxonomies from phenotypical to molecular and will enable personalized/precision therapeutics (Bielekova, Vodovotz, An, & Hallenbeck, 2014). identity (Gemmati et al., 2020). Systems biologists often use “omics” data (genomics, transcriptomics, proteomics, metabolomics) to provide broad insight into the molecular dynamics that accompany genetic, cultural, ethnic and societal influences on a living organism. For example, a high daily intake of fat (related to cultural and societal factors) has a negative influence on the metabolic performance of tissues in the body and increases the risk of type II diabetes. Systems biologists use omics data to understand a tissue’s metabolic status over time. Since there is sex and gender bias in clinical and omics data (Gemmati et al., 2020), current biological models apply to cisgender men, whereas models of other bodies and genders are under-represented.

Gendered Innovations 1: Collecting Data on Sexual Dimorphism in Gene Expression Improves Biological Modeling

Omics studies of animal organs and body fluids have demonstrated sex differences in biochemistry and the regulation of gene activities. In the liver, for example, over 1000 genes are expressed differently in females and males. Sex differences in metabolic and gene regulatory pathways have also been demonstrated in humans. For example, metabolomic analysis of serum from participants in the German KORA cohort showed significant sex differences in concentration for 102 of 131 examined metabolites (Mittelstrass et al., 2011). Sex-specific Genome Wide Association Studies (GWAS) showed that some of these sex differences resulted from genetic differences. Despite these indicative results, most systems biology studies have not considered sex.

Data on human sexual dimorphism are limited. Because biopsy material cannot be taken from many of the organs of living individuals, new strategies need to be designed to intensify studies on sexual dimorphism in genome expression, such as the use of iPSC (induced pluripotent stem cells) or organoids. Ideally, such studies would take into account differences in lifestyle, ethnicity, cultural and social background to incorporate intersectional factors into the data sets.

Gendered Innovation 2: Collecting Sex and Gender Data Fuels Integrative Omics

Integrative omics serves as a foundation for precision medicine, pharmacotherapy and nutrition (Karczewski & Snyder, 2018). Meta-analysis of omics data is a way to find novel diagnostic biomarkers, drug targets or nutritional pathways to prevent the onset of disease. Integrating sex and gender identity into the source data for integrative omics will be an essential first step towards more personalized medicine. Current datasets under-represent females and lack information about participants’ gender identity. Concerning information on the influence of gender identity, in 2018 the gender dysphoria treatment in Sweden (GETS) study was announced to investigate if and how cross-sex hormone treatment to align gender identity with physical appearance leads to metabolic and functional changes including at the genomic and epigenomic levels (Wiik et al., 2018). Big data that includes social and cultural information can open the way to sex-specific and gender-specific precision medicine (Das et al., 2020).

Gendered Innovation 3: Analyzing Sex Improves Understanding of the Role of the Microbiome in Health and Disease

Awareness of the gut microbiome’s role in health and disease is growing. Using gene sequencing to construct a Metagenome, researchers can now analyze the composition of the microbiome and quantify the different types of microorganisms present in our gut. The microbiome is influenced by the genetic make-up of the host as well as by cultural and social circumstances. Such sequencing has found women’s and men’s microbiomes differ. Sex differences in the microbiome may not only contribute to sex differences of gut-related diseases, but could also underlie sex differences in neurologic and neurodegenerative disorders (Thion et al., 2018) (H2020-EU.3.1.1. ID:733100, SYSCID). Research has shown that women tend to have microbiomes with significantly higher microbial diversity than men (Chen et al. 2018). The make-up of the microbiome is probably influenced by contraceptives with estrogen, which modify the growth conditions in the gut. Women’s microbiomes display higher resistance to various classes of antibiotics, which seem to follow differences in antibiotics use (Sinha et al., 2019) (H2020-EU.1.1. ID: 715772, BabyVir). These novel observations warrant more profound investigation and may result in sex-specific analysis of health and disease.

Gendered Innovations 4: Integrating Sex Difference into Systems Biology Models

Because biomedical experiments more often use male animals and humans, more data about males than females are available to systems biology researchers. Consequently, mathematical models in biology are based primarily on male data. In order to close this sex gap, journals and granting agencies should promote data collection on females, which is a prerequisite for the generation of female-based models in parallel to the existing male-based models. In addition, available omics data sets are often composed of mixed female and male data, while in the analysis sex and gender are regarded as confounders. It is recommended that systems biologists systematically label the sex of the data and take this into account in the models they are building.

Naik et al. (2014) constructed the in silico model SteatoNet using liver metabolic data, to enhance understanding in research on liver disease etiology—but this model is valid for males only (Naik, Rozman, & Belic, 2014). Cvitanovic Tomas et al. (Cvitanovic Tomas, Urlep, Moskon, Mraz, & Rozman, 2018) have adapted SteatoNet to include sex differences by using data on estrogen and androgen receptor responses and consequent differences in growth hormone release. They call their computational model LiverSex and hope it will provide insight into sex-dependent liver pathologies. It has been validated for the mouse, but not yet for humans.

In the context of physiology and metabolism, researchers at the universities of Luxembourg and Leiden recently presented two validated, sex-specific, whole-body metabolic reconstructions, named Harvey and Harvetta (Thiele et al., 2018). The models represent the metabolism of twenty organs, six sex organs, six types of blood cells, the systemic blood circulation, the blood-brain barrier, and the gastrointestinal lumen with microbiome. There is potential for such models to be used to determine treatment methods in situations in which experimental testing is impossible, such as for pregnant women.