title: Molecular targets for rapid identification of Brucella spp.
abstract: BACKGROUND
  Brucella is an intracellular pathogen capable of infecting animals and humans. There are six recognized species of Brucella that differ in their host preference. The genomes of the three Brucella species have been recently sequenced. Comparison of the three revealed over 98% sequence similarity at the protein level and enabled computational identification of common and differentiating genes. We validated these computational predictions and examined the expression patterns of the putative unique and differentiating genes, using genomic and reverse transcription PCR. We then screened a set of differentiating genes against classical Brucella biovars and showed the applicability of these regions in the design of diagnostic tests.
  
  
  RESULTS
  We have identified and tested set of molecular targets that are associated in unique patterns with each of the sequenced Brucella spp. A comprehensive comparison was made among the published genome sequences of B. abortus, B. melitensis and B. suis. The comparison confirmed published differences between the three Brucella genomes, and identified subsets of features that were predicted to be of interest in a functional comparison of B. melitensis and B. suis to B. abortus. Differentiating sequence regions from B. abortus, B. melitensis and B. suis were used to develop PCR primers to test for the existence and in vitro transcription of these genes in these species. Only B. suis is found to have a significant number of unique genes, but combinations of genes and regions that exist in only two out of three genomes and are therefore useful for diagnostics were identified and confirmed.
  
  
  CONCLUSIONS
  Although not all of the differentiating genes identified were transcribed under steady state conditions, a group of genes sufficient to discriminate unambiguously between B. suis, B. melitensis, and B. abortus was identified. We present an overview of these genomic differences and the use of these features to discriminate among a number of Brucella biovars.
authors.affiliation: Department of Computer Science, College of Information Technology, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. vgratush@uncc.edu
authors.forename: Vladyslava G
authors.initials: VG
authors.lastname: Ratushna
authors.forename: David M
authors.initials: DM
authors.lastname: Sturgill
authors.forename: Sheela
authors.initials: S
authors.lastname: Ramamoorthy
authors.forename: Sherry A
authors.initials: SA
authors.lastname: Reichow
authors.forename: Yongqun
authors.initials: Y
authors.lastname: He
authors.forename: Raju
authors.initials: R
authors.lastname: Lathigra
authors.forename: Nammalwar
authors.initials: N
authors.lastname: Sriranganathan
authors.forename: Shirley M
authors.initials: SM
authors.lastname: Halling
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Boyle
authors.forename: Cynthia J
authors.initials: CJ
authors.lastname: Gibas
mesh_terms: D015373:Bacterial Typing Techniques; D002002:Brucella / Q000145:classification* / Q000235:genetics / Q000302:isolation & purification; D019295:Computational Biology; D005798:Genes, Bacterial; D014644:Genetic Variation; D016680:Genome, Bacterial; D016133:Polymerase Chain Reaction / Q000379:methods*; D012329:RNA, Bacterial / Q000235:genetics / Q000378:metabolism; D020133:Reverse Transcriptase Polymerase Chain Reaction; D017422:Sequence Analysis, DNA
chemical_list: D012329:RNA, Bacterial