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Genomic Tools Used in Molecular Clinical Aging Research

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Clinical Genetics and Genomics of Aging

Abstract

The development of next-generation sequencing (NGS) technologies to study the genome, the transcriptome, and the epigenome has revolutionized the research in all life sciences. Whole-genome sequencing and related emerging techniques are changing the perspective of researchers about the complexity of all biological phenomena. Now, the interaction between molecules and their cellular functions can be studied with high-throughput methods and in a relatively easy way, although most technologies are still expensive. In this regard, this era of post-genomic studies needs to be the time for our better understanding of the aging process and related diseases. Moreover, these technologies are a major promise to catapult biomedical research into true clinical applications as prognostics or even more as therapeutic tools for many human conditions such as aging and associated diseases. In this chapter, we reviewed how different NGS strategies have been used in the study of longevity, aging, and age-related diseases. These NGS strategies include exome and whole-genome sequencing, transcriptome sequencing, single-cell whole-genome sequencing, mitochondrial genome sequencing, DNA methylation sequencing, and ChIP-seq.

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Abbreviations

ChIP:

Chromatin immunoprecipitation

ChIP-seq:

ChIP sequencing

DMR:

Differentially methylated regions

DNA:

Deoxyribonucleic acid

DNAmet:

DNA methylation

FACS:

Fluorescence-activated cell sorting

LCM:

Laser capture microdissection

mtDNA:

Mitochondrial DNA

NGS:

Next-generation sequencing

RNA:

Ribonucleic acid

RNA-seq:

RNA sequencing

scRNA-seq:

Single-cell RNA-seq

SCS:

Single-cell sequencing

SMS:

Single-molecule sequencing

WES:

Whole-exome sequencing

WGS:

Whole-genome sequencing

References

  1. Bao R, et al. Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing. Cancer Inform. 2014;13:67–82. https://doi.org/10.4137/CIN.S13779.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Hayden E, Technology C. The $1,000 genome. Nature. 2014;507:294–5. https://doi.org/10.1038/507294a.

    Article  CAS  PubMed  Google Scholar 

  3. Mitra RD, Shendure J, Olejnik J, Edyta Krzymanska O, Church GM. Fluorescent in situ sequencing on polymerase colonies. Anal Biochem. 2003;320:55–65. https://doi.org/10.1016/s0003-2697(03)00291-4.

    Article  CAS  PubMed  Google Scholar 

  4. Shendure J, et al. Accurate multiplex polony sequencing of an evolved bacterial genome. Science. 2005;309:1728–32. https://doi.org/10.1126/science.1117389.

    Article  CAS  PubMed  Google Scholar 

  5. Adessi C, et al. Solid phase DNA amplification: characterisation of primer attachment and amplification mechanisms. Nucleic Acids Res. 2000;28:E87. https://doi.org/10.1093/nar/28.20.e87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Dressman D, Yan H, Traverso G, Kinzler KW, Vogelstein B. Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci U S A. 2003;100:8817–22. https://doi.org/10.1073/pnas.1133470100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mitra RD, Church GM. In situ localized amplification and contact replication of many individual DNA molecules. Nucleic Acids Res. 1999;27:e34. https://doi.org/10.1093/nar/27.24.e34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bentley DR, et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature. 2008;456:53–9. https://doi.org/10.1038/nature07517.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Clark MJ, et al. Performance comparison of exome DNA sequencing technologies. Nat Biotechnol. 2011;29:908–14. https://doi.org/10.1038/nbt.1975.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Liu Y, Zhou J, White KP. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics. 2014;30:301–4. https://doi.org/10.1093/bioinformatics/btt688.

    Article  CAS  PubMed  Google Scholar 

  11. Ziller MJ, Hansen KD, Meissner A, Aryee MJ. Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nat Methods. 2015;12:230–2., 231 p following 232. https://doi.org/10.1038/nmeth.3152.

