Biology subjects

Weghorn, D.

Publications and source records attributed to Weghorn, D..

9 recordsLinked to original sources

bioRxiv2025

Dissecting DNA-mismatch-repair-driven mutational processes in human cells

Mismatch repair (MMR) is an essential DNA repair pathway that preserves genome stability by correcting replication errors and other DNA lesions. MMR deficiency (MMRd) leads to increased mutation rates and microsatellite instability, and contributes to tumorigenesis in multiple cancer types, yet the full spectrum of mutational patterns caused by MMRd remains incompletely defined. Here, using CRISPR-Cas9 knockouts in human isogenic cell lines, we systematically profile mutations arising in MMR-deficient cells. We observe a marked increase in mutation burden and the appearance of established MMRd-associated mutational signatures. Unexpectedly, we also detect signatures SBS57 and DBS14 and connect them to germline single-nucleotide polymorphisms that co-occur with MMR-driven indels. In addition, we provide experimental genomic evidence supporting a role of MMR in preventing mutations underlying the widespread SBS1 signature, and we link MMR deficiency to the indel signature ID12, which currently lacks a known aetiology. Finally, comparing in vitro MMRd profiles from rapidly dividing cells without exogenous mutagen exposure to in vivo tumour genomes that reflect lifelong mutation accumulation reveals pronounced differences in signature composition, underscoring the context dependence of MMR-linked mutagenesis. Together, our results broaden the known mutational footprint of MMR deficiency and illuminate previously uncharacterised mechanisms relevant to cancer development.

genetics↗

bioRxiv2025

Transcription start sites experience a high influx of heritable variants fuelled by early development

Mutations drive evolution and genetic diversity, but the impact of transcription on germline mutagenesis remains poorly understood. Here, we identify a hypermutation phenomenon at transcription start sites in the human germline, spanning several hundred base pairs in both directions. We link this TSS mutational hotspot to divergent transcription, RNA polymerase II stalling, R-loops, and mitotic--but not meiotic--double-strand breaks, revealing a recombination-independent mechanism distinct from known processes. Notably, the hotspot is absent in de novo mutation data. We reconcile this by showing that TSS mutations are significantly enriched with early mosaic variants often filtered out in de novo mutation calls, indicating that the hotspot arises during early embryogenesis. Mutational signature analysis reinforces these findings and implicates alternative non-homologous end joining and maternal mutation clusters. Our study provides the first detailed description of a germline TSS mutation hotspot, with broad evolutionary and biomedical implications.

genomics↗

bioRxiv2023

High-density sampling reveals volume growth in human tumours

In growing cell populations such tumours, mutations can serve as markers that allow tracking the past evolution from current samples. The genomic analyses of bulk samples and samples from multiple regions have shed light on the evolutionary forces acting on tumours. However, little is known empirically on the spatio-temporal dynamics of tumour evolution. Here, we leverage published data from resected hepatocellular carcinomas, each with several hundred samples taken in two and three dimensions. Using spatial metrics of evolution, we find that tumour cells grow predominantly uniformly within the tumour volume instead of at the surface. We determine how mutations and cells are dispersed throughout the tumour and how cell death contributes to the overall tumour growth. Our methods shed light on the early evolution of tumours in vivo and can be applied to high-resolution data in the emerging field of spatial biology.

cancer biology↗

bioRxiv2023

Mutational signature decomposition with deep neural networks reveals origins of clock-like processes and hypoxia dependencies

DNA mutational processes generate patterns of somatic and germline mutations. A multitude of such mutational processes has been identified and linked to biochemical mechanisms of DNA damage and repair. Cancer genomics relies on these so-called mutational signatures to classify tumours into subtypes, navigate treatment, determine exposure to mutagens, and characterise the origin of individual mutations. Yet, state-of-the-art methods to quantify the contributions of different mutational signatures to a tumour sample frequently fail to detect certain mutational signatures, work well only for a relatively high number of mutations, and do not provide comprehensive error estimates of signature contributions. Here, we present a novel approach to signature decomposition using artificial neural networks that addresses these problems. We show that our approach, SigNet, outperforms existing methods by learning the prior frequencies of signatures and their correlations present in real data. Unlike any other method we tested, SigNet achieves high prediction accuracy even with few mutations. We used this to generate estimates of signature weights for more than 7500 tumours for which only whole-exome sequencing data are available. We then identified systematic differences in signature activity both as a function of epigenetic covariates and over the course of tumour evolution. This allowed us to decipher the origins of signatures SBS3, SBS5 and SBS40. We further discovered novel associations of mutational signatures with hypoxia, including strong positive correlations with the activities of clock-like and defective DNA repair mutational processes. These results provide new insights into the interplay between tumour biology and mutational processes and demonstrate the utility of our novel approach to mutational signature decomposition, a crucial part of cancer genomics studies.

