Identification of key immune regulatory genes in HIV-1 progression

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

doi:10.1016/j.gene.2021.145735

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Transcriptome analyses, including weighted network co-expression analysis (WGCNA) and gene expression trend analysis, represent useful methods with which to survey host anti-infection immune molecules because they provide genome-wide profiles of gene expression [ 28 , 29 , 30 ]. In humans, a set of key immune genes, including IFN, interferon regulatory factor (IRF) 1, IRF 7, etc., interacting with human immunodeficiency virus (HIV)-1 have been defined by using WGCNA to construct gene co-expression networks based on transcriptome data from 52 patients [ 31 ]. Several antiviral immune genes, including laboratory of genetics and physiology 2 (LGP2), transforming growth factor-β-activated kinase 1 (TAK1) and zinc finger protein 36 (ZFP36), activated during encephalomyocarditis virus infection, have also been identified through gene expression trend analysis based on transcriptome data [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Identification and Expression Analysis of Potential Antiviral Tripartite Motif Proteins (TRIMs) in Grass Carp (Ctenopharyngodon idella)

Qin

Xiao

Ding

et al. 2021

Biology

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Tripartite motif proteins (TRIMs), especially B30.2 domain-containing TRIMs (TRIMs-B30.2), are increasingly well known for their antiviral immune functions in mammals, while antiviral TRIMs are far from being identified in teleosts. In the present study, we identified a total of 42 CiTRIMs from the genome of grass carp, Ctenopharyngodon idella, an important cultured teleost in China, based on hmmsearch and SMART analysis. Among these CiTRIMs, the gene loci of 37 CiTRIMs were located on different chromosomes and shared gene collinearities with homologous counterparts from human and zebrafish genomes. They possessed intact conserved RBCC or RB domain assemblies at their N-termini and eight different domains, including the B30.2 domain, at their C-termini. A total of 19 TRIMs-B30.2 were identified, and most of them were clustered into a large branch of CiTRIMs in the dendrogram. Tissue expression analysis showed that 42 CiTRIMs were universally expressed in various grass carp tissues. A total of 11 significantly differentially expressed CiTRIMs were found in two sets of grass carp transcriptomes during grass carp reovirus (GCRV) infection. Three of them, including Cibtr40, CiTRIM103 and CiTRIM109, which all belonged to TRIMs-B30.2, were associated with the type I interferon response during GCRV infection by weighted network co-expression and gene expression trend analyses, suggesting their involvement in antiviral immunity. These findings may offer useful information for understanding the structure, evolution, and function of TRIMs in teleosts and provide potential antiviral immune molecule markers for grass carp.

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Section: Introductionmentioning

confidence: 99%

Genome-Wide Identification and Expression Analysis of Potential Antiviral Tripartite Motif Proteins (TRIMs) in Grass Carp (Ctenopharyngodon idella)

Qin

Xiao

Ding

et al. 2021

Biology

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Identification of Gene Communities in Liver Hepatocellular Carcinoma: An OffsetNMF-Based Integrative Technique

Hossain

Halsana

2022

Intelligent Data Communication Technologies and Internet of Things

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Pan-cancer classification by regularized multi-task learning

Hossain

Khatun

Ray

et al. 2021

Sci Rep

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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.

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Identification of key immune regulatory genes in HIV-1 progression

Cited by 5 publications

References 39 publications

Genome-Wide Identification and Expression Analysis of Potential Antiviral Tripartite Motif Proteins (TRIMs) in Grass Carp (Ctenopharyngodon idella)

Genome-Wide Identification and Expression Analysis of Potential Antiviral Tripartite Motif Proteins (TRIMs) in Grass Carp (Ctenopharyngodon idella)

Identification of Gene Communities in Liver Hepatocellular Carcinoma: An OffsetNMF-Based Integrative Technique

Pan-cancer classification by regularized multi-task learning

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