Prioritizing transcriptional factors in gene regulatory networks with PageRank

Ding, Hongxu; Yang, Ying; Xue, Yuanqing; Seninge, Lucas; Gong, Henry; Safavi, Rojin; Califano, Andrea; Stuart, Joshua M.

doi:10.1016/j.isci.2020.102017

Cited by 11 publications

(5 citation statements)

References 34 publications

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“…We followed the workflow as previously described in ref. 64 . Specifically, we (1) retrieved the genomic information of the differentially expressed genes from the Bioconductor R TxDb.Mmusculus.UCSC.mm10.knownGene annotation using the function genes in the Bioconductor R package GenomicFeatures; (2) identified the promoter regions of the differentially expressed genes using the function promoters in the Bioconductor R package Genomi-cRanges; (3) retrieved TF position frequency matrices (PFMs) that are documented in the Bioconductor R JASPAR2020 database using the function getMatrixSet in the Bioconductor R package TFBSTools; (4) retrieved the promoter sequences of the differentially expressed genes from the Bioconductor R BSgenome.Mmusculus.UCSC.mm10 database, and match such sequences with the TF PFMs using functions matchMotifs and motifMatches in the Bioconductor R package motifmatchr.…”

Section: Motif Analysismentioning

confidence: 99%

Genetic and pharmacologic inhibition of ALDH1A3 as a treatment of β-cell failure

Son

Esposito

et al. 2023

Nat Commun

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Type 2 diabetes (T2D) is associated with β-cell dedifferentiation. Aldehyde dehydrogenase 1 isoform A3 (ALHD1A3) is a marker of β-cell dedifferentiation and correlates with T2D progression. However, it is unknown whether ALDH1A3 activity contributes to β-cell failure, and whether the decrease of ALDH1A3-positive β-cells (A+) following pair-feeding of diabetic animals is due to β-cell restoration. To tackle these questions, we (i) investigated the fate of A+ cells during pair-feeding by lineage-tracing, (ii) somatically ablated ALDH1A3 in diabetic β-cells, and (iii) used a novel selective ALDH1A3 inhibitor to treat diabetes. Lineage tracing and functional characterization show that A+ cells can be reconverted to functional, mature β-cells. Genetic or pharmacological inhibition of ALDH1A3 in diabetic mice lowers glycemia and increases insulin secretion. Characterization of β-cells following ALDH1A3 inhibition shows reactivation of differentiation as well as regeneration pathways. We conclude that ALDH1A3 inhibition offers a therapeutic strategy against β-cell dysfunction in diabetes.

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Section: Motif Analysismentioning

confidence: 99%

Genetic and pharmacologic inhibition of ALDH1A3 as a treatment of β-cell failure

Son

Esposito

et al. 2023

Nat Commun

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“…Then, they apply this technique to metric space to more applications. In (Ding et al 2020) Ding and other authors prioritized the dynamic changes of the biological states in the gene regulatory networks with the responsible role of transcriptional factors using PageRank centrality. Later, they introduce the multiplex PageRank for multilayer biological networks.…”

Section: Pagerankmentioning

confidence: 99%

Centrality Measures in Finding Influential Nodes for the Big-Data Network

Gopalakrishnan

Sridharan

Swaminathan

2021

Handbook of Smart Materials, Technologies, and Devices

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Network analysis determines node-to-node connectivity and examines each node's features. Various tools are available in the literature to study networks like graph theoretical, statistical, and many. As the graph plays a vital role in representing networks, the centrality measurements play an essential role in network analysis. The centrality measures use various metrics for evaluating how important the node is. The centrality predicts the characteristics and importance of the nodes in the network. In this review, selected 16 centrality measures presented along with their implementations.

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“…PageRank then computes the percent chance of arriving at any given webpage [10]. Notably, PageRank has been applied to many biological contexts including identifying candidate genes [11], topologically expressed genes [12], protein function prediction [13], gene evaluation from microarray results [14], finding functional gene modules [15, 16], entity linking [17], prioritizing transcriptional factors in gene regulatory networks [18], and semantic similarity for disease-target associations [19].…”

Section: Introductionmentioning

confidence: 99%

PRRGO: A Tool for Visualizing and Mapping Globally Expressed Genes in Public Gene Expression Omnibus RNA-Sequencing Studies to PageRank-scored Gene Ontology Terms

Solano,

D’Sa,

Nikolaidis

2024

Preprint

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We herein report PageRankeR Gene Ontology (PRRGO), a downloadable web application that can integrate differentially expressed gene (DEG) data from the gene expression omnibus (GEO) GEO2R web tool with the gene ontology (GO) database. Unlike existing tools, PRRGO computes the PageRank for the entire GO network and can generate both interactive GO networks on the web interface and comma-separated values (CSV) files containing the DEG statistics categorized by GO term. These hierarchical and tabular GO-DEG data are especially conducive to hypothesis generation and overlap studies with the use of PageRank data, which can provide a metric of GO term centrality. We verified the tool for accuracy and reliability across nine independent heat shock (HS) studies for which the RNA-seq data was publicly available on GEO and found that the tool produced increasing concordance between study DEGs, GO terms, and select HS-specific GO terms.

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Prioritizing transcriptional factors in gene regulatory networks with PageRank

Cited by 11 publications

References 34 publications

Genetic and pharmacologic inhibition of ALDH1A3 as a treatment of β-cell failure

Genetic and pharmacologic inhibition of ALDH1A3 as a treatment of β-cell failure

Centrality Measures in Finding Influential Nodes for the Big-Data Network

PRRGO: A Tool for Visualizing and Mapping Globally Expressed Genes in Public Gene Expression Omnibus RNA-Sequencing Studies to PageRank-scored Gene Ontology Terms

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