Comparison of diagnostic tests for the detection of Brucella spp. in camel sera.

BACKGROUND: Brucellosis in livestock causes enormous losses for economies of developing countries and poses a severe health risk to consumers of dairy products. Little information is known especially on camel brucellosis and its impact on human health. For surveillance and control of the disease, sensitive and reliable detection methods are needed. Although serological tests are the mainstay of diagnosis in camel brucellosis, these tests have been directly transposed from cattle without adequate validation. To date, little information on application of real-time PCR for detection of Brucella in camel serum is available. Therefore, this study was performed to compare the diagnostic efficiency of different serological tests and real-time PCR in order to identify the most sensitive, rapid and simple combination of tests for detecting Brucella infection in camels. FINDINGS: A total of 895 serum samples collected from apparently healthy Sudanese camels was investigated. Sudan is a well documented endemic region for brucellosis with cases in humans, ruminants, and camels. Rose Bengal Test (RBT), Complement Fixation Test (CFT), Slow Agglutination Test (SAT), Competitive Enzyme Linked Immunosorbant Assay (cELISA) and Fluorescence Polarization Assay (FPA) as well as real-time PCR were used. Our findings revealed that bcsp31 kDa real-time PCR detected Brucella DNA in 84.8% (759/895) of the examined samples, of which 15.5% (118/759) were serologically negative. Our results show no relevant difference in sensitivity between the different serological tests. FPA detected the highest number of positive cases (79.3%) followed by CFT (71.4%), RBT (70.7%), SAT (70.6%) and cELISA (68.8%). A combination of real-time PCR with one of the used serological tests identified brucellosis in more than 99% of the infected animals. 59.7% of the examined samples were positive in all serological tests and real-time PCR. A subpopulation of 6.8% of animals was positive in all serological tests but negative in real-time PCR assays. The high percentage of positive cases in this study does not necessarily reflect the seroprevalence of the disease in the country but might be caused by the fact that the camels were imported from brucellosis infected herds of Sudan, accidentally. Seroprevalence of brucellosis in camels should be examined in confirmatory studies to evaluate the importance of brucellosis in this animal species. CONCLUSION: We suggest combining bcsp31 real-time PCR with either FPA, CFT, RBT or SAT to screen camels for brucellosis.

Annotating novel genes by integrating synthetic lethals and genomic information.

BACKGROUND: Large scale screening for synthetic lethality serves as a common tool in yeast genetics to systematically search for genes that play a role in specific biological processes. Often the amounts of data resulting from a single large scale screen far exceed the capacities of experimental characterization of every identified target. Thus, there is need for computational tools that select promising candidate genes in order to reduce the number of follow-up experiments to a manageable size. RESULTS: We analyze synthetic lethality data for arp1 and jnm1, two spindle migration genes, in order to identify novel members in this process. To this end, we use an unsupervised statistical method that integrates additional information from biological data sources, such as gene expression, phenotypic profiling, RNA degradation and sequence similarity. Different from existing methods that require large amounts of synthetic lethal data, our method merely relies on synthetic lethality information from two single screens. Using a Multivariate Gaussian Mixture Model, we determine the best subset of features that assign the target genes to two groups. The approach identifies a small group of genes as candidates involved in spindle migration. Experimental testing confirms the majority of our candidates and we present she1 (YBL031W) as a novel gene involved in spindle migration. We applied the statistical methodology also to TOR2 signaling as another example. CONCLUSION: We demonstrate the general use of Multivariate Gaussian Mixture Modeling for selecting candidate genes for experimental characterization from synthetic lethality data sets. For the given example, integration of different data sources contributes to the identification of genetic interaction partners of arp1 and jnm1 that play a role in the same biological process.

Network analysis of systems elements.

A central goal of postgenomic research is to assign a function to every predicted gene. Because genes often cooperate in order to establish and regulate cellular events the examination of a gene has also included the search for at least a few interacting genes. This requires a strong hypothesis about possible interaction partners, which has often been derived from what was known about the gene or protein beforehand. Many times, though, this prior knowledge has either been completely lacking, biased towards favored concepts, or only partial due to the theoretically vast interaction space. With the advent of high-throughput technology and robotics in biological research, it has become possible to study gene function on a global scale, monitoring entire genomes and proteomes at once. These systematic approaches aim at considering all possible dependencies between genes or their products, thereby exploring the interaction space at a systems scale. This chapter provides an introduction to network analysis and illustrates the corresponding concepts on the basis of gene expression data. First, an overview of existing methods for the identification of co-regulated genes is given. Second, the issue of topology inference is discussed and as an example a specific inference method is presented. And lastly, the application of these techniques is demonstrated for the Arabidopsis thaliana isoprenoid pathway.