In vitro characterization of neonatal, juvenile, and adult porcine islet oxygen demand, ß-cell function, and transcriptomes.

BACKGROUND: There is currently a shortage of human donor pancreata which limits the broad application of islet transplantation as a treatment for type 1 diabetes. Porcine islets have demonstrated potential as an alternative source, but a study evaluating islets from different donor ages under unified protocols has yet to be conducted. METHODS: Neonatal porcine islets (NPI; 1-3 days), juvenile porcine islets (JPI; 18-21 days), and adult porcine islets (API; 2+ years) were compared in vitro, including assessments of oxygen consumption rate, membrane integrity determined by FDA/PI staining, ß-cell proliferation, dynamic glucose-stimulated insulin secretion, and RNA sequencing. RESULTS: Oxygen consumption rate normalized to DNA was not significantly different between ages. Membrane integrity was age dependent, and API had the highest percentage of intact cells. API also had the highest glucose-stimulated insulin secretion response during a dynamic insulin secretion assay and had 50-fold higher total insulin content compared to NPI and JPI. NPI and JPI had similar glucose responsiveness, ß-cell percentage, and ß-cell proliferation rate. Transcriptome analysis was consistent with physiological assessments. API transcriptomes were enriched for cellular metabolic and insulin secretory pathways, while NPI exhibited higher expression of genes associated with proliferation. CONCLUSIONS: The oxygen demand, membrane integrity, ß-cell function and proliferation, and transcriptomes of islets from API, JPI, and NPI provide a comprehensive physiological comparison for future studies. These assessments will inform the optimal application of each age of porcine islet to expand the availability of islet transplantation.

Immunoglobulin-like receptors in chickens: identification, functional characterization, and renaming to cluster homolog of immunoglobulin-like receptors.

The cluster homolog of immunoglobulin-like receptors (CHIRs), previously known as the "chicken homolog of immunogloublin-like receptors," represents is a large group of transmembrane glycoproteins that direct the immune response. However, the full repertoire of putatively activating, inhibitory, or dual function CHIRA, CHIRB, and CHIRAB on chickens' immune responses is poorly understood. Herein, the study objective was to determine the genes encoding CHIR proteins and predict their function by searching canonical protein structure. A bioinformatics pipeline based on previous work was employed to search for the CHIRs from the newly updated broiler and layer genomes. The categorization into CHIRA, CHIRB, and CHIRAB types was assigned through motif searches, multiple sequence alignment, and phylogeny. In total, 150 protein-encoding genes on Chromosome 31 were identified as CHIRs. Gene members of each functional group (CHIRA, CHIRB, CHIRAB) were classified in accordance with previously recognized proteins. The genes were renamed to "cluster homolog of immunoglobulin-like receptors" (CHIRs) to allow for the naming of orthologous genes in other avian species. Additionally, expression analysis of the classified CHIRs across various reinforces their importance as immune regulators and activation in inflammatory tissues. Furthermore, over 1,000 diverse and rare CHIRs variants associated with differential Marek's disease response (P < 0.05) emphasize the impact of CHIRs on shaping avian immune responses in diverse contexts. The practical applications of these findings encompass advancing immunology, improving poultry health management, optimizing breeding programs for disease resistance, and enhancing overall animal health through a deeper understanding of the roles and functions of CHIRA, CHIRB, and CHIRAB types in avian immune responses.

Comparative proteomic analysis of tail regeneration in the green anole lizard, Anolis carolinensis.

As amniote vertebrates, lizards are the most closely related organisms to humans capable of appendage regeneration. Lizards can autotomize, or release their tails as a means of predator evasion, and subsequently regenerate a functional replacement. Green anoles (Anolis carolinensis) can regenerate their tails through a process that involves differential expression of hundreds of genes, which has previously been analyzed by transcriptomic and microRNA analysis. To investigate protein expression in regenerating tissue, we performed whole proteomic analysis of regenerating tail tip and base. This is the first proteomic data set available for any anole lizard. We identified a total of 2,646 proteins - 976 proteins only in the regenerating tail base, 796 only in the tail tip, and 874 in both tip and base. For over 90% of these proteins in these tissues, we were able to assign a clear orthology to gene models in either the Ensembl or NCBI databases. For 13 proteins in the tail base, 9 proteins in the tail tip, and 10 proteins in both regions, the gene model in Ensembl and NCBI matched an uncharacterized protein, confirming that these predictions are present in the proteome. Ontology and pathways analysis of proteins expressed in the regenerating tail base identified categories including actin filament-based process, ncRNA metabolism, regulation of phosphatase activity, small GTPase mediated signal transduction, and cellular component organization or biogenesis. Analysis of proteins expressed in the tail tip identified categories including regulation of organelle organization, regulation of protein localization, ubiquitin-dependent protein catabolism, small GTPase mediated signal transduction, morphogenesis of epithelium, and regulation of biological quality. These proteomic findings confirm pathways and gene families activated in tail regeneration in the green anole as well as identify uncharacterized proteins whose role in regrowth remains to be revealed. This study demonstrates the insights that are possible from the integration of proteomic and transcriptomic data in tail regrowth in the green anole, with potentially broader application to studies in other regenerative models.

