High Throughput Selection of Effective Serodiagnostics for Trypanosoma cruzi infection

The diagnosis of Trypanosoma cruzi infection (the cause of human Chagas disease) is difficult because the symptoms of the infection are often absent or non-specific, and because the parasites themselves are usually below the level of detection in the infected subjects. Therefore, diagnosis generally depends on the measurement of T. cruzi–specific antibodies produced in response to the infection. However, current methods to detect anti–T. cruzi antibodies are relatively poor. In this study, we have conducted a broad screen of >400 T. cruzi proteins to identify those proteins which are best able to detect anti–T. cruzi antibodies. Using a set of proteins selected by this screen, we were able to detect 100% of >100 confirmed positive human cases of T. cruzi infection, as well as suspect cases that were negative using existing tests. This protein panel was also able to detect apparent changes in infection status following drug treatment of individuals with chronic T. cruzi infection. The results of this study should allow for significant improvements in the detection of T. cruzi infection and better screening methods to avoid blood transfusion–related transmission of the infection, and offer a crucial tool for determining the success or failure of drug treatment and other intervention strategies to limit the impact of Chagas disease

Genome-wide transcriptional profiling of peripheral blood leukocytes from cattle infected with Mycobacterium bovis reveals suppression of host immune genes

Background Mycobacterium bovis is the causative agent of bovine tuberculosis (BTB), a pathological infection with significant economic impact. Recent studies have highlighted the role of functional genomics to better understand the molecular mechanisms governing the host immune response to M. bovis infection. Furthermore, these studies may enable the identification of novel transcriptional markers of BTB that can augment current diagnostic tests and surveillance programmes. In the present study, we have analysed the transcriptome of peripheral blood leukocytes (PBL) from eight M. bovis-infected and eight control non-infected age-matched and sex-matched Holstein-Friesian cattle using the Affymetrix® GeneChip® Bovine Genome Array with 24,072 gene probe sets representing more than 23,000 gene transcripts. Results Control and infected animals had similar mean white blood cell counts. However, the mean number of lymphocytes was significantly increased in the infected group relative to the control group (P = 0.001), while the mean number of monocytes was significantly decreased in the BTB group (P = 0.002). Hierarchical clustering analysis using gene expression data from all 5,388 detectable mRNA transcripts unambiguously partitioned the animals according to their disease status. In total, 2,960 gene transcripts were differentially expressed (DE) between the infected and control animal groups (adjusted P-value threshold ≤ 0.05); with the number of gene transcripts showing decreased relative expression (1,563) exceeding those displaying increased relative expression (1,397). Systems analysis using the Ingenuity® Systems Pathway Analysis (IPA) Knowledge Base revealed an over-representation of DE genes involved in the immune response functional category. More specifically, 64.5% of genes in the affects immune response subcategory displayed decreased relative expression levels in the infected animals compared to the control group. Conclusions This study demonstrates that genome-wide transcriptional profiling of PBL can distinguish active M. bovis-infected animals from control non-infected animals. Furthermore, the results obtained support previous investigations demonstrating that mycobacterial infection is associated with host transcriptional suppression. These data support the use of transcriptomic technologies to enable the identification of robust, reliable transcriptional markers of active M. bovis infection.This work was supported by Investigator Grants from Science Foundation Ireland (Nos: SFI/01/F.1/B028 and SFI/08/IN.1/B2038), a Research Stimulus Grant from the Department of Agriculture, Fisheries and Food (No: RSF 06 405) and a European Union Framework 7 Project Grant (No: KBBE-211602-MACROSYS). KEK is supported by the Irish Research Council for Science, Engineering and Technology (IRCSET) funded Bioinformatics and Systems Biology PhD Programme http://bioinfo-casl.ucd.ie/PhD