Publications by authors named "Cathy R Gresham"

8 Publications

  • Page 1 of 1

Identification of active deubiquitinases in the chicken tissues.

Proteomics 2021 Oct 13:e2100122. Epub 2021 Oct 13.

Department of Microbiology and Cell Science, College of Agricultural and Life Sciences, University of Florida, Gainesville, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/pmic.202100122DOI Listing
October 2021

An atlas of the catalytically active liver and spleen kinases in chicken identified by chemoproteomics.

J Proteomics 2020 08 2;225:103850. Epub 2020 Jun 2.

Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, USA. Electronic address:

Phosphorylation is a post-translational protein modification regulating most known cellular processes. While protein kinases constitute a large family of highly conserved enzymes, identification of active kinases is challenging due to a low abundance of some of these signaling molecules. Although chicken is the first agricultural animal to have a sequenced genome, annotation of the kinome, i.e., a complement of all protein kinases in the genome is limited. We used chemical probes consisting of ATP and ADP derivatives binding to specific lysine (Lys) residues within the ATP-binding pocket of kinases, combined with proteomics, to identify 267 peptides labeled with the ATP and ADP acyl derivatives and 188 corresponding chicken kinases in chicken spleen and liver. Our description of active chicken kinases and ATP binding sites will support future studies focused on identifying the role of this important class of enzymes in chicken health and disease. SIGNIFICANCE: Advances made in understanding chicken enzymes are critical for the improved knowledge of the regulatory pathways controlling physiological processes in chicken. Since protein phosphorylation controls multiple aspects of cell fate, it is often linked to pathological conditions, and understanding of the kinase expression in chicken is essential for future therapeutic approaches. We coupled proteomics and labeling with active-site probes binding to Lys residues within the ATP-binding pocket of kinases to identify 188 kinases and corresponding 267 peptides labeled with the ATP and ADP acyl derivatives in chicken spleen and liver. Results of the present study describing catalytically active kinases is a starting point for chemoproteomic-based interrogation of kinases in chicken exposed to different conditions. Kinases identified in this study are available through the Chickspress genome browser that has previously published mRNA, miRNA, and shotgun proteomics data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jprot.2020.103850DOI Listing
August 2020

Chickspress: a resource for chicken gene expression.

Database (Oxford) 2019 01;2019

School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson AZ, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/baz058DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556980PMC
January 2019

HPIDB 2.0: a curated database for host-pathogen interactions.

Database (Oxford) 2016 3;2016. Epub 2016 Jul 3.

Institute for Genomics, Biocomputing and Biotechnology, College of Veterinary Medicine, Institute for Genomics, Mississippi State University, Mississippi State, MS 39762, USA

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/database/baw103DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930832PMC
November 2017

Three crocodilian genomes reveal ancestral patterns of evolution among archosaurs.

Science 2014 Dec 11;346(6215):1254449. Epub 2014 Dec 11.

Departamento de Desarrollo Biotecnológico, Instituto de Higiene, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1126/science.1254449DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4386873PMC
December 2014

AgBase: supporting functional modeling in agricultural organisms.

Nucleic Acids Res 2011 Jan 12;39(Database issue):D497-506. Epub 2010 Nov 12.

Department of Basic Sciences, College of Veterinary Medicine, PO Box 6100, Mississippi State University, MS 39762, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/nar/gkq1115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3013706PMC
January 2011

An atlas of bovine gene expression reveals novel distinctive tissue characteristics and evidence for improving genome annotation.

Genome Biol 2010 20;11(10):R102. Epub 2010 Oct 20.

USDA-ARS US Meat Animal Research Center, State Spur 18 D, Clay Center, NE 68901, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/gb-2010-11-10-r102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218658PMC
June 2011

Facilitating functional annotation of chicken microarray data.

BMC Bioinformatics 2009 Oct 8;10 Suppl 11:S2. Epub 2009 Oct 8.

Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, MS 39762, USA.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2105-10-S11-S2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3226191PMC
October 2009
-->