The DNA methylome of human sperm is distinct from blood with little evidence for tissue-consistent obesity associations
Autoři:
Fredrika Åsenius aff001; Tyler J. Gorrie-Stone aff002; Ama Brew aff003; Yasmin Panchbhaya aff004; Elizabeth Williamson aff005; Leonard C. Schalkwyk aff002; Vardhman K. Rakyan aff003; Michelle L. Holland aff006; Sarah J. Marzi aff007; David J. Williams aff001
Působiště autorů:
UCL EGA Institute for Women’s Health, University College London, London, United Kingdom
aff001; School of Biological Sciences, University of Essex, Colchester, United Kingdom
aff002; The Blizard Institute, Queen Mary University of London, London, United Kingdom
aff003; UCL Genomics, Great Ormond Street Institute of Child Health, London, United Kingdom
aff004; Fertility & reproductive medicine laboratory, University College Hospital, London, United Kingdom
aff005; Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
aff006; UK Dementia Research Institute, Imperial College London, London, United Kingdom
aff007; Department of Brain Sciences, Imperial College London, London, United Kingdom
aff008
Vyšlo v časopise:
The DNA methylome of human sperm is distinct from blood with little evidence for tissue-consistent obesity associations. PLoS Genet 16(10): e1009035. doi:10.1371/journal.pgen.1009035
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009035
Souhrn
Epidemiological research suggests that paternal obesity may increase the risk of fathering small for gestational age offspring. Studies in non-human mammals indicate that such associations could be mediated by DNA methylation changes in spermatozoa that influence offspring development in utero. Human obesity is associated with differential DNA methylation in peripheral blood. It is unclear, however, whether this differential DNA methylation is reflected in spermatozoa. We profiled genome-wide DNA methylation using the Illumina MethylationEPIC array in a cross-sectional study of matched human blood and sperm from lean (discovery n = 47; replication n = 21) and obese (n = 22) males to analyse tissue covariation of DNA methylation, and identify obesity-associated methylomic signatures. We found that DNA methylation signatures of human blood and spermatozoa are highly discordant, and methylation levels are correlated at only a minority of CpG sites (~1%). At the majority of these sites, DNA methylation appears to be influenced by genetic variation. Obesity-associated DNA methylation in blood was not generally reflected in spermatozoa, and obesity was not associated with altered covariation patterns or accelerated epigenetic ageing in the two tissues. However, one cross-tissue obesity-specific hypermethylated site (cg19357369; chr4:2429884; P = 8.95 × 10−8; 2% DNA methylation difference) was identified, warranting replication and further investigation. When compared to a wide range of human somatic tissue samples (n = 5,917), spermatozoa displayed differential DNA methylation across pathways enriched in transcriptional regulation. Overall, human sperm displays a unique DNA methylation profile that is highly discordant to, and practically uncorrelated with, that of matched peripheral blood. We observed that obesity was only nominally associated with differential DNA methylation in sperm, and therefore suggest that spermatozoal DNA methylation is an unlikely mediator of intergenerational effects of metabolic traits.
Klíčová slova:
Blood – Body Mass Index – DNA methylation – DNA replication – Gene ontologies – Obesity – Single nucleotide polymorphisms – Sperm
Zdroje
1. Wahl S, Drong A, Lehne B, Loh M, Scott WR, Kunze S, et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature. 2017;541(7635):81–+. doi: 10.1038/nature20784 28002404
2. Joehanes R, Just AC, Marioni RE, Pilling LC, Reynolds LM, Mandaviya PR, et al. Epigenetic Signatures of Cigarette Smoking. Circulation-Cardiovascular Genetics. 2016;9(5):436–47. doi: 10.1161/CIRCGENETICS.116.001506 27651444
3. Mendelson MM, Marioni RE, Joehanes R, Liu CY, Hedman AK, Aslibekyan S, et al. Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach. Plos Medicine. 2017;14(1).
