The existing protein annotation in chicken is mostly limited to computational predictions based on orthology to other proteins, which often leads to a significant underestimation of the function of these proteins. Genome-scale experimental annotation can provide insight into the actual enzymatic activities of chicken proteins. Amongst post-translational modifications, ubiquitination is of interest as anomalies in ubiquitination are implicated in such diseases as inflammatory disorders, infectious diseases, or malignancies. Ubiquitination is controlled by deubiquitinases (DUBs), which remove ubiquitin from protein substrates. However, the DUBs have not been systematically annotated and quantified in chicken tissues. Here we used a chemoproteomics approach, which is based on active-site probes specific to DUBs, and identified 26 active DUBs in the chicken spleen, cecum, and liver.

ABSTRACT Background Network studies of host-pathogen interactions (HPI) are critical in understanding the mechanisms of pathogenesis. However, accessible HPI data for agriculturally important pathogens are limited. This lack of HPI data impedes network analysis to study agricultural pathogens, for preventing and reducing the severity of diseases of relevance to agriculture. Results To rapidly provide HPIs for a broad range of pathogens, we use an interolog-based approach. This approach uses sequence similarity to transfer known HPIs from better studied host-pathogen pairs and predicts 389,878 HPIs for 23 host-pathogen systems of relevance to US agriculture. Each predicted HPI is qualitatively assessed using co-localization, infection related processes, and interacting domains and this information is provided as a confidence indicator for the prediction. Evaluation of predicted HPIs demonstrates that the host proteins predicted to be involved in pathogen interactions include hubs and bottlenecks in the network, as reported in curated host proteins. Moreover, we demonstrate that the use of the predicted HPIs adds value to network analysis and recapitulates known aspects of host-pathogen biology. Access to the predicted HPIs for these agricultural host-pathogen systems is available via the Host Pathogen Interaction Database (HPIDB, hpidb.igbb.msstate.edu ), and can be downloaded in standard MITAB file format for subsequent network analysis. Conclusions This core set of interolog-based HPIs will enable animal health researchers to incorporate network analysis into their research and help identify host-pathogen interactions that may be tested and experimentally validated. Moreover, the development of a larger set of experimentally validated HPI will inform future predictions. Our approach of transferring biologically relevant HPIs based on interologs is broadly applicable to many host-microbe systems and can be extended to support network modeling of other pathogens, as well as interactions between non-pathogenic microbes.

Chicken is the first agricultural animal to have a sequenced genome, but current kinase annotations of Gallus gallus are overwhelmingly limited to the predictions generated based on homology or isolated studies focused on specific kinases. Our approach used chemical probes consisting of ATP and ADP derivatives binding to specific lysine residues within the ATP-binding pocket of kinases. Collectively, we identified 188 chicken kinases and corresponding 267 peptides labeled with the ATP and ADP acyl derivatives in chicken spleen and liver. The kinases identified here are publicly available through the database, Chickspress genome browser ( http://geneatlas.arl.arizona.edu/cgi-bin/GeneAtlas/chickspress_genes.cgii ). Analysis of putative functions of these chicken kinases indicates that kinases identified in this study might regulate hematological system development, necrosis, apoptosis, epithelial neoplasm, and other processes. The identified tissue-specific expression atlas of active chicken kinases along with the ATP binding sites of kinases provide the basis for the development of specific drug targets for multiple chicken diseases as well as starting point for inhibitor selectivity studies in this agriculturally important species. Moreover, this study will support future studies focused on identifying the role of these kinases in chicken growth, metabolism, and disease.

