Eliot C. Bush

Whole Genome DNA Methylation Profile of the Jewel Wasp (Nasonia vitripennis)

The epigenetic mark of DNA methylation, the addition of a methyl (CH3) group to a cytosine residue, has been extensively studied in many mammalian genomes and, although it is commonly found at the promoter regions of genes, it is also involved in a number of different biological functions. In other complex animals, such as social insects, DNA methylation has been determined to be involved in caste differentiation and to occur primarily in gene bodies. The role of methylation in nonsocial insects, however, has not yet been explored thoroughly. Here, we present the whole-genome DNA methylation profile of the nonsocial hymenopteran, the jewel wasp (Nasonia vitripennis). From high-throughput sequencing of bisulfite-converted gDNA extracted from male Nasonia thoraces, we were able to determine which cytosine residues are methylated in the entire genome. We found that an overwhelming majority of methylated sites (99.7%) occur at cytosines followed by a guanine in the 3′ direction (CpG sites). Additionally, we found that a majority of methylation in Nasonia occurs within exonic regions of the genome (more than 62%). Overall, methylation is sparse in Nasonia, occurring only at 0.18% of all sites and at 0.63% of CpGs. Our analysis of the Nasonia methylome revealed that in contrast to the methylation profile typically seen in mammals, methylation is sparse and is constrained primarily to exons. This methylation profile is more similar to that of the social hymenopteran species, the honey bee (Apis mellifera). In presenting the Nasonia methylome, we hope to promote future investigation of the regulatory function of DNA methylation in both social and nonsocial hymenoptera.

The dynamic DNA methylation cycle from egg to sperm in the honey bee Apis mellifera

In honey bees (Apis mellifera), the epigenetic mark of DNA methylation is central to the developmental regulation of caste differentiation, but may also be involved in additional biological functions. In this study, we examine the whole genome methylation profiles of three stages of the haploid honey bee genome: unfertilised eggs, the adult drones that develop from these eggs and the sperm produced by these drones. These methylomes reveal distinct patterns of methylation. Eggs and sperm show 381 genes with significantly different CpG methylation patterns, with the vast majority being more methylated in eggs. Adult drones show greatly reduced levels of methylation across the genome when compared with both gamete samples. This suggests a dynamic cycle of methylation loss and gain through the development of the drone and during spermatogenesis. Although fluxes in methylation during embryogenesis may account for some of the differentially methylated sites, the distinct methylation patterns at some genes suggest parent-specific epigenetic marking in the gametes. Extensive germ line methylation of some genes possibly explains the lower-than-expected frequency of CpG sites in these genes. We discuss the potential developmental and evolutionary implications of methylation in eggs and sperm in this eusocial insect species.

When CS 1 is biology 1: crossdisciplinary collaboration as CS context

We present the curriculum, deployment, and initial evaluation of a course, BioCS1, designed to serve as CS1 and Biology1 for majors of either (or both) disciplines. Cotaught by professors in both fields, BioCS1 interweaves fundamental biology and computational topics in a manner similar to contextual approaches to CS1. In contrast to other contextual approaches, however, BioCS1 emphasizes both CS and its context equally. The results suggest that cross-disciplinary collaborations can succeed at the introductory level, as they have at later stages of the curriculum.

Context dependent substitution biases vary within the human genome

BackgroundModels of sequence evolution typically assume that different nucleotide positions evolve independently. This assumption is widely appreciated to be an over-simplification. The best known violations involve biases due to adjacent nucleotides. There have also been suggestions that biases exist at larger scales, however this possibility has not been systematically explored.ResultsTo address this we have developed a method which identifies over- and under-represented substitution patterns and assesses their overall impact on the evolution of genome composition. Our method is designed to account for biases at smaller pattern sizes, removing their effects. We used this method to investigate context bias in the human lineage after the divergence from chimpanzee. We examined bias effects in substitution patterns between 2 and 5 bp long and found significant effects at all sizes. This included some individual three and four base pair patterns with relatively large biases. We also found that bias effects vary across the genome, differing between transposons and non-transposons, between different classes of transposons, and also near and far from genes.ConclusionsWe found that nucleotides beyond the immediately adjacent one are responsible for substantial context effects, and that these biases vary across the genome.

