Sperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder
Offspring affected by sperm small RNAs
Paternal dietary conditions in mammals influence the metabolic phenotypes of offspring. Although prior work suggests the involvement of epigenetic pathways, the mechanisms remains unclear. Two studies now show that altered paternal diet affects the level of small RNAs in mouse sperm. Chen et al. injected sperm transfer RNA (tRNA) fragments from males that had been kept on a high-fat diet into normal oocytes. The progeny displayed metabolic disorders and concomitant alteration of genes in metabolic pathways. Sharma et al. observed the biogenesis and function of small tRNA-derived fragments during sperm maturation. Further understanding of the mechanisms by which progeny are affected by parental exposure may affect human diseases such as diet-induced metabolic disorders.
Abstract
Increasing evidence indicates that metabolic disorders in offspring can result from the father’s diet, but the mechanism remains unclear. In a paternal mouse model given a high-fat diet (HFD), we showed that a subset of sperm transfer RNA–derived small RNAs (tsRNAs), mainly from 5′ transfer RNA halves and ranging in size from 30 to 34 nucleotides, exhibited changes in expression profiles and RNA modifications. Injection of sperm tsRNA fractions from HFD males into normal zygotes generated metabolic disorders in the F1 offspring and altered gene expression of metabolic pathways in early embryos and islets of F1 offspring, which was unrelated to DNA methylation at CpG-enriched regions. Hence, sperm tsRNAs represent a paternal epigenetic factor that may mediate intergenerational inheritance of diet-induced metabolic disorders.
Get full access to this article
View all available purchase options and get full access to this article.
Supplementary Material
Summary
Materials and Methods
Figs. S1 to S13
Tables S1 to S12
Resources
File (aad7977-chen-sm-table-s10.xlsx)
- Download
- 28.50 KB
File (aad7977-chen-sm-table-s12.xlsx)
- Download
- 440.59 KB
File (aad7977-chen-sm-table-s6.xlsx)
- Download
- 54.03 KB
File (aad7977-chen-sm-table-s8.xlsx)
- Download
- 13.54 KB
File (aad7977-chen-sm-table-s9.xlsx)
- Download
- 166.23 KB
File (aad7977-chen-sm.pdf)
- Download
- 3.88 MB
File (aad7977-chen-sm_corrected.pdf)
- Download
- 4.14 MB
REFERENCES AND NOTES
1
Daxinger L., Whitelaw E., Understanding transgenerational epigenetic inheritance via the gametes in mammals. Nat. Rev. Genet. 13, 153–162 (2012).
2
Rechavi O., Houri-Ze’evi L., Anava S., Goh W. S., Kerk S. Y., Hannon G. J., Hobert O., Starvation-induced transgenerational inheritance of small RNAs in C. elegans. Cell 158, 277–287 (2014).
3
Carone B. R., Fauquier L., Habib N., Shea J. M., Hart C. E., Li R., Bock C., Li C., Gu H., Zamore P. D., Meissner A., Weng Z., Hofmann H. A., Friedman N., Rando O. J., Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals. Cell 143, 1084–1096 (2010).
4
Ng S. F., Lin R. C., Laybutt D. R., Barres R., Owens J. A., Morris M. J., Chronic high-fat diet in fathers programs β-cell dysfunction in female rat offspring. Nature 467, 963–966 (2010).
5
Rando O. J., Daddy issues: Paternal effects on phenotype. Cell 151, 702–708 (2012).
6
Radford E. J., Ito M., Shi H., Corish J. A., Yamazawa K., Isganaitis E., Seisenberger S., Hore T. A., Reik W., Erkek S., Peters A. H., Patti M. E., Ferguson-Smith A. C., In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science 345, 1255903 (2014).
7
Wei Y., Yang C. R., Wei Y. P., Zhao Z. A., Hou Y., Schatten H., Sun Q. Y., Paternally induced transgenerational inheritance of susceptibility to diabetes in mammals. Proc. Natl. Acad. Sci. U.S.A. 111, 1873–1878 (2014).
