Hossain, S. M. M. | bioRxiv Science

bioRxiv2020

Identification of key immune regulatory genes in HIV-1Progression

In the last few decades, application of DNA microarray technology has sprung up as a powerful technique for discovering stage specific changes in expression pattern of a disease progression. Human Immunodeficiency Virus (HIV) infection causes Acquired Immunodeficiency Syndrome (AIDS) which is one of the most devastating diseases affecting humankind. Here, we have proposed a framework to examine the difference among microarray gene expression data of uninfected and three different HIV-1 infection stages using module preservation statistics. Initially, we detected differentially expressed genes among all the stages and identified coexpression modules by using topological overlap as a dissimilarity measure. To examine relationship among co-expression modules, we have compiled a module eigenegene network for each sample category which models similarity among all coexpression modules. To further examine the network, we have found clusters in it which are termed as meta-modules. Different module preservation statistics with two composite statistics: "Zsummary" and "MedianRank" are utilized to examine changes in structure of coexpression modules. We have applied our proposed methodology to discover modular changes between uninfected and acute samples, acute and chronic samples, chronic and AIDS samples. We have found several interesting results on preservation characteristics of gene modules across different stages. Some genes are identified to be preserved in a pair of stages while alter their characteristics across other stages. We further validated the obtained results using permutation test and classification techniques. Biological significance of the obtained modules have been examined using gene ontology and pathway based analysis. Additionally, we have detected key immune regulatory hub genes in the associated protein-protein interaction networks (PPINs) of the differentially expressed genes (DEGs) using twelve topological and centrality analysis methods. Moreover, we have analyzed the key immune regulatory genes which interacts with HIV-1 proteins inside the preserved and perturbed meta-modules across different HIV-1 stages and thus likely to act as potential biomarkers in HIV-1 progression.

bioinformatics↗

bioRxiv2020

Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model

Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer (PC), late detection of which leads to its therapeutic failure. This study aims to find out key regulatory genes and their impact on the progression of the disease helping the etiology of the disease which is still largely unknown. We leverage the landmark advantages of time-series gene expression data of this disease, and thereby the identified key regulators capture the characteristics of gene activity patterns in the progression of the cancer. We have identified the key modules and predicted gene functions of top genes from the compiled gene association network (GAN). Here, we have used the natural cubic spline regression model (splineTimeR) to identify differentially expressed genes (DEG) from the PDAC microarray time-series data downloaded from gene expression omnibus (GEO). First, we have identified key transcriptomic regulators (TR) and DNA binding transcription factors (DbTF). Subsequently, the Dirichlet process and Gaussian process (DPGP) mixture model is utilized to identify the key gene modules. A variation of the partial correlation method is utilized to analyze GAN, which is followed by a process of gene function prediction from the network. Finally, a panel of key genes related to PDAC is highlighted from each of the analyses performed. Please note: Abbreviations should be introduced at the first mention in the main text - no abbreviations lists. Suggested structure of main text (not enforced) is provided below.

bioinformatics↗