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Gene editing using clustered regularly interspaced short palindromic repeats/Cas9 has great potential for improving the compatibility of porcine organs with human recipients. However, the risk of detrimental off-target mutations in gene-edited pigs remains largely undefined. We have previously generated GGTA1 knock-in pigs for xenotransplantation using FokI-dCas9, a variant of Cas9 that is reported to reduce the frequency of off-target mutagenesis. In this study, we used whole genome sequencing (WGS) and optimized bioinformatic analysis to assess the fidelity of FokI-dCas9 editing in the generation of these pigs. Genomic DNA was isolated from porcine cells before and after gene editing and sequenced by WGS. The genomic sequences were analyzed using GRIDSS variant-calling software to detect putative structural variations (SVs), which were validated by PCR of DNA from knock-in and wild-type pigs. Platypus variant-calling software was used to detect single-nucleotide variations (SNVs) and small insertions/deletions (indels). GRIDSS analysis confirmed the precise integration of one copy of the knock-in construct in the gene-edited cells. Three additional SVs were detected by GRIDSS: deletions in intergenic regions in chromosome 6 and the X chromosome and a duplication of part of the CALD1 gene on chromosome 18. These mutations were not associated with plausible off-target sites, and were not detected in a second line of knock-in pigs generated using the same pair of guide RNAs, suggesting that they were the result of background mutation rather than off-target activity. Platypus identified 1375 SNVs/indels after quality filtering, but none of these were located in proximity to potential off-target sites, indicating that they were probably also spontaneous mutations. This is the first WGS analysis of pigs generated from FokI-dCas9-edited cells. Our results demonstrate that FokI-dCas9 is capable of high-fidelity gene editing with negligible off-target or undesired on-target mutagenesis.

Purpose: Posterior scleral remodeling accompanies myopia. In guinea pigs developing myopia, the region around the optic nerve (peripapillary zone, PPZ) rapidly expands followed by inhibition in eye size in the periphery. We studied the differential gene expression in the sclera that accompanies these changes. Methods: Guinea pigs were form-deprived (FD) for 2 weeks to induce myopia, while the fellow eye served as a control. After 2 weeks, the PPZ and the peripheral temporal sclera were isolated in representative animals to extract the RNA. RNA sequencing was undertaken using an Illumina HiSeq 2000, with differential expression analyzed using Voom and pathways analyzed using the Ingenuity Pathway Analysis tool. RNA from additional PPZ and peripheral temporal sclera in FD and fellow eyes was used for validation of gene expression using quantitative real-time PCR (qRT-PCR). Results: In myopic sclera, 348 genes were differentially expressed between PPZ and the peripheral temporal region (corrected P < 0.05), of which 61 were differentially expressed in the PPZ between myopic and control eyes. Pathway analyses of these gene sets showed the involvement of Gαi signaling along with previously reported gamma-aminobutyric acid (GABA) and glutamate receptors among numerous novel pathways. The expression pattern of three novel genes and two myopia-related genes was validated using qRT-PCR. Conclusions: Gene expression changes are associated with the rapid elongation that occurs around the optic nerve region during the development of myopia. A prominent change in Gαi signaling, which affects cAMP synthesis and thus collagen levels, may be critical in mediating the regional changes in myopic sclera.

We report a ribosomal tag pyrosequencing study of the phylogenetic diversity of Archaea, Bacteria and Eucarya over a depth profile at the Hawaii Ocean Time-Series Station, ALOHA. The V9 region of the SSU rRNA gene was amplified from samples representing the epi- (10 m), meso- (800 m) and bathy- (4400 m) pelagia. The primers used are expected to amplify representatives of approximately 80% of known phylogenetic diversity across all three domains. Comparisons of unique sequences revealed a remarkably low degree of overlap between communities at each depth. The 444 147 sequence tags analyzed represented 62 975 unique sequences. Of these, 3707 (5.9%) occurred at two depths, and only 298 (0.5%) were observed at all three depths. At this level of phylogenetic resolution, Bacteria diversity decreased with depth but was still equivalent to that reported elsewhere for different soil types. Archaea diversity was highest in the two deeper samples. Eucarya observations and richness estimates are almost one order of magnitude higher than any previous marine microbial Eucarya richness estimates. The associations of many Eucarya sequences with putative parasitic organisms may have significant impacts on our understanding of the mechanisms controlling host population density and diversity, and point to a more significant role for microbial Eucarya in carbon flux through the microbial loop. We posit that the majority of sequences detected from the deep sea that have closest matches to sequences from non-pelagic sources are indeed native to the marine environment, and are possibly responsible for key metabolic processes in global biogeochemical cycles.

