MiRNA and menstrual cycle
Description
Study question: Do ovarian hormone levels influence cell-free or “circulating” microRNA (cf-miRNA) levels across the menstrual cycle? Summary answer: This exploratory study suggests that hormonal levels fluctuations throughout the menstrual cycle may alter cf-miRNAs levels. Study design, size, duration: A prospective, monocentric study conducted between March and November 2021. Since this a pilot study, sample size was based on feasibility as well as previous, similar human studies conducted in different tissues. A total of 20 subjects were involved in the study. Participants/materials, setting, methods: We conducted an exploratory study where blood samples were collected from sixteen eumenorrheic females in the early follicular phase, the ovulation phase and the mid-luteal phase of the menstrual cycle. Ovarian hormones oestrogen, progesterone, luteinizing hormone (LH) and follicle-stimulating hormone (FSH) were measured in serum by electrochemiluminescence. The levels of 179 plasma-enriched miRNAs were profiled using a PCR-based panel, including stringent internal and external controls to account for the potential differences in RNA extraction and reverse-transcription stemming from low-RNA input samples. Main results and the role of chance: This exploratory study suggests that cf-miRNAs may play an active role in the regulation of the female cycle by mediating the expression of genes fluctuating with hormonal changes. Linear mixed-models adjusted for the relevant variables showed numerous associations between phases of the menstrual cycle, ovarian hormones and plasma cf-miRNA levels. Validated gene targets of the cf-miRNAs varying with the menstrual cycle were enriched within the female reproductive tissues and primarily involved in cell proliferation and apoptosis.
Files
Steps to reproduce
Cq values were calculated by applying the second derivative method and transformed into arbitrary units using the formula (2^Ct) x (10^10). Melting curves were examined automatically using the LightCycler 480 software version 1.5.0, and individual data points were manually excluded if a double peak or a “shoulder” was visible, leading to the exclusion of 181 data points (1.97%). Six miRNA datasets presented an average Cq value > 35 and were excluded from further analysis. Following this, all individual Cq values > 35 remaining in the dataset were excluded from further analysis, leading to the exclusion of an additional 189 data points (2.29%). Of the remaining miRNA datasets, eleven (6.45%) missed more than 20% of their individual data points and were excluded on this basis. For the remaining 157 miRNAs, the arithmetic mean of the transformed Cq values (arbitrary units) of the six endogenous UniSp3 quality controls was calculated for each plate, allocated a random value of 1 and used to account for inter-plate variability. Finally, the geometric mean of the transformed Cq values (arbitrary units) of all miRNAs considered for the final stage of the analysis was used for global normalization