Search for differentially methylated regions in ancient and modern genomes

Currently, active research is focused on investigating the mechanisms that regulate the development of various pathologies and their evolutionary dynamics. Epigenetic mechanisms, such as DNA methylation, play a significant role in evolutionary processes, as their changes have a faster impact on the phenotype compared to mutagenesis. In this study, we attempted to develop an algorithm for identifying differentially methylated regions associated with metabolic syndrome, which have undergone methylation changes in humans during the transition from a hunter­gatherer to a sedentary lifestyle. The application of existing whole­genome bisulfite sequencing methods is limited for ancient samples due to their low quality and fragmentation, and the approach to obtaining DNA methylation profiles differs significantly between ancient hunter­gatherer samples and modern tissues. In this study, we validated DamMet, an algorithm for reconstructing ancient methylomes. Application of DamMet to Neanderthal and Denisovan genomes showed a moderate level of correlation with previously published methylation profiles and demonstrated an underestimation of methylation levels in the reconstructed profiles by an average of 15–20 %. Additionally, we developed a new Python­based algorithm that allows for the comparison of methylomes in ancient and modern samples, despite the absence of methylation profiles in modern bone tissue within the context of obesity. This analysis involves a two­step data processing approach, where the first step involves the identification and  filtration of tissue­specific methylation regions, and the second step focuses on the direct search for differentially methylated regions in specific areas associated with the researcher’s target condition. By applying this algorithm to test data, we identified 38 differentially methylated regions associated with obesity, the majority of which were located in promoter regions. The pipeline demonstrated sufficient efficiency in detecting these regions. These results confirm the feasibility of reconstructing DNA methylation profiles in ancient samples and comparing them with modern methylomes. Furthermore, possibilities for further methodological development and the implementation of a new step for studying differentially methylated positions associated with evolutionary processes are discussed.

1. Angermueller C., Lee H.J., Reik W., Stegle O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 2017;18(1):67. DOI 10.1186/s13059-017-1189-z

2. Bock C., Reither S., Mikeska T., Paulsen M., Walter J., Lengauer T. BiQ Analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing. Bioinformatics. 2005;21(21): 4067-4068. DOI 10.1093/bioinformatics/bti652

3. Bolger A.M., Lohse M., Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114-2120. DOI 10.1093/bioinformatics/btu170

4. Briggs A.W., Stenzel U., Johnson P.L.F., Green R.E., Kelso J., Prüfer K., Meyer M., Krause J., Ronan M.T., Lachmann M., Pääbo S. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl. Acad. Sci. USA. 2007;104(37):14616-14621. DOI 10.1073/pnas.0704665104

5. Briggs A.W., Good J.M., Green R.E., Krause J., Maricic T., Stenzel U., Lalueza-Fox C., Rudan P., Brajković D., Kućan Ž., Gušić I., Schmitz R., Doronichev V.B., Golovanova L.V., de la Rasilla M.,

6. Fortea J., Rosas A., Pääbo S. Targeted retrieval and analysis of five Neandertal mtDNA genomes. Science. 2009a;325(5938):318-321. DOI 10.1126/science.1174462

7. Briggs A.W., Good J.M., Green R.E., Krause J., Maricic T., Stenzel U., Pääbo S. Primer extension capture: targeted sequence retrieval from heavily degraded DNA sources. J. Vis. Exp. 2009b;31:1573. DOI 10.3791/1573

8. Briggs A.W., Stenzel U., Meyer M., Krause J., Kircher M., Pääbo S. Removal of deaminated cytosines and detection of in vivo methylation in ancient DNA. Nucleic Acids Res. 2010;38(6):e87. DOI 10.1093/nar/gkp1163

9. Clark S.J., Harrison J., Paul C.L., Frommer M. High sensitivity mapping of methylated cytosines. Nucleic Acids Res. 1994;22(15):2990-2997. DOI 10.1093/nar/22.15.2990

10. Feinberg A.P., Irizarry R.A. Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proc. Natl. Acad. Sci. USA. 2010;107(Suppl.1):1757-1764. DOI 10.1073/pnas.0906183107

11. Fu Q., Li H., Moorjani P., Jay F., Slepchenko S.M., Bondarev A.A., Johnson P.L.F., Aximu-Petri A., Prüfer K., de Filippo C., Meyer M., Zwyns N., Salazar-García D.C., Kuzmin Y.V., Keates S.G., Kosintsev P.A., Razhev D.I., Richards M.P., Peristov N.V., Lachmann M., Douka K., Higham T.F.G., Slatkin M., Hublin J.J., Reich D., Kelso J., Viola T.B., Pääbo S. Genome sequence of a 45,000-year-old modern human from western Siberia. Nature. 2014;514(7523):445-449. DOI 10.1038/nature13810