    Article  CAS  PubMed  Google Scholar 

  12. Ozsolak F, Milos PM. Transcriptome profiling using single-molecule direct RNA sequencing. Methods Mol Biol. 2011;733:51–61. https://doi.org/10.1007/978-1-61779-089-8_4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities. Nat Rev Genet. 2011;12:87–98. https://doi.org/10.1038/nrg2934.

    Article  CAS  PubMed  Google Scholar 

  14. Zhou X, et al. The next-generation sequencing technology and application. Protein Cell. 2010;1:520–36. https://doi.org/10.1007/s13238-010-0065-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Choi M, et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci U S A. 2009;106:19096–101. https://doi.org/10.1073/pnas.0910672106.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Fang H, et al. Reducing INDEL calling errors in whole genome and exome sequencing data. Genome Med. 2014;6:89. https://doi.org/10.1186/s13073-014-0089-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kaeberlein M. Genome-wide approaches to understanding human ageing. Hum Genomics. 2006;2:422–8.

    Article  CAS  Google Scholar 

  18. Perls TT, Bochen K, Freeman M, Alpert L, Silver MH. Validity of reported age and centenarian prevalence in New England. Age Ageing. 1999;28:193–7. https://doi.org/10.1093/ageing/28.2.193.

    Article  CAS  PubMed  Google Scholar 

  19. Puca AA, et al. A genome-wide scan for linkage to human exceptional longevity identifies a locus on chromosome 4. Proc Natl Acad Sci U S A. 2001;98:10505–8. https://doi.org/10.1073/pnas.181337598.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Geesaman BJ, et al. Haplotype-based identification of a microsomal transfer protein marker associated with the human lifespan. Proc Natl Acad Sci U S A. 2003;100:14115–20. https://doi.org/10.1073/pnas.1936249100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Di Leo E, et al. Mutations in MTP gene in abeta- and hypobeta-lipoproteinemia. Atherosclerosis. 2005;180:311–8. https://doi.org/10.1016/j.atherosclerosis.2004.12.004.

    Article  CAS  PubMed  Google Scholar 

  22. Gregg RE, Wetterau JR. The molecular basis of abetalipoproteinemia. Curr Opin Lipidol. 1994;5:81–6.

    Article  CAS  Google Scholar 

  23. Kammerer S, et al. Amino acid variant in the kinase binding domain of dual-specific A kinase-anchoring protein 2: a disease susceptibility polymorphism. Proc Natl Acad Sci U S A. 2003;100:4066–71. https://doi.org/10.1073/pnas.2628028100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. van den Akker EB, Deelen J, Slagboom PE, Beekman M. Exome and whole genome sequencing in aging and longevity. Adv Exp Med Biol. 2015;847:127–39. https://doi.org/10.1007/978-1-4939-2404-2_6.

    Article  CAS  PubMed  Google Scholar 

  25. Flachsbart F, et al. Association of FOXO3A variation with human longevity confirmed in German centenarians. Proc Natl Acad Sci U S A. 2009;106:2700–5. https://doi.org/10.1073/pnas.0809594106.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Willcox BJ, et al. FOXO3A genotype is strongly associated with human longevity. Proc Natl Acad Sci U S A. 2008;105:13987–92. https://doi.org/10.1073/pnas.0801030105.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Gierman HJ, et al. Whole-genome sequencing of the world’s oldest people. PLoS One. 2014;9:e112430. https://doi.org/10.1371/journal.pone.0112430.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lacaze P, et al. The Medical Genome Reference Bank: a whole-genome data resource of 4000 healthy elderly individuals. Rationale and cohort design. Eur J Hum Genet. 2019;27:308–16. https://doi.org/10.1038/s41431-018-0279-z.

    Article  PubMed  Google Scholar 

  29. Erikson GA, et al. Whole-genome sequencing of a healthy aging cohort. Cell. 2016;165:1002–11. https://doi.org/10.1016/j.cell.2016.03.022.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Harris SE, Deary IJ. The genetics of cognitive ability and cognitive ageing in healthy older people. Trends Cogn Sci. 2011;15:388–94. https://doi.org/10.1016/j.tics.2011.07.004.

    Article  PubMed  Google Scholar 

  31. Lalli MA, et al. Whole-genome sequencing suggests a chemokine gene cluster that modifies age at onset in familial Alzheimer’s disease. Mol Psychiatry. 2015;20:1294–300. https://doi.org/10.1038/mp.2015.131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ridge PG, et al. Linkage, whole genome sequence, and biological data implicate variants in RAB10 in Alzheimer’s disease resilience. Genome Med. 2017;9:100. https://doi.org/10.1186/s13073-017-0486-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Vardarajan BN, et al. Whole genome sequencing of Caribbean Hispanic families with late-onset Alzheimer’s disease. Ann Clin Transl Neurol. 2018;5:406–17. https://doi.org/10.1002/acn3.537.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Alexander J, et al. Neuropathology-driven whole-genome sequencing study points to novel candidate genes for healthy brain aging. Alzheimer Dis Assoc Disord. 2019;33:7–14. https://doi.org/10.1097/WAD.0000000000000294.

    Article  CAS  PubMed  Google Scholar 

  35. Zheng HF, et al. Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature. 2015;526:112–7. https://doi.org/10.1038/nature14878.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ye K, et al. Aging as accelerated accumulation of somatic variants: whole-genome sequencing of centenarian and middle-aged monozygotic twin pairs. Twin Res Hum Genet. 2013;16:1026–32. https://doi.org/10.1017/thg.2013.73.

    Article  PubMed  Google Scholar 

  37. Park JS, et al. Brain somatic mutations observed in Alzheimer’s disease associated with aging and dysregulation of tau phosphorylation. Nat Commun. 2019;10:3090. https://doi.org/10.1038/s41467-019-11000-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Guerreiro RJ, et al. Exome sequencing reveals an unexpected genetic cause of disease: NOTCH3 mutation in a Turkish family with Alzheimer’s disease. Neurobiol Aging. 2012;33:1008 e1017–23. https://doi.org/10.1016/j.neurobiolaging.2011.10.009.

    Article  CAS  Google Scholar 

  39. Benitez BA, et al. Missense variant in TREML2 protects against Alzheimer’s disease. Neurobiol Aging. 2014;35:1510 e1519–26. https://doi.org/10.1016/j.neurobiolaging.2013.12.010.

    Article  CAS  Google Scholar 

  40. Shameer K, Klee EW, Dalenberg AK, Kullo IJ. Whole exome sequencing implicates an INO80D mutation in a syndrome of aortic hypoplasia, premature atherosclerosis, and arterial stiffness. Circ Cardiovasc Genet. 2014;7:607–14. https://doi.org/10.1161/CIRCGENETICS.113.000233.

    Article  CAS  PubMed  Google Scholar 

  41. Jansen IE, et al. Discovery and functional prioritization of Parkinson’s disease candidate genes from large-scale whole exome sequencing. Genome Biol. 2017;18:22. https://doi.org/10.1186/s13059-017-1147-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Akhtarkhavari T, et al. Genetic investigation of an Iranian supercentenarian by whole exome sequencing. Arch Iran Med. 2015;18:688–97, 0151/810/AIM.009.

    PubMed  Google Scholar 

  43. Nygaard HB, et al. Whole exome sequencing of an exceptional longevity cohort. J Gerontol A Biol Sci Med Sci. 2018; https://doi.org/10.1093/gerona/gly098.

  44. Chang YH, et al. Use of whole-exome sequencing to determine the genetic basis of signs of skin youthfulness in Korean women. J Eur Acad Dermatol Venereol. 2017;31:e138–41. https://doi.org/10.1111/jdv.13904.

    Article  CAS  PubMed  Google Scholar 

  45. Huentelman MJ, et al. Associations of MAP 2K3 gene variants with superior memory in superagers. Front Aging Neurosci. 2018;10:155. https://doi.org/10.3389/fnagi.2018.00155.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Anisimov SV, Boheler KR. Aging-associated changes in cardiac gene expression: large scale transcriptome analysis. Adv Gerontol. 2003;11:67–75.

    CAS  PubMed  Google Scholar 

  47. Xing W, et al. Long non-coding RNAs in aging organs and tissues. Clin Exp Pharmacol Physiol. 2017;44(Suppl 1):30–7. https://doi.org/10.1111/1440-1681.12795.

    Article  CAS  PubMed  Google Scholar 

  48. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63. https://doi.org/10.1038/nrg2484.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years Nat Rev Gen. 2019. https://doi.org/10.1038/s41576-019-0150-2.

  50. Farr JN, et al. Effects of age and estrogen on skeletal gene expression in humans as assessed by RNA sequencing. PLoS One. 2015;10:e0138347. https://doi.org/10.1371/journal.pone.0138347.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Roforth MM, et al. Global transcriptional profiling using RNA sequencing and DNA methylation patterns in highly enriched mesenchymal cells from young versus elderly women. Bone. 2015;76:49–57. https://doi.org/10.1016/j.bone.2015.03.017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wang Y, et al. Sex differences in transcriptomic profiles in aged kidney cells of renin lineage. Aging (Albany NY). 2018;10:606–21. https://doi.org/10.18632/aging.101416.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. de la Torre Gomez C, Goreham RV, Bech Serra JJ, Nann T, Kussmann M. “Exosomics”-a review of biophysics, biology and biochemistry of exosomes with a focus on human breast milk. Front Genet. 2018;9:92. https://doi.org/10.3389/fgene.2018.00092.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Saugstad JA, et al. Analysis of extracellular RNA in cerebrospinal fluid. J Extracell Vesicles. 2017;6:1317577. https://doi.org/10.1080/20013078.2017.1317577.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Greer JB, Schmale MC, Fieber LA. Whole-transcriptome changes in gene expression accompany aging of sensory neurons in Aplysia californica. BMC Genomics. 2018;19:529. https://doi.org/10.1186/s12864-018-4909-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Mansfeld J, et al. Branched-chain amino acid catabolism is a conserved regulator of physiological ageing. Nat Commun. 2015;6:10043. https://doi.org/10.1038/ncomms10043.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Mateos J, et al. Next-generation sequencing and quantitative proteomics of Hutchinson-Gilford progeria syndrome-derived cells point to a role of nucleotide metabolism in premature aging. PLoS One. 2018;13:e0205878. https://doi.org/10.1371/journal.pone.0205878.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Barry G, Guennewig B, Fung S, Kaczorowski D, Weickert CS. Long non-coding RNA expression during aging in the human subependymal zone. Front Neurol. 2015;6:45. https://doi.org/10.3389/fneur.2015.00045.

    Article  PubMed  PubMed Central  Google Scholar 

  59. He Z, Bammann H, Han D, Xie G, Khaitovich P. Conserved expression of lincRNA during human and macaque prefrontal cortex development and maturation. RNA. 2014;20:1103–11. https://doi.org/10.1261/rna.043075.113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. White RR, et al. Comprehensive transcriptional landscape of aging mouse liver. BMC Genomics. 2015;16:899. https://doi.org/10.1186/s12864-015-2061-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Chen LL, Carmichael GG. Altered nuclear retention of mRNAs containing inverted repeats in human embryonic stem cells: functional role of a nuclear noncoding RNA. Mol Cell. 2009;35:467–78. https://doi.org/10.1016/j.molcel.2009.06.027.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bianchessi V, et al. The mitochondrial lncRNA ASncmtRNA-2 is induced in aging and replicative senescence in endothelial cells. J Mol Cell Cardiol. 2015;81:62–70. https://doi.org/10.1016/j.yjmcc.2015.01.012.

    Article  CAS  PubMed  Google Scholar 

  63. Yang D, Yang K, Yang M. Circular RNA in aging and age-related diseases. Adv Exp Med Biol. 2018;1086:17–35. https://doi.org/10.1007/978-981-13-1117-8_2.

