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BackgroundStudying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.ResultsWe used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.ConclusionThe results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

Rice paddies are a major source of methane. How to reduce the methane emission in the paddy field without decreasing the yield has become a major concern of scientists, environmental groups, and agricultural policymakers worldwide. Azolla, used as a dual crop in rice cultivation, has multiple agronomic benefits. However, the effects of the dual cropping of Azolla on methane emissions of double rice cropping paddies have not yet been reported. Here, we conducted a 3-year field experiment to evaluate the impacts of rice + Azolla on methane emission and rice yield in a double rice cropping system. The results indicated that the rice + Azolla without N fertilizer and with moderate N fertilizer (200 kg N ha −1 a −1 ) significantly reduced methane emissions over the rice cycle by 12.3 and 25.3% compared with the conventional rice cropping with common N fertilizer (400 kg N ha −1 a −1 ), respectively. The reason for the trend was because the dual cropping of Azolla has significant effect on dissolved oxygen and soil redox potential, which are key factors for methane emission in this study. The rice yield under the rice + Azolla with moderate N fertilizer annually averaged 12.7 Mg ha −1 , which was comparable with that of the conventional rice cropping with common N fertilizer. Moreover, the rice + Azolla with moderate N fertilizer had the lowest yieldscaled methane (25.2 kg Mg −1 grain yield). Here, we showed for the first time that Azolla planting allows sustainable rice production coupled with methane mitigation in double rice cropping systems.