genomics↗

bioRxiv2023

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. Author SummaryAn important question in genetics is how the effects of mutations combine to alter phenotypes. Genetic interactions (epistasis) describe non-additive effects of pairs of mutations, but can also involve higher-order (three- and four-way etc.) combinations. Quantifying higher-order interactions is experimentally very challenging requiring a large number of measurements. Techniques based on deep mutational scanning (DMS) represent valuable sources of data to study epistasis. However, the best way to extract the relevant pairwise and higher-order epistatic coefficients (genetic interaction terms) from this data for the task of phenotypic prediction remains an unresolved problem. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values. Critically, this formalism currently only allows for two alleles (amino acid or nucleotide states) per sequence position, hampering applications in more biologically realistic scenarios. Here we present an extension of the Walsh-Hadamard transform that overcomes this limitation and demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic genetic landscapes.

systems biology↗

bioRxiv2022

Evolutionary conservation of A/T-ending codons reflects co-regulation of expression and complex formation.

BackgroundIn a wide variety of organisms, synonymous codons are used with different frequencies, a phenomenon known as codon bias that plays an important role in determining expression levels. However, the importance of codon bias to facilitate the simultaneous turnover of thousands of protein-coding transcripts to bring about phenotypic changes in cellular programs such as development, has not yet been investigated in detail. ResultsHere, we discover that genes with A/T-ending codon preferences are expressed coordinately and display a high codon conservation in mammals. This feature is not observed in genes enriched in G/C-ending codons. A paradigmatic case of this phenomenon is KRAS, from the RAS family, an A/T-rich gene with a high codon conservation (95%) in comparison to HRAS (76%). Also, we find that genes with similar codon composition are more likely to be part of the same protein complex, and that genes with A/T-ending codons are more prone to form protein complexes than those rich in G/C. The codon preferences of genes with A/T-ending codons are conserved among vertebrates. We propose that codon conservation, a feature of expression-coordinated transcripts, is linked to the high expression variation and coordination of tRNA isoacceptors reading A/T-ending codons. ConclusionsOur data indicate that cells exploit A/T-ending codons to generate coordinated, fine-tuned changes of protein-coding transcripts. We suggest that this orchestration contributes to tissue-specific and ontogenetic-specific expression, which can facilitate, for instance, timely protein complex formation.

evolutionary biology↗

bioRxiv2019

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. This reduction was 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 the footprint of selection when using introns as proxies of neutrality. Here we compiled and analyzed published germline de novo mutation (DNM) data to test if the exonic mutation rate is also reduced in germ cells. We detected ascertainment bias in studies using DNM data from diseased probands and investigated the impact of extended nucleotide context on de novo mutation rate. After controlling for these factors, we found no reduction in the mutation rate in exons compared to introns in the germline genome, in contrast to what has been previously described in somatic cells. Therefore, there is no evidence of an enhanced mismatch repair system activity in exons with respect to adjacent introns in germline cells.

evolutionary biology↗

bioRxiv2018

Discovery of cancer driver genes based on nucleotide context

Many cancer genomes contain large numbers of somatic mutations, but few of these mutations drive tumor development. Current approaches to identify cancer driver genes are largely based on mutational recurrence, i.e. they search for genes with an increased number of nonsynonymous mutations relative to the local background mutation rate. Multiple studies have noted that the sensitivity of recurrence-based methods is limited in tumors with high background mutation rates, because passenger mutations dilute their statistical power. Here, we observe that passenger mutations tend to occur in characteristic nucleotide sequence contexts, while driver mutations follow a different distribution pattern determined by the location of functionally relevant genomic positions along the protein-coding sequence. To discover new cancer genes, we searched for genes with an excess of mutations in unusual nucleotide contexts that deviate from the characteristic context around passenger mutations. By applying this statistical framework to whole-exome sequencing data from 12,004 tumors, we discovered a long tail of novel candidate cancer genes with mutation frequencies as low as 1% and functional supporting evidence. Our results show that considering both the number and the nucleotide context around mutations helps identify novel cancer driver genes, particularly in tumors with high background mutation rates.

genetics↗

bioRxiv2018

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 [1]. 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 dataset [2]. 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.

genetics↗