4. Horvath S. DNA methylation age of human tissues and cell types. Genome Biology. 2013;14(10).
5. Barbosa TD, Ingerslev LR, Alm PS, Versteyhe S, Massart J, Rasmussen M, et al. High-fat diet reprograms the epigenome of rat spermatozoa and transgenerationally affects metabolism of the offspring. Molecular Metabolism. 2016;5(3):184–97. doi: 10.1016/j.molmet.2015.12.002 26977389
6. Sakai K, Ideta-Otsuka M, Saito H, Hiradate Y, Hara K, Igarashi K, et al. Effects of doxorubicin on sperm DNA methylation in mouse models of testicular toxicity. Biochemical and Biophysical Research Communications. 2018;498(3):674–9. doi: 10.1016/j.bbrc.2018.03.044 29524425
7. Dias BG, Ressier KJ. Parental olfactory experience influences behavior and neural structure in subsequent generations. Nature Neuroscience. 2014;17(1):89–96. doi: 10.1038/nn.3594 24292232
8. Watkins AJ, Dias I, Tsuro H, Allen D, Emes RD, Moreton J, et al. Paternal diet programs offspring health through sperm- and seminal plasma-specific pathways in mice. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(40):10064–9. doi: 10.1073/pnas.1806333115 30150380
9. Youngson NA, Lecomte V, Maloney CA, Leung P, Liu J, Hesson LB, et al. Obesity-induced sperm DNA methylation changes at satellite repeats are reprogrammed in rat offspring. Asian Journal of Andrology. 2016;18(6):930–6. doi: 10.4103/1008-682X.163190 26608942
10. Radford EJ, Ito M, Shi H, Corish JA, Yamazawa K, Isganaitis E, et al. In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science. 2014;345(6198):785–+.
11. Huypens P, Sass S, Wu M, Dyckhoff D, Tschop M, Theis F, et al. Epigenetic germline inheritance of diet-induced obesity and insulin resistance. Nature Genetics. 2016;48(5):497–+. doi: 10.1038/ng.3527 26974008
12. Wei YC, Yang CR, Wei YP, Zhao ZA, Hou Y, Schatten H, et al. Paternally induced transgenerational inheritance of susceptibility to diabetes in mammals. Proceedings of the National Academy of Sciences of the United States of America. 2014;111(5):1873–8. doi: 10.1073/pnas.1321195111 24449870
13. Tang WWC, Dietmann S, Irie N, Leitch HG, Floros VI, Bradshaw CR, et al. A Unique Gene Regulatory Network Resets the Human Germline Epigenome for Development. Cell. 2015;161(6):1453–67. doi: 10.1016/j.cell.2015.04.053 26046444
14. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518(7539):317–30. doi: 10.1038/nature14248 25693563
15. Hannon E, Lunnon K, Schalkwyk L, Mill J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics. 2015;10(11):1024–32. doi: 10.1080/15592294.2015.1100786 26457534
16. Soubry A, Murphy SK, Wang F, Huang Z, Vidal AC, Fuemmeler BF, et al. Newborns of obese parents have altered DNA methylation patterns at imprinted genes. International Journal of Obesity. 2015;39(4):650–7. doi: 10.1038/ijo.2013.193 24158121
17. Oldereid NB, Wennerholm UB, Pinborg A, Loft A, Laivuori H, Petzold M, et al. The effect of paternal factors on perinatal and paediatric outcomes: a systematic review and meta-analysis. Human Reproduction Update. 2018;24(3):320–89. doi: 10.1093/humupd/dmy005 29471389
18. McCowan LME, North RA, Kho EM, Black MA, Chan EHY, Dekker GA, et al. Paternal Contribution to Small for Gestational Age Babies: A Multicenter Prospective Study. Obesity. 2011;19(5):1035–9. doi: 10.1038/oby.2010.279 21127471
19. Tyrrell JS, Yaghootkar H, Freathy RM, Hattersley AT, Frayling TM. Parental diabetes and birthweight in 236 030 individuals in the UK Biobank Study. International Journal of Epidemiology. 2013;42(6):1714–23. doi: 10.1093/ije/dyt220 24336895
20. Krausz C, Sandoval J, Sayols S, Chianese C, Giachini C, Heyn H, et al. Novel Insights into DNA Methylation Features in Spermatozoa: Stability and Peculiarities. Plos One. 2012;7(10).
21. Clough E, Barrett T. The Gene Expression Omnibus Database. Statistical Genomics: Methods and Protocols. 2016;1418:93–110.
22. Barlow DP, Bartolomei MS. Genomic Imprinting in Mammals. Cold Spring Harbor Perspectives in Biology. 2014;6(2).
23. Schulz R, Woodfine K, Menheniott TR, Bourc’his D, Bestor T, Oakey RJ. WAMIDEX: a web atlas of murine genomic imprinting and differential expression. Epigenetics. 2008;3(2):89–96. doi: 10.4161/epi.3.2.5900 18398312
24. Carbon S, Dietze H, Lewis SE, Mungall CJ, Munoz-Torres MC, Basu S, et al. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Research. 2017;45(D1):D331–D8. doi: 10.1093/nar/gkw1108 27899567
25. NCBI. dbSNP Human Build 151 database 2019 [https://www.ncbi.nlm.nih.gov/snp/.
26. Gunasekara CJ, Scott CA, Laritsky E, Baker MS, MacKay H, Duryea JD, et al. A Genomic Atlas of Systemic Interindividual Epigenetic Variation in Humans. Environmental and Molecular Mutagenesis. 2019;60:51–2.
27. Van Baak TE, Coarfa C, Dugue PA, Fiorito G, Laritsky E, Baker MS, et al. Epigenetic supersimilarity of monozygotic twin pairs. Genome Biology. 2018;19.
28. McClay JL, Shabalin AA, Dozmorov MG, Adkins DE, Kumar G, Nerella S, et al. High density methylation QTL analysis in human blood via next-generation sequencing of the methylated genomic DNA fraction. Genome Biology. 2015;16.
29. Noor N, Cardenas A, Rifas-Shiman SL, Pan H, Dreyfuss JM, Oken E, et al. Association of Periconception Paternal Body Mass Index With Persistent Changes in DNA Methylation of Offspring in Childhood. JAMA Netw Open. 2019;2(12):e1916777. doi: 10.1001/jamanetworkopen.2019.16777 31880793
30. Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schonfels W, Ahrens M, et al. Obesity accelerates epigenetic aging of human liver. Proceedings of the National Academy of Sciences of the United States of America. 2014;111(43):15538–43. doi: 10.1073/pnas.1412759111 25313081
31. Nevalainen T, Kananen L, Marttila S, Jylhävä J, Mononen N, Kähönen M, et al. Obesity accelerates epigenetic aging in middle-aged but not in elderly individuals. Clin Epigenetics. 2017;9:20. doi: 10.1186/s13148-016-0301-7 28289477
32. Ryan J, Wrigglesworth J, Loong J, Fransquet PD, Woods RL. A Systematic Review and Meta-analysis of Environmental, Lifestyle, and Health Factors Associated With DNA Methylation Age. J Gerontol A Biol Sci Med Sci. 2020;75(3):481–94. doi: 10.1093/gerona/glz099 31001624
33. Jenkins TG, Aston KI, Cairns B, Smith A, Carrell DT. Paternal germ line aging: DNA methylation age prediction from human sperm. Bmc Genomics. 2018;19.
34. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573–91.
35. Urdinguio RG, Bayon GF, Dmitrijeva M, Torano EG, Bravo C, Fraga MF, et al. Aberrant DNA methylation patterns of spermatozoa in men with unexplained infertility. Human Reproduction. 2015;30(5):1014–28. doi: 10.1093/humrep/dev053 25753583
36. Rakyan VK, Down TA, Thorne NP, Flicek P, Kulesha E, Graf S, et al. An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). Genome Research. 2008;18(9):1518–29. doi: 10.1101/gr.077479.108 18577705
37. Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biology. 2016;17.
