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BackgroundPseudomonas aeruginosa(PA) is an opportunistic Gram-negative bacterium that causes serious life threatening and nosocomial infections including pneumonia.PAhas the ability to alter host genome to facilitate its invasion, thus increasing the virulence of the organism. Sphingosine-1- phosphate (S1P), a bioactive lipid, is known to play a key role in facilitating infection. Sphingosine kinases (SPHK) 1&2 phosphorylate sphingosine to generate S1P in mammalian cells. We reported earlier thatSphk2−/−mice offered significant protection against lung inflammation, compared to wild type (WT) animals. Therefore, we profiled the differential expression of genes between the protected group ofSphk2−/−and the wild type controls to better understand the underlying protective mechanisms related to theSphk2deletion in lung inflammatory injury. Whole transcriptome shotgun sequencing (RNA-Seq) was performed on mouse lung tissue using NextSeq 500 sequencing system.ResultsTwo-way analysis of variance (ANOVA) analysis was performed and differentially expressed genes followingPAinfection were identified using whole transcriptome ofSphk2−/−mice and their WT counterparts. Pathway (PW) enrichment analyses of the RNA seq data identified several signaling pathways that are likely to play a crucial role in pneumonia caused byPAsuch as those involved in: 1. Immune response toPAinfection and NF-κB signal transduction; 2. PKC signal transduction; 3. Impact on epigenetic regulation; 4. Epithelial sodium channel pathway; 5. Mucin expression; and 6. Bacterial infection related pathways.Our genomic data suggests a potential role for SPHK2 inPA-induced pneumonia through elevated expression of inflammatory genes in lung tissue. Further, validation by RT-PCR on 10 differentially expressed genes showed 100% concordance in terms of vectoral changes as well as significant fold change.ConclusionUsingSphk2−/−mice and differential gene expression analysis, we have shown here that S1P/SPHK2 signaling could play a key role in promotingPApneumonia. The identified genes promote inflammation and suppress others that naturally inhibit inflammation and host defense. Thus, targeting SPHK2/S1P signaling inPA-induced lung inflammation could serve as a potential therapy to combatPA-induced pneumonia.

Background The application of machine learning to classification problems that depend only on positive examples is gaining attention in the computational biology community. We and others have described the use of two-class machine learning to identify novel miRNAs. These methods require the generation of an artificial negative class. However, designation of the negative class can be problematic and if it is not properly done can affect the performance of the classifier dramatically and/or yield a biased estimate of performance. We present a study using one-class machine learning for microRNA (miRNA) discovery and compare one-class to two-class approaches using naïve Bayes and Support Vector Machines. These results are compared to published two-class miRNA prediction approaches. We also examine the ability of the one-class and two-class techniques to identify miRNAs in newly sequenced species. Results Of all methods tested, we found that 2-class naive Bayes and Support Vector Machines gave the best accuracy using our selected features and optimally chosen negative examples. One class methods showed average accuracies of 70–80% versus 90% for the two 2-class methods on the same feature sets. However, some one-class methods outperform some recently published two-class approaches with different selected features. Using the EBV genome as and external validation of the method we found one-class machine learning to work as well as or better than a two-class approach in identifying true miRNAs as well as predicting new miRNAs. Conclusion One and two class methods can both give useful classification accuracies when the negative class is well characterized. The advantage of one class methods is that it eliminates guessing at the optimal features for the negative class when they are not well defined. In these cases one-class methods can be superior to two-class methods when the features which are chosen as representative of that positive class are well defined. Availability The OneClassmiRNA program is available at: [1]