O-135 Sperm-borne small ribonucleic acid profile significantly impacts embryo development
Bibliographic record
Abstract
Abstract Study question Do normozoospermic males with reduced pre-implantation embryo development have aberrant sperm small RNA profiles? Summary answer Small RNA sequencing suggests the small RNA profile may differ in normozoospermic males with low blastocyst development rates, compared to males with higher blastocyst progression. What is known already Current male factor infertility diagnostics are insufficient, with 30-50% of subfertile males having unknown etiology. Spermatozoa contain a complex, epigenetically-marked genome and a collection of RNAs and proteins, which are not adequately assessed by current diagnostic methods. The sperm small RNA payload is reportedly modified during epididymal transit and in response to paternal exposures, influencing which sperm small RNA species are delivered to the oocyte. Mechanistic animal studies and correlative human and animal studies have suggested that sperm small RNAs may be important for early embryonic development and health of offspring, though their diagnostic and therapeutic value are still unclear. Study design, size, duration Human semen samples were collected between April 2017 and August 2020 from a total of 56 male patients presenting to CReATe Fertility Centre for fertility evaluation. Clinical data was accessed retrospectively. All patients were normozoospermic, according to standard semen analysis and were using donor oocytes. Samples were divided into high (n = 20), average (n = 16), and low (n = 20) fertility groups based on their deviation (1 standard deviation) from the mean blastocyst rate. Participants/materials, setting, methods Semen analysis was undertaken immediately following sample collection and spermatozoa were isolated by centrifugation. Sperm small RNA was purified and eluted using the RNeasy and MiRNeasy Kits (Qiagen). Barcoded and amplified cDNA libraries were prepared from small RNA using the NEXTFLEX Small RNA-Seq Kit v3 (Bioo Scientific). Resulting libraries were pooled, size-selected to a range of 140-190 base pairs, denatured and diluted for sequencing. Single-end, 75 bp sequencing was performed using the NextSeq 550 (Illumina). Main results and the role of chance Sequencing generated approximately 300 million raw reads, with 30 samples exceeding 2 million reads included in the differential expression analysis. Most reads were mapped to rRNAs (69%), miRNAs (11%), and piRNAs (12%). However, transfer RNA fragments from tRNA-Gly-GCC and tRNA-Val-CAC were the most abundant sequences. Top annotated miRNAs include: miR-12136-5p; miR-21-5p; and miR-122-5p. Principal component analysis revealed 222 genes that were differentially expressed between the high (n = 14) and low (n = 11) fertility groups (p < 0.05). Interestingly, the top 50 differentially expressed sRNAs are sufficient to effectively cluster sperm with poor blastocyst development rates. Limitations, reasons for caution The results are limited by a relatively low sequencing depth (mean of 4.1 million reads per sample) and sample size. Fertility groups were determined by blastocyst rates, which can be confounded by non-sperm-derived variables, including technical skill, embryo culturing conditions, and maternal factors (though donor oocytes were used). Wider implications of the findings With additional validation, a clinically-useful panel of differentially expressed sperm small RNAs could be used to predict IVF success and evaluate therapies aimed at improving male reproductive health. Augmenting traditional semen analytics with diagnostic sperm small RNA analysis could reduce time to pregnancy and the psychosocial impacts of fertility treatment. Trial registration number Not applicable
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".