Improved detection of methylation in ancient DNA

Abstract Reconstructing premortem DNA methylation levels in ancient DNA (aDNA) has led to breakthrough studies such as the prediction of anatomical features of the Denisovan, as well as the castration status of ancient horses. These studies relied on computationally inferring methylation levels from damage signals in naturally deaminated cytosines. Because of statistical constraints, this inference requires high-coverage sequencing, and is thus not only expensive but also restricted to samples with exceptional DNA preservation. Instead, a method to directly measure methylation levels in aDNA, as exists in modern DNA samples, would open the door to a more thorough and cost effective ability to study ancient DNA methylation. We have tested two methods for direct methylation measurement developed for modern DNA based on either bisulfite or enzymatic methylation treatments. We find that both methods preserve sufficient DNA yields to allow for methylation measurement. Bisulfite treatment, combined with a single stranded library preparation, shows the least reduction in DNA yields compared to no methylation treatment, as well as the least biases during methylation conversion. In addition, we show that applying bisulfite treatment to ∼0.4-fold coverage sample provides a methylation signal that is comparable to, or even better, than the computationally inferred one. We thus present a method to directly measure methylation in ancient DNA that is cost effective and can be used on a wide variety of ancient samples..

Medienart:

Preprint

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

bioRxiv.org - (2023) vom: 06. Nov. Zur Gesamtaufnahme - year:2023

Sprache:

Englisch

Beteiligte Personen:

Sawyer, Susanna [VerfasserIn]
Gelabert, Pere [VerfasserIn]
Yakir, Benjamin [VerfasserIn]
Lizcano, Alejandro Llanos [VerfasserIn]
Sperduti, Alessandra [VerfasserIn]
Bondioli, Luca [VerfasserIn]
Cheronet, Olivia [VerfasserIn]
Neugebauer-Maresch, Christine [VerfasserIn]
Teschler-Nicola, Maria [VerfasserIn]
Novak, Mario [VerfasserIn]
Pap, Ildikó [VerfasserIn]
Szikossy, Ildikó [VerfasserIn]
Hajdu, Tamás [VerfasserIn]
Meshorer, Eran [VerfasserIn]
Carmel, Liran [VerfasserIn]
Pinhasi, Ron [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

570
Biology

doi:

10.1101/2023.10.31.564722

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XBI041416228