Metabolomics and proteomics techniques are valuable for researching infertility, according to a recent study published in Frontiers in Endocrinology.
Takeaways
- Infertility affects 8% to 12% of reproductive-aged couples worldwide, with 50% of cases attributed to female infertility. Factors influencing natural conception include the intention-to-conceive period, female partner's age, and medical conditions causing infertility.
- Metabolomics involves investigating metabolites within an organism, providing insights into metabolic pathway activity and changes in metabolite concentration. Proteomics assesses an organism's protein composition, structure, and function. Both techniques are complementary in understanding an organism's metabolic state and physiological functions.
- Analyzing metabolites in pregnant patients can enhance understanding of metabolic abnormalities in infertility, leading to the identification of biomarkers for diagnosis and treatment. Similarly, studying proteins linked to infertility may improve understanding of associated diseases.
- A Mendelian randomization (MR) analysis was conducted to evaluate the impact of metabolites and proteins on infertility onset. The study included 504 metabolites and 971 proteins, revealing 5 proteins and 2 metabolites with potential causal relationships to female infertility.
- The study identified specific proteins and metabolites potentially linked to female infertility. GRAM domain-containing protein 1C showed a positive association, while 4 other proteins exhibited negative associations. Both metabolites, fibrinogen cleavage peptides ADpSGEGDFXAEGGGVR and 3-Hydroxybutyrate, showed a positive correlation with infertility.
Infertility impacts 8% to 12% of reproductive-aged couples worldwide, and 50% of infertility cases are attributable to female infertility. The natural conception rate is primarily impacted by the period of intention to conceive, the female partner’s age, and infertility connected to medical conditions.
In metabolomics, metabolites within an organism are investigated, allowing metabolic pathway activity, metabolite concentration changes, and metabolite interactions to be identified. This increases the understanding of an organism’s metabolic state and physiological functions.
In comparison, proteomics evaluates the composition, structure, and function of an organism’s proteins. Protein metabolism creates metabolites, making metabolomics and proteomics complementary.
Analyzing the metabolites in pregnant patients may allow for an improved understanding of metabolic abnormalities in infertility. This can lead to biomarkers being applied to diagnose and treat patients. Determining proteins linked to infertility may also improve understanding of associated diseases.
Investigators conducted a mendelian randomization (MR) analysis to evaluate the impact of metabolites and proteins on infertility onset. The open GWAS website was assessed for metabolite data, with 975 metabolites contained.
Of metabolites, 452 were human blood metabolites, 150 metabolites of human immune system symptoms, 123 circulating metabolites, and 249 metabolic traits. There were 4490 distinct data from proteins obtained for proteomics data.
Infertility data was obtained from the Finngen population database. Instrumental variables were selected based on correlation with the outcome variable, unrelatedness, and exclusivity. A whole-genome significant level and no linkage disequilibrium with other Single Nucleotide Polymorphisms were selected as instrumental variables.
There were 504 metabolites and 971 proteins included in the final analysis. Five proteins and 2 metabolites had potential causal relationships during the MR analysis. The metabolites were identified as fibrinogen cleavage peptides ADpSGEGDFXAEGGGVR and 3-Hydroxybutyrate.
The proteins were identified as GRAM domain-containing protein 1C, Interleukin-3 receptor subunit alpha, Thrombospondin type-1 domain-containing protein 1, Intestinal-type alkaline phosphatase, and platelet and endothelial cell adhesion molecule 1.
A positive association was found between GRAM domain-containing protein 1C and infertility. However, the other 4 proteins were negatively associated with infertility. In comparison, both metabolites with a potential causal relationship had a positive correlation with infertility.
These results indicated several potential relationships between metabolites, proteins, and female infertility. Investigators concluded these findings provide new targets for the diagnosis and treatment of female infertility.
Reference
Shi J, Wu X, Qi H, Xu X, Hong S. Application and discoveries of metabolomics and proteomics in the study of female infertility. Front Endocrinol. 2024;14. doi:10.3389/fendo.2023.1315099