    Article  CAS  PubMed  Google Scholar 

  64. Guo JU, Agarwal V, Guo H, Bartel DP. Expanded identification and characterization of mammalian circular RNAs. Genome Biol. 2014;15:409. https://doi.org/10.1186/s13059-014-0409-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hansen TB, et al. Natural RNA circles function as efficient microRNA sponges. Nature. 2013;495:384–8. https://doi.org/10.1038/nature11993.

    Article  CAS  PubMed  Google Scholar 

  66. Ashwal-Fluss R, et al. circRNA biogenesis competes with pre-mRNA splicing. Mol Cell. 2014;56:55–66. https://doi.org/10.1016/j.molcel.2014.08.019.

    Article  CAS  PubMed  Google Scholar 

  67. Calarco JA, et al. Regulation of vertebrate nervous system alternative splicing and development by an SR-related protein. Cell. 2009;138:898–910. https://doi.org/10.1016/j.cell.2009.06.012.

    Article  CAS  PubMed  Google Scholar 

  68. Brown JB, et al. Diversity and dynamics of the Drosophila transcriptome. Nature. 2014;512:393–9. https://doi.org/10.1038/nature12962.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Abdelmohsen K, et al. Circular RNAs in monkey muscle: age-dependent changes. Aging (Albany NY). 2015;7:903–10. https://doi.org/10.18632/aging.100834.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Mahmoudi E, Cairns MJ. Circular RNAs are temporospatially regulated throughout development and ageing in the rat. Sci Rep. 2019;9:2564. https://doi.org/10.1038/s41598-019-38860-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Nelms BD, et al. CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types. Genome Biol. 2016;17:201. https://doi.org/10.1186/s13059-016-1062-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Bonham LW, Sirkis DW, Yokoyama JS. The transcriptional landscape of microglial genes in aging and neurodegenerative disease. Front Immunol. 2019;10:1170. https://doi.org/10.3389/fimmu.2019.01170.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Plaza-Zabala A, Sierra-Torre V, Sierra A. Autophagy and microglia: novel partners in neurodegeneration and aging. Int J Mol Sci. 2017;18:598. https://doi.org/10.3390/ijms18030598.

    Article  CAS  PubMed Central  Google Scholar 

  74. Huang S. Non-genetic heterogeneity of cells in development: more than just noise. Development. 2009;136:3853–62. https://doi.org/10.1242/dev.035139.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Li L, Clevers H. Coexistence of quiescent and active adult stem cells in mammals. Science. 2010;327:542–5. https://doi.org/10.1126/science.1180794.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Shalek AK, et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature. 2014;510:363–9. https://doi.org/10.1038/nature13437.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018;50:96. https://doi.org/10.1038/s12276-018-0071-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Trapnell C, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6. https://doi.org/10.1038/nbt.2859.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Stubbington MJT, et al. T cell fate and clonality inference from single-cell transcriptomes. Nat Methods. 2016;13:329–32. https://doi.org/10.1038/nmeth.3800.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Liang J, Cai W, Sun Z. Single-cell sequencing technologies: current and future. J Genet Genomics. 2014;41:513–28. https://doi.org/10.1016/j.jgg.2014.09.005.

    Article  PubMed  Google Scholar 

  81. Tang F, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6:377–82. https://doi.org/10.1038/nmeth.1315.

    Article  CAS  PubMed  Google Scholar 

  82. Ramskold D, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30:777–82. https://doi.org/10.1038/nbt.2282.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Macosko EZ, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14. https://doi.org/10.1016/j.cell.2015.05.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Enge M, et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell. 2017;171:321–30.e314. https://doi.org/10.1016/j.cell.2017.09.004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Xin Y, et al. Single-cell RNAseq reveals that pancreatic beta-cells from very old male mice have a young gene signature. Endocrinology. 2016;157:3431–8. https://doi.org/10.1210/en.2016–1235.