Bio1 as CS1: evaluating a crossdisciplinary CS context

We present the curriculum, deployment, and initial evaluation of a course, BioCS1, designed to serve as an introductory course in both biology and CS. Co-taught by professors in both fields, BioCS1 interweaves fundamental biology and computational topics in a manner similar to contextual approaches to CS1. In contrast to other contextual approaches, however, BioCS1 emphasizes both CS and its context equally. The results suggest that such cross-disciplinary collaborations can thrive at the introductory level, just as they have later in the curriculum.

Integrating Genomics Research throughout the Undergraduate Curriculum: A Collection of Inquiry-Based Genomics Lab Modules

We wish to let CBE—Life Sciences Education readers know about a portal to a set of curricular lab modules designed to integrate genomics and bioinformatics into commonly taught courses at all levels of the undergraduate curriculum. Through a multi-year, collaborative process, we developed, implemented, and peer-reviewed inquiry-based, integrated instructional units (I3Us) adaptable to a range of teaching settings, with a focus on both model and nonmodel systems. Each of the products is built on vetted design principles: 1) they have clear pedagogical objectives; 2) they are integrated with lessons taught in the lecture; 3) they are designed to integrate the learning of science content with learning about the process of science; and 4) they require student reflection and discussion (Figure 1; National Research Council [NRC], 2005). Eleven I3Us were designed and implemented as multi-week modules within the context of an existing biology course (e.g., microbiology, comparative anatomy, introduction to neurobiology), and three I3Us were incorporated into interdisciplinary biology/computer science classes. Our collection of genomics instructional units, together with extensive supporting material for each module, is accessible on a dedicated website (http://serc.carleton.edu/genomics/activities.html) that also provides links to bioinformatics tools and online assessment and pedagogical resources for teaching genomics. Figure 1. Pedagogical elements of the I3U, which was based on the findings of Americas Lab Report (NRC, 2005 ) and was used as the primary curricular design framework for this project. Rapid advances in genome sequencing and analysis offer unparalleled opportunity and challenge for biology educators. More data are being generated than can be analyzed and contextualized in traditional teaching or research models. Indeed, this explosion of data has spawned rapid growth in the discipline of bioinformatics, which is focused on development of the computational tools and approaches for extracting biologically meaningful insights from genomic data. At the same time, access to vast quantities of genomic data stored in publicly available databases can offer educators ways to engage undergraduates in authentic research and to democratize research that was previously possible only at research-intensive universities with massive instrumentation infrastructures. The integration of genomic and bioinformatic approaches into undergraduate curricula represents one response to the national calls for biology teaching that is more quantitative and promotes deeper understanding of biological systems through interdisciplinary analyses (National Academy of Sciences, 2003 ; Association of American Medical Colleges and Howard Hughes Medical Institute [HHMI], 2009 ; NRC, 2009 ; American Association for the Advancement of Science, 2011 ). Yet relatively few faculty members who teach undergraduate biology have expertise in the fields of genomics or bioinformatics, and they may therefore feel inadequately prepared to develop their own new curricular modules capitalizing on this dispersed abundance of available resources. Our Teagle Foundation–funded genomics education initiative, Bringing Big Science to Small Colleges: A Genomics Collaboration, was designed to address the challenges of helping faculty members integrate genome-scale science into the undergraduate classroom. The goal of the project was to create and disseminate self-contained curricular units that stimulate students and faculty alike to think in new ways and at different scales of biological inquiry. To this end, a series of three workshops over 3 yr brought together a total of 34 faculty participants from 19 institutions and a diverse array of disciplines—including biology, computer science, and science education—to develop a set of lab modules containing a substantial genomics component. We believe that these modules are suitable for integration into existing courses in the biology curriculum and are adaptable to a variety of teaching settings. The project website serves as a portal to activity sheets describing each I3U, complete with learning goals, teaching tips, and links to teaching materials, as well as downloadable resources and assessment tools (Figure 2), that can be customized by any interested educator. Each I3U was peer-reviewed by fellow participants, as well as by a professional project consultant who has extensive experience with Web-based description of teaching materials using this format (Manduca et al., 2006 ). The goals of this review process were to ensure that each I3U met the design criteria articulated above, and to evaluate whether the activity sheet provided both an easily accessible overview of the content and enough detailed information for other instructors to adapt and implement the material and its associated assessment strategies. This peer review was complemented by each participants own explicitly framed evaluation of his/her I3U through a formal reflection form (accessible at http://serc.carleton.edu/genomics/workshop09/index.html). Although these I3Us were designed for courses currently taught by the project participants within the specific institutions’ curricula, we propose that they can be inserted into other courses encompassing similar content (Tables 1 and ​and2)2) and/or learning goals (e.g., Figure 2). We have received many communications from colleagues at other institutions who have adapted our I3Us for their courses. Figure 2. Excerpt from an activity sheet from the Genomics Instructional Units Minicollection describing one of the curricular modules developed within the Bringing Big Science to Small Colleges program (for the complete activity sheet, see http://serc.carleton.edu/genomics/units/19163.html ... Table 1. List of I3Us generated in the Bringing Big Science to Small Colleges collaborative project, grouped by the general level in the curriculum in which they were originally taught Table 2. Pedagogical attributes (scale of biological organization, genomic level of analysis, and bioinformatic skills taught) of I3Us developed in this project and disseminated on the projects website One fundamental characteristic of each I3U in our collection is the focus on guided inquiry. The benefits to an undergraduate of hands-on participation in research are well documented (Nagda et al., 1998 ; Gafney, 2001 ; Hunter et al., 2007 ; Kardash et al., 2008 ; Lopatto, 2009 ). Integrating authentic research experiences into the undergraduate curriculum allows this powerful learning model to be scaled from use with only a few students to use with entire laboratory sections (Lopatto 2009 ; Lopatto et al. 2008 ). Like other national participatory genomic teaching initiatives (Campbell et al., 2006 , 2007 ; Ditty et al., 2010 ; Shaffer et al., 2010 ; HHMI, 2011 ), our model for curriculum development in genomics emphasizes synergies between student-centered research and education. However, in contrast with some of these other projects, our grassroots approach leveraged a wealth of existing expertise by providing opportunities for individual faculty members to develop, implement, modify, evaluate, and share undergraduate teaching modules that stem from their own research and/or teaching interests. In this regard, our project most closely resembles the Genome Consortium for Active Teaching, which provides faculty members and their undergraduates access to microarrays from a variety of organisms, allowing participants to define their own research questions in a model system with which they are already familiar (Campbell et al., 2006 , 2007 ). Our collaborative effort among biologists, computer scientists, and science educators has yielded a collection of pedagogical resources that can be adapted for use in a wide variety of educational settings. We invite other biologists to begin building on our work by using and providing feedback on our I3Us. Faculty who have tested materials that exemplify our design principles are encouraged to add them to our collection. For further information, please contact the corresponding author.

