Fitness landscape for nucleosome positioning

Coupling between Sequence-Mediated Nucleosome Organization and Genome Evolution

The nucleosome is a major modulator of DNA accessibility to other cellular factors. Nucleosome positioning has a critical importance in regulating cell processes such as transcription, replication, recombination or DNA repair. The DNA sequence has an influence on the position of nucleosomes on genomes, although other factors are also implicated, such as ATP-dependent remodelers or competition of the nucleosome with DNA binding proteins. Different sequence motifs can promote or inhibit the nucleosome formation, thus influencing the accessibility to the DNA. Sequence-encoded nucleosome positioning having functional consequences on cell processes can then be selected or counter-selected during evolution. We review the interplay between sequence evolution and nucleosome positioning evolution. We first focus on the different ways to encode nucleosome positions in the DNA sequence, and to which extent these mechanisms are responsible of genome-wide nucleosome positioning in vivo. Then, we discuss the findings about selection of sequences for their nucleosomal properties. Finally, we illustrate how the nucleosome can directly influence sequence evolution through its interactions with DNA damage and repair mechanisms. This review aims to provide an overview of the mutual influence of sequence evolution and nucleosome positioning evolution, possibly leading to complex evolutionary dynamics.

Central dogma rates and the trade-off between precision and economy in gene expression

Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise.

A thousand empirical adaptive landscapes and their navigability

The adaptive landscape is an iconic metaphor that pervades evolutionary biology. It was mostly applied in theoretical models until recent years, when empirical data began to allow partial landscape reconstructions. Here, we exhaustively analyse 1,137 complete landscapes from 129 eukaryotic species, each describing the binding affinity of a transcription factor to all possible short DNA sequences. We find that the navigability of these landscapes through single mutations is intermediate to that of additive and shuffled null models, suggesting that binding affinity-and thereby gene expression-is readily fine-tuned via mutations in transcription factor binding sites. The landscapes have few peaks that vary in their accessibility and in the number of sequences they contain. Binding sites in the mouse genome are enriched in sequences found in the peaks of especially navigable landscapes and the genetic diversity of binding sites in yeast increases with the number of sequences in a peak. Our findings suggest that landscape navigability may have contributed to the enormous success of transcriptional regulation as a source of evolutionary adaptations and innovations.

Increasing Nucleosome Occupancy Is Correlated with an Increasing Mutation Rate so Long as DNA Repair Machinery Is Intact

Deciphering the multitude of epigenomic and genomic factors that influence the mutation rate is an area of great interest in modern biology. Recently, chromatin has been shown to play a part in this process. To elucidate this relationship further, we integrated our own ultra-deep sequenced human nucleosomal DNA data set with a host of published human genomic and cancer genomic data sets. Our results revealed, that differences in nucleosome occupancy are associated with changes in base-specific mutation rates. Increasing nucleosome occupancy is associated with an increasing transition to transversion ratio and an increased germline mutation rate within the human genome. Additionally, cancer single nucleotide variants and microindels are enriched within nucleosomes and both the coding and non-coding cancer mutation rate increases with increasing nucleosome occupancy. There is an enrichment of cancer indels at the theoretical start (74 bp) and end (115 bp) of linker DNA between two nucleosomes. We then hypothesized that increasing nucleosome occupancy decreases access to DNA by DNA repair machinery and could account for the increasing mutation rate. Such a relationship should not exist in DNA repair knockouts, and we thus repeated our analysis in DNA repair machinery knockouts to test our hypothesis. Indeed, our results revealed no correlation between increasing nucleosome occupancy and increasing mutation rate in DNA repair knockouts. Our findings emphasize the linkage of the genome and epigenome through the nucleosome whose properties can affect genome evolution and genetic aberrations such as cancer.

Universality and predictability in molecular quantitative genetics

Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait's genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology.