Abstract
The discovery of RNAs (for example, messenger RNAs, non-coding RNAs) in sperm has opened the possibility that sperm may function by delivering additional paternal information aside from solely providing the DNA1. Increasing evidence now suggests that sperm small non-coding RNAs (sncRNAs) can mediate intergenerational transmission of paternally acquired phenotypes, including mental stress2,3 and metabolic disorders4,5,6. How sperm sncRNAs encode paternal information remains unclear, but the mechanism may involve RNA modifications. Here we show that deletion of a mouse tRNA methyltransferase, DNMT2, abolished sperm sncRNA-mediated transmission of high-fat-diet-induced metabolic disorders to offspring. Dnmt2 deletion prevented the elevation of RNA modifications (m5C, m2G) in sperm 30–40 nt RNA fractions that are induced by a high-fat diet. Also, Dnmt2 deletion altered the sperm small RNA expression profile, including levels of tRNA-derived small RNAs and rRNA-derived small RNAs, which might be essential in composing a sperm RNA ‘coding signature’ that is needed for paternal epigenetic memory. Finally, we show that Dnmt2-mediated m5C contributes to the secondary structure and biological properties of sncRNAs, implicating sperm RNA modifications as an additional layer of paternal hereditary information.
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References
Chen, Q., Yan, W. & Duan, E. Epigenetic inheritance of acquired traits through sperm RNAs and sperm RNA modifications. Nat. Rev. Genet. 17, 733–743 (2016).
Gapp, K. et al. Implication of sperm RNAs in transgenerational inheritance of the effects of early trauma in mice. Nat. Neurosci. 17, 667–669 (2014).
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. USA 112, 13699–13704 (2015).
Chen, Q. et al. Sperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder. Science 351, 397–400 (2016).
Grandjean, V. et al. RNA-mediated paternal heredity of diet-induced obesity and metabolic disorders. Sci. Rep. 5, 18193 (2015).
Sharma, U. et al. Biogenesis and function of tRNA fragments during sperm maturation and fertilization in mammals. Science 351, 391–396 (2016).
Miska, E. A. & Ferguson-Smith, A. C. Transgenerational inheritance: models and mechanisms of non-DNA sequence-based inheritance. Science 354, 59–63 (2016).
Wei, Y. et al. Paternally induced transgenerational inheritance of susceptibility to diabetes in mammals. Proc. Natl Acad. Sci. USA 111, 1873–1878 (2014).
Radford, E. J. et al. In utero effects. In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science 345, 1255903 (2014).
Siklenka, K. et al. Disruption of histone methylation in developing sperm impairs offspring health transgenerationally. Science 350, aab2006 (2015).
Liebers, R., Rassoulzadegan, M. & Lyko, F. Epigenetic regulation by heritable RNA. PLoS Genet. 10, e1004296 (2014).
Kiani, J. et al. RNA-mediated epigenetic heredity requires the cytosine methyltransferase Dnmt2. PLoS Genet. 9, e1003498 (2013).
Nelson, V. R., Heaney, J. D., Tesar, P. J., Davidson, N. O. & Nadeau, J. H. Transgenerational epigenetic effects of the Apobec1 cytidine deaminase deficiency on testicular germ cell tumor susceptibility and embryonic viability. Proc. Natl Acad. Sci. USA 109, 2766–2773 (2012).
Zhang, X., Cozen, A. E., Liu, Y., Chen, Q. & Lowe, T. M. Small RNA modifications: integral to function and disease. Trends Mol. Med. 22, 1025–1034 (2016).
Lyko, F. The DNA methyltransferase family: a versatile toolkit for epigenetic regulation. Nat. Rev. Genet. 19, 81–92 (2018).
Goll, M. G. et al. Methylation of tRNAAsp by the DNA methyltransferase homolog Dnmt2. Science 311, 395–398 (2006).
Tuorto, F. et al. RNA cytosine methylation by Dnmt2 and NSun2 promotes tRNA stability and protein synthesis. Nat. Struct. Mol. Biol. 19, 900–905 (2012).
Tuorto, F. et al. The tRNA methyltransferase Dnmt2 is required for accurate polypeptide synthesis during haematopoiesis. EMBO J. 34, 2350–2362 (2015).
Peng, H. et al. A novel class of tRNA-derived small RNAs extremely enriched in mature mouse sperm. Cell Res. 22, 1609–1612 (2012).
Holland, M. L. et al. Early-life nutrition modulates the epigenetic state of specific rDNA genetic variants in mice. Science 353, 495–498 (2016).
