Preservation affinity in consensus modules among stages of HIV-1 progression

Pan-cancer classification by regularized multi-task learning

Classifying pan-cancer samples using gene expression patterns is a crucial challenge for the accurate diagnosis and treatment of cancer patients. Machine learning algorithms have been considered proven tools to perform downstream analysis and capture the deviations in gene expression patterns across diversified diseases. In our present work, we have developed PC-RMTL, a pan-cancer classification model using regularized multi-task learning (RMTL) for classifying 21 cancer types and adjacent normal samples using RNASeq data obtained from TCGA. PC-RMTL is observed to outperform when compared with five state-of-the-art classification algorithms, viz. SVM with the linear kernel (SVM-Lin), SVM with radial basis function kernel (SVM-RBF), random forest (RF), k-nearest neighbours (kNN), and decision trees (DT). The PC-RMTL achieves 96.07% accuracy and 95.80% MCC score for a completely unknown independent test set. The only method that appears as the real competitor is SVM-Lin, which nearly equalizes the accuracy in prediction of PC-RMTL but only when complete feature sets are provided for training; otherwise, PC-RMTL outperformed all other classification models. To the best of our knowledge, this is a significant improvement over all the existing works in pan-cancer classification as they have failed to classify many cancer types from one another reliably. We have also compared gene expression patterns of the top discriminating genes across the cancers and performed their functional enrichment analysis that uncovers several interesting facts in distinguishing pan-cancer samples.

Identification of key immune regulatory genes in HIV-1 progression

Human immunodeficiency virus (HIV) infection causes acquired immunodeficiency syndrome (AIDS), one of the most devastating diseases affecting humankind. Here, we have proposed a framework to examine the differences among microarray gene expression data of uninfected and three different HIV-1 infection stages using module preservation statistics. We leverage the advantage of gene co-expression networks (GCN) constructed for each infection stages to detect the topological and structural changes of a group of differentially expressed genes. We examine the relationship among a set of co-expression modules by constructing a module eigengene network considering the overall similarity/dissimilarity among the genes within the modules. We have utilized different module preservation statistics with two composite statistics: "Z_summary" and "MedianRank" to examine the changes in co-expression patterns between modules. We have found several interesting results on the preservation characteristics of gene modules across different stages. Some genes are identified to be preserved in a pair of stages while altering their characteristics across other stages. We further validated the obtained results using permutation test and classification techniques. The biological significances of the obtained modules have also been examined using gene ontology and pathway-based analysis. Additionally, we have identified a set of key immune regulatory hub genes in the associated protein-protein interaction networks (PPINs) of the differentially expressed (DE) genes, which interacts with HIV-1 proteins and are likely to act as potential biomarkers in HIV-1 progression.

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, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV–host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV–host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV–human protein–protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, “shock and kill” and “block and lock” approaches to cure latent infection.

A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer's disease progression

BACKGROUND

Alzheimer's disease (AD) is a chronic neuro-degenerative disruption of the brain which involves in large scale transcriptomic variation. The disease does not impact every regions of the brain at the same time, instead it progresses slowly involving somewhat sequential interaction with different regions. Analysis of the expression patterns of the genes in different regions of the brain influenced in AD surely contribute for a enhanced comprehension of AD pathogenesis and shed light on the early characterization of the disease.

RESULTS

Here, we have proposed a framework to identify perturbation and preservation characteristics of gene expression patterns across six distinct regions of the brain ("EC", "HIP", "PC", "MTG", "SFG", and "VCX") affected in AD. Co-expression modules were discovered considering a couple of regions at once. These are then analyzed to know the preservation and perturbation characteristics. Different module preservation statistics and a rank aggregation mechanism have been adopted to detect the changes of expression patterns across brain regions. Gene ontology (GO) and pathway based analysis were also carried out to know the biological meaning of preserved and perturbed modules.

CONCLUSIONS

In this article, we have extensively studied the preservation patterns of co-expressed modules in six distinct brain regions affected in AD. Some modules are emerged as the most preserved while some others are detected as perturbed between a pair of brain regions. Further investigation on the topological properties of preserved and non-preserved modules reveals a substantial association amongst "betweenness centrality" and "degree" of the involved genes. Our findings may render a deeper realization of the preservation characteristics of gene expression patterns in discrete brain regions affected by AD.