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Live cells are not affected by dead sperm in liquid boar semen: New insights based on a thermo-resistance test.

Monike QuirinoUlrike JakopAna Paula Gonçalves MellagiFernando Pandolfo BortolozzoMarkus JungMartin Schulze
Published in: Reproduction in domestic animals = Zuchthygiene (2022)
This study evaluated the effect of different proportions of dead spermatozoa on the quality of liquid boar semen during a thermo-resistance test (TRT). After 3 days of storage (17°C), 54 conventional artificial insemination semen doses (~23 × 10 6 sperm/ml in ~88 ml of BTS) were split into three 15 ml-treatments (25%, 50%, and 75% dead sperm cells) by mixing two subsamples containing 75% (I) and 0% (II) of live cells. Spermatozoa were evaluated after TRT at 30 (on-test) and 300 min (off-test) incubation at 38°C. At the on-test, treatments of 25%, 50%, and 75% dead sperm cells showed medians for total sperm motility of 77.6%, 50.2%, and 25.6%, respectively. Considering the absolute variation of sperm motility during TRT, doses with 25% dead sperm lost more percentage points (pp) (-9.4 pp) compared to doses containing 50% (-8.2 pp) and 75% dead sperm (-4.5 pp). The lowest loss was observed for doses with 75% dead sperm (p < .01). However, data showed that treatments lost similar proportion of motile cells over the TRT: 25% dead sperm = -11.9%, 50% dead sperm = -16.0%, and 75% dead sperm = -17.5% (p = .31). Regarding the flow cytometry parameters (plasma and acrosomal membrane integrity, mitochondrial activity of cells with intact plasma membrane, high degree of lipid disorder, and apoptotic cells), the absolute variations did not surpass values of -1.8, 3.4, -5.4, and 4.7 pp, respectively. Furthermore, the relative variation suggested that dead sperm did not substantially change their values over the TRT. In conclusion, dead sperm cells did not influence the quality of contemporary live cells during the period and in conditions of a TRT.
Keyphrases
  • induced apoptosis
  • cell cycle arrest
  • cell death
  • oxidative stress
  • endoplasmic reticulum stress
  • signaling pathway
  • flow cytometry
  • escherichia coli
  • deep learning
  • cell proliferation
  • quality improvement