Shea, J. M. et al. Genetic and epigenetic variation, but not diet, shape the sperm methylome. Dev. Cell 35, 750–758 (2015).
Teh, A. L. et al. The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Res. 24, 1064–1074 (2014).
Wang, X. & Moazed, D. DNA sequence-dependent epigenetic inheritance of gene silencing and histone H3K9 methylation. Science 356, 88–91 (2017).
Laprell, F., Finkl, K. & Muller, J. Propagation of polycomb-repressed chromatin requires sequence-specificrecruitment to DNA. Science 356, 85–88 (2017).
Klosin, A., Casas, E., Hidalgo-Carcedo, C., Vavouri, T. & Lehner, B. Transgenerational transmission of environmental information in C. elegans. Science 356, 320–323 (2017).
Ciabrelli, F. et al. Stable Polycomb-dependent transgenerational inheritance of chromatin states in Drosophila. Nat. Genet. 49, 876–886 (2017).
Coleman, R. T. & Struhl, G. Causal role for inheritance of H3K27me3 in maintaining the OFF state of a Drosophila HOX gene. Science 356, eaai8236 (2017).
Su, D. et al. Quantitative analysis of ribonucleoside modifications in tRNA by HPLC-coupled mass spectrometry. Nat. Protoc. 9, 828–841 (2014).
Liu, F. et al. ALKBH1-mediated tRNA demethylation regulates translation. Cell 167, 816–828 (2016).
Chu, C. et al. A sequence of 28S rRNA-derived small RNAs is enriched in mature sperm and various somatic tissues and possibly associates with inflammation. J. Mol. Cell Biol. 9, 256–259 (2017).
Legrand, C. et al. Statistically robust methylation calling for whole-transcriptome bisulfite sequencing reveals distinct methylation patterns for mouse RNAs. Genome Res. 27, 1589–1596 (2017).
Kim, H. K. et al. A transfer-RNA-derived small RNA regulates ribosome biogenesis. Nature 552, 57–62 (2017).
Gebetsberger, J., Wyss, L., Mleczko, A. M., Reuther, J. & Polacek, N. A tRNA-derived fragment competes with mRNA for ribosome binding and regulates translation during stress. RNA Biol. 14, 1364–1373 (2017).
Schorn, A. J., Gutbrod, M. J., LeBlanc, C. & Martienssen, R. LTR-retrotransposon control by tRNA-derived small RNAs. Cell 170, 61–71 (2017).
Schimmel, P. The emerging complexity of the tRNA world: mammalian tRNAs beyond protein synthesis. Nat. Rev. Mol. Cell Biol. 19, 45–58 (2018).
Jeltsch, A. et al. Mechanism and biological role of Dnmt2 in nucleic acid methylation. RNA Biol. 14, 1108–1123 (2017).
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
McStay, B. & Grummt, I. The epigenetics of rRNA genes: from molecular to chromosome biology. Annu. Rev. Cell Dev. Biol. 24, 131–157 (2008).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Yang, X. et al. Single sample expression-anchored mechanisms predict survival in head and neck cancer. PLoS Comput. Biol. 8, e1002350 (2012).
Acknowledgements
This research was supported by the Ministry of Science and Technology of China (2016YFA0500903 to E.D., 2015CB943000 to Ying Z., 2012CBA01300 to Q.Zhou, 2017YFC1001401 to E.D.), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA01020101 to Q.Zhou, XDB19000000 to Q.Zhai and XDA12030204 to M.Y.), the National Natural Science Foundation of China (31671568 and 81490742 to E.D., 31671201 to Ying Z., 31630037 to Q.Zhai, 31701308 to Z.C., 31670830 and 81472181 to M.Y), Youth Innovation Promotion Association, CAS (no. 2016081 to Ying Z.), NIH grant (R01HD092431 and P30GM110767-03 to Q.C.; HD085506 and P30GM110767 to W.Y.), Templeton Foundation (PID: 50183 to W.Y.), Nevada INBRE (GM103440 to D.Q., M.P. and Q.C.), Baden-Württemberg Stiftung (Forschungsprogramm ‘nicht-kodierende RNAs’) and Deutsche Forschungsgemeinschaft (Priority Programme 1784) to F.L. F.T. is supported by the Institute of Genetics and Biophysics A. Buzzati-Traverso, C.N.R., Italy.
