The role of transcript regions and amino acid choice in nucleosome positioning.

Eukaryotic DNA is organized and compacted in a string of nucleosomes, DNA-wrapped protein cylinders. The positions of nucleosomes along DNA are not random but show well-known base pair sequence preferences that result from the sequence-dependent elastic and geometric properties of the DNA double helix. Here, we focus on DNA around transcription start sites, which are known to typically attract nucleosomes in multicellular life forms through their high GC content. We aim to understand how these GC signals, as observed in genome-wide averages, are produced and encoded through different genomic regions (mainly 5' UTRs, coding exons, and introns). Our study uses a bioinformatics approach to decompose the genome-wide GC signal into between-region and within-region signals. We find large differences in GC signal contributions between vertebrates and plants and, remarkably, even between closely related species. Introns contribute most to the GC signal in vertebrates, while in plants the exons dominate. Further, we find signal strengths stronger on DNA than on mRNA, suggesting a biological function of GC signals along the DNA itself, as is the case for nucleosome positioning. Finally, we make the surprising discovery that both the choice of synonymous codons and amino acids contribute to the nucleosome positioning signal.

Multiplexing mechanical and translational cues on genes.

The genetic code gives precise instructions on how to translate codons into amino acids. Due to the degeneracy of the genetic code-18 out of 20 amino acids are encoded for by more than one codon-more information can be stored in a basepair sequence. Indeed, various types of additional information have been discussed in the literature, e.g., the positioning of nucleosomes along eukaryotic genomes and the modulation of the translating efficiency in ribosomes to influence cotranslational protein folding. The purpose of this study is to show that it is indeed possible to carry more than one additional layer of information on top of a gene. In particular, we show how much translation efficiency and nucleosome positioning can be adjusted simultaneously without changing the encoded protein. We achieve this by mapping genes on weighted graphs that contain all synonymous genes, and then finding shortest paths through these graphs. This enables us, for example, to readjust the disrupted translational efficiency profile after a gene has been introduced from one organism (e.g., human) into another (e.g., yeast) without greatly changing the nucleosome landscape intrinsically encoded by the DNA molecule.

Shortest paths through synonymous genomes.

The elasticity of the DNA double helix varies with the underlying base pair sequence. This allows one to put mechanical cues into sequences that in turn influence the packaging of DNA into nucleosomes, DNA-wrapped protein cylinders. Nucleosomes dictate a broad range of biological processes, ranging from gene regulation, recombination, and replication to chromosome condensation. Here we map base pair sequences onto graphs and use shortest paths algorithms to determine which DNA stretches are easiest or hardest to bend inside a nucleosome. We further demonstrate how genetic and mechanical information can be multiplexed by studying paths through graphs of synonymous codons. Using this method we find that nucleosomes can be placed by mechanical cues nearly everywhere on the genome of baker's yeast (Saccharomyces cerevisiae).

Physics behind the mechanical nucleosome positioning code.

The positions along DNA molecules of nucleosomes, the most abundant DNA-protein complexes in cells, are influenced by the sequence-dependent DNA mechanics and geometry. This leads to the "nucleosome positioning code", a preference of nucleosomes for certain sequence motives. Here we introduce a simplified model of the nucleosome where a coarse-grained DNA molecule is frozen into an idealized superhelical shape. We calculate the exact sequence preferences of our nucleosome model and find it to reproduce qualitatively all the main features known to influence nucleosome positions. Moreover, using well-controlled approximations to this model allows us to come to a detailed understanding of the physics behind the sequence preferences of nucleosomes.