Shane C. Burgess

The Gene Ontology project in 2008

The Gene Ontology (GO) project (http://www.geneontology.org/) provides a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://www.sequenceontology.org/). The ontologies have been extended and refined for several biological areas, and improvements to the structure of the ontologies have been implemented. To improve the quantity and quality of gene product annotations available from its public repository, the GO Consortium has launched a focused effort to provide comprehensive and detailed annotation of orthologous genes across a number of ‘reference’ genomes, including human and several key model organisms. Software developments include two releases of the ontology-editing tool OBO-Edit, and improvements to the AmiGO browser interface.

Comprehensive proteomic analysis of bovine spermatozoa of varying fertility rates and identification of biomarkers associated with fertility.

BackgroundMale infertility is a major problem for mammalian reproduction. However, molecular details including the underlying mechanisms of male fertility are still not known. A thorough understanding of these mechanisms is essential for obtaining consistently high reproductive efficiency and to ensure lower cost and time-loss by breeder.ResultsUsing high and low fertility bull spermatozoa, here we employed differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT) and identified 125 putative biomarkers of fertility. We next used quantitative Systems Biology modeling and canonical protein interaction pathways and networks to show that high fertility spermatozoa differ from low fertility spermatozoa in four main ways. Compared to sperm from low fertility bulls, sperm from high fertility bulls have higher expression of proteins involved in: energy metabolism, cell communication, spermatogenesis, and cell motility. Our data also suggests a hypothesis that low fertility sperm DNA integrity may be compromised because cell cycle: G2/M DNA damage checkpoint regulation was most significant signaling pathway identified in low fertility spermatozoa.ConclusionThis is the first comprehensive description of the bovine spermatozoa proteome. Comparative proteomic analysis of high fertility and low fertility bulls, in the context of protein interaction networks identified putative molecular markers associated with high fertility phenotype.

Three crocodilian genomes reveal ancestral patterns of evolution among archosaurs

INTRODUCTION Crocodilians and birds are the two extant clades of archosaurs, a group that includes the extinct dinosaurs and pterosaurs. Fossils suggest that living crocodilians (alligators, crocodiles, and gharials) have a most recent common ancestor 80 to 100 million years ago. Extant crocodilians are notable for their distinct morphology, limited intraspecific variation, and slow karyotype evolution. Despite their unique biology and phylogenetic position, little is known about genome evolution within crocodilians. Evolutionary rates of tetrapods inferred from DNA sequences anchored by ultraconserved elements. Evolutionary rates among reptiles vary, with especially low rates among extant crocodilians but high rates among squamates. We have reconstructed the genomes of the common ancestor of birds and of all archosaurs (shown in gray silhouette, although the morphology of these species is uncertain). RATIONALE Genome sequences for the American alligator, saltwater crocodile, and Indian gharial—representatives of all three extant crocodilian families—were obtained to facilitate better understanding of the unique biology of this group and provide a context for studying avian genome evolution. Sequence data from these three crocodilians and birds also allow reconstruction of the ancestral archosaurian genome. RESULTS We sequenced shotgun genomic libraries from each species and used a variety of assembly strategies to obtain draft genomes for these three crocodilians. The assembled scaffold N50 was highest for the alligator (508 kilobases). Using a panel of reptile genome sequences, we generated phylogenies that confirm the sister relationship between crocodiles and gharials, the relationship with birds as members of extant Archosauria, and the outgroup status of turtles relative to birds and crocodilians. We also estimated evolutionary rates along branches of the tetrapod phylogeny using two approaches: ultraconserved element–anchored sequences and fourfold degenerate sites within stringently filtered orthologous gene alignments. Both analyses indicate that the rates of base substitution along the crocodilian and turtle lineages are extremely low. Supporting observations were made for transposable element content and for gene family evolution. Analysis of whole-genome alignments across a panel of reptiles and mammals showed that the rate of accumulation of micro-insertions and microdeletions is proportionally lower in crocodilians, consistent with a single underlying cause of a reduced rate of evolutionary change rather than intrinsic differences in base repair machinery. We hypothesize that this single cause may be a consistently longer generation time over the evolutionary history of Crocodylia. Low heterozygosity was observed in each genome, consistent with previous analyses, including the Chinese alligator. Pairwise sequential Markov chain analysis of regional heterozygosity indicates that during glacial cycles of the Pleistocene, each species suffered reductions in effective population size. The reduction was especially strong for the American alligator, whose current range extends farthest into regions of temperate climates. CONCLUSION We used crocodilian, avian, and outgroup genomes to reconstruct 584 megabases of the archosaurian common ancestor genome and the genomes of key ancestral nodes. The estimated accuracy of the archosaurian genome reconstruction is 91% and is higher for conserved regions such as genes. The reconstructed genome can be improved by adding more crocodilian and avian genome assemblies and may provide a unique window to the genomes of extinct organisms such as dinosaurs and pterosaurs. To provide context for the diversification of archosaurs—the group that includes crocodilians, dinosaurs, and birds—we generated draft genomes of three crocodilians: Alligator mississippiensis (the American alligator), Crocodylus porosus (the saltwater crocodile), and Gavialis gangeticus (the Indian gharial). We observed an exceptionally slow rate of genome evolution within crocodilians at all levels, including nucleotide substitutions, indels, transposable element content and movement, gene family evolution, and chromosomal synteny. When placed within the context of related taxa including birds and turtles, this suggests that the common ancestor of all of these taxa also exhibited slow genome evolution and that the comparatively rapid evolution is derived in birds. The data also provided the opportunity to analyze heterozygosity in crocodilians, which indicates a likely reduction in population size for all three taxa through the Pleistocene. Finally, these data combined with newly published bird genomes allowed us to reconstruct the partial genome of the common ancestor of archosaurs, thereby providing a tool to investigate the genetic starting material of crocodilians, birds, and dinosaurs.

Sequencing three crocodilian genomes to illuminate the evolution of archosaurs and amniotes

The International Crocodilian Genomes Working Group (ICGWG) will sequence and assemble the American alligator (Alligator mississippiensis), saltwater crocodile (Crocodylus porosus) and Indian gharial (Gavialis gangeticus) genomes. The status of these projects and our planned analyses are described.

Characterization of malignant tissue cells by laser-induced breakdown spectroscopy

Cancer diagnosis and classification is extremely complicated and, for the most part, relies on subjective interpretation of biopsy material. Such methods are laborious and in some cases might result in different results depending on the histopathologist doing the examination. Automated, real-time diagnostic procedures would greatly facilitate cancer diagnosis and classification. Laser-induced breakdown spectroscopy (LIBS) is used for the first time to our knowledge to distinguish normal and malignant tumor cells from histological sections. We found that the concentration of trace elements in normal and tumor cells was significantly different. For comparison, the tissue samples were also analyzed by an inductively coupled plasma emission spectroscopy (ICPES) system. The results from the LIBS measurement and ICPES analysis were in good agreement.

Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project

We describe the organization of a nascent international effort, the Functional Annotation of Animal Genomes (FAANG) project, whose aim is to produce comprehensive maps of functional elements in the genomes of domesticated animal species.

AgBase: a unified resource for functional analysis in agriculture

Analysis of functional genomics (transcriptomics and proteomics) datasets is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation. To facilitate systems biology in these species we have established the curated, web-accessible, public resource ‘AgBase’ (). We have improved the structural annotation of agriculturally important genomes by experimentally confirming the in vivo expression of electronically predicted proteins and by proteogenomic mapping. Proteogenomic data are available from the AgBase proteogenomics link. We contribute Gene Ontology (GO) annotations and we provide a two tier system of GO annotations for users. The ‘GO Consortium’ gene association file contains the most rigorous GO annotations based solely on experimental data. The ‘Community’ gene association file contains GO annotations based on expert community knowledge (annotations based directly from author statements and submitted annotations from the community) and annotations for predicted proteins. We have developed two tools for proteomics analysis and these are freely available on request. A suite of tools for analyzing functional genomics datasets using the GO is available online at the AgBase site. We encourage and publicly acknowledge GO annotations from researchers and provide an online mechanism for agricultural researchers to submit requests for GO annotations.

Identification of the neoplastically transformed cells in Marek's disease herpesvirus-induced lymphomas: recognition by the monoclonal antibody AV37.