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Q.C., YF.Z. and Ying Z. conceived the idea and designed experiments. Q.C. and YF.Z. wrote the main manuscript and integrated inputs from all authors. YF.Z., X.Z. and Xin L. performed the mouse breeding, embryo manipulation related experiments and phenotype analyses, with help from YS.L., SC.L, Y.C., XH.L., L.Z., Y.Q., J.Q., Z.C., Y.W., HL.Z., Ying Z., L.L. and under the supervision of Qi Z. and E.D. YF.Z., X.Z. and Ying Z. contributed to the RNA modifications analysis with help from Ying L., M.Y., Qiwei Z., R.W., D.Q. and under the supervision of Ying Z. and Q.C. YF.Z. prepared sperm RNA samples for next-generation sequencing with the help of HY.P., W.Y. and under the supervision of Ying Z. J.S. performed bioinformatics analyses for small RNA-seq and transcriptome data with help from T.Z., and the data were interpreted by J.S., Q.C. and T.Z. RNA modification and RNA secondary structure experiments were performed by YF.Z., M.P. and under the supervision of Q.C. Northern blot, bisulfite sequencing and Dnmt2 mice fertility analyses were performed by F.T. and YF.Z. with help from F.L., M.P. and R.L. X.Z. performed cell transfection experiments. Q.C. communicated with the editor and coordinated communications with Ying Z., Qi. Z., and E.D., who supervised different aspects of the paper.
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Supplementary Figure 1 Expression of tsRNAs and Dnmt2, breeding strategies and the metabolic parameters of F1 males generated by Dnmt2–/– mice breeding.
(a) Northern blot for tRNAs and tsRNAs in sperm and testis from Dnmt2+/+ and Dnmt2–/– mice. Blue arrows indicate tRNAs, Red arrows indicate tsRNAs. Blots are shown as representatives of two independent experiments with similar results. (b) The relative expression level of Dnmt2 mRNA in mouse testis, caput epididymis and cauda epididymis under HFD or ND condition. Each dot represents one biologically independent experiment, n = 6 biologically independent experiment. **P < 0.01, NS: not significant. (c) Breeding strategy to generate Dnmt2+/+ and Dnmt2+/+ F0 mice for experiments in Fig. 1. (d,e) Illustration of zygotic injection of sperm (d) total RNAs, (e) 30–40 nt RNAs and injection of water as control to generate F1 male offspring for phenotypic examination in Fig. 2. (f,g) Representative resampling test between Dnmt2+/+ HFD vs Dnmt2–/– HFD in Fig. 2f, which were performed by reducing the n number of both groups by 1/3 (f), or by 10–50% (g), demonstrating the robustness of statistical significant difference between these two groups. (h) Mating strategy to generate F1 male for phenotypic analyses in (i–m). (i) Body weights of F1 male fed on ND. Each dot represents one mouse, data pooled from 2 experiments. (j) Blood glucose of F1 male during the GTT. n = 7 mice for F1 male generated from Dnmt2–/– HFD group, and n = 10 mice for F1 male generated from Dnmt2–/– ND group; data pooled from 2 GTT experiments. **P < 0.01, ****P < 0.0001. (k) Area under the curve (AUC) statistics for GTT in (j). (l) Relative blood glucose of F1 male during the ITT. n = 6 mice for F1 male generated from Dnmt2–/– HFD group, and n = 5 mice for F1 male generated from Dnmt2–/– ND group; data pooled from 2 ITT experiments. *P < 0.05, **P < 0.01 (m) AUC statistics for ITT in (l). All data are plotted as mean ± SEM. Statistical analysis for (b,I,k,m) are performed by two-tailed unpaired Student's t-test, and (j,l) are performed by two-tailed, two-way Anova, uncorrected Fisher's LSD. All statistic source data and P values are provided in Supplementary Table 1.
Supplementary Figure 2 The level of different type of RNA modifications in sperm RNA fractions extracted from F0 Dnmt2+/+ and Dnmt2–/– males under ND and HFD.
The relative level of unmodified (a) C, (b) G, (c) A, (d) U in different sperm RNA fractions (15–25 nt, 30–40 nt, 40–100 nt and >100 nt and the relative level of modified nucleotide in (e) Cm, (f) Gm, (g) m1G, (h) m7G, (i) Am, (j) I, (k) Im, (l) m6A, (m) Um, (n) m5U from different sperm RNA fractions (15–25 nt, 30–40 nt, 40–100 nt and >100 nt) of F0 Dnmt2 +/+ and Dnmt2–/– males under ND and HFD. Value for each dot are generated by pooled sperm RNAs from 8 mice, as to reach enough RNA amount in each fraction to perform LC-MS/MS. Number of dots on the figure represents number of biologically independent experiments, which is also detailed in Supplementary Table 1. All data are plotted as mean ± SEM (n = number of biologically independent experiments). All statistical analysis was performed by two-tailed, one-way Anova, uncorrected Fisher's LSD. All statistic source data and P values are provided in Supplementary Table 1.