8
Holoch D., Moazed D., RNA-mediated epigenetic regulation of gene expression. Nat. Rev. Genet. 16, 71–84 (2015).
9
Grandjean V., Gounon P., Wagner N., Martin L., Wagner K. D., Bernex F., Cuzin F., Rassoulzadegan M., The miR-124-Sox9 paramutation: RNA-mediated epigenetic control of embryonic and adult growth. Development 136, 3647–3655 (2009).
10
Wagner K. D., Wagner N., Ghanbarian H., Grandjean V., Gounon P., Cuzin F., Rassoulzadegan M., RNA induction and inheritance of epigenetic cardiac hypertrophy in the mouse. Dev. Cell 14, 962–969 (2008).
11
Rassoulzadegan M., Grandjean V., Gounon P., Vincent S., Gillot I., Cuzin F., RNA-mediated non-Mendelian inheritance of an epigenetic change in the mouse. Nature 441, 469–474 (2006).
12
Yuan S., Oliver D., Schuster A., Zheng H., Yan W., Breeding scheme and maternal small RNAs affect the efficiency of transgenerational inheritance of a paramutation in mice. Sci. Rep. 5, 9266 (2015).
13
Rodgers A. B., Morgan C. P., Leu N. A., Bale T. L., Transgenerational epigenetic programming via sperm microRNA recapitulates effects of paternal stress. Proc. Natl. Acad. Sci. U.S.A. 122, 13699–13704 (2015).
14
Fullston T., Ohlsson Teague E. M., Palmer N. O., DeBlasio M. J., Mitchell M., Corbett M., Print C. G., Owens J. A., Lane M., Paternal obesity initiates metabolic disturbances in two generations of mice with incomplete penetrance to the F2 generation and alters the transcriptional profile of testis and sperm microRNA content. FASEB J. 27, 4226–4243 (2013).
15
Gapp K., Jawaid A., Sarkies P., Bohacek J., Pelczar P., Prados J., Farinelli L., Miska E., Mansuy I. M., Implication of sperm RNAs in transgenerational inheritance of the effects of early trauma in mice. Nat. Neurosci. 17, 667–669 (2014).
16
Jodar M., Selvaraju S., Sendler E., Diamond M. P., Krawetz S. A.Reproductive Medicine Network, The presence, role and clinical use of spermatozoal RNAs. Hum. Reprod. Update 19, 604–624 (2013).
17
Yan W., Potential roles of noncoding RNAs in environmental epigenetic transgenerational inheritance. Mol. Cell. Endocrinol. 398, 24–30 (2014).
18
Liebers R., Rassoulzadegan M., Lyko F., Epigenetic regulation by heritable RNA. PLOS Genet. 10, e1004296 (2014).
19
Peng H., Shi J., Zhang Y., Zhang H., Liao S., Li W., Lei L., Han C., Ning L., Cao Y., Zhou Q., Chen Q., Duan E., A novel class of tRNA-derived small RNAs extremely enriched in mature mouse sperm. Cell Res. 22, 1609–1612 (2012).
20
Lane M., Robker R. L., Robertson S. A., Parenting from before conception. Science 345, 756–760 (2014).
21
Siklenka K., Erkek S., Godmann M., Lambrot R., McGraw S., Lafleur C., Cohen T., Xia J., Suderman M., Hallett M., Trasler J., Peters A. H., Kimmins S., Disruption of histone methylation in developing sperm impairs offspring health transgenerationally. Science 350, aab2006 (2015).
22
Zhang Y., Zhang Y., Shi J., Zhang H., Cao Z., Gao X., Ren W., Ning Y., Ning L., Cao Y., Chen Y., Ji W., Chen Z. J., Chen Q., Duan E., Identification and characterization of an ancient class of small RNAs enriched in serum associating with active infection. J. Mol. Cell Biol. 6, 172–174 (2014).
23
Yan M., Wang Y., Hu Y., Feng Y., Dai C., Wu J., Wu D., Zhang F., Zhai Q., A high-throughput quantitative approach reveals more small RNA modifications in mouse liver and their correlation with diabetes. Anal. Chem. 85, 12173–12181 (2013).