12. Gansauge M.-T., Meyer M. Single-stranded DNA library preparation for the sequencing of ancient or damaged DNA. Nat. Protoc. 2013; 8(4):737-748. DOI 10.1038/nprot.2013.038

13. Gokhman D., Lavi E., Prüfer K., Fraga M.F., Riancho J.A., Kelso J., Pääbo S., Meshorer E., Carmel L. Reconstructing the DNA methylation maps of the Neandertal and the Denisovan. Science. 2014; 344(6183):523-527. DOI 10.1126/science.1250368

14. Gokhman D., Nissim-Rafinia M., Agranat-Tamir L., Housman G., García-Pérez R., Lizano E., Cheronet O., Mallick S., Nieves-Colón M.A., Li H., Alpaslan-Roodenberg S., Novak M., Gu H., Osinski J.M., Ferrando-Bernal M., Gelabert P., Lipende I., Mjungu D., Kondova I., Bontrop R., Kullmer O., Weber G., Shahar T., Dvir-Ginzberg M., Faerman M., Quillen E.E., Meissner A., Lahav Y., Kandel L., Liebergall M., Prada M.E., Vidal J.M., Gronostajski R.M., Stone A.C., Yakir B., Lalueza-Fox C., Pinhasi R., Reich D., Marques-Bonet T., Meshorer E., Carmel L. Differential DNA methylation of vocal and facial anatomy genes in modern humans. Nat. Commun. 2020; 11(1):1189. DOI 10.1038/s41467-020-15020-6

15. Gu H., Smith Z.D., Bock C., Boyle P., Gnirke A., Meissner A. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 2011;6(4): 468-481. DOI 10.1038/nprot.2010.190

16. Günther T., Malmström H., Svensson E.M., Omrak A., Sánchez-Quin to F., Kılınç G.M., Krzewińska M., Eriksson G., Fraser M., Edlund H., Munters A.R., Coutinho A., Simões L.G., Vicente M., Sjölander A., Sellevold B.J., Jørgensen R., Claes P., Shriver M.D., Valdiosera C., Netea M.G., Apel J., Lidén K., Skar B., Storå J., Götherström A., Jakobsson M. Population genomics of Mesolithic Scandinavia: investigating early postglacial migration routes and high-latitude adaptation. PLoS Biol. 2018;16(1):e2003703. DOI 10.1371/journal.pbio.2003703

17. Hanghøj K., Seguin-Orlando A., Schubert M., Madsen T., Pedersen J.S., Willerslev E., Orlando L. Fast, accurate and automatic ancient nucleosome and methylation maps with epiPALEOMIX. Mol. Biol. Evol. 2016;33(12):3284-3298. DOI 10.1093/molbev/msw184

18. Hanghøj K., Renaud G., Albrechtsen A., Orlando L. DamMet: ancient methylome mapping accounting for errors, true variants, and post-mortem DNA damage. GigaScience. 2019;8(4):giz025. DOI 10.1093/gigascience/giz025

19. Jablonka E., Raz G. Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. Q. Rev. Biol. 2009;84(2):131-176. DOI 10.1086/598822

20. Jun G., Wing M.K., Abecasis G.R., Kang H.M. An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data. Genome Res. 2015;25(6): 918-925. DOI 10.1101/gr.176552.114

21. Krueger F., Andrews S. RBismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011;27(11): 1571-1572. DOI 10.1093/bioinformatics/btr167

22. Loyfer N., Magenheim J., Peretz A., Cann G., Bredno J., Klochendler A., Fox-Fisher I., Shabi-Porat S., Hecht M., Pelet T., Moss J., Drawshy Z., Amini H., Moradi P., Nagaraju S., Bauman D., Shveiky D., Porat S., Dior U., Rivkin G., Or O., Hirshoren N., Carmon E., Pikarsky A., Khalaileh A., Zamir G., Grinbaum R., Gazala M.A., Mizrahi I., Shussman N., Korach A., Wald O., Izhar U., Erez E., Yutkin V., Samet Y., Golinkin D.R., Spalding K.L., Druid H., Arner P., Shapiro A.M.J., Grompe M., Aravanis A., Venn O., Jamshidi A., Shemer R., Dor Y., Glaser B., Kaplan T. A DNA methylation atlas of normal human cell types. Nature. 2023;613(7943):355-364. DOI 10.1038/s41586-022-05580-6

