| Literature DB >> 35456493 |
Marianne Dehasque1,2,3, Patrícia Pečnerová4, Vendela Kempe Lagerholm1,5, Erik Ersmark1,2,5, Gleb K Danilov6, Peter Mortensen7, Sergey Vartanyan8, Love Dalén1,2,3.
Abstract
Rapid and cost-effective retrieval of endogenous DNA from ancient specimens remains a limiting factor in palaeogenomic research. Many methods have been developed to increase ancient DNA yield, but modifications to existing protocols are often based on personal experience rather than systematic testing. Here, we present a new silica column-based extraction protocol, where optimizations were tested in controlled experiments. Using relatively well-preserved permafrost samples, we tested the efficiency of pretreatment of bone and tooth powder with a bleach wash and a predigestion step. We also tested the recovery efficiency of MinElute and QIAquick columns, as well as Vivaspin columns with two molecular weight cut-off values. Finally, we tested the effect of uracil-treatment with two different USER enzyme concentrations. We find that neither bleach wash combined with a predigestion step, nor predigestion by itself, significantly increased sequencing efficiency. Initial results, however, suggest that MinElute columns are more efficient for ancient DNA extractions than QIAquick columns, whereas different molecular weight cut-off values in centrifugal concentrator columns did not have an effect. Uracil treatments are effective at removing DNA damage even at concentrations of 0.15 U/µL (as compared to 0.3 U/µL) of ancient DNA extracts.Entities:
Keywords: DNA extraction; ancient DNA; bone; high-throughput sequencing; woolly mammoth
Mesh:
Substances:
Year: 2022 PMID: 35456493 PMCID: PMC9032354 DOI: 10.3390/genes13040687
Source DB: PubMed Journal: Genes (Basel) ISSN: 2073-4425 Impact factor: 4.141
Figure 1Relative efficiency of predigestion, bleach wash followed by predigestion, different silica columns, and centrifugal concentrator filters with different weight cut-offs, measured on each individual samples’ endogenous DNA content, complexity, average fragment length, GC content, and coverage. The dotted horizontal line marks the one-fold threshold. Bars below or above this threshold indicate that they performed worse or better, respectively, for that parameter compared to the control.
Figure 2Boxplots of relative efficiency of predigestion, bleach wash followed by predigestion, different silica columns, and centrifugal concentrator filters with different weight cut-offs on (from left to right) endogenous DNA content, complexity, average fragment length, GC content, and coverage. Circles represent outlier values. Statistically significant results are marked with one (p < 0.05) or two (p < 0.01) asterisk(s). Whiskers represent 1.5 interquartile range, whereas boxes represent the first and third quartile. Values above one-fold indicate an increase in the treatment compared to the control for that parameter, whereas values under one-fold indicate a decrease.
Results of statistical tests.
| Paired Test | Test Statistic | |||
|---|---|---|---|---|
| Predigestion ( | Complexity | t = 4.061 | 0.00486 | |
| Endogenous content | t = −3.135 | 0.01291 | ||
| Average fragment length | t = 2.019 | 0.04974 | ||
| GC content | Wilcoxon signed ranks | V = 9.5 | 0.6241 | |
| Coverage | t = −1.562 | 0.08947 | ||
| Damage parameter δD | t = 1.633 | 0.08168 | ||
| Damage parameter δS | t = −0.009815 | 0.4963 | ||
| Damage parameter λ | t = 1.765 | 0.06888 | ||
| Bleach wash and Predigestion ( | Complexity | t = 5.156 | 0.001052 | |
| Endogenous content | t = −1.748 | 0.06555 | ||
| Average fragment length | t = −0.1617 | 0.4384 | ||
| GC content | t = 4.514 | 0.002023 | ||
| Coverage | t = −1.327 | 0.1164 | ||
| Damage parameter δD | t = −4.912 | 0.00134 | ||
| Damage parameter δD | t = −8.278 | 0.00008414 | ||
| Damage parameter λ | t = −3.797 | 0.004502 | ||
| MinElute column ( | Endogenous content | Wilcoxon signed ranks | V = 7 | 0.5 |
| Complexity | t = −0.615 | 0.2859 | ||
| Average fragment length | t = −2.3524 | 0.03916 | ||
| GC content | Wilcoxon signed ranks | V = 15 | 0.03125 | |
| Coverage | Wilcoxon signed ranks | V = 5 | 0.3125 | |
| Damage parameter δD | Wilcoxon signed ranks | V = 7 | 0.5 | |
| Damage parameter δS | Wilcoxon signed ranks | V = 12 | 0.1562 | |
| Damage parameter λ | Wilcoxon signed ranks | V = 15 | 0.03125 | |
| 10 MWCO filter ( | Endogenous content | t = −1.815 | 0.0597 | |
| Complexity | t = −0.06492 | 0.47515 | ||
| Average fragment length | t = 2.002 | 0.0461 | ||
| GC content | Wilcoxon signed ranks | V = 1 | 0.5 | |
| Coverage | Wilcoxon signed ranks | V = 18 | 0.28905 | |
| Damage parameter δD | Wilcoxon signed ranks | V = 18 | 0.28905 | |
| Damage parameter δS | Wilcoxon signed ranks | V = 25 | 0.03906 | |
| Damage parameter λ | Wilcoxon signed ranks | V = 18 | 0.28905 |
Figure 3Relative efficiency of different USER concentrations on each samples’ endogenous DNA content, complexity, average fragment length, GC content, and coverage. The dotted horizontal line marks the one-fold threshold. Bars below or above this threshold indicate that the performance of the treatment was worse or better, respectively, for that parameter compared to the control.
Figure 4Conceptual figure to show how library complexity becomes more important at higher sequencing depths. The dark grey line depicts a library with high endogenous DNA content but low complexity, whereas the light grey depicts a library with low endogenous DNA content but high complexity. Although the high endogenous library results in higher coverage at lower sequencing depths (here <80 million on-target sequencing reads), the high complexity library becomes more efficient at higher sequencing depths.