High-throughput sequencing and proteomics technologies are markedly increasing the amount of RNA and peptide data that are available to researchers, which are typically made publicly available via data repositories such as the NCBI Sequence Read Archive and proteome archives, respectively. These data sets contain valuable information about when and where gene products are expressed, but this information is not readily obtainable from archived data sets. Here we report Chickspress (http://geneatlas.arl.arizona.edu), the first publicly available gene expression resource for chicken tissues. Since there is no single source of chicken gene models, Chickspress incorporates both NCBI and Ensembl gene models and links these gene sets with experimental gene expression data and QTL information. By linking gene models from both NCBI and Ensembl gene prediction pipelines, researchers can, for the first time, easily compare gene models from each of these prediction workflows to available experimental data for these products. We use Chickspress data to show the differences between these gene annotation pipelines. Chickspress also provides rapid search, visualization and download capacity for chicken gene sets based upon tissue type, developmental stage and experiment type. This first Chickspress release contains 161 gene expression data sets, including expression of mRNAs, miRNAs, proteins and peptides. We provide several examples demonstrating how researchers may use this resource.

Protein-protein interactions between a microbial pathogen and its host play a vital role in initiating and maintaining infection, and host-pathogen interactions (HPI) are required for understanding disease. The Host-Pathogen Interaction Database (HPIDB, http://www.agbase.msstate.edu/hpi/main.html ) is a public resource that facilitates annotation, modeling, analysis and computational prediction of HPIs. HPIDB provides manually curated, experimentally verified HPI and integrates existing HPI compiled from multiple databases. The HPIDB curation effort complies with IMEx consortium standards and focuses on livestock pathogens. To date (February 2017), HPIDB contains a total of 52,953 HPI entries (58 hosts and 524 pathogens). Despite this current progress in experimental HPIs data, many diseases have little or no available HPIs. To aid researchers in targeting and experimentally validating other important HPIs, we are developing a prediction model by leveraging the existing experimentally verified HPIs. We use Machine Learning to compare multiple protein-related features and computationally predict novel HPIs for a broad range of pathogens. We will report on the effort to develop this prediction tool, including accuracy and ability to recover known HPIs. Our goal is to develop this prediction workflow as an online tool so that researchers can rapidly predict and model interactions for their own pathogens of interest in a genome wide scale; we expect that this modeling will complement wet lab work to identify potential therapeutic targets.

Background: Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to GeneOntology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information. Results: Wehave improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%.We have also identified themost significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GOMapper (AGOM) tool to help researchers to quickly retrieve corresponding functional information for their dataset. Conclusion: Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using AGOM tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and will be updated on regular basis.

The American alligator, Alligator mississippiensis, like all crocodilians, has temperature-dependent sex determination, in which the sex of an embryo is determined by the incubation temperature of the egg during a critical period of development. The lack of genetic differences between male and female alligators leaves open the question of how the genes responsible for sex determination and differentiation are regulated. Insight into this question comes from the fact that exposing an embryo incubated at male-producing temperature to estrogen causes it to develop ovaries. Because estrogen response elements are known to regulate genes over long distances, a contiguous genome assembly is crucial for predicting and understanding their impact. We present an improved assembly of the American alligator genome, scaffolded with in vitro proximity ligation (Chicago) data. We use this assembly to scaffold two other crocodilian genomes based on synteny. We perform RNA sequencing of tissues from American alligator embryos to find genes that are differentially expressed between embryos incubated at male- versus female-producing temperature. Finally, we use the improved contiguity of our assembly along with the current model of CTCF-mediated chromatin looping to predict regions of the genome likely to contain estrogen-responsive genes. We find that these regions are significantly enriched for genes with female-biased expression in developing gonads after the critical period during which sex is determined by incubation temperature. We thus conclude that estrogen signaling is a major driver of female-biased gene expression in the post-temperature sensitive period gonads.

The tarnished plant bug (TPB), Lygus lineolaris (Palisot de Beauvois) is a polyphagous, phytophagous insect that has emerged as a major pest of cotton, alfalfa, fruits, and vegetable crops in the eastern United States and Canada. Using its piercing-sucking mouthparts, TPB employs a "lacerate and flush" feeding strategy in which saliva injected into plant tissue degrades cell wall components and lyses cells whose contents are subsequently imbibed by the TPB. It is known that a major component of TPB saliva is the polygalacturonase enzymes that degrade the pectin in the cell walls. However, not much is known about the other components of the saliva of this important pest. In this study, we explored the salivary gland transcriptome of TPB using Illumina sequencing. After in silico conversion of RNA sequences into corresponding polypeptides, 25,767 putative proteins were discovered. Of these, 19,540 (78.83%) showed significant similarity to known proteins in the either the NCBI nr or Uniprot databases. Gene ontology (GO) terms were assigned to 7,512 proteins, and 791 proteins in the sialotranscriptome of TPB were found to collectively map to 107 Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathways. A total of 3,653 Pfam domains were identified in 10,421 sialotranscriptome predicted proteins resulting in 12,814 Pfam annotations; some proteins had more than one Pfam domain. Functional annotation revealed a number of salivary gland proteins that potentially facilitate degradation of host plant tissues and mitigation of the host plant defense response. These transcripts/proteins and their potential roles in TPB establishment are described.