24
Tuorto F., Liebers R., Musch T., Schaefer M., Hofmann S., Kellner S., Frye M., Helm M., Stoecklin G., Lyko F., RNA cytosine methylation by Dnmt2 and NSun2 promotes tRNA stability and protein synthesis. Nat. Struct. Mol. Biol. 19, 900–905 (2012).
25
Kiani J., Grandjean V., Liebers R., Tuorto F., Ghanbarian H., Lyko F., Cuzin F., Rassoulzadegan M., RNA-mediated epigenetic heredity requires the cytosine methyltransferase Dnmt2. PLOS Genet. 9, e1003498 (2013).
26
Cho Y. S., Chen C.-H., Hu C., Long J., Hee Ong R. T., Sim X., Takeuchi F., Wu Y., Go M. J., Yamauchi T., Chang Y.-C., Kwak S. H., Ma R. C. W., Yamamoto K., Adair L. S., Aung T., Cai Q., Chang L.-C., Chen Y.-T., Gao Y., Hu F. B., Kim H.-L., Kim S., Kim Y. J., Lee J. J.-M., Lee N. R., Li Y., Liu J. J., Lu W., Nakamura J., Nakashima E., Ng D. P.-K., Tay W. T., Tsai F.-J., Wong T. Y., Yokota M., Zheng W., Zhang R., Wang C., So W. Y., Ohnaka K., Ikegami H., Hara K., Cho Y. M., Cho N. H., Chang T.-J., Bao Y., Hedman Å. K., Morris A. P., McCarthy M. I., Takayanagi R., Park K. S., Jia W., Chuang L.-M., Chan J. C. N., Maeda S., Kadowaki T., Lee J.-Y., Wu J.-Y., Teo Y. Y., Tai E. S., Shu X. O., Mohlke K. L., Kato N., Han B.-G., Seielstad M., Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in East Asians. Nat. Genet. 44, 67–72 (2012).
27
Jiménez-Palomares M., López-Acosta J. F., Villa-Pérez P., Moreno-Amador J. L., Muñoz-Barrera J., Fernández-Luis S., Heras-Pozas B., Perdomo G., Bernal-Mizrachi E., Cózar-Castellano I., Cyclin C stimulates β-cell proliferation in rat and human pancreatic β-cells. Am. J. Physiol. Endocrinol. Metab. 308, E450–E459 (2015).
28
Dibble C. C., Cantley L. C., Regulation of mTORC1 by PI3K signaling. Trends Cell Biol. 25, 545–555 (2015).
29
Storey J. D., Tibshirani R., Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440–9445 (2003).
30
Benjamini Y., Hochberg Y., Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B Methodol. 57, 289–300 (1995).
31
Wang L., Feng Z., Wang X., Wang X., Zhang X., DEGseq: An R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26, 136–138 (2010).
32
Zhao X. Y., Li W., Lv Z., Liu L., Tong M., Hai T., Hao J., Guo C. L., Ma Q. W., Wang L., Zeng F., Zhou Q., iPS cells produce viable mice through tetraploid complementation. Nature 461, 86–90 (2009).
33
Li D. S., Yuan Y. H., Tu H. J., Liang Q. L., Dai L. J., A protocol for islet isolation from mouse pancreas. Nat. Protoc. 4, 1649–1652 (2009).
34
Kim D., Pertea G., Trapnell C., Pimentel H., Kelley R., Salzberg S. L., TopHat2: Accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
35
Trapnell C., Roberts A., Goff L., Pertea G., Kim D., Kelley D. R., Pimentel H., Salzberg S. L., Rinn J. L., Pachter L., Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).
36
Sturn A., Quackenbush J., Trajanoski Z., Genesis: Cluster analysis of microarray data. Bioinformatics 18, 207–208 (2002).
37
Xi Y., Li W., BSMAP: Whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232 (2009).