Current challenges and future of agricultural genomes to phenomes in the USA.

Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies expand public-private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the "Thinking Big: Visualizing the Future of AG2PI" two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome to phenome (AG2P) work and envision the future of the AG2P field. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: (1) innovate in genetic improvement methods development and evaluation and (2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research.

AgBase: supporting functional modeling in agricultural organisms.

AgBase (http://www.agbase.msstate.edu/) provides resources to facilitate modeling of functional genomics data and structural and functional annotation of agriculturally important animal, plant, microbe and parasite genomes. The website is redesigned to improve accessibility and ease of use, including improved search capabilities. Expanded capabilities include new dedicated pages for horse, cat, dog, cotton, rice and soybean. We currently provide 590 240 Gene Ontology (GO) annotations to 105 454 gene products in 64 different species, including GO annotations linked to transcripts represented on agricultural microarrays. For many of these arrays, this provides the only functional annotation available. GO annotations are available for download and we provide comprehensive, species-specific GO annotation files for 18 different organisms. The tools available at AgBase have been expanded and several existing tools improved based upon user feedback. One of seven new tools available at AgBase, GOModeler, supports hypothesis testing from functional genomics data. We host several associated databases and provide genome browsers for three agricultural pathogens. Moreover, we provide comprehensive training resources (including worked examples and tutorials) via links to Educational Resources at the AgBase website.

Proteomic insight into human directed selection of the domesticated chicken Gallus gallus.

Chicken domestication began at least 3,500 years ago for purposes of divination, cockfighting, and food. Prior to industrial scale chicken production, domestication selected larger birds with increased egg production. In the mid-20th century companies began intensive selection with the broiler (meat) industry focusing on improved feed conversion, rapid growth, and breast muscle yield. Here we present proteomic analysis comparing the modern broiler line, Ross 708, with the UIUC legacy line which is not selected for growth traits. Breast muscle proteome analysis identifies cellular processes that have responded to human directed artificial selection. Mass spectrometry was used to identify protein level differences in the breast muscle of 6-day old chicks from Modern and Legacy lines. Our results indicate elevated levels of stress proteins, ribosomal proteins and proteins that participate in the innate immune pathway in the Modern chickens. Furthermore, the comparative analyses indicated expression differences for proteins involved in multiple biochemical pathways. In particular, the Modern line had elevated levels of proteins affecting the pentose phosphate pathway, TCA cycle and fatty acid oxidation while proteins involved in the first phase of glycolysis were reduced compared to the Legacy line. These analyses provide hypotheses linking the morphometric changes driven by human directed selection to biochemical pathways. These results also have implications for the poultry industry, specifically Wooden Breast disease which is linked to rapid breast muscle growth.

Systemic response to Campylobacter jejuni infection by profiling gene transcription in the spleens of two genetic lines of chickens.

Campylobacter jejuni (C. jejuni) is a leading cause of human bacterial enteritis worldwide with poultry products being a major source of C. jejuni contamination. The chicken is the natural reservoir of C. jejuni where bacteria colonize the digestive tract of poultry, but rarely cause symptoms of disease. To understand the systemic molecular response mechanisms to C. jejuni infection in chickens, total splenic RNA was isolated and applied to a whole genome chicken microarray for comparison between infected (I) and non-infected (N) chickens within and between genetic lines A and B. There were more total splenic host genes responding to the infection in resistant line A than in susceptible line B. Specifically, genes for lymphocyte activation, differentiation and humoral response, and Ig light and heavy chain were upregulated in the resistant line. In the susceptible line, genes for regulation of erythrocyte differentiation, hemopoiesis, and RNA biosynthetic process were all downregulated. An interaction analysis between genetic lines and treatment demonstrated distinct defense mechanisms between lines: the resistant line promoted apoptosis and cytochrome c release from mitochondria, whereas the susceptible line responded with a downregulation of both functions. This was the first time that such systemic defensive mechanisms against C. jejuni infection have been reported. The results of this study revealed novel molecular mechanisms of the systemic host responses to C. jejuni infection in chickens that warrant further investigation.

