An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity.

Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions.

Applicability of the Mutation-Selection Balance Model to Population Genetics of Heterozygous Protein-Truncating Variants in Humans.

The fate of alleles in the human population is believed to be highly affected by the stochastic force of genetic drift. Estimation of the strength of natural selection in humans generally necessitates a careful modeling of drift including complex effects of the population history and structure. Protein-truncating variants (PTVs) are expected to evolve under strong purifying selection and to have a relatively high per-gene mutation rate. Thus, it is appealing to model the population genetics of PTVs under a simple deterministic mutation-selection balance, as has been proposed earlier (Cassa et al. 2017). Here, we investigated the limits of this approximation using both computer simulations and data-driven approaches. Our simulations rely on a model of demographic history estimated from 33,370 individual exomes of the Non-Finnish European subset of the ExAC data set (Lek et al. 2016). Additionally, we compared the African and European subset of the ExAC study and analyzed de novo PTVs. We show that the mutation-selection balance model is applicable to the majority of human genes, but not to genes under the weakest selection.

Fitness landscape for nucleosome positioning.

Histone-DNA complexes, so-called nucleosomes, are the building blocks of DNA packaging in eukaryotic cells. The histone-binding affinity of a local DNA segment depends on its elastic properties and determines its accessibility within the nucleus, which plays an important role in the regulation of gene expression. Here, we derive a fitness landscape for intergenic DNA segments in yeast as a function of two molecular phenotypes: their elasticity-dependent histone affinity and their coverage with transcription factor binding sites. This landscape reveals substantial selection against nucleosome formation over a wide range of both phenotypes. We use it as the core component of a quantitative evolutionary model for intergenic DNA segments. This model consistently predicts the observed diversity of histone affinities within wild Saccharomyces paradoxus populations, as well as the affinity divergence between neighboring Saccharomyces species. Our analysis establishes histone binding and transcription factor binding as two separable modes of sequence evolution, each of which is a direct target of natural selection.

The TP53-activated E3 ligase RNF144B is a tumour suppressor that prevents genomic instability.

BACKGROUND: TP53, the most frequently mutated gene in human cancers, orchestrates a complex transcriptional program crucial for cancer prevention. While certain TP53-dependent genes have been extensively studied, others, like the recently identified RNF144B, remained poorly understood. This E3 ubiquitin ligase has shown potent tumor suppressor activity in murine Eµ Myc-driven lymphoma, emphasizing its significance in the TP53 network. However, little is known about its targets and its role in cancer development, requiring further exploration. In this work, we investigate RNF144B's impact on tumor suppression beyond the hematopoietic compartment in human cancers. METHODS: Employing TP53 wild-type cells, we generated models lacking RNF144B in both non-transformed and cancerous cells of human and mouse origin. By using proteomics, transcriptomics, and functional analysis, we assessed RNF144B's impact in cellular proliferation and transformation. Through in vitro and in vivo experiments, we explored proliferation, DNA repair, cell cycle control, mitotic progression, and treatment resistance. Findings were contrasted with clinical datasets and bioinformatics analysis. RESULTS: Our research underscores RNF144B's pivotal role as a tumor suppressor, particularly in lung adenocarcinoma. In both human and mouse oncogene-expressing cells, RNF144B deficiency heightened cellular proliferation and transformation. Proteomic and transcriptomic analysis revealed RNF144B's novel function in mediating protein degradation associated with cell cycle progression, DNA damage response and genomic stability. RNF144B deficiency induced chromosomal instability, mitotic defects, and correlated with elevated aneuploidy and worse prognosis in human tumors. Furthermore, RNF144B-deficient lung adenocarcinoma cells exhibited resistance to cell cycle inhibitors that induce chromosomal instability. CONCLUSIONS: Supported by clinical data, our study suggests that RNF144B plays a pivotal role in maintaining genomic stability during tumor suppression.

Genes enriched in A/T-ending codons are co-regulated and conserved across mammals.

Codon usage influences gene expression distinctly depending on the cell context. Yet, the importance of codon bias in the simultaneous turnover of specific groups of protein-coding genes remains to be investigated. Here, we find that genes enriched in A/T-ending codons are expressed more coordinately in general and across tissues and development than those enriched in G/C-ending codons. tRNA abundance measurements indicate that this coordination is linked to the expression changes of tRNA isoacceptors reading A/T-ending codons. Genes with similar codon composition are more likely to be part of the same protein complex, especially for genes with A/T-ending codons. The codon preferences of genes with A/T-ending codons are conserved among mammals and other vertebrates. We suggest that this orchestration contributes to tissue-specific and ontogenetic-specific expression, which can facilitate, for instance, timely protein complex formation.

