Abstract: PD08-09
Sources of Funding: none

Introduction

Older fathers have a significantly higher chance of generating offspring with a high prevalence of genetic abnormalities, childhood cancers, and neuropsychiatric disorders, including schizophrenia, autism, and bipolar disorder (1). We know very little about how paternal aging affects future generations or why certain syndromes are particularly susceptible to the paternal-age effect. A recent trend toward delayed paternity (in the US, births to fathers over 40 years has risen from 7.5% in 1992 to 10.1% in 2000 (2)) and a widespread use of assisted reproductive technologies (which resulted in 61,610 US infants in 2011 (3)) makes it imperative for our species to understand how aging effects germ cells and how paternal factors affect the next generation. The current studies on paternal aging are mostly descriptive and we lack animal models to address the mechanisms.

Methods

While a diverse pool of RNAs exists in sperm, it was the dogma that only sperm contribute DNA to the next generation. However, recent studies have shown that the effects of an animal’s environment, such as traumatic stress and high fat diet, during adolescence can be passed down to the next generation through sperm RNAs(4–6). In this study, we sequenced both small RNAs (miRNAs and piRNAs) and long RNAs (mRNAs, transposable elements, and non-coding RNAs) from sperm of C57/B6 wild-type mice of the ages 8 weeks, 15 months and 21 months.

Results

We detected a distinct miRNA profile during paternal aging and an age-dependent decrease in RNA surveillance for transposable elements in male germ cells.

Conclusions

In conclusion, the sperm RNA changes can be used as biomarkers to evaluate the aging process in old males. We envision that this work will pave the way for further studies on sperm RNA transgenerational effects. _x000D_ Reference_x000D_ 1. D. Malaspina, C. Gilman, T. M. Kranz, Fertil Steril 103, 1392 (2015)._x000D_ 2. M. King, P. Bearman, Int J Epidemiol 38, 1224 (2009)._x000D_ 3. S. Sunderam et al., MMWR Surveill Summ 63, 1 (2014)._x000D_ 4. K. Gapp et al., Nat Neurosci 17, 667 (2014)._x000D_ 5. U. Sharma et al., Science 351, 391 (2016)._x000D_ 6. Q. Chen et al., Science 351, 397 (2016)._x000D_

Funding

none