38. Xia B, Yan Y, Baron M, Wagner F, Barkley D, Chiodin M, et al. Widespread Transcriptional Scanning in the Testis Modulates Gene Evolution Rates. Cell. 2020;180(2):248–62.e21. doi: 10.1016/j.cell.2019.12.015 31978344
39. Braun PR, Han SZ, Hing B, Nagahama Y, Gaul LN, Heinzman JT, et al. Genome-wide DNA methylation comparison between live human brain and peripheral tissues within individuals. Translational Psychiatry. 2019;9.
40. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–9. doi: 10.4161/epi.23470 23314698
41. Price EM, Cotton AM, Lam LL, Farre P, Emberly E, Brown CJ, et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics & Chromatin. 2013;6.
42. Kirchner H, Sinha I, Gao H, Ruby MA, Schonke M, Lindvall JM, et al. Altered DNA methylation of glycolytic and lipogenic genes in liver from obese and type 2 diabetic patients. Molecular Metabolism. 2016;5(3):171–83. doi: 10.1016/j.molmet.2015.12.004 26977391
43. Spiers H, Hannon E, Schalkwyk LC, Smith R, Wong CCY, O’Donovan MC, et al. Methylomic trajectories across human fetal brain development. Genome Research. 2015;25(3):338–52. doi: 10.1101/gr.180273.114 25650246
44. Illumina. Pub. No. 1070-2015-008-B. Infinium MethylationEPIC BeadChip Datasheet. Illumina; 2017.
45. Donkin I, Versteyhe S, Ingerslev LR, Qian K, Mechta M, Nordkap L, et al. Obesity and Bariatric Surgery Drive Epigenetic Variation of Spermatozoa in Humans. Cell Metabolism. 2016;23(2):369–78. doi: 10.1016/j.cmet.2015.11.004 26669700
46. Camprubi C, Salas-Huetos A, Aiese-Cigliano R, Godo A, Pons MC, Castellano G, et al. Spermatozoa from infertile patients exhibit differences of DNA methylation associated with spermatogenesis-related processes: an array-based analysis. Reproductive Biomedicine Online. 2016;33(6):709–19. doi: 10.1016/j.rbmo.2016.09.001 27692602
47. Jenkins TG, Aston KI, Meyer TD, Hotaling JM, Shamsi MB, Johnstone EB, et al. Decreased fecundity and sperm DNA methylation patterns. Fertility and Sterility. 2016;105(1):51–+. doi: 10.1016/j.fertnstert.2015.09.013 26453269
48. Gorrie-Stone TJ, Smart MC, Saffari A, Malki K, Hannon E, Burrage J, et al. Bigmelon: tools for analysing large DNA methylation datasets. Bioinformatics. 2019;35(6):981–6. doi: 10.1093/bioinformatics/bty713 30875430
49. Sharp GC, Alfano R, ‘The Pregnancy and Childhood Epigenetics (PACE) consortium’, Lawlor DA, Sorensen TI, London SJ, et al. Paternal body mass index and offspring DNA methylation: findings from the PACE consortium [Preprint]. 2020.
50. Sharma U, Conine CC, Shea JM, Boskovic A, Derr AG, Bing XY, et al. Biogenesis and function of tRNA fragments during sperm maturation and fertilization in mammals. Science. 2016;351(6271):391–6. doi: 10.1126/science.aad6780 26721685
51. Chen Q, Yan MH, Cao ZH, Li X, Zhang YF, Shi JC, et al. Sperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder. Science. 2016;351(6271):397–400. doi: 10.1126/science.aad7977 26721680
52. Åsenius F, Danson AF, Marzi SJ. DNA methylation in human sperm: a systematic review. Hum Reprod Update. 2020. doi: 10.1093/humupd/dmaa025 32790874
53. World Health Organization. WHO laboratory manual for the examination and processing of human semen- Fifth Edition. WHO, editor. Geneva, Switzerland: WHO; 2010.