Supplementary Figure 3 Small RNA-seq analysis of sperm RNAs from F0 Dnmt2+/+ and Dnmt2–/– males under ND and HFD.
The small RNA-seq quality control, statistics, and RNA profiling. (Data and images shown in this figure are representatives of two biological replicates, raw data for both replicates are archived in the Gene Expression Omnibus under accession number GSM2574268). (a-d) Electrophoretic size distribution of RNAs in (a) Dnmt2+/+ ND, (b) Dnmt2+/+ HFD, (c) Dnmt2–/– HFD, (d) Dnmt2–/– ND, analyzed by Agilent Bioanalyzer. (e) Summary of small RNA reads. Information for RNA reads annotation was analyzed by genome match information, miRNAs, piRNAs, tRNAs and rRNAs annotation according to mouse genome database (GRCm38/mm10), miRBase211, piRNA database2,3, GtRNAdb4, NCBI Nucleotide database, Ensembl mouse ncRNA database (GRCm38/mm10) and Rfam 11.0 database5. (f-i) Length distribution and pattern changes of different sncRNAs (miRNAs, piRNAs, tsRNAs and rsRNAs) in (f) Dnmt2+/+ ND, (g) Dnmt2+/+ HFD, (h) Dnmt2–/– HFD and (i) Dnmt2–/– ND sperm. (j-m) loci mapping information of rsRNAs in different rRNAs (5sRNA, 5.8sRNA, 18sRNA and 28sRNA) for (j) Dnmt2+/+ ND, (k) Dnmt2+/+ HFD, (l) Dnmt2–/– HFD and (m) Dnmt2–/– ND sperm. RPM: reads per million.
Supplementary Figure 4 Dnmt2-depended m5C modification regulate biological properties of tsRNA.
(a) Dnmt2-dependent C38 methylation in testis. Bisulfite sequencing maps for the three known tRNA targets of Dnmt2 (tRNA-Asp, tRNA-Gly and tRNA-Val in mouse testis (Dnmt2+/+ and Dnmt2–/–). Each row represents one sequence read, each column a cytosine residue. Yellow boxes represent unmethylated cytosine residues; blue boxes indicate methylated cytosine residues (m5C), sequencing gaps are shown in white. Numbers above the maps indicate the number of reads. Cytosine C38 is labeled in red, other cytosine sites are in black. (b) The sequence of chemically synthesized 3’tsRNA-Gly that harbors five m5C according to the Dnmt2+/+ condition (5 × m5C), 3’tsRNA-Gly with four m5C, lacking a Dnmt2-mediated m5C at C38 position (4 × m5C), and 3’tsRNA-Gly without any RNA modifications (no m5C). (c) By transfecting the above three types of tsRNAs into 3T3 cell lines followed by transcriptome sequencing (n = 2 biologically independent RNA samples). Each dot in the box plot represent the geneset score of one transcriptome data, the upper boundary of the box represents the maxima, the lower boundary represents the minima, and the centre represent the average. We observed that the same tsRNAs sequence with different number of m5C modifications (no m5C, five m5C and four m5C respectively) can induced different cellular transcriptome changes compared to the empty transfection: while all three types of tsRNAs similarly induced an overall elevated gene expression of Citrate Acid Circle pathway (which might be induced by and dependent on their specific sequence); the three types of tsRNAs with different m5C modification induced distinct gene responses of Ribosome pathway (which might be sensitive to the RNA structural changes caused by m5C), which may reflect recent studies reporting the function of tsRNAs in regulating Ribosome functions6–9.
Supplementary Figure 5
Unprocessed gel blots
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Supplementary Figures 1–5, and legends for Supplementary Tables 1 and 2.
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Zhang, Y., Zhang, X., Shi, J. et al. Dnmt2 mediates intergenerational transmission of paternally acquired metabolic disorders through sperm small non-coding RNAs. Nat Cell Biol 20, 535–540 (2018). https://doi.org/10.1038/s41556-018-0087-2
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DOI: https://doi.org/10.1038/s41556-018-0087-2
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