Discovering preservation pattern from co-expression modules in progression of HIV-1 disease: An eigengene based approach

Ray, Sumanta; Hossain, Sk Md Mosaddek; Khatun, Lutfunnesa

doi:10.1109/icacci.2016.7732146

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…For instance, the analysis of gene expression using co-expression networks is shown in the work by Pedragosa et al [ 41 ], where the infection caused by Lymphocytic Choriomeningitis Virus (LCMV) is studied over time in mice spleen using GCNs. In Ray et al [ 42 ], GCNs are reconstructed from different microarray expression data in order to study HIV-1 progression, revealing important changes across the different infection stages. Similarly, in the work presented by McDermott et al [ 43 ], the over- and under-stimulation of the innate immune response to severe acute respiratory syndrome coronavirus (SARS-CoV) infection is studied.…”

Section: Related Workmentioning

confidence: 99%

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

Delgado-Chaves

Gómez-Vela

Divina

et al. 2020

Genes

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Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E Δ H S C compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E Δ H S C mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E Δ H S C mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches.

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Section: Related Workmentioning

confidence: 99%

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

Delgado-Chaves

Gómez-Vela

Divina

et al. 2020

Genes

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“…Moreover, the expression profiles of the genes belonging to a particular module were summarized by Module Eigengene (ME) using the singular value decomposition (SVD) technique. Subsequently, we have constructed eigengene networks separately for each category of samples from which by computing the dissimilarity among the module eigengenes, meta modules were constructed utilizing the procedure mentioned in [5], [7], [21].…”

Section: Identifying Co-expressed Modulesmentioning

confidence: 99%

“…Gene co-expression network analysis is a popular technique, widely used in several articles to model dependency structure among gene expression, such as: used to investigate the changes of expression patterns across the disease progression stages [5], [6], [7], [8], [9], [10], [11], used to identify hub genes and relevant pathways [12], [13], used to distinguish cancer risk modules [14] and utilized for biomarker selection in cancer prognosis [15]. Gene co-expression analysis in different stages of HIV progression is noticed to be carried out in literatures [5], [7], [16]. However, in most of the cases the co-expression changes are examined between acute, chronic and non-progressor stages of infection by using different methodologies.…”

Section: Introductionmentioning

confidence: 99%

Identification of key immune regulatory genes in HIV–1 Progression

Hossain

Khatun

Ray

et al. 2020

Preprint

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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.

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“…The area of gene expression analysis has undergone several major advancements in biomedical research. With increased efficiency and quality, these measurements have led to improvements in disease sub-classification, gene identification problems, and studying progression characteristics of diseases [1][2][3][4][5][6][7] . Biological mechanisms are dynamic in nature, therefore its activities must be supervised at multiple time points.…”

Section: Introductionmentioning

confidence: 99%

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

Hossain

Halsana

Khatun

et al. 2020

Preprint

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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.

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Discovering preservation pattern from co-expression modules in progression of HIV-1 disease: An eigengene based approach

Cited by 13 publications

References 15 publications

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks

Identification of key immune regulatory genes in HIV–1 Progression

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

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