Identification and analysis of host–pathogen interactions (HPI) is essential to study infectious diseases. However, HPI data are sparse in existing molecular interaction databases, especially for agricultural host–pathogen systems. Therefore, resources that annotate, predict and display the HPI that underpin infectious diseases are critical for developing novel intervention strategies. HPIDB 2.0 (http://www.agbase.msstate.edu/hpi/main.html) is a resource for HPI data, and contains 45, 238 manually curated entries in the current release. Since the first description of the database in 2010, multiple enhancements to HPIDB data and interface services were made that are described here. Notably, HPIDB 2.0 now provides targeted biocuration of molecular interaction data. As a member of the International Molecular Exchange consortium, annotations provided by HPIDB 2.0 curators meet community standards to provide detailed contextual experimental information and facilitate data sharing. Moreover, HPIDB 2.0 provides access to rapidly available community annotations that capture minimum molecular interaction information to address immediate researcher needs for HPI network analysis. In addition to curation, HPIDB 2.0 integrates HPI from existing external sources and contains tools to infer additional HPI where annotated data are scarce. Compared to other interaction databases, our data collection approach ensures HPIDB 2.0 users access the most comprehensive HPI data from a wide range of pathogens and their hosts (594 pathogen and 70 host species, as of February 2016). Improvements also include enhanced search capacity, addition of Gene Ontology functional information, and implementation of network visualization. The changes made to HPIDB 2.0 content and interface ensure that users, especially agricultural researchers, are able to easily access and analyse high quality, comprehensive HPI data. All HPIDB 2.0 data are updated regularly, are publically available for direct download, and are disseminated to other molecular interaction resources. Database URL: http://www.agbase.msstate.edu/hpi/main.html

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.

Mature mammalian sperm contain a complex population of RNAs some of which might regulate spermatogenesis while others probably play a role in fertilization and early development. Due to this limited knowledge, the biological functions of sperm RNAs remain enigmatic. Here we report the first characterization of the global transcriptome of the sperm of fertile stallions. The findings improved understanding of the biological significance of sperm RNAs which in turn will allow the discovery of sperm-based biomarkers for stallion fertility. The stallion sperm transcriptome was interrogated by analyzing sperm and testes RNA on a 21,000-element equine whole-genome oligoarray and by RNA-seq. Microarray analysis revealed 6,761 transcripts in the sperm, of which 165 were sperm-enriched, and 155 were differentially expressed between the sperm and testes. Next, 70 million raw reads were generated by RNA-seq of which 50% could be aligned with the horse reference genome. A total of 19,257 sequence tags were mapped to all horse chromosomes and the mitochondrial genome. The highest density of mapped transcripts was in gene-rich ECA11, 12 and 13, and the lowest in gene-poor ECA9 and X; 7 gene transcripts originated from ECAY. Structural annotation aligned sperm transcripts with 4,504 known horse and/or human genes, rRNAs and 82 miRNAs, whereas 13,354 sequence tags remained anonymous. The data were aligned with selected equine gene models to identify additional exons and splice variants. Gene Ontology annotations showed that sperm transcripts were associated with molecular processes (chemoattractant-activated signal transduction, ion transport) and cellular components (membranes and vesicles) related to known sperm functions at fertilization, while some messenger and micro RNAs might be critical for early development. The findings suggest that the rich repertoire of coding and non-coding RNAs in stallion sperm is not a random remnant from spermatogenesis in testes but a selectively retained and functionally coherent collection of RNAs.