    Article  CAS  PubMed  Google Scholar 

  86. Davie K, et al. A single-cell transcriptome atlas of the aging drosophila brain. Cell. 2018;174:982–98. e920. https://doi.org/10.1016/j.cell.2018.05.057.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Lodato MA, et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science. 2018;359:555–9. https://doi.org/10.1126/science.aao4426.

    Article  CAS  PubMed  Google Scholar 

  88. Taanman JW. The mitochondrial genome: structure, transcription, translation and replication. Biochim Biophys Acta. 1999;1410:103–23. https://doi.org/10.1016/s0005-2728(98)00161-3.

    Article  CAS  PubMed  Google Scholar 

  89. Alston CL, Rocha MC, Lax NZ, Turnbull DM, Taylor RW. The genetics and pathology of mitochondrial disease. J Pathol. 2017;241:236–50. https://doi.org/10.1002/path.4809.

    Article  CAS  PubMed  Google Scholar 

  90. DeBalsi KL, Hoff KE, Copeland WC. Role of the mitochondrial DNA replication machinery in mitochondrial DNA mutagenesis, aging and age-related diseases. Ageing Res Rev. 2017;33:89–104. https://doi.org/10.1016/j.arr.2016.04.006.

    Article  CAS  PubMed  Google Scholar 

  91. Islam MT. Oxidative stress and mitochondrial dysfunction-linked neurodegenerative disorders. Neurol Res. 2017;39:73–82. https://doi.org/10.1080/01616412.2016.1251711.

    Article  CAS  PubMed  Google Scholar 

  92. Stewart JB, Chinnery PF. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat Rev Genet. 2015;16:530–42. https://doi.org/10.1038/nrg3966.

    Article  CAS  PubMed  Google Scholar 

  93. Koch RE, Josefson CC, Hill GE. Mitochondrial function, ornamentation, and immunocompetence. Biol Rev Camb Philos Soc. 2017;92:1459–74. https://doi.org/10.1111/brv.12291.

    Article  PubMed  Google Scholar 

  94. Tang S, et al. Transition to next generation analysis of the whole mitochondrial genome: a summary of molecular defects. Hum Mutat. 2013;34:882–93. https://doi.org/10.1002/humu.22307.

    Article  CAS  PubMed  Google Scholar 

  95. Sondheimer N, et al. Neutral mitochondrial heteroplasmy and the influence of aging. Hum Mol Genet. 2011;20:1653–9. https://doi.org/10.1093/hmg/ddr043.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Duan M, Tu J, Lu Z. Recent advances in detecting mitochondrial DNA heteroplasmic variations. Molecules. 2018;23:E323. https://doi.org/10.3390/molecules23020323.

    Article  CAS  PubMed  Google Scholar 

  97. Maitra A, et al. The human MitoChip: a high-throughput sequencing microarray for mitochondrial mutation detection. Genome Res. 2004;14:812–9. https://doi.org/10.1101/gr.2228504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Raju R, Jian B, Hubbard W, Chaudry I. The mitoscriptome in aging and disease. Aging Dis. 2011;2:174–80.

    PubMed  PubMed Central  Google Scholar 

  99. Casoli T, Spazzafumo L, Di Stefano G, Conti F. Role of diffuse low-level heteroplasmy of mitochondrial DNA in Alzheimer’s disease neurodegeneration. Front Aging Neurosci. 2015;7:142. https://doi.org/10.3389/fnagi.2015.00142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Palculict ME, Zhang VW, Wong LJ, Wang J. Comprehensive mitochondrial genome analysis by massively parallel sequencing. Methods Mol Biol. 2016;1351:3–17. https://doi.org/10.1007/978-1-4939-3040-1_1.

    Article  CAS  PubMed  Google Scholar 

  101. Li H, et al. Aging-associated mitochondrial DNA mutations alter oxidative phosphorylation machinery and cause mitochondrial dysfunctions. Biochim Biophys Acta Mol basis Dis. 2017;1863:2266–73. https://doi.org/10.1016/j.bbadis.2017.05.022.