A first course in computing with applications to biology

We believe that undergraduate biology students must acquire a foundational background in computing including how to formulate a computational problem; develop an algorithmic solution; implement their solution in software and then test, document and use their code to explore biological phenomena. Moreover, by learning these skills in the first year, students acquire a powerful tool set that they can use and build on throughout their studies. To address this need, we have developed a first-year undergraduate course that teaches students the foundations of computational thinking and programming in the context of problems in biology. This article describes the structure and content of the course and summarizes assessment data on both affective and learning outcomes.

Modeling the Role of Negative Cooperativity in Metabolic Regulation and Homeostasis

A significant proportion of enzymes display cooperativity in binding ligand molecules, and such effects have an important impact on metabolic regulation. This is easiest to understand in the case of positive cooperativity. Sharp responses to changes in metabolite concentrations can allow organisms to better respond to environmental changes and maintain metabolic homeostasis. However, despite the fact that negative cooperativity is almost as common as positive, it has been harder to imagine what advantages it provides. Here we use computational models to explore the utility of negative cooperativity in one particular context: that of an inhibitor binding to an enzyme. We identify several factors which may contribute, and show that acting together they can make negative cooperativity advantageous.

xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria

BackgroundGenomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative, have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree.ResultsWe have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree.ConclusionsxenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species.