The proteogenomic mapping tool.

BACKGROUND: High-throughput mass spectrometry (MS) proteomics data is increasingly being used to complement traditional structural genome annotation methods. To keep pace with the high speed of experimental data generation and to aid in structural genome annotation, experimentally observed peptides need to be mapped back to their source genome location quickly and exactly. Previously, the tools to do this have been limited to custom scripts designed by individual research groups to analyze their own data, are generally not widely available, and do not scale well with large eukaryotic genomes. RESULTS: The Proteogenomic Mapping Tool includes a Java implementation of the Aho-Corasick string searching algorithm which takes as input standardized file types and rapidly searches experimentally observed peptides against a given genome translated in all 6 reading frames for exact matches. The Java implementation allows the application to scale well with larger eukaryotic genomes while providing cross-platform functionality. CONCLUSIONS: The Proteogenomic Mapping Tool provides a standalone application for mapping peptides back to their source genome on a number of operating system platforms with standard desktop computer hardware and executes very rapidly for a variety of datasets. Allowing the selection of different genetic codes for different organisms allows researchers to easily customize the tool to their own research interests and is recommended for anyone working to structurally annotate genomes using MS derived proteomics data.

Workflows for Rapid Functional Annotation of Diverse Arthropod Genomes.

Genome sequencing of a diverse array of arthropod genomes is already underway, and these genomes will be used to study human health, agriculture, biodiversity, and ecology. These new genomes are intended to serve as community resources and provide the foundational information required to apply 'omics technologies to a more diverse set of species. However, biologists require genome annotation to use these genomes and derive a better understanding of complex biological systems. Genome annotation incorporates two related, but distinct, processes: Demarcating genes and other elements present in genome sequences (structural annotation); and associating a function with genetic elements (functional annotation). While there are well-established and freely available workflows for structural annotation of gene identification in newly assembled genomes, workflows for providing the functional annotation required to support functional genomics studies are less well understood. Genome-scale functional annotation is required for functional modeling (enrichment, networks, etc.). A first-pass genome-wide functional annotation effort can rapidly identify under-represented gene sets for focused community annotation efforts. We present an open-source, open access, and containerized pipeline for genome-scale functional annotation of insect proteomes and apply it to various arthropod species. We show that the performance of the predictions is consistent across a set of arthropod genomes with varying assembly and annotation quality.

Proteome profile of the developing maize (Zea mays L.) rachis.

In this study, we performed the first high-throughput proteomic analysis of developing rachis (cob) from maize genotype Mp313E. Using two proteomic approaches, 2-DE and 2-D LC, we identified 967 proteins. A 2-D proteome reference map was established. Functional classification of identified proteins revealed that proteins involved in various cellular metabolisms, response to stimulus and transport, were the most abundant.

Analysis of Bovine Viral Diarrhea Viruses-infected monocytes: identification of cytopathic and non-cytopathic biotype differences.