38
Shannon P., Markiel A., Ozier O., Baliga N. S., Wang J. T., Ramage D., Amin N., Schwikowski B., Ideker T., Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
(0)eLetters
eLetters is a forum for ongoing peer review. eLetters are not edited, proofread, or indexed, but they are screened. eLetters should provide substantive and scholarly commentary on the article. Embedded figures cannot be submitted, and we discourage the use of figures within eLetters in general. If a figure is essential, please include a link to the figure within the text of the eLetter. Please read our Terms of Service before submitting an eLetter.
Log In to Submit a ResponseNo eLetters have been published for this article yet.
Information & Authors
Information
Published In
Science
Volume 351 | Issue 6271
22 January 2016
22 January 2016
Copyright
Copyright © 2016, American Association for the Advancement of Science.
Article versions
You are viewing the most recent version of this article.
Submission history
Received: 4 November 2015
Accepted: 11 December 2015
Published in print: 22 January 2016
Acknowledgments
Raw data are archived in the Gene Expression Omnibus under accession number GSE75544. This research was supported by the National Basic Research Program of China (grants 2012CBA01300 to Q.Zho., 2015CB943000 to Q.C., and 2014CB542300 to Q.Zha.), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant XDA01000000 to Q.Zho. and E.D), and the National Natural Science Foundation of China (grants 81490742 to E.D., 31200879 to Q.C., 31300960 to H.P., 31300957 to Y.Z., 81472181 to M.Y., and 31470768 and 81321062 to Q.Zha.).
Authors
Metrics & Citations
Metrics
Article Usage
Altmetrics
Citations
Cite as
- Qi Chen et al.
Export citation
Select the format you want to export the citation of this publication.
Cited by
- The role of tRNA-derived small RNAs in aging, BMB Reports, 56, 2, (49-55), (2023).https://doi.org/10.5483/BMBRep.2022-0199
- Intergenerational Inheritance of Hepatic Steatosis in a Mouse Model of Childhood Obesity: Potential Involvement of Germ-Line microRNAs, Nutrients, 15, 5, (1241), (2023).https://doi.org/10.3390/nu15051241
- Sperm RNA Payload: Implications for Intergenerational Epigenetic Inheritance, International Journal of Molecular Sciences, 24, 6, (5889), (2023).https://doi.org/10.3390/ijms24065889
- Using Small Non-Coding RNAs in Extracellular Vesicles of Semen as Biomarkers of Male Reproductive System Health: Opportunities and Challenges, International Journal of Molecular Sciences, 24, 6, (5447), (2023).https://doi.org/10.3390/ijms24065447
- Transgenerational Epigenetic Inheritance of Traumatic Experience in Mammals, Genes, 14, 1, (120), (2023).https://doi.org/10.3390/genes14010120
- tRNA-derived small RNAs in plant response to biotic and abiotic stresses, Frontiers in Plant Science, 14, (2023).https://doi.org/10.3389/fpls.2023.1131977
- tRNA derived fragments:A novel player in gene regulation and applications in cancer, Frontiers in Oncology, 13, (2023).https://doi.org/10.3389/fonc.2023.1063930
- Differential expression profiling of tRNA-Derived small RNAs and their potential roles in methamphetamine self-administered rats, Frontiers in Genetics, 14, (2023).https://doi.org/10.3389/fgene.2023.1088498
- Multigenerational mistimed feeding drives circadian reprogramming with an impaired unfolded protein response, Frontiers in Endocrinology, 14, (2023).https://doi.org/10.3389/fendo.2023.1157165
- Stress‐sensitive dynamics of miRNAs and Elba1 in Drosophila embryogenesis , Molecular Systems Biology, (2023).https://doi.org/10.15252/msb.202211148
- See more
Loading...
View Options
Check Access
Log in to view the full text
AAAS login provides access to Science for AAAS Members, and access to other journals in the Science family to users who have purchased individual subscriptions.
- Become a AAAS Member
- Activate your AAAS ID
- Purchase Access to Other Journals in the Science Family
- Account Help
Log in via OpenAthens.
Log in via Shibboleth.
More options
Register for free to read this article
As a service to the community, this article is available for free. Login or register for free to read this article.
Buy a single issue of Science for just $15 USD.