Sperm mobility is defined as sperm movement against resistance at body temperature. Although all mobile sperm are motile, not all motile sperm are mobile. Sperm mobility is a primary determinant of male fertility in the chicken. Previous work explained phenotypic variation at the level of the sperm cell and the mitochondrion. The present work was conducted to determine if phenotypic variation could be explained at the level of the proteome using semen donors from lines of chickens selected for low or high sperm mobility. We began by testing the hypothesis that premature mitochondrial failure, and hence sperm immobility, arose from Ca 2+ overloading. The hypothesis was rejected because staining with a cell permeant Ca 2+-specific dye was not enhanced in the case of low mobility sperm. The likelihood that sperm require little energy before ejaculation and the realization that the mitochondrial permeability transition can be induced by oxidative stress arising from inadequate NADH led to the hypothesis that glycolytic enzymes might differ between lines. This possibility was confirmed by 2-dimensional electrophoresis for aldolase and phosphoglycerate kinase 1. This outcome warranted evaluation of the whole cell proteome by differential detergent fractionation and mass spectrometry. Bioinformatics evaluation of proteins with different expression levels confirmed the likelihood that ATP metabolism and glycolysis differ between lines. This experimental outcome corroborated differences observed between lines in previous work, which include mitochondrial ultrastructure, sperm cell oxygen consumption, and straight line velocity. Although glycolytic proteins were more abundant within highly mobile sperm, quantitative PCR of representative testis RNA, which included mRNA for phosphoglycerate kinase 1, found no difference between lines. In summary, we propose a proteome-based model for sperm mobility phenotype in which a genetic predisposition puts sperm cells at risk of premature mitochondrial failure as they pass through the excurrent ducts of the testis. In other words, we attribute mitochondrial failure to sperm cell and reproductive tract attributes that interact to affect sperm in a stochastic manner before ejaculation. In conclusion, our work provides a starting point for understanding chicken semen quality in terms of gene networks. © 2011 American Society of Animal Science. All rights reserved.
Marek's disease (MDV) virus is mainly known for the induction of visceral lymphomas and lymphoid infiltration of peripheral nerves. Recently, additional tropism for the central nervous system has been recognised as a distinct feature of disease induced by very virulent MDV isolates. During the analysis of changes in the peripheral blood leukocyte subpopulations in chickens infected with either a virulent (HPRS-16)or a very virulent (C12/130) strain of MDV, we observed a marked monocytosis in chickens infected with C12/130. Perivascular cuffing in brain and mononuclear cell infiltration into the meninges-of chickens infected with C12/130 were associated with the appearance of the monocytosis from 6-10 days post-infection. Our results show that a peripheral blood monocytosis may be a contributory factor in establishing or accelerating the severity of mononuclear infiltration into the meninges and perivascular spaces in the brain during infection by very virulent C12/130 strain of MDV.
PMID: 22979947;PMCID: PMC3472249;Abstract:
Background: Marek's Disease (MD) is a hyperproliferative, lymphomatous, neoplastic disease of chickens caused by the oncogenic Gallid herpesvirus type 2 (GaHV-2; MDV). Like several human lymphomas the neoplastic MD lymphoma cells overexpress the CD30 antigen (CD30 hi) and are in minority, while the non-neoplastic cells (CD30 lo) form the majority of population. MD is a unique natural in-vivo model of human CD30 hi lymphomas with both natural CD30 hi lymphomagenesis and spontaneous regression. The exact mechanism of neoplastic transformation from CD30 lo expressing phenotype to CD30 hi expressing neoplastic phenotype is unknown. Here, using microarray, proteomics and Systems Biology modeling; we compare the global gene expression of CD30 lo and CD30 hi cells to identify key pathways of neoplastic transformation. We propose and test a specific mechanism of neoplastic transformation, and genetic resistance, involving the MDV oncogene Meq, host gene products of the Nuclear Factor Kappa B (NF-κB) family and CD30; we also identify a novel Meq protein interactome.Results: Our results show that a) CD30 lo lymphocytes are pre-neoplastic precursors and not merely reactive lymphocytes; b) multiple transformation mechanisms exist and are potentially controlled by Meq; c) Meq can drive a feed-forward cycle that induces CD30 transcription, increases CD30 signaling which activates NF-κB, and, in turn, increases Meq transcription; d) Meq transcriptional repression or activation of the CD30 promoter generally correlates with polymorphisms in the CD30 promoter distinguishing MD-lymphoma resistant and susceptible chicken genotypes e) MDV oncoprotein Meq interacts with proteins involved in physiological processes central to lymphomagenesis.Conclusions: In the context of the MD lymphoma microenvironment (and potentially in other CD30 hi lymphomas as well), our results show that the neoplastic transformation is a continuum and the non-neoplastic cells are actually pre-neoplastic precursor cells and not merely immune bystanders. We also show that NF-κB is a central player in MDV induced neoplastic transformation of CD30-expressing lymphocytes in vivo. Our results provide insights into molecular mechanisms of neoplastic transformation in MD specifically and also herpesvirus induced lymphoma in general. © 2012 Kumar et al.; licensee BioMed Central Ltd.
PMID: 23161678;PMCID: PMC3531070;Abstract:
The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bio-informatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources. © The Author(s) 2012.
PMID: 18047724;PMCID: PMC2099493;Abstract:
Background: Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data. Results: ProtQuant was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. ProtQuant has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,ProtQuant implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr. Conclusion: ProtQuant is a tool for protein quantification built for multi-platform use with an intuitive user interface. ProtQuant efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, ProtQuant is available as a self-installing executable for the Windows environment used by many bench scientists. © 2007 Bridges et al; licensee BioMed Central Ltd.