ABSTRACT Understanding the interactions between herpesviruses and their host cells and also the interactions between neoplastically transformed cells and the host immune system is fundamental to understanding the mechanisms of herpesvirus oncology. However, this has been difficult as no animal models of herpesvirus-induced oncogenesis in the natural host exist in which neoplastically transformed cells are also definitively identified and may be studied in vivo. Mareks disease (MD) herpesvirus (MDV) of poultry, although a recognized natural oncogenic virus causing T-cell lymphomas, is no exception. In this work, we identify for the first time the neoplastically transformed cells in MD as the CD4+ major histocompatibility complex (MHC) class Ihi, MHC class IIhi, interleukin-2 receptor α-chain-positive, CD28lo/−, phosphoprotein 38-negative (pp38−), glycoprotein B-negative (gB−), αβ T-cell-receptor-positive (TCR+) cells which uniquely overexpress a novel host-encoded extracellular antigen that is also expressed by MDV-transformed cell lines and recognized by the monoclonal antibody (MAb) AV37. Normal uninfected leukocytes and MD lymphoma cells were isolated directly ex vivo and examined by flow cytometry with MAb recognizing AV37, known leukocyte antigens, and MDV antigens pp38 and gB. CD28 mRNA was examined by PCR. Cell cycle distribution and in vitro survival were compared for each lymphoma cell population. We demonstrate for the first time that the antigen recognized by AV37 is expressed at very low levels by small minorities of uninfected leukocytes, whereas particular MD lymphoma cells uniquely express extremely high levels of the AV37 antigen; the AV37hi MD lymphoma cells fulfill the accepted criteria for neoplastic transformation in vivo (protection from cell death despite hyperproliferation, presence in all MD lymphomas, and not supportive of MDV production); the lymphoma environment is essential for AV37+ MD lymphoma cell survival; pp38 is an antigen expressed during MDV-productive infection and is not expressed by neoplastically transformed cells in vivo; AV37+ MD lymphoma cells have the putative immune evasion mechanism of CD28 down-regulation; AV37hi peripheral blood leukocytes appear early after MDV infection in both MD-resistant and -susceptible chickens; and analysis of TCR variable β chain gene family expression suggests that MD lymphomas have polyclonal origins. Identification of the neoplastically transformed cells in MD facilitates a detailed understanding of MD pathogenesis and also improves the utility of MD as a general model for herpesvirus oncology.

Prediction of peptides observable by mass spectrometry applied at the experimental set level

BackgroundWhen proteins are subjected to proteolytic digestion and analyzed by mass spectrometry using a method such as 2D LC MS/MS, only a portion of the proteotypic peptides associated with each protein will be observed. The ability to predict which peptides can and cannot potentially be observed for a particular experimental dataset has several important applications in proteomics research including calculation of peptide coverage in terms of potentially detectable peptides, systems biology analysis of data sets, and protein quantification.ResultsWe have developed a methodology for constructing artificial neural networks that can be used to predict which peptides are potentially observable for a given set of experimental, instrumental, and analytical conditions for 2D LC MS/MS (a.k.a Multidimensional Protein Identification Technology [MudPIT]) datasets. Neural network classifiers constructed using this procedure for two MudPIT datasets exhibit 10-fold cross validation accuracy of about 80%. We show that a classifier constructed for one dataset has poor predictive performance with the other dataset, thus demonstrating the need for dataset specific classifiers. Classification results with each dataset are used to compute informative percent amino acid coverage statistics for each protein in terms of the predicted detectable peptides in addition to the percent coverage of the complete sequence. We also demonstrate the utility of predicted peptide observability for systems analysis to help determine if proteins that were expected but not observed generate sufficient peptides for detection.ConclusionClassifiers that accurately predict the likelihood of detecting proteotypic peptides by mass spectrometry provide proteomics researchers with powerful new approaches for data analysis. We demonstrate that the procedure we have developed for building a classifier based on an individual experimental data set results in classifiers with accuracy comparable to those reported in the literature based on large training sets collected from multiple experiments. Our approach allows the researcher to construct a classifier that is specific for the experimental, instrument, and analytical conditions of a single experiment and amenable to local, condition-specific, implementation. The resulting classifiers have application in a number of areas such as determination of peptide coverage for protein identification, pathway analysis, and protein quantification.

Prediction of Cell Penetrating Peptides by Support Vector Machines

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.