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Your search keyword '"Segun Jung"' showing total 2 results
Start Over Author "Segun Jung" Journal bmc genomics
2 results on '"Segun Jung"'

1. Genetic deletion of Sphk2 confers protection against Pseudomonas aeruginosa mediated differential expression of genes related to virulent infection and inflammation in mouse lung

Author

David L. Ebenezer, Panfeng Fu, Yashaswin Krishnan, Mark Maienschein-Cline, Hong Hu, Segun Jung, Ravi Madduri, Zarema Arbieva, Anantha Harijith, and Viswanathan Natarajan

Subjects
Pseudomonas aeruginosa, Pneumonia, Sphingosine kinase 2, Sphingolipids, Genomics, bacterial resistance, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background Pseudomonas aeruginosa (PA) is an opportunistic Gram-negative bacterium that causes serious life threatening and nosocomial infections including pneumonia. PA has 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 that Sphk2 −/− 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 of Sphk2 −/− and the wild type controls to better understand the underlying protective mechanisms related to the Sphk2 deletion in lung inflammatory injury. Whole transcriptome shotgun sequencing (RNA-Seq) was performed on mouse lung tissue using NextSeq 500 sequencing system. Results Two-way analysis of variance (ANOVA) analysis was performed and differentially expressed genes following PA infection were identified using whole transcriptome of Sphk2 −/− 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 by PA such as those involved in: 1. Immune response to PA infection 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 in PA-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. Conclusion Using Sphk2 −/− mice and differential gene expression analysis, we have shown here that S1P/SPHK2 signaling could play a key role in promoting PA pneumonia. The identified genes promote inflammation and suppress others that naturally inhibit inflammation and host defense. Thus, targeting SPHK2/S1P signaling in PA-induced lung inflammation could serve as a potential therapy to combat PA-induced pneumonia.

Published
2019
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2. Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping

Author

Segun Jung, Yingtao Bi, and Ramana V. Davuluri

Subjects
Discretization, exon-array, Feature selection, Biology, Machine Learning, Multiclass classification, 03 medical and health sciences, 0302 clinical medicine, RNA Isoforms, Genetics, Cluster Analysis, Humans, 030304 developmental biology, data discretization, 0303 health sciences, business.industry, Gene Expression Profiling, Research, Computational Biology, platform transition, Pattern recognition, multi-class classification, Class (biology), Expression (mathematics), Random forest, Statistical classification, Identification (information), ComputingMethodologies_PATTERNRECOGNITION, 030220 oncology & carcinogenesis, Artificial intelligence, RNA-seq, Glioblastoma, business, Algorithms, Biotechnology
Abstract

Background Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. Results We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. Conclusions The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms.

Published
2015
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