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Expression correlation attenuates within and between key signaling pathways in chronic kidney disease

Hui Yu, Danqian Chen, Olufunmilola Oyebamiji, Ying-Yong Zhao, Yan Guo
2020 BMC Medical Genomics  
Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. In particular, global loss of transcriptome correlation was previously observed in aging mice, and a most recent study found genetic and environmental perturbations on human subjects tended to cause universal attenuation of transcriptome coherence. While methodological progresses surrounding differential coexpression
more » ... have helped with research on several human diseases, there has not been an investigation of coexpression disruptions in chronic kidney disease (CKD) yet. RNA-seq was performed on total RNAs of kidney tissue samples from 140 CKD patients. A combination of differential coexpression methods were employed to analyze the transcriptome transition in CKD from the early, mild phase to the late, severe kidney damage phase. We discovered a global expression correlation attenuation in CKD progression, with pathway Regulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Well-known relevant genes, such as ACTN4, were characterized with widespread correlation disassociation with partners from a wide array of signaling pathways. Altogether, our analysis reported a global expression correlation attenuation within and between key signaling pathways in chronic kidney disease, and presented a list of vanishing hub genes and disrupted correlations within and between key signaling pathways, illuminating on the pathophysiological mechanisms of CKD progression.
doi:10.1186/s12920-020-00772-3 pmid:32957963 pmcid:PMC7504859 fatcat:6btwwt3xy5eq5hvqji3ezd4kme

Genomic Positional Dissection of RNA Editomes in Tumor and Normal Samples

Michael Chigaev, Hui Yu, David C. Samuels, Quanhu Sheng, Olufunmilola Oyebamiji, Scott Ness, Wei Yue, Ying-yong Zhao, Yan Guo
2019 Frontiers in Genetics  
RNA editing is phenomenon that occurs in both protein coding and non-coding RNAs. Increasing evidence have shown that adenosine-to-inosine RNA editing can potentially rendering substantial functional effects throughout the genome. Using RNA editing datasets from two large consortiums: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) project, we quantitatively analyzed human genome-wide RNA editing events derived from tumor or normal tissues. Generally, a common RNA editing
more » ... te tends to have a higher editing level in tumors as compared to normal samples. Of the 14 tumor-normal-paired cancer types examined, Eleven of the 14 cancers tested had overall increased RNA editing levels in the tumors. The editomes in cancer or normal tissues were dissected by genomic locations, and significant RNA editing locational difference was found between cancerous and healthy subjects. Additionally, our results indicated a significant correlation between the RNA editing rate and the gene density across chromosomes, highlighted hyper RNA editing clusters through visualization of running RNA editing rates along chromosomes, and identified hyper RNA edited genes (protein-coding genes, lincRNAs, and pseudogenes) that embody a large portion of cancer prognostic predictors. This study reinforces the potential functional effects of RNA editing in protein-coding genes, and also makes a strong foundation for further exploration of RNA editing's roles in non-coding regions.
doi:10.3389/fgene.2019.00211 pmid:30949194 pmcid:PMC6435843 fatcat:gpmofromz5huxbckgjmtiveoly

MetaGSCA: A tool for meta-analysis of gene set differential coexpression

Yan Guo, Hui Yu, Haocan Song, Jiapeng He, Olufunmilola Oyebamiji, Huining Kang, Jie Ping, Scott Ness, Yu Shyr, Fei Ye
2021 PLoS Computational Biology  
Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta
more » ... e Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.
doi:10.1371/journal.pcbi.1008976 pmid:33945541 pmcid:PMC8121311 fatcat:bmo6uhzbxbghbfi4pqurzzly7a

A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus

Robert N. Bone, Olufunmilola Oyebamiji, Sayali Talware, Sharmila Selvaraj, Preethi Krishnan, Farooq Syed, Huanmei Wu, Carmella Evans-Molina
2020 Diabetes  
The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow
more » ... our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. In parallel, we generated an RNA-sequencing dataset from human islets treated with brefeldin A (BFA), a known GA stress inducer. Overlapping the T1D and T2D groups with the BFA dataset, we identified 120 and 204 differentially expressed genes, respectively. In both the T1D and T2D models, pathway analyses revealed that the top pathways were associated with GA integrity, organization, and trafficking. Quantitative RT-PCR was used to validate a common signature of GA stress that included ATF3, ARF4, CREB3, and COG6 Taken together, these data indicate that GA-associated genes are dysregulated in diabetes and identify putative markers of β-cell GA stress.
doi:10.2337/db20-0636 pmid:32820009 pmcid:PMC7576569 fatcat:eovvdffifnd43bciegrw7ny7ey

Non-canonical RNA-DNA differences and other human genomic features are enriched within very short tandem repeats

Hui Yu, Shilin Zhao, Scott Ness, Huining Kang, Quanhu Sheng, David C. Samuels, Olufunmilola Oyebamiji, Ying-yong Zhao, Yan Guo, Lilia M. Iakoucheva
2020 PLoS Computational Biology  
Samuels, Olufunmilola Oyebamiji, Yan Guo.  ... 
doi:10.1371/journal.pcbi.1007968 pmid:32511223 fatcat:f75dvytjlrfjzjd2rh3vao3y7a

Heterogeneity of Circulating Tumor Cell Neoplastic Subpopulations Outlined by Single-Cell Transcriptomics

Christine M. Pauken, Shelby Ray Kenney, Kathryn J. Brayer, Yan Guo, Ursa A. Brown-Glaberman, Dario Marchetti
2021 Cancers  
Olufunmilola (Mary) Oyebamiji provided bioinformatics statistical analysis for the RNA-Seq data.  ... 
doi:10.3390/cancers13194885 pmid:34638368 fatcat:aqwntuc2cbfjrljsg4ewe2oeva