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1.  Inferring modules from human protein interactome classes 
BMC Systems Biology  2010;4:102.
The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features.
We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated) data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules.
Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.
PMCID: PMC2923113  PMID: 20653930
2.  Development of a Sensitive and Specific Enzyme-Linked Immunosorbent Assay Based on Recombinant Antigens for Rapid Detection of Antibodies against Mycoplasma agalactiae in Sheep▿ †  
Clinical and Vaccine Immunology  2007;14(4):420-425.
We developed a new recombinant enzyme-linked immunosorbent assay (rELISA) for serodiagnosis of contagious agalactia (CA), a disease caused by Mycoplasma agalactiae in sheep and goats. The assay is based on two M. agalactiae surface proteins, namely, P80 and P55. Identification of these immunodominant and common antigens was accomplished by examining the antibody response elicited in sheep during experimental infection and comparing it to the protein expression profiles of 75 M. agalactiae field strains. Our rELISA was tested with 343 sera, collected from sheep with a laboratory-confirmed diagnosis of CA (n = 223) and from healthy animals (n = 120). All sera had previously been tested by Western blotting (WB) for reactivity against M. agalactiae. In addition, our rELISA was compared with a commercial routine ELISA based on inactivated antigens (CHEKiT). Among the 223 samples that were WB positive for M. agalactiae, 209 (93.7%) tested positive for rP80-P55 with our ELISA, whereas only 164 (73.8%) tested positive with the CHEKiT ELISA. Among the 120 samples tested that were WB negative for M. agalactiae, 96.7% were confirmed as negative with our rELISA, while only 75.8% were confirmed as negative with the CHEKiT ELISA. A comparison of the results with receiver operating characteristic curves indicated that the differences observed between our rELISA and the CHEKiT ELISA are statistically significant. The use of recombinant peptides instead of inactivated antigens could significantly improve the discrimination of positive and negative animals, bringing significant advantages in controlling the import/export of live animals and helping in eradication of this economically detrimental disease.
PMCID: PMC1865618  PMID: 17287317

Results 1-2 (2)