23. Meyer M., Kircher M., Gansauge M.-T., Li H., Racimo F., Mallick S., Schraiber J.G., Jay F., Prüfer K., de Filippo C., Sudmant P.H., Alkan C., Fu Q., Do R., Rohland N., Tandon A., Siebauer M., Green R.E., Bryc K., Briggs A.W., Stenzel U., Dabney J., Shendure J., Kitzman J., Hammer M.F., Shunkov M.V., Derevianko A.P., Patterson N., Andrés A.M., Eichler E.E., Slatkin M., Reich D., Kelso J., Pääbo S. A high-coverage genome sequence from an archaic Denisovan individual. Science. 2012;338(6104):222-226. DOI 10.1126/science.1224344

24. Moreno-Mayar J., Potter B., Vinner L., Steinrücken M., Rasmussen S., Terhorst J., Kamm J., Albrechtsen A., Malaspinas A., Sikora M., Reuther J., Irish J., Malhi R., Orlando L., Song Y., Nielsen R., Meltzer D., Willerslev E. Terminal Pleistocene Alaskan genome reveals first founding population of Native Americans. Nature. 2018a; 553(7687):203-207. DOI 10.1038/nature25173

25. Moreno-Mayar J.V., Vinner L., Damgaard P.B., de la Fuente C., Chan J., Spence J.P., Allentoft M.E., Vimala T., Racimo F., Pinotti T., Rasmussen S., Margaryan A., Orbegozo M.I., Mylopotamitaki D., Wooller M., Bataille C., Becerra-Valdivia L., Chivall D., Comeskey D., Devièse T., Grayson D.K., George L., Harry H., Alexan dersen V., Primeau C., Erlandson J., Rodrigues-Carvalho C., Reis S., Bastos M.Q.R., Cybulski J., Vullo C., Morello F., Vilar M., Wells S., Gregersen K., Hansen K.L., Lynnerup N., Mirazón Lahr M., Kjær K., Strauss A., Alfonso-Durruty M., Salas A., Schroeder H., Higham T., Malhi R.S., Rasic J.T., Souza L., Santos F.R., Malaspinas A.-S., Sikora M., Nielsen R., Song Y.S., Meltzer D.J., Willerslev E. Early human dispersals within the Americas. Science. 2018b;362(6419). DOI 10.1126/science.aav2621

26. Niiranen L., Leciej D., Edlund H., Bernhardsson C., Fraser M., Sánchez Quinto F., Herzig K.H., Jakobsson M., Walkowiak J., Thalmann O. Epigenomic modifications in modern and ancient genomes. Genes. 2022;13(2):178. DOI 10.3390/genes13020178

27. Ohm J.E., Mali P., Van Neste L., Berman D.M., Liang L., Pandiyan K., Briggs K.J., Zhang W., Argani P., Simons B., Yu W., Matsui W., Van Criekinge W., Rassool F.V., Zambidis E., Schuebel K.E., Cope L., Yen J., Mohammad H.P., Cheng L., Baylin S.B. Cancer-related epigenome changes associated with reprogramming to induced pluripotent stem cells. Cancer Res. 2010;70(19):7662-7673. DOI 10.1158/0008-5472.CAN-10-1361

28. Olova N., Krueger F., Andrews S., Oxley D., Berrens R.V., Branco M.R., Reik W. Comparison of whole-genome bisulfite sequencing library preparation strategies identifies sources of biases affecting DNA methylation data. Genome Biol. 2018;19(1):33. DOI 10.1186/s13059-018-1408-2

29. Orlando L., Gilbert M.T.P., Willerslev E. Reconstructing ancient genomes and epigenomes. Nat. Rev. Genet. 2015;16(7):395-408. DOI 10.1038/nrg3935

30. Pedersen B.S., Schwartz D.A., Yang I.V., Kechris K.J. Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values. Bioinformatics. 2012;28(22):2986-2988. DOI 10.1093/bioinformatics/bts545

31. Poplin R., Ruano-Rubio V., DePristo M.A., Fennell T.J., Carneiro M.O., Van der Auwera G.A., Kling D.E., Gauthier L.D., Levy-Moonshine A., Roazen D., Shakir K., Thibault J., Chandran S., Whelan C., Lek M., Gabriel S., Daly M.J., Neale B., MacArthur D.G., Banks E. Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv. 2017. DOI 10.1101/201178

32. Prüfer K., Racimo F., Patterson N., Jay F., Sankararaman S., Sawyer S., Heinze A., Renaud G., Sudmant P.H., de Filippo C., Li H., Mallick S., Dannemann M., Fu Q., Kircher M., Kuhlwilm M., Lachmann M., Meyer M., Ongyerth M., Siebauer M., Theunert C., Tandon A., Moorjani P., Pickrell J., Mullikin J.C., Vohr S.H., Green R.E., Hellmann I., Blanche H., Cann H., Kitzman J.O., Shendure J., Eichler E.E., Lein E.S., Bakken T.E., Golovanova L.V., Doronichev V.B., Shunkov M.V., Derevianko A.P., Viola B., Slatkin M., Reich D., Kelso J., Pääbo S. The complete genome sequence of a Neanderthal from the Altai Mountains. Nature. 2014;505(7481): 43-49. DOI 10.1038/nature12886

33. Prüfer K., de Filippo C., Grote S., Mafessoni F., Korlević P., Hajdinjak M., Vernot B., Skov L., Hsieh P., Peyrégne S., Reher D., Hopfe C., Nagel S., Maricic T., Fu Q., Theunert C., Rogers R., Skoglund P., Chintalapati M., Dannemann B., Nelson B.J., Key F.M., Rudan P., Kućan Ž., Gušić I., Golovanova L.V., Doronichev V.B., Patterson N., Reich D., Eichler E.E., Slatkin M., Schierup M.H., Andrés A.M., Kelso J., Meyer M., Pääbo S. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science. 2017;358(6363):655-658. DOI 10.1126/science.aao1887

34. Saag L., Vasilyev S.V., Varul L., Kosorukova N.V., Gerasimov D.V., Oshibkina S.V., Griffith S.J., Solnik A., Saag L., D’Atanasio E., Metspalu E., Reidla M., Rootsi S., Kivisild T., Scheib C.L., Tambets K., Kriiska A., Metspalu M. Genetic ancestry changes in Stone to Bronze Age transition in the East European plain. Sci. Adv. 2021;7:eabd6535. DOI 10.1126/sciadv.abd6535

35. Sawyer S., Krause J., Guschanski K., Savolainen V., Pääbo S. Temporal patterns of nucleotide misincorporations and DNA fragmentation in ancient DNA. PLoS One. 2012;7(3):e34131. DOI 10.1371/journal.pone.0034131

36. Seguin-Orlando A., Donat R., Der Sarkissian C., Southon J., Thèves C.,

37. Manen C., Tchérémissinoff Y., Crubézy E., Shapiro B., Deleuze J., Dalén L., Guilaine J., Orlando L. Heterogeneous hunter-gatherer and steppe-related ancestries in Late Neolithic and Bell Beaker genomes from present-day France. Curr. Biol. 2021;31(5):1072-1083. DOI 10.1016/j.cub.2020.12.015

38. Sikora M., Seguin-Orlando A., Sousa V.C., Albrechtsen A., Korneliussen T., Ko A., Rasmussen S., Dupanloup I., Nigst P.R., Bosch M.D., Renaud G., Allentoft M.E., Margaryan A., Vasilyev S.V., Veselovskaya E.V., Borutskaya S.B., Deviese T., Comeskey D., Higham T., Manica A., Foley R., Meltzer D.J., Nielsen R., Excoffier L., Lahr M.M., Orlando L., Willerslev E. Ancient genomes show social and reproductive behavior of early Upper Paleolithic foragers. Science. 2017;358(6363):659-662. DOI 10.1126/science.aao1807

39. Suzuki M., Liao W., Wos F., Johnston A.D., DeGrazia J., Ishii J., Bloom T., Zody M.C., Germer S., Greally J.M. Whole-genome bisulfite sequencing with improved accuracy and cost. Genome Res. 2018;28(9):1364-1371. DOI 10.1101/gr.232587.117

40. Zhur K.V., Trifonov V.A., Prokhortchouk E.B. Progress and prospects in epigenetic studies of ancient DNA. Biochemistry (Mosc.). 2021; 86(12-13):1563-1571. DOI 10.1134/S0006297921120051

41. Zou L.S., Erdos M.R., Taylor D.L., Chines P.S., Varshney A., Parker S.C.J., Collins F.S., Didion J.P. BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues. BMC Genomics. 2018;19(1):390. DOI 10.1186/s12864-018-4766-y