Germline de novo mutation rates on exons versus introns in humans.

A main assumption of molecular population genetics is that genomic mutation rate does not depend on sequence function. Challenging this assumption, a recent study has found a reduction in the mutation rate in exons compared to introns in somatic cells, ascribed to an enhanced exonic mismatch repair system activity. If this reduction happens also in the germline, it can compromise studies of population genomics, including the detection of selection when using introns as proxies for neutrality. Here we compile and analyze published germline de novo mutation data to test if the exonic mutation rate is also reduced in germ cells. After controlling for sampling bias in datasets with diseased probands and extended nucleotide context dependency, we find no reduction in the mutation rate in exons compared to introns in the germline. Therefore, there is no evidence that enhanced exonic mismatch repair activity determines the mutation rate in germline cells.

Identification of cancer driver genes based on nucleotide context.

Cancer genomes contain large numbers of somatic mutations but few of these mutations drive tumor development. Current approaches either identify driver genes on the basis of mutational recurrence or approximate the functional consequences of nonsynonymous mutations by using bioinformatic scores. Passenger mutations are enriched in characteristic nucleotide contexts, whereas driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver-gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified according to mutations in unusual nucleotide contexts.

Bayesian inference of negative and positive selection in human cancers.

Cancer genomics efforts have identified genes and regulatory elements driving cancer development and neoplastic progression. From a microevolution standpoint, these are subject to positive selection. Although elusive in current studies, genes whose wild-type coding sequences are needed for tumor growth are also of key interest. They are expected to experience negative selection and stay intact under pressure of incessant mutation. The detection of significantly mutated (or undermutated) genes is completely confounded by the genomic heterogeneity of cancer mutation. Here we present a hierarchical framework that allows modeling of coding point mutations. Application of the model to sequencing data from 17 cancer types demonstrates an increased power to detect known cancer driver genes and identifies new significantly mutated genes with highly plausible biological functions. The signal of negative selection is very subtle, but is detectable in several cancer types and in a pan-cancer data set. It is enriched in cell-essential genes identified in a CRISPR screen, as well as in genes with reported roles in cancer.

Identifying DNase I hypersensitive sites as driver distal regulatory elements in breast cancer.

Efforts to identify driver mutations in cancer have largely focused on genes, whereas non-coding sequences remain relatively unexplored. Here we develop a statistical method based on characteristics known to influence local mutation rate and a series of enrichment filters in order to identify distal regulatory elements harboring putative driver mutations in breast cancer. We identify ten DNase I hypersensitive sites that are significantly mutated in breast cancers and associated with the aberrant expression of neighboring genes. A pan-cancer analysis shows that three of these elements are significantly mutated across multiple cancer types and have mutation densities similar to protein-coding driver genes. Functional characterization of the most highly mutated DNase I hypersensitive sites in breast cancer (using in silico and experimental approaches) confirms that they are regulatory elements and affect the expression of cancer genes. Our study suggests that mutations of regulatory elements in tumors likely play an important role in cancer development.Cancer driver mutations can occur within noncoding genomic sequences. Here, the authors develop a statistical approach to identify candidate noncoding driver mutations in DNase I hypersensitive sites in breast cancer and experimentally demonstrate they are regulatory elements of known cancer genes.

Estimating the selective effects of heterozygous protein-truncating variants from human exome data.

The evolutionary cost of gene loss is a central question in genetics and has been investigated in model organisms and human cell lines. In humans, tolerance of the loss of one or both functional copies of a gene is related to the gene's causal role in disease. However, estimates of the selection and dominance coefficients in humans have been elusive. Here we analyze exome sequence data from 60,706 individuals to make genome-wide estimates of selection against heterozygous loss of gene function. Using this distribution of selection coefficients for heterozygous protein-truncating variants (PTVs), we provide corresponding Bayesian estimates for individual genes. We find that genes under the strongest selection are enriched in embryonic lethal mouse knockouts, Mendelian disease-associated genes, and regulators of transcription. Screening by essentiality, we find a large set of genes under strong selection that are likely to have crucial functions but have not yet been thoroughly characterized.