Microorganisms act both as drivers and indicators of perturbations in the marine environment. In an effort to establish baselines to predict the response of marine habitats to environmental change, here we report a broad survey of microbial diversity across the Indian Ocean, including the first microbial samples collected in the pristine lagoon of Salomon Islands, Chagos Archipelago. This was the first large-scale ecogenomic survey aboard a private yacht employing a ‘citizen oceanography’ approach and tools and protocols easily adapted to ocean going sailboats. Our data highlighted biogeographic patterns in microbial community composition across the Indian Ocean. Samples from within the Salomon Islands lagoon contained a community which was different even from adjacent samples despite constant water exchange, driven by the dominance of the photosynthetic cyanobacterium Synechococcus. In the lagoon, Synechococcus was also responsible for driving shifts in the metatranscriptional profiles. Enrichment of transcripts related to photosynthesis and nutrient cycling indicated bottom-up controls of community structure. However a five-fold increase in viral transcripts within the lagoon during the day, suggested a concomitant top-down control by bacteriophages. Indeed, genome recruitment against Synechococcus reference genomes suggested a role of viruses in providing the ecological filter for determining the β-diversity patterns in this system.

In considering the best possible solutions for answering phylogenetic questions from genomic sequences, we have chosen a strategy that we suggest is superior to others that have gone previously. We have ignored multigene families and instead have used single-gene families. This minimizes the inadvertent analysis of paralogs. We have employed strict data controls and have reasoned that if a protein is not capable of recovering the uncontroversial parts of a phylogenetic tree, then why should we use it for the more controversial parts? We have sliced and diced the data in as many ways as possible in order to uncover the signals in that data. Using this strategy, we have tested two controversial hypotheses concerning eukaryotic phylogenetic relationships: the placement of arthropoda and nematodes and the relationships of animals, plants, and fungi. We have constructed phylogenetic trees from 780 single-gene families from 10 completed genomes and amalgamated these into a single supertree. We have also carried out a total evidence analysis on the only universally distributed protein families that can accurately reconstruct the uncontroversial parts of the phylogenetic tree: a total of five families. In doing so, we ignore the majority of single-gene families that are universally distributed as they do not have the appropriate signals to recover the uncontroversial parts of the tree. We have also ignored every protein that has ever been used previously to address this issue, simply because none of them meet our strict criteria. Using these data controls, site stripping, and multiple analyses, 24 out of 26 analyses strongly support the grouping of vertebrates with arthropods (Coelomata hypothesis) and plants with animals. In the other two analyses, the data were ambivalent. The latter finding overturns an 11-year theory of Eukaryotic evolution; the first confirms what has already been said by others. In the light of this new tree, we re-analyze the evolution of intron gain and loss in the rpL14 gene and find that it is much more compatible with the hypothesis presented here than with the Opisthokonta hypothesis.

The extent to which prokaryotic evolution has been influenced by horizontal gene transfer (HGT) and therefore might be more of a network than a tree is unclear. Here we use supertree methods to ask whether a definitive prokaryotic phylogenetic tree exists and whether it can be confidently inferred using orthologous genes. We analysed an 11-taxon dataset spanning the deepest divisions of prokaryotic relationships, a 10-taxon dataset spanning the relatively recent gamma-proteobacteria and a 61-taxon dataset spanning both, using species for which complete genomes are available. Congruence among gene trees spanning deep relationships is not better than random. By contrast, a strong, almost perfect phylogenetic signal exists in gamma-proteobacterial genes. Deep-level prokaryotic relationships are difficult to infer because of signal erosion, systematic bias, hidden paralogy and/or HGT. Our results do not preclude levels of HGT that would be inconsistent with the notion of a prokaryotic phylogeny. This approach will help decide the extent to which we can say that there is a prokaryotic phylogeny and where in the phylogeny a cohesive genomic signal exists.

We live on a vast, underexplored planet that is largely ocean. Despite modern technology, Global Positioning System (GPS) navigation, and advanced engineering of ocean vessels, the ocean is unforgiving, especially in rough weather. Coastal ocean navigation, with risks of running aground and inconsistent weather and sea patterns, can also be challenging and hazardous. In 2012, more than 100 international incidents of ships sinking, foundering, grounding, or being lost at sea were reported (http://en.wikipedia.org/wiki/List_of_shipwrecks_in_2012). Even a modern jetliner can disappear in the ocean with little or no trace [1], and the current costs and uncertainty associated with search and rescue make the prospects of finding an object in the middle of the ocean daunting [2].Notwithstanding satellite constellations, autonomous vehicles, and more than 300 research vessels worldwide (www.wikipedia.org/wiki/List_of_research_vessels_by_country), we lack fundamental data relating to our oceans. These missing data hamper our ability to make basic predictions about ocean weather, narrow the trajectories of floating objects, or estimate the impact of ocean acidification and other physical, biological, and chemical characteristics of the world's oceans. To cope with this problem, scientists make probabilistic inferences by synthesizing models with incomplete data. Probabilistic modeling works well for certain questions of interest to the scientific community, but it is difficult to extract unambiguous policy recommendations from this approach. The models can answer important questions about trends and tendencies among large numbers of events but often cannot offer much insight into specific events. For example, probabilistic models can tell us with some precision the extent to which storm activity will be intensified by global climate change but cannot yet attribute the severity of a particular storm to climate change. Probabilistic modeling can provide important insights into the global traffic patterns of floating debris but is not of much help to search-and-rescue personnel struggling to learn the likely trajectory of a particular piece of debris left by a wreck.Oceanographic data are incomplete because it is financially and logistically impractical to sample everywhere. Scientists typically sample over time, floating with the currents and observing their temporal evolution (the Langrangian approach), or they sample across space to cover a gradient of conditions—such as temperature or nutrients (the Eulerian approach). These observational paradigms have various strengths and weaknesses, but their fundamental weakness is cost. A modern ocean research vessel typically costs more than US$30,000 per day to operate—excluding the full cost of scientists, engineers, and the cost of the research itself. Even an aggressive expansion of oceanographic research budgets would not do much to improve the precision of our probabilistic models, let alone to quickly and more accurately locate missing objects in the huge, moving, three-dimensional seascape. Emerging autonomous technologies such as underwater gliders and in situ biological samplers (e.g., environmental sample processors) help fill gaps but are cost prohibitive to scale up. Similarly, drifters (e.g., the highly successful Argo floats program) have proven very useful for better defining currents, but unless retrieved after their operational lifetime, they become floating trash, adding to a growing problem.Long-term sampling efforts such as the continuous plankton recorder in the North Sea and North Atlantic [3] provide valuable data on decadal trends and leveraged English Channel ferries to accomplish much of the sampling. Modernizing and expanding this approach is a goal of citizen science initiatives. How do we leverage cost-effective technologies and economies of scale given shrinking federal research budgets?

The last universal common ancestor of contemporary biology (LUCA) used a precise set of 20 amino acids as a standard alphabet with which to build genetically encoded protein polymers. Considerable evidence indicates that some of these amino acids were present through nonbiological syntheses prior to the origin of life, while the rest evolved as inventions of early metabolism. However, the same evidence indicates that many alternatives were also available, which highlights the question: what factors led biological evolution on our planet to define its standard alphabet? One possibility is that natural selection favored a set of amino acids that exhibits clear, nonrandom properties-a set of especially useful building blocks. However, previous analysis that tested whether the standard alphabet comprises amino acids with unusually high variance in size, charge, and hydrophobicity (properties that govern what protein structures and functions can be constructed) failed to clearly distinguish evolution's choice from a sample of randomly chosen alternatives. Here, we demonstrate unambiguous support for a refined hypothesis: that an optimal set of amino acids would spread evenly across a broad range of values for each fundamental property. Specifically, we show that the standard set of 20 amino acids represents the possible spectra of size, charge, and hydrophobicity more broadly and more evenly than can be explained by chance alone.

Mutation and selection are the principle forces governing gene and protein sequence. Mutation is the major source of variation, and selection removes variation. Although many mutations are likely to be neutral with respect to natural selection, much of the extant sequence that is functionally important has experienced selective pressures in the past. By examining the history of DNA sequences, we can infer the functional importance of particular residues and the selective pressures that have influenced their evolution. In this chapter, we review the most interesting approaches for inferring the evolutionary history of DNA and protein sequences and indicate how these analyses can be useful in the drug discovery process.