Atlantic cod (Gadus morhua) is a large, cold-adapted teleost that sustains long-standing commercial fisheries and incipient aquaculture1,2. Here we present the genome sequence of Atlantic cod, showing evidence for complex thermal adaptations in its haemoglobin gene cluster and an unusual immune architecture compared to other sequenced vertebrates. The genome assembly was obtained exclusively by 454 sequencing of shotgun and paired-end libraries, and automated annotation identified 22,154 genes. The major histocompatibility complex (MHC) II is a conserved feature of the adaptive immune system of jawed vertebrates3,4, but we show that Atlantic cod has lost the genes for MHCII, CD4 and Ii that are essential for the function of this pathway. Nevertheless, Atlantic cod is not exceptionally susceptible to disease under natural conditions5. We find a highly expanded number of MHCI genes and a unique composition of its Toll-like receptor (TLR) families. This suggests how the Atlantic cod immune system has evolved compensatory mechanisms within both adaptive and innate immunity in the absence of MHCII. These observations affect fundamental assumptions about the evolution of the adaptive immune system and its components in vertebrates.

Current three-dimensional (3D) genome modeling platforms are limited by their inability to account for radial placement of loci in the nucleus. We present Chrom3D, a user-friendly whole-genome 3D computational modeling framework that simulates positions of topologically-associated domains (TADs) relative to each other and to the nuclear periphery. Chrom3D integrates chromosome conformation capture (Hi-C) and lamin-associated domain (LAD) datasets to generate structure ensembles that recapitulate radial distributions of TADs detected in single cells. Chrom3D reveals unexpected spatial features of LAD regulation in cells from patients with a laminopathy-causing lamin mutation. Chrom3D is freely available on github.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1146-2) contains supplementary material, which is available to authorized users.

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Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge-based potentials based on pairwise distances – so-called “potentials of mean force” (PMFs) – have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state – a necessary component of these potentials – is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities “reference ratio distributions” deriving from the application of the “reference ratio method.” This new view is not only of theoretical relevance but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.

Differentiation of osteoblasts from mesenchymal stem cells (MSCs) is an integral part of bone development and homeostasis, and may when improperly regulated cause disease such as bone cancer or osteoporosis. Using unbiased high-throughput methods we here characterize the landscape of global changes in gene expression, histone modifications, and DNA methylation upon differentiation of human MSCs to the osteogenic lineage. Furthermore, we provide a first genome-wide characterization of DNA binding sites of the bone master regulatory transcription factor Runt-related transcription factor 2 (RUNX2) in human osteoblasts, revealing target genes associated with regulation of proliferation, migration, apoptosis, and with a significant overlap with p53 regulated genes. These findings expand on emerging evidence of a role for RUNX2 in cancer, including bone metastases, and the p53 regulatory network. We further demonstrate that RUNX2 binds to distant regulatory elements, promoters, and with high frequency to gene 3 0 ends. Finally, we identify TEAD2 and GTF2I as novel regulators of osteogenesis. STEM CELLS

The development of many sporadic cancers is directly initiated by carcinogen exposure. Carcinogens induce malignancies by creating DNA lesions (i.e., adducts) that can result in mutations if left unrepaired. Despite this knowledge, there has been remarkably little investigation into the regulation of susceptibility to acquire DNA lesions. In this study, we present the first quantitative human genome-wide map of DNA lesions induced by ultraviolet (UV) radiation, the ubiquitous carcinogen in sunlight that causes skin cancer. Remarkably, the pattern of carcinogen susceptibility across the genome of primary cells significantly reflects mutation frequency in malignant melanoma. Surprisingly, DNase-accessible euchromatin is protected from UV, while lamina-associated heterochromatin at the nuclear periphery is vulnerable. Many cancer driver genes have an intrinsic increase in carcinogen susceptibility, including the oncogene that has the highest mutation frequency in melanoma. These findings provide a genome-wide snapshot of DNA injuries at the earliest stage of carcinogenesis. Furthermore, they identify carcinogen susceptibility as an origin of genome instability that is regulated by nuclear architecture and mirrors mutagenesis in cancer.

Combining genome-wide structural models with phenomenological data is at the forefront of efforts to understand the organizational principles regulating the human genome. Here, we use chromosome-chromosome contact data as knowledge-based constraints for large-scale three-dimensional models of the human diploid genome. The resulting models remain minimally entangled and acquire several functional features that are observed in vivo and that were never used as input for the model. We find, for instance, that gene-rich, active regions are drawn towards the nuclear center, while gene poor and lamina associated domains are pushed to the periphery. These and other properties persist upon adding local contact constraints, suggesting their compatibility with non-local constraints for the genome organization. The results show that suitable combinations of data analysis and physical modelling can expose the unexpectedly rich functionally-related properties implicit in chromosome-chromosome contact data. Specific directions are suggested for further developments based on combining experimental data analysis and genomic structural modelling.

Chrom3D is a computational platform for 3D genome modeling that simulates the spatial positioning of chromosome domains relative to each other and relative to the nuclear periphery. In Chrom3D, chromosomes are modeled as chains of contiguous beads, in which each bead represents a genomic domain. In this protocol, a bead represents a topologically associated domain (TAD) mapped from ensemble Hi-C data. Chrom3D takes as input data significant pairwise TAD-TAD interactions determined from a Hi-C contact matrix, and TAD interactions with the nuclear periphery, determined by ChIP-sequencing of nuclear lamins to define lamina-associated domains (LADs). Chrom3D is based on Monte Carlo simulations initiated from a starting random bead configuration. During the optimization process, TAD-TAD interactions constrain bead positions relative to each other, whereas LAD information constrains the corresponding bead toward the nuclear periphery. Optimization can be repeated many times to generate an ensemble of 3D genome models. Analyses of the models enable estimations of the radial positioning of genomic sites in the nucleus across cells in a population. Chrom3D provides opportunities to reveal spatial relationships between TADs and LADs. More generally, predictions from Chrom3D models can be experimentally tested in the laboratory. We describe the entire Chrom3D protocol for modeling a 3D diploid human genome, from the creation of input files to the final rendering of 3D genome structures. The procedure takes ∼18 h. Chrom3D is freely available on GitHub.