A public candidate gene testing pipeline for resistance to aflatoxin accumulation or Aspergillus flavus infection in maize is presented here. The pipeline consists of steps for identifying, testing, and verifying the association of selected maize gene sequences with resistance under field conditions. Resources include a database of genetic and protein sequences associated with the reduction in aflatoxin contamination from previous studies; eight diverse inbred maize lines for polymorphism identification within any maize gene sequence; four Quantitative Trait Loci (QTL) mapping populations and one association mapping panel, all phenotyped for aflatoxin accumulation resistance and associated phenotypes; and capacity for Insertion/Deletion (InDel) and SNP genotyping in the population(s) for mapping. To date, ten genes have been identified as possible candidate genes and put through the candidate gene testing pipeline, and results are presented here to demonstrate the utility of the pipeline.

Background A comprehensive transcriptome survey, or gene atlas, provides information essential for a complete understanding of the genomic biology of an organism. We present an atlas of RNA abundance for 92 adult, juvenile and fetal cattle tissues and three cattle cell lines. Results The Bovine Gene Atlas was generated from 7.2 million unique digital gene expression tag sequences (300.2 million total raw tag sequences), from which 1.59 million unique tag sequences were identified that mapped to the draft bovine genome accounting for 85% of the total raw tag abundance. Filtering these tags yielded 87,764 unique tag sequences that unambiguously mapped to 16,517 annotated protein-coding loci in the draft genome accounting for 45% of the total raw tag abundance. Clustering of tissues based on tag abundance profiles generally confirmed ontology classification based on anatomy. There were 5,429 constitutively expressed loci and 3,445 constitutively expressed unique tag sequences mapping outside annotated gene boundaries that represent a resource for enhancing current gene models. Physical measures such as inferred transcript length or antisense tag abundance identified tissues with atypical transcriptional tag profiles. We report for the first time the tissue-specific variation in the proportion of mitochondrial transcriptional tag abundance. Conclusions The Bovine Gene Atlas is the deepest and broadest transcriptome survey of any livestock genome to date. Commonalities and variation in sense and antisense transcript tag profiles identified in different tissues facilitate the examination of the relationship between gene expression, tissue, and gene function.

BACKGROUND:Aspergillus flavus Link:Fr, an opportunistic fungus that produces aflatoxin, is pathogenic to maize and other oilseed crops. Aflatoxin is a potent carcinogen, and its presence markedly reduces the value of grain. Understanding and enhancing host resistance to A. flavus infection and/or subsequent aflatoxin accumulation is generally considered an efficient means of reducing grain losses to aflatoxin. Different proteomic, genomic and genetic studies of maize (Zea mays L.) have generated large data sets with the goal of identifying genes responsible for conferring resistance to A. flavus, or aflatoxin.RESULTS:In order to maximize the usage of different data sets in new studies, including association mapping, we have constructed a relational database with web interface integrating the results of gene expression, proteomic (both gel-based and shotgun), Quantitative Trait Loci (QTL) genetic mapping studies, and sequence data from the literature to facilitate selection of candidate genes for continued investigation. The Corn Fungal Resistance Associated Sequences Database (CFRAS-DB) (http://agbase.msstate.edu/) was created with the main goal of identifying genes important to aflatoxin resistance. CFRAS-DB is implemented using MySQL as the relational database management system running on a Linux server, using an Apache web server, and Perl CGI scripts as the web interface. The database and the associated web-based interface allow researchers to examine many lines of evidence (e.g. microarray, proteomics, QTL studies, SNP data) to assess the potential role of a gene or group of genes in the response of different maize lines to A. flavus infection and subsequent production of aflatoxin by the fungus.CONCLUSIONS:CFRAS-DB provides the first opportunity to integrate data pertaining to the problem of A. flavus and aflatoxin resistance in maize in one resource and to support queries across different datasets. The web-based interface gives researchers different query options for mining the database across different types of experiments. The database is publically available at http://agbase.msstate.edu.