Data from large-scale protein interaction screens for humans and model eukaryotes have been invaluable for developing systems-level models of biological processes. Despite this value, only a limited amount of interaction data is available for prokaryotes. Here we report the systematic identification of protein interactions for the bacterium Campylobacter jejuni, a food-borne pathogen and a major cause of gastroenteritis worldwide. Using high-throughput yeast two-hybrid screens we detected and reproduced 11,687 interactions. The resulting interaction map includes 80% of the predicted C. jejuni NCTC11168 proteins and places a large number of poorly characterized proteins into networks that provide initial clues about their functions. We used the map to identify a number of conserved subnetworks by comparison to protein networks from Escherichia coli and Saccharomyces cerevisiae. We also demonstrate the value of the interactome data for mapping biological pathways by identifying the C. jejuni chemotaxis pathway. Finally, the interaction map also includes a large subnetwork of putative essential genes that may be used to identify potential new antimicrobial drug targets for C. jejuni and related organisms. The C. jejuni protein interaction map is one of the most comprehensive yet determined for a free-living organism and nearly doubles the binary interactions available for the prokaryotic kingdom. This high level of coverage facilitates pathway mapping and function prediction for a large number of C. jejuni proteins as well as orthologous proteins from other organisms. The broad coverage also facilitates cross-species comparisons for the identification of evolutionarily conserved subnetworks of protein interactions.
Abstract Surface oxidation states of ultrafine particulate matter can influence the proinflammatory responses and reactive oxygen species levels in tissue. Surface active species of vehicle-emission soot can serve as electron transfer-mediators in mitochondrion. Revealing the role of surface oxidation state in particles-proteins interaction will promote the understanding on metabolism and toxicity. Here, the surface oxidation state was modeled by nitro/amino ligands on nanoparticles, the interaction with blood proteins were evaluated by capillary electrophoresis quantitatively. The nitro shown larger affinity than amino. On the other hand, the affinity to hemoglobin is 10 3 times larger than that to BSA. Further, molecular docking indicated the difference of binding intensity were mainly determined by hydrophobic forces and hydrogen bonds. These will deepen the quantitative understanding of protein-nanoparticles interaction from the perspective of surface chemical state.
Protein-protein interactions are important in many aspects of cellular processes. Discovery of protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. High-throughput experimental screens developed so far show high error rates in terms of false positives and false negatives. There is thus a great need for new computational approaches to enable the prediction of new protein-protein interactions and to enhance the reliability of experimentally derived interaction maps. Many of the computational approaches developed thus far are based on strong biological assumptions, resulting in biases towards certain types of predictions. As a first step towards a more complete and accurate interaction map, we propose to predict protein-protein interactions using existing experimental data combined with the Gene Ontology (GO) annotations of proteins. We do not use strong prior rules about GO patterns and proteinprotein interactions and thus avoid biases associated with various assumptions. We show that GO annotations can be a useful predictor for proteinprotein interactions and that prediction performance can be improved by combining the results from both decision trees and Bayesian networks.
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