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      Analysis of Babesia bovis infection-induced gene expression changes in larvae from the cattle tick, Rhipicephalus (Boophilus) microplus

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          Abstract

          Background

          Cattle babesiosis is a tick-borne disease of cattle that has severe economic impact on cattle producers throughout the world’s tropical and subtropical countries. The most severe form of the disease is caused by the apicomplexan, Babesia bovis, and transmitted to cattle through the bite of infected cattle ticks of the genus Rhipicephalus, with the most prevalent species being Rhipicephalus (Boophilus) microplus. We studied the reaction of the R. microplus larval transcriptome in response to infection by B. bovis.

          Methods

          Total RNA was isolated for both uninfected and Babesia bovis-infected larval samples. Subtracted libraries were prepared by subtracting the B. bovis-infected material with the uninfected material, thus enriching for expressed genes in the B. bovis-infected sample. Expressed sequence tags from the subtracted library were generated, assembled, and sequenced. To complement the subtracted library method, differential transcript expression between samples was also measured using custom high-density microarrays. The microarray probes were fabricated using oligonucleotides derived from the Bmi Gene Index database (Version 2). Array results were verified for three target genes by real-time PCR.

          Results

          Ticks were allowed to feed on a B. bovis-infected splenectomized calf and on an uninfected control calf. RNA was purified in duplicate from whole larvae and subtracted cDNA libraries were synthesized from Babesia-infected larval RNA, subtracting with the corresponding uninfected larval RNA. One thousand ESTs were sequenced from the larval library and the transcripts were annotated. We used a R. microplus microarray designed from a R. microplus gene index, BmiGI Version 2, to look for changes in gene expression that were associated with infection of R. microplus larvae. We found 24 transcripts were expressed at a statistically significant higher level in ticks feeding upon a B. bovis-infected calf contrasted to ticks feeding on an uninfected calf. Six transcripts were expressed at a statistically significant lower level in ticks feeding upon a B. bovis-infected calf contrasted to ticks feeding on an uninfected calf.

          Conclusion

          Our experimental approaches yielded specific differential gene expression associated with the infection of R. microplus by B. bovis. Overall, an unexpectedly low number of transcripts were found to be differentially expressed in response to B. bovis infection. Although the BmiGI Version 2 gene index ( http://compbio.dfci.harvard.edu/tgi/cgi-bin/tgi/gimain.pl?gudb=b_microplus) was a useful database to help assign putative function to some transcripts, a majority of the differentially expressed transcripts did not have annotation that was useful for assignment of function and specialized bioinformatic approaches were necessary to increase the information from these transcriptome experiments.

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          Most cited references25

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            In silico prediction of protein-protein interactions in human macrophages

            Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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              UniRef: comprehensive and non-redundant UniProt reference clusters.

              Redundant protein sequences in biological databases hinder sequence similarity searches and make interpretation of search results difficult. Clustering of protein sequence space based on sequence similarity helps organize all sequences into manageable datasets and reduces sampling bias and overrepresentation of sequences. The UniRef (UniProt Reference Clusters) provide clustered sets of sequences from the UniProt Knowledgebase (UniProtKB) and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. Currently covering >4 million source sequences, the UniRef100 database combines identical sequences and subfragments from any source organism into a single UniRef entry. UniRef90 and UniRef50 are built by clustering UniRef100 sequences at the 90 or 50% sequence identity levels. UniRef100, UniRef90 and UniRef50 yield a database size reduction of approximately 10, 40 and 70%, respectively, from the source sequence set. The reduced redundancy increases the speed of similarity searches and improves detection of distant relationships. UniRef entries contain summary cluster and membership information, including the sequence of a representative protein, member count and common taxonomy of the cluster, the accession numbers of all the merged entries and links to rich functional annotation in UniProtKB to facilitate biological discovery. UniRef has already been applied to broad research areas ranging from genome annotation to proteomics data analysis. UniRef is updated biweekly and is available for online search and retrieval at http://www.uniprot.org, as well as for download at ftp://ftp.uniprot.org/pub/databases/uniprot/uniref. Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Parasit Vectors
                Parasit Vectors
                Parasites & Vectors
                BioMed Central
                1756-3305
                2012
                7 August 2012
                : 5
                : 162
                Affiliations
                [1 ]USDA-ARS, Knipling Bushland US Livestock Insect Research Laboratory, 2700 Fredericksburg Rd, Kerrville, TX, 78028, USA
                [2 ]Department of Mathematics, University of Texas at El Paso, El Paso, TX, 79968, USA
                [3 ]USDA-ARS Animal Disease Research Unit, Pullman, WA, 99164, USA
                [4 ]The Institute for Genetics and Bioinformatics, University of New England, Armidale, NSW, 2351, Australia
                [5 ]International Livestock Research Institute (ILRI) and Biosciences eastern and central Africa (BecA) Hub, PO Box 30709, Nairobi, Kenya
                [6 ]Program in Vector-Borne Diseases, Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, 99164, USA
                Article
                1756-3305-5-162
                10.1186/1756-3305-5-162
                3436708
                22871314
                1f5bedba-d142-49f1-a91e-b24b4e925bf4
                Copyright ©2012 Heekin et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 May 2012
                : 26 July 2012
                Categories
                Research

                Parasitology
                rhipicephalus (boophilus) microplus,larva,serine proteinase inhibitor,transcriptome,babesia bovis,cattle tick

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