54. Laqqan M, Tierling S, Alkhaled Y, LoPorto C, Hammadeh ME. Alterations in sperm DNA methylation patterns of oligospermic males. Reproductive Biology. 2017;17(4):396–400. doi: 10.1016/j.repbio.2017.10.007 29108863
55. Qiagen. QIAamp. DNA Mini and Blood Mini Handbook 1102728. Fifth edition ed: Qiagen HB-0329-004; May 2016.
56. Danson AF, Marzi SJ, Lowe R, Holland ML, Rakyan VK. Early life diet conditions the molecular response to post-weaning protein restriction in the mouse. Bmc Biology. 2018;16. doi: 10.1186/s12915-018-0516-5 29720174
57. Illumina. Infinium HD Assay Methylation Protocol Guide Document # 15019519 [PDF]: Illumina, Inc; 2015. http://emea.support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/infinium_assays/infinium_hd_methylation/infinium-hd-methylation-guide-15019519-01.pdf.
58. Pidsley R, Wong CCY, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. Bmc Genomics. 2013;14.
59. Filzmoser P, Maronna R, Werner M. Outlier identification in high dimensions. Computational Statistics & Data Analysis. 2008;52(3):1694–711.
60. Lawrence M, Huber W, Pages H, Aboyoun P, Carlson M, Gentleman R, et al. Software for Computing and Annotating Genomic Ranges. Plos Computational Biology. 2013;9(8).
61. Ritchie ME, Phipson B, Wu D, Hu YF, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research. 2015;43(7).
62. Phipson B, Maksimovic J, Oshlack A. missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics. 2016;32(2):286–8. doi: 10.1093/bioinformatics/btv560 26424855
63. Millard SP. EnvStats: An R Package for Environmental Statistics: Springer; 2013. https://www.springer.com/gb/book/9781461484554.
64. Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K. cluster: Cluster Analysis Basics and Extensions. R package version 2.1.0. 2019.
65. Andrews SV, Ladd-Acosta C, Feinberg AP, Hansen KD, Fallin MD. "Gap hunting" to characterize clustered probe signals in Illumina methylation array data. Epigenetics & Chromatin. 2016;9.
66. Bates D, Machler M, Bolker BM, Walker SC. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67(1):1–48.
67. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. Bmc Bioinformatics. 2012;13.
68. Mansell G, Gorrie-Stone TJ, Bao YC, Kumari M, Schalkwyk LS, Mill J, et al. Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. Bmc Genomics. 2019;20.
69. Hillman S, Peebles DM, Williams DJ. Paternal metabolic and cardiovascular risk factors for fetal growth restriction: a case-control study. Diabetes Care. 2013;36(6):1675–80. doi: 10.2337/dc12-1280 23315598
70. UCLH Clinical Biochemistry. UCLH Clinical Biochemistry Test Information University College London Hospital2017 [Biochemistry test information]. https://www.uclh.nhs.uk/OurServices/ServiceA-Z/PATH/PATHBIOMED/CBIO/Pages/InformationforGPs.aspx.
71. Gayoso-Diz P, Otero-Gonzalez A, Rodriguez-Alvarez MX, Gude F, Garcia F, De Francisco A, et al. Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: effect of gender and age: EPIRCE cross-sectional study. Bmc Endocrine Disorders. 2013;13.
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 10
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- „Jednohubky“ z klinického výzkumu – 2024/44
Nejčtenější v tomto čísle
- Evaluation of both exonic and intronic variants for effects on RNA splicing allows for accurate assessment of the effectiveness of precision therapies
- RNA-directed DNA Methylation
- The DNA methylome of human sperm is distinct from blood with little evidence for tissue-consistent obesity associations
- Correction: Molecular predictors of brain metastasis-related microRNAs in lung adenocarcinoma