Human and bats genome robustness under COSMIC mutational signatures

Abstract

Carcinogenesis is an evolutionary process, and mutations can fix the selected phenotypes in selective microenvironments. Both normal and neoplastic cells are robust to the mutational stressors in the microenvironment to the extent that secure their fitness. To test the robustness of genes under a range of mutagens, we developed a sequential mutation simulator, Sinabro, to simulate single base substitution under a given mutational process. Then, we developed a pipeline to measure the robustness of genes and cells under those mutagenesis processes. We discovered significant human genome robustness to the APOBEC mutational signature SBS2, which is associated with viral defense mechanisms and is implicated in cancer. Robustness evaluations across over 70,000 sequences against 41 signatures showed higher resilience under signatures predominantly causing C-to-T (G-to-A) mutations. Principal component analysis indicates the GC content at the codon's wobble position significantly influences robustness, with increased resilience noted under transition mutations compared to transversions. Then, we tested our results in bats at extremes of the lifespan-to-mass relationship and found the long-lived bat is more robust to APOBEC than the short-lived one. By revealing robustness to APOBEC ranked highest in human (and bats with much more than number of APOBEC) genome, this work bolsters the key potential role of APOBECs in aging and cancer, as well as evolved countermeasures to this innate mutagenic process. It also provides the baseline of the human and bat genome robustness under mutational processes associated with aging and cancer.

Keywords: APOBEC; Cancer evolution; GC Wobble; Mutational signature; Robustness and evolvability.

Figure 1.. Schematic representation of Sinabro algorithm for sequential mutations under mutational signatures.

For a given coding sequence, Sinabro traverses trinucleotide context through the sequence and calls in the percentage of corresponding mutation type based on mutational signature from the pre-computed matrix. The matrix is then normalized to the sum of the matrix to make it a probability matrix. A single base substitution of the sequence is selected based on the probability matrix, and the mutation is compared to the original codon to determine whether it is non-synonymous. The process loops until the mutation is non-synonymous.

Figure 2.. The average and variation of robustness against each mutational signature of the human genome.

a. The top 10 mutational signatures rank by average. b. The top 10 mutational signatures rank by standard deviation. c. The bottom 10 mutational signatures rank by average. d. The bottom 10 mutational signatures rank by standard deviation.

Figure 3.. Principal component analysis (PCA) of mutational signature.

a. Heat map colored by the weight of mutational signatures in each principal component. The Y-axis is sorted based on the GC targeting preference of each mutational signature. b. Shoulder plot of cumulative explained variance ratio. PC1 and PC2 explain 73% of the total variance, and from PC1 to PC10 explain 90% of the total variance. c. PC1-PC2 plot of all protein-coding sequences of the human genome colored by the GC contents at the wobble position. A sequence with high GC contents at the wobble position has a high PC1 value.

Figure 4.. The weight of each mutational signature on PCs 1–10.

The Y-axis is sorted based on the weight of each mutational signature of each principal component. SBS1 appeared to be the highest for PC3, PC4, and PC10 (a, b, and h); SBS10a for PC5 (c); SBS7d for PC6 (d); SBS7a for PC7 (e); SBS84 for PC8 (f); SBS22 for PC9 (g).

Figure 5.. Impact of mutational signatures’ specificity and the transition mutation ratio on robustness.

Specificity decreases with increasing entropy. a. The average robustness under mutational signature versus the specificity of the mutational signatures plot is colored by the GC targeting preference of the mutational signatures. b. The standard deviation of robustness under mutational signatures versus the specificity of the mutational signatures plot is colored by the GC targeting preference of the mutational signatures. c. Average robustness under mutational signature versus the specificity of the mutational signatures plot is colored by the transition mutation ratio of the mutational signatures. d. Linear regression plot of the average robustness of mutational signatures against the transition ratio of the mutational signatures. The two variables have a significant positive correlation (R² = 0.715, F(1, 58) = 145.5, p < 2.2 × 10⁻¹⁶).

Figure 6.. Motif preference patterns of bat APOBECs in cytidine deamination.

Each bat APOBEC enzyme was incubated with a 369 bp artificial DNA substrate, followed by PCR amplification of the resultant DNA and Next Generation Sequencing analysis. Each bar shows the average percentage of the C to T mutation rates of the corresponding NNC from the full length of the DNA sequence.

Figure 7.. Comparison of robustness under mutational signatures between human and two bat genomes.

Robustness under COSMIC mutational signature (a) SBS1 spontaneous deamination, (b) SBS4 tobacco smoking, (c) SBS6 defective DNA mismatch repair, (d) SBS18 damage by ROS, (e and f) SBS2 and SBS13 APOBEC activity. Pair wise Wilcoxon rank sum test in Table S2. g. Robustness under their native APOBEC3A activity. M. myotis showed the highest and M. molossus showed the lowest robustness.