Construction And Characterization Of The Single Nucleotide Resolution Transcriptome Map And Protein Protein Interaction Network Of Actinobacillus Pleuropneumoniae

Actinobacillus pleuropneumoniae, a Gram-negative pathogen of the Pasteurellaceae family, is the primary etiologic agent of porcine contagious pleuropneumonia, a severe respiratory disease causing great economic losses to the worldwide swine industry. The A. pleuropneumoniae genome annotations were made by computational analysis based on gene prediction algorithms. However, due to the inherent limitations of the computational methods for prokaryotic gene annotation, the existing annotations of the A. pleuropneumoniae genomes are far from complete. High-throughput RNA sequencing（RNA-seq） has been applied to study the transcriptional landscape of bacteria, which can efficiently and accurately identify gene expression regions and unknown transcriptional units, especially small non-coding RNAs（sRNAs）, UTRs and regulatory regions.Proteins are the main function carrier and implementation of the process of biological life. Cellular processes in vivo are achieved through protein-protein interactions. The better understanding of protein-protein interactions is crucial for elucidating the structural functional relations of proteins, investigating their roles in associated disease development, and determining potential drug targets for clinical applications. At present, the protein-protein interaction data are very scarce, this greatly limits the understanding the pathogenic mechanism, gene function and drug targets screening.Based on RNA-seq data, we analyze the transcriptome of A. pleuropneumoniae in order to improve the existing genome annotation and promote our understanding of A. pleuropneumoniae gene structures and RNA-based regulation. We constructed the APP protein-protein interaction network using a homogenous protein mapping method. Analysis of the protein-protein interaction network has provided a number of valuable clues for exploration of signal transduction mechanism and determining potential drug targets. The main research was described as follows: 1. Construction and characterization of the single nucleotide resolution transcriptome map of APPRNA samples of A. pleuropneumoniae cultured to mid-log phase were sequenced by RNA-seq technology. More than 3.8 million high-quality reads（average length ⁹0 bp） from a cDNA library were generated and aligned to the reference genome. The obtained reads represent 170-fold A. pleuropneumoniae genome lengths. Uniquely mapped reads accounts for >90% of all reads. We utilized RNA-seq to construct a single nucleotide resolution transcriptome map of A. pleuropneumoniae. Based on this transcriptome map, we identified 1933（approximately 90%） out of the 2147 predicted genes and 1845 out of the 2036 protein-coding genes which were expressed. The functions of the expressed genes were distributed across all categories of COG database. We also identified 1221 previously un-annotated expressed intergenic regions（EIRs） with a minimum length of 30 bp.Overall, 32 novel protein coding regions were identified according to the transcriptome map. The average length of proteins encoded by these regions was around 47 amino acids（ranged from 26 to 90 amino acids）. The start site for 35 genes was corrected based on the current genome annotation. We identified 5’-UTR for 715 annotated genes and 3’-UTR for 384 annotated genes in A. pleuropneumoniae JL03.Furthermore, 51 sRNAs in the A. pleuropneumoniae genome were discovered, of which 40 sRNAs were never reported in previous studies. And 11 sRNAs of A. pleuropneumoniae were homologous to well characterized sRNAs in other species.Based on RNA-seq data, 840 co-expressed pairs of genes that could be organized into 351 operons were identified. We randomly selected 26 pairs of genes which were predicted to be co-transcribed for authentication.The RNA-seq based transcriptome map of A. pleuropneumoniae JL03 validated annotated genes and corrected annotations of open reading frames in the genome, and led to the identification of many functional elements（e.g. regions encoding novel proteins, non-coding sRNAs and operon structures）. As a consequence, the accuracy of existing genome annotations was significantly enhanced. The transcriptional units described in this study provide a foundation for future studies concerning the gene functions and the transcriptional regulatory architectures of this pathogen. 2. Prediction and characterization of protein-protein interaction network in APPIn this study, a virtual protein interaction network of A. pleuropneumoniae was constructed based on the homologous protein mapping（HPM） approach, consisting of 2737 non-redundant interactions pairs and 533 proteins. The functions of the 533 proteins were distributed across all categories of COG database. Proteins were significantly enriched in three COG categories “Translation”（J category）, “Amino acid transport and metabolism”（E category） and “Function-unassigned”（S category）. We calculated the topological parameters of PPI networks by using the Network Analysis of Cytoscape software. The scale-free and small-world network topological properties are founded in the PPI network, which illustrate the PPI network has good fault tolerance, stability. The sub-network of APL₀448（hns） was chosen for validations that would further evaluate the quality of our PPI data.The PPI network of A. pleuropneumoniae was used to predict the biological role of 23 hypothetical proteins based on the functions of interacting proteins. The PPI network contains 131 proteins which exist in 32 in biological pathways. The complex protein interactions which exist in APP biological pathways, can maintain biological pathway, when the expression of a few proteins were restrained under pressure from environment. We constructed the PPI network of Cell wall biogenesis pathway and Pyrimidine metabolism Pyrimidine metabolism to predict the potential drug targets. Through combining the protein database and references, we identified the known drug targets 22, potential drug target with literature reported 13 and 18 new candidate drug target.There are 15 proteins which were participated in signal transduction in APP PPI network. We constructed the signal transduction PPI network and analyzed the protein-protein interactions involved in signal transduction pathways. We found that the protein-protein interactions between the proteins in COG categories “signal transduction”（T category） and “transcription”（K category） were important in signal transduction pathways, such as the protein Crp can regulate gene expression through interact with RpoA, RpoD and PurR. This suggested that there are complex signal transduction and gene regulation mode existing in APP.The PPI network described here provides valuable information for the research of lifestyle, regulation, signal transduction, metabolic network, environmental adaptation and pathogenic mechanisms of APP.