BACKGROUND: Bovine Viral Diarrhea Virus (BVDV) infection is widespread in cattle worldwide, causing important economic losses. Pathogenesis of the disease caused by BVDV is complex, as each BVDV strain has two biotypes: non-cytopathic (ncp) and cytopathic (cp). BVDV can cause a persistent latent infection and immune suppression if animals are infected with an ncp biotype during early gestation, followed by a subsequent infection of the cp biotype. The molecular mechanisms that underscore the complex disease etiology leading to immune suppression in cattle caused by BVDV are not well understood. RESULTS: Using proteomics, we evaluated the effect of cp and ncp BVDV infection of bovine monocytes to determine their role in viral immune suppression and uncontrolled inflammation. Proteins were isolated by differential detergent fractionation and identified by 2D-LC ESI MS/MS. We identified 137 and 228 significantly altered bovine proteins due to ncp and cp BVDV infection, respectively. Functional analysis of these proteins using the Gene Ontology (GO) showed multiple under- and over- represented GO functions in molecular function, biological process and cellular component between the two BVDV biotypes. Analysis of the top immunological pathways affected by BVDV infection revealed that pathways representing macropinocytosis signalling, virus entry via endocytic pathway, integrin signalling and primary immunodeficiency signalling were identified only in ncp BVDV-infected monocytes. In contrast, pathways like actin cytoskeleton signalling, RhoA signalling, clathrin-mediated endocytosis signalling and interferon signalling were identified only in cp BDVD-infected cells. Of the six common pathways involved in cp and ncp BVDV infection, acute phase response signalling was the most significant for both BVDV biotypes. Although, most shared altered host proteins between both BVDV biotypes showed the same type of change, integrin alpha 2b (ITGA2B) and integrin beta 3 (ITGB3) were down- regulated by ncp BVDV and up- regulated by cp BVDV infection. CONCLUSIONS: This study shows that, as we expected, there are significant functional differences in the host proteins that respond to cp or ncp BVDV infection. The combined use of GO and systems biology network modelling facilitated a better understanding of host-pathogen interactions.

GOModeler--a tool for hypothesis-testing of functional genomics datasets.

BACKGROUND: Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating the molecular mechanisms that govern the biology under investigation. A number of publicly available 'discovery-based' computational tools use the computationally amenable Gene Ontology (GO) for hypothesis generation. However, there are few tools that support hypothesis-based testing using the GO and none that support testing with user defined hypothesis terms.Here, we present GOModeler, a tool that enables researchers to conduct hypothesis-based testing of high throughput datasets using the GO. GOModeler summarizes the overall effect of a user defined gene/protein differential expression dataset on specific GO hypothesis terms selected by the user to describe a biological experiment. The design of the tool allows the user to complement the functional information in the GO with his/her domain specific expertise for comprehensive hypothesis testing. RESULTS: GOModeler tests the relevance of the hypothesis terms chosen by the user for the input gene dataset by providing the individual effects of the genes on the hypothesis terms and the overall effect of the entire dataset on each of the hypothesis terms. It matches the GO identifiers (ids) of the genes with the GO ids of the hypothesis terms and parses the names of those ids that match to assign effects. We demonstrate the capabilities of GOModeler with a dataset of nine differentially expressed cytokine genes and compare the results to those obtained through manual analysis of the dataset by an immunologist. The direction of overall effects on all hypothesis terms except one was consistent with the results obtained by manual analysis. The tool's editing capability enables the user to augment the information extracted. GOModeler is available as a part of the AgBase tool suite (http://www.agbase.msstate.edu). CONCLUSIONS: GOModeler allows hypothesis driven analysis of high throughput datasets using the GO. Using this tool, researchers can quickly evaluate the overall effect of quantitative expression changes of gene set on specific biological processes of interest. The results are provided in both tabular and graphical formats.

Gene Ontology annotation quality analysis in model eukaryotes.

Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers' ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases).

ArrayIDer: automated structural re-annotation pipeline for DNA microarrays.

BACKGROUND: Systems biology modeling from microarray data requires the most contemporary structural and functional array annotation. However, microarray annotations, especially for non-commercial, non-traditional biomedical model organisms, are often dated. In addition, most microarray analysis tools do not readily accept EST clone names, which are abundantly represented on arrays. Manual re-annotation of microarrays is impracticable and so we developed a computational re-annotation tool (ArrayIDer) to retrieve the most recent accession mapping files from public databases based on EST clone names or accessions and rapidly generate database accessions for entire microarrays. RESULTS: We utilized the Fred Hutchinson Cancer Research Centre 13K chicken cDNA array - a widely-used non-commercial chicken microarray - to demonstrate the principle that ArrayIDer could markedly improve annotation. We structurally re-annotated 55% of the entire array. Moreover, we decreased non-chicken functional annotations by 2 fold. One beneficial consequence of our re-annotation was to identify 290 pseudogenes, of which 66 were previously incorrectly annotated. CONCLUSION: ArrayIDer allows rapid automated structural re-annotation of entire arrays and provides multiple accession types for use in subsequent functional analysis. This information is especially valuable for systems biology modeling in the non-traditional biomedical model organisms.

Facilitating functional annotation of chicken microarray data.

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 Gene Ontology (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: We have 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 the most 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 GO Mapper (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.

Structural and functional-annotation of an equine whole genome oligoarray.

