Single genome retrieval of context-dependent variability in mutation rates for human germline

TP53: the unluckiest of genes?

The transcription factor p53 plays a key role in the cellular defense against cancer development. It is inactivated in virtually every tumor, and in every second tumor this inactivation is due to a mutation in the TP53 gene. In this perspective, we show that this diverse mutational spectrum is unique among all other cancer-associated proteins and discuss what drives the selection of TP53 mutations in cancer. We highlight that several factors conspire to make the p53 protein particularly vulnerable to inactivation by the mutations that constantly plague our genome. It appears that the TP53 gene has emerged as a victim of its own evolutionary past that shaped its structure and function towards a pluripotent tumor suppressor, but came with an increased structural fragility of its DNA-binding domain. TP53 loss of function - with associated dominant-negative effects - is the main mechanism that will impair TP53 tumor suppressive function, regardless of whether a neomorphic phenotype is associated with some of these variants.

Towards the genomic sequence code of DNA fragility for machine learning

Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity for strand breaks remains elusive. Here, by analyzing the differences and commonalities across myriads of genomic breakage datasets, we extract the sequence-linked rules and patterns behind DNA fragility. We show the overall deconvolution of the sequence influence into short-, mid- and long-range effects, and the stressor-dependent differences in defining the range and compositional effects on DNA fragility. We summarize and release our feature compendium as a library that can be seamlessly incorporated into genomic machine learning procedures, where DNA fragility is of concern, and train a generalized DNA fragility model on cancer-associated breakages. Structural variants (SVs) tend to stabilize regions in which they emerge, with the effect most pronounced for pathogenic SVs. In contrast, the effects of chromothripsis are seen across regions less prone to breakages. We find that viral integration may bring genome fragility, particularly for cancer-associated viruses. Overall, this work offers novel insights into the genomic sequence basis of DNA fragility and presents a powerful machine learning resource to further enhance our understanding of genome (in)stability and evolution.

Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning

We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These models, however, treat the DNA as a mere collection of four, A, T, G and C, letters, dismissing the past advancements in science that can enable the use of more intricate information from nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical (QM) and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming QM methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A->C mutation rates.

Generalised interrelations among mutation rates drive the genomic compliance of Chargaff's second parity rule

Chargaff's second parity rule (PR-2), where the complementary base and k-mer contents are matching within the same strand of a double stranded DNA (dsDNA), is a phenomenon that invited many explanations. The strict compliance of nearly all nuclear dsDNA to PR-2 implies that the explanation should also be similarly adamant. In this work, we revisited the possibility of mutation rates driving PR-2 compliance. Starting from the assumption-free approach, we constructed kinetic equations for unconstrained simulations. The results were analysed for their PR-2 compliance by employing symbolic regression and machine learning techniques. We arrived to a generalised set of mutation rate interrelations in place in most species that allow for their full PR-2 compliance. Importantly, our constraints explain PR-2 in genomes out of the scope of the prior explanations based on the equilibration under mutation rates with simpler no-strand-bias constraints. We thus reinstate the role of mutation rates in PR-2 through its molecular core, now shown, under our formulation, to be tolerant to previously noted strand biases and incomplete compositional equilibration. We further investigate the time for any genome to reach PR-2, showing that it is generally earlier than the compositional equilibrium, and well within the age of life on Earth.

8-Oxo-7,8-dihydroguanine, friend and foe: Epigenetic-like regulator versus initiator of mutagenesis

A high flux of reactive oxygen species during oxidative stress results in oxidative modification of cellular components including DNA. Oxidative DNA "damage" to the heterocyclic bases is considered deleterious because polymerases may incorrectly read the modifications causing mutations. A prominent member in this class is the oxidized guanine base 8-oxo-7,8-dihydroguanine (OG) that is moderately mutagenic effecting G→T transversion mutations. Recent reports have identified that formation of OG in G-rich regulatory elements in the promoters of the VEGF, TNFα, and SIRT1 genes can increase transcription via activation of the base excision repair (BER) pathway. Work in our laboratory with the G-rich sequence in the promoter of VEGF concluded that BER drives a shift in structure to a G-quadruplex conformation leading to gene activation in mammalian cells. More specifically, removal of OG from the duplex context by 8-oxoguanine glycosylase 1 (OGG1) produces an abasic site (AP) that destabilizes the duplex, shifting the equilibrium toward the G-quadruplex fold because of preferential extrusion of the AP into a loop. The AP is bound but inefficiently cleaved by apurinic/apyrimidinic endoDNase I (APE1) that likely allows recruitment of activating transcription factors for gene induction. The ability of OG to induce transcription ascribes a regulatory or epigenetic-like role for this oxidatively modified base. We compare OG to the 5-methylcytosine (5mC) epigenetic pathway including its oxidized derivatives, some of which poise genes for transcription while also being substrates for BER. The mutagenic potential of OG to induce only ∼one-third the number of mutations (G→T) compared to deamination of 5mC producing C→T mutations is described. These comparisons blur the line between friendly epigenetic base modifications and those that are foes, i.e. DNA "damage," causing genetic mutations.