O-135 Sperm-borne small ribonucleic acid profile significantly impacts embryo development
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».