Application and discoveries of metabolomics and proteomics in the study of female infertility

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Infertility is a disease characterized by the failure to establish a clinical pregnancy after 12 months of regular and unprotected sexual intercourse. It is estimated to affect between 8 and 12% of reproductive-aged couples worldwide. Males are found to be solely responsible for 20-30% of infertility cases but contribute to 50% of cases overall. Secondary infertility is the most common form of female infertility around the globe, often due to reproductive tract infections. The three major factors influencing the spontaneous probability of conception are the time of unwanted non-conception, the age of the female partner and the disease-related infertility. The chance of becoming spontaneously pregnant declines with the duration before conception. The fertility decline in female already starts around 25-30 years of age and the median age at last birth is 40-41 years in most studied populations experiencing natural fertility. The disease-related infertility may affect both genders or be specific to one gender. The factors affecting both genders' fertility are hypogonadotrophic hypogonadism, hyperprolactinemia, disorders of ciliary function, cystic fibrosis, infections, systemic diseases and lifestyle related factors/diseases. Premature ovarian insufficiency, polycystic ovary syndrome, endometriosis, uterine fibroids and endometrial polyps may play a role in female infertility. Male infertility may be due to testicular and post-testicular deficiencies. Semen decline that has been observed over the years, endocrine disrupting chemicals and consanguinity are other factors that may be involved.

Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes, and as such the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating, workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS based untargeted metabolomics studies – specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.