    Article  CAS  PubMed  Google Scholar 

  102. Zhang R, Wang Y, Ye K, Picard M, Gu Z. Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC Genomics. 2017;18:890. https://doi.org/10.1186/s12864-017-4287-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Norddahl GL, et al. Accumulating mitochondrial DNA mutations drive premature hematopoietic aging phenotypes distinct from physiological stem cell aging. Cell Stem Cell. 2011;8:499–510. https://doi.org/10.1016/j.stem.2011.03.009.

    Article  CAS  PubMed  Google Scholar 

  104. Li E, Zhang Y. DNA methylation in mammals. Cold Spring Harb Perspect Biol. 2014;6:a019133. https://doi.org/10.1101/cshperspect.a019133.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Frommer M, et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A. 1992;89:1827–31. https://doi.org/10.1073/pnas.89.5.1827.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Yang Y, et al. Quantitative and multiplexed DNA methylation analysis using long-read single-molecule real-time bisulfite sequencing (SMRT-BS). BMC Genomics. 2015;16:350. https://doi.org/10.1186/s12864-015-1572-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Masser DR, et al. Analysis of DNA modifications in aging research. Geroscience. 2018;40:11–29. https://doi.org/10.1007/s11357-018-0005-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Koch CM, Wagner W. Epigenetic-aging-signature to determine age in different tissues. Aging (Albany NY). 2011;3:1018–27. https://doi.org/10.18632/aging.100395.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Heyn H, et al. Distinct DNA methylomes of newborns and centenarians. Proc Natl Acad Sci U S A. 2012;109:10522–7. https://doi.org/10.1073/pnas.1120658109.

    Article  PubMed  PubMed Central  Google Scholar 

  110. McClay JL, et al. A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects. Hum Mol Genet. 2014;23:1175–85. https://doi.org/10.1093/hmg/ddt511.

    Article  CAS  PubMed  Google Scholar 

  111. Raddatz G, et al. Aging is associated with highly defined epigenetic changes in the human epidermis. Epigenetics Chromatin. 2013;6:36. https://doi.org/10.1186/1756-8935-6-36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Zhang S, et al. Genome-wide analysis of DNA methylation profiles in a senescence-accelerated mouse prone 8 brain using whole-genome bisulfite sequencing. Bioinformatics. 2017;33:1591–5. https://doi.org/10.1093/bioinformatics/btx040.

    Article  CAS  PubMed  Google Scholar 

  113. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19:371–84. https://doi.org/10.1038/s41576-018-0004-3.

    Article  CAS  PubMed  Google Scholar 

  114. Lowe D, Horvath S, Raj K. Epigenetic clock analyses of cellular senescence and ageing. Oncotarget. 2016;7:8524–31. https://doi.org/10.18632/oncotarget.7383.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Furey TS. ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet. 2012;13:840–52. https://doi.org/10.1038/nrg3306.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. O’Brown ZK, Van Nostrand EL, Higgins JP, Kim SK. The inflammatory transcription factors NFkappaB, STAT1 and STAT3 drive age-associated transcriptional changes in the human kidney. PLoS Genet. 2015;11:e1005734. https://doi.org/10.1371/journal.pgen.1005734.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Liu L, et al. Chromatin modifications as determinants of muscle stem cell quiescence and chronological aging. Cell Rep. 2013;4:189–204. https://doi.org/10.1016/j.celrep.2013.05.043.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Stefanelli G, et al. Learning and age-related changes in genome-wide H2A.Z binding in the mouse hippocampus. Cell Rep. 2018;22:1124–31. https://doi.org/10.1016/j.celrep.2018.01.020.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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García-Venzor, A., Mandujano-Tinoco, E.A. (2020). Genomic Tools Used in Molecular Clinical Aging Research. In: Gomez-Verjan, J., Rivero-Segura, N. (eds) Clinical Genetics and Genomics of Aging. Springer, Cham. https://doi.org/10.1007/978-3-030-40955-5_5

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