BACKGROUND: The horse genome is sequenced, allowing equine researchers to use high-throughput functional genomics platforms such as microarrays; next-generation sequencing for gene expression and proteomics. However, for researchers to derive value from these functional genomics datasets, they must be able to model this data in biologically relevant ways; to do so requires that the equine genome be more fully annotated. There are two interrelated types of genomic annotation: structural and functional. Structural annotation is delineating and demarcating the genomic elements (such as genes, promoters, and regulatory elements). Functional annotation is assigning function to structural elements. The Gene Ontology (GO) is the de facto standard for functional annotation, and is routinely used as a basis for modelling and hypothesis testing, large functional genomics datasets. RESULTS: An Equine Whole Genome Oligonucleotide (EWGO) array with 21,351 elements was developed at Texas A&M University. This 70-mer oligoarray was designed using the approximately 7 x assembled and annotated sequence of the equine genome to be one of the most comprehensive arrays available for expressed equine sequences. To assist researchers in determining the biological meaning of data derived from this array, we have structurally annotated it by mapping the elements to multiple database accessions, including UniProtKB, Entrez Gene, NRPD (Non-Redundant Protein Database) and UniGene. We next provided GO functional annotations for the gene transcripts represented on this array. Overall, we GO annotated 14,531 gene products (68.1% of the gene products represented on the EWGO array) with 57,912 annotations. GAQ (GO Annotation Quality) scores were calculated for this array both before and after we added GO annotation. The additional annotations improved the meanGAQ score 16-fold. This data is publicly available at AgBase http://www.agbase.msstate.edu/. CONCLUSION: Providing additional information about the public databases which link to the gene products represented on the array allows users more flexibility when using gene expression modelling and hypothesis-testing computational tools. Moreover, since different databases provide different types of information, users have access to multiple data sources. In addition, our GO annotation underpins functional modelling for most gene expression analysis tools and enables equine researchers to model large lists of differentially expressed transcripts in biologically relevant ways.

Introduction to the proceedings of the Avian Genomics and Gene Ontology Annotation Workshop.

The Avian Genomics Conference and Gene Ontology Annotation Workshop brought together researchers and students from around the world to present their latest research addressing the delivery of value from the billions of base-pairs of Archosaur sequence that have become available in the last few years. This editorial describes the conference itself and introduces the ten peer-reviewed manuscripts accepted for publications in the proceedings. These manuscripts address issues ranging from the poultry industry view of USDA genomics policy to the genomics of a wide variety of Archeosaur species including chicken, duck, alligator, and condors and their pathogens.

The Chicken Gene Nomenclature Committee report.

Comparative genomics is an essential component of the post-genomic era. The chicken genome is the first avian genome to be sequenced and it will serve as a model for other avian species. Moreover, due to its unique evolutionary niche, the chicken genome can be used to understand evolution of functional elements and gene regulation in mammalian species. However comparative biology both within avian species and within amniotes is hampered due to the difficulty of recognising functional orthologs. This problem is compounded as different databases and sequence repositories proliferate and the names they assign to functional elements proliferate along with them. Currently, genes can be published under more than one name and one name sometimes refers to unrelated genes. Standardized gene nomenclature is necessary to facilitate communication between scientists and genomic resources. Moreover, it is important that this nomenclature be based on existing nomenclature efforts where possible to truly facilitate studies between different species. We report here the formation of the Chicken Gene Nomenclature Committee (CGNC), an international and centralized effort to provide standardized nomenclature for chicken genes. The CGNC works in conjunction with public resources such as NCBI and Ensembl and in consultation with existing nomenclature committees for human and mouse. The CGNC will develop standardized nomenclature in consultation with the research community and relies on the support of the research community to ensure that the nomenclature facilitates comparative and genomic studies.

Sequential detergent extraction prior to mass spectrometry analysis.

Sequential detergent extraction of proteins from eukaryotic cells has been used to increase proteome coverage of 2D-PAGE. We have adapted sequential detergent extraction for use with the high-throughput non-electrophoretic proteomics method of liquid chromatography and electrospray ionisation tandem mass spectrometry. This method of extraction yields comprehensive proteomes that include up to twice as many membrane proteins as other published methods. Two thirds of these membrane proteins have more than one transmembrane domain and many of these have multiple transmembrane domains. Since sequential detergent extraction (SDE) separates proteins based upon their physicochemistry and sub-cellular localisation, this method also provides useful data about cellular localisation.