Error-free and error-prone DNA repair shape mutation landscapes in human tumors

Many processes can cause the same nucleotide change in a genome, making the identification of the mechanisms causing mutations a difficult challenge. Here, we show that clustered mutations provide a more precise fingerprint of mutagenic processes. Of nine clustered mutation signatures identified from >1,000 tumor genomes, three relate to variable APOBEC activity and three are associated with tobacco smoking. An additional signature matches the spectrum of translesion DNA polymerase eta (POLH). In lymphoid cells, these mutations target promoters, consistent with AID-initiated somatic hypermutation. In solid tumors, however, they are associated with UV exposure and alcohol consumption and target the H3K36me3 chromatin of active genes in a mismatch repair (MMR)-dependent manner. These regions normally have a low mutation rate because error-free MMR also targets H3K36me3 chromatin. Carcinogens and error-prone repair therefore redistribute mutations to the more important regions of the genome, contributing a substantial mutation load in many tumors, including driver mutations

Scales and mechanisms of somatic mutation rate variation across the human genome

Cancer genome sequencing has revealed that somatic mutation rates vary substantially across the human genome and at scales from megabase-sized domains to individual nucleotides. Here we review recent work that has both revealed the major mutation biases that operate across the genome and the molecular mechanisms that cause them. The default mutation rate landscape in mammalian genomes results in active genes having low mutation rates because of a combination of factors that increase DNA repair: early DNA replication, transcription, active chromatin modifications and accessible chromatin. Therefore, either an increase in the global mutation rate or a redistribution of mutations from inactive to active DNA can increase the rate at which consequential mutations are acquired in active genes. Several environmental carcinogens and intrinsic mechanisms operating in tumor cells likely cause cancer by this second mechanism: by specifically increasing the mutation rate in active regions of the genome

Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity

BACKGROUND: There are a number of methods (also called: measures) currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can introduce strong methodological bias into measurements; therefore we attempted to develop a method free from such dependencies. One of the common applications of codon usage analyses is to quantitatively predict gene expressivity. RESULTS: We compared the performance of several commonly used measures and a novel method we introduce in this paper – Measure Independent of Length and Composition (MILC). Large, randomly generated sequence sets were used to test for dependence on (i) sequence length, (ii) overall amount of codon bias and (iii) codon bias discrepancy in the sequences. A derivative of the method, named MELP (MILC-based Expression Level Predictor) can be used to quantitatively predict gene expression levels from genomic data. It was compared to other similar predictors by examining their correlation with actual, experimentally obtained mRNA or protein abundances. CONCLUSION: We have established that MILC is a generally applicable measure, being resistant to changes in gene length and overall nucleotide composition, and introducing little noise into measurements. Other methods, however, may also be appropriate in certain applications. Our efforts to quantitatively predict gene expression levels in several prokaryotes and unicellular eukaryotes met with varying levels of success, depending on the experimental dataset and predictor used. Out of all methods, MELP and Rainer Merkl's GCB method had the most consistent behaviour. A 'reference set' containing known ribosomal protein genes appears to be a valid starting point for a codon usage-based expressivity prediction

Systematic discovery of germline cancer predisposition genes through the identification of somatic second hits

The genetic causes of cancer include both somatic mutations and inherited germline variants. Large-scale tumor sequencing has revolutionized the identification of somatic driver alterations but has had limited impact on the identification of cancer predisposition genes (CPGs). Here we present a statistical method, ALFRED, that tests Knudson’s two-hit hypothesis to systematically identify CPGs from cancer genome data. Applied to ~10, 000 tumor exomes the approach identifies known and putative CPGs – including the chromatin modifier NSD1 – that contribute to cancer through a combination of rare germline variants and somatic loss-of-heterozygosity (LOH). Rare germline variants in these genes contribute substantially to cancer risk, including to ~14% of ovarian carcinomas, ~7% of breast tumors, ~4% of uterine corpus endometrial carcinomas, and to a median of 2% of tumors across 17 cancer types

Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers.

We thank Luis Garcia-Jimeno for assistance with permutation. S.P. is supported by the Agencia Estatal de Investigacion, Ministerio de Ciencia e Innovacion (MCIN/AEI/10.13039/501100011033) through the RETOS project PID2019-109571RA-I00. This work was funded by the European Research Council (ERC) Starting grant (HYPER-INSIGHT, 757700) to F.S. and ERC Consolidator (IR-DC, 616434) and Advanced (MUTANOMICS, 883742) grants to B.L. F.S. and B.L. are funded by the ICREA Research Professor program. S.P., F.S., and B.L. acknowledge the support of the Severo Ochoa Centres of Excellence program to the CNIO, IRB Barcelona, and to the CRG (MCIN/AEI/10.13039/50110001103), respectively. B.L. and F.S. Work is funded with the grants BFU2017-89488-P and RegioMut BFU2017-89833-P (MCIN/AEI/10.13039/501100011033/FEDER "A way to make Europe"), respectively. B.L. is further supported by the Bettencourt Schueller Foundation, the Agencia de Gestio d'Ajuts Universitaris i de Recerca (2017 SGR 1322), and the Centres de Recerca de Catalunya (CERCA) program/Generalitat de Catalunya. B.L. also acknowledges the support of the Spanish Ministry of Economy, Industry, and Competitiveness to the European Molecular Biology Laboratory (EMBL) partnership. The results shown here are in whole or part based upon data generated by the TCGA Research Network.The classic two-hit model posits that both alleles of a tumor suppressor gene (TSG) must be inactivated to cause cancer. In contrast, for some oncogenes and haploinsufficient TSGs, a single genetic alteration can suffice to increase tumor fitness. Here, by quantifying the interactions between mutations and copy number alterations (CNAs) across 10,000 tumors, we show that many cancer genes actually switch between acting as one-hit or two-hit drivers. Third order genetic interactions identify the causes of some of these switches in dominance and dosage sensitivity as mutations in other genes in the same biological pathway. The correct genetic model for a gene thus depends on the other mutations in a genome, with a second hit in the same gene or an alteration in a different gene in the same pathway sometimes representing alternative evolutionary paths to cancer.S

Signatures of conformational stability and oxidation resistance in proteomes of pathogenic bacteria

Protein oxidation is known to compromise vital cellular functions. Therefore, invading pathogenic bacteria must resist damage inflicted by host defenses via reactive oxygen species. Using comparative genomics and experimental approaches, we provide multiple lines of evidence that proteins from pathogenic bacteria have acquired resistance to oxidative stress by an increased conformational stability. Representative pathogens exhibited higher survival upon HSP90 inhibition and a less-oxidation-prone proteome. A proteome signature of the 46 pathogenic bacteria encompasses 14 physicochemical features related to increasing protein conformational stability. By purifying ten representative proteins, we demonstrate in vitro that proteins with a pathogen-like signature are more resistant to oxidative stress as a consequence of their increased conformational stability. A compositional signature of the pathogens’ proteomes allowed the design of protein fragments more resilient to both unfolding and carbonylation, validating the relationship between conformational stability and oxidability with implications for synthetic biology and antimicrobial strategies

REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret