Global expression and CpG methylation analysis of primary endothelial cells before and after TNFa stimulation reveals gene modules enriched in inflammatory and infectious diseases and associated DMRs

Rhead, Brooke; Shao, Xiaorong; Quach, Hong; Ghai, Poonam; Barcellos, Lisa F.; Bowcock, Anne M.

doi:10.1371/journal.pone.0230884

Cited by 12 publications

(15 citation statements)

References 40 publications

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“…For each out of 8 test samples, we constructed a subnetwork selecting 140 the most relevant genes and deleting singletones, that lead to subnetworks consisting of mainly 130 vertices. Remarkably, the green gene module, which was the most strongly correlated one with TNFα upregulation (Rhead et al, 2020) showed significant association (adjusted p-value < 0.05) with the combined set of subnetwork genes, with genes found in the majority of subnetworks and also with 5 of the 8 subnetworks (Supplementary File S1). At the same significance level the turquoise gene module described in (Rhead et al, 2020) was strongly associated with 2 of 8 subnetworks and with genes found in all 8 subnetworks.…”

Section: Genes Selected By Glrp Correlate With Modules Identified By mentioning

confidence: 99%

“…Remarkably, the green gene module, which was the most strongly correlated one with TNFα upregulation (Rhead et al, 2020) showed significant association (adjusted p-value < 0.05) with the combined set of subnetwork genes, with genes found in the majority of subnetworks and also with 5 of the 8 subnetworks (Supplementary File S1). At the same significance level the turquoise gene module described in (Rhead et al, 2020) was strongly associated with 2 of 8 subnetworks and with genes found in all 8 subnetworks. In addition, both the green and the turquoise modules showed moderate association (adjusted p-value < 0.1) with the majority of gene sets defined on the basis of the test subnetworks.…”

Section: Genes Selected By Glrp Correlate With Modules Identified By mentioning

confidence: 99%

“…To check the biological relevance of subnetwork genes prioritized by GLRP we also used the gene expression data from human umbilical vein endothelial cells (HUVECs) treated or not treated with tumor necrosis factor alpha TNFα (Rhead et al, 2020) The data, provided by the same authors (GEO database series: GSE144803), is suitable for a binary classification task and is balanced. The data was quantile normalized and mapped to the vertices of HPRD PPI resulting in 7798 genes in the main connected component.…”

Section: Validationmentioning

confidence: 99%

“…The data was quantile normalized and mapped to the vertices of HPRD PPI resulting in 7798 genes in the main connected component. We compared gene sets identified in our subnetworks to gene modules and differentially expressed genes in response to TNFα identified by (Rhead et al, 2020) (Supplementary File S1). (Rhead et al, 2020) used weighted gene co-expression network analysis (WGCNA) (Zhang and Horvath, 2005) constructing networks as gene modules.…”

Section: Validationmentioning

confidence: 99%

“…We compared gene sets identified in our subnetworks to gene modules and differentially expressed genes in response to TNFα identified by (Rhead et al, 2020) (Supplementary File S1). (Rhead et al, 2020) used weighted gene co-expression network analysis (WGCNA) (Zhang and Horvath, 2005) constructing networks as gene modules. Associations between subnetwork genes sets and 16 gene modules defined by (Rhead et al, 2020) as well as 589 upregulated genes (log-fold change > 0.5, FDR < 0.01), 425 downregulated genes (logfold change < -0.5, FDR < 0.01) and the combined set of 1014 DE genes were analyzed using the Functional classification tool of the geneXplain platform (Kolpakov et al, 2011) Fisher test calculations were carried out with a total contingency table count corresponding to the number of genes in (Rhead et al, 2020, file S1 of) after mapping to Ensembl (Yates et al, 2020) gene ids (10022 genes).…”

Section: Validationmentioning

confidence: 99%

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Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

Chereda

Bleckmann

Menck

et al. 2020

Preprint

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MotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g. distant metastasis in cancer, for each individual patient.ResultsWe extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset, and then applied the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. As a result this method could be potentially highly useful on interpreting classification results on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrphttps://frankkramer-lab.github.io/MetaRelSubNetVis/Contacttim.beissbarth@bioinf.med.uni-goettingen.de

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Section: Genes Selected By Glrp Correlate With Modules Identified By mentioning

confidence: 99%

Section: Genes Selected By Glrp Correlate With Modules Identified By mentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

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Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

Chereda

Bleckmann

Menck

et al. 2020

Preprint

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Identification of genes associated with abiotic stress tolerance in sweetpotato using weighted gene co‐expression network analysis

Kitavi,

Gemenet,

Wood

et al. 2023

Plant Direct

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Sweetpotato, Ipomoea batatas (L.), a key food security crop, is negatively impacted by heat, drought, and salinity stress. The orange‐fleshed sweetpotato cultivar “Beauregard” was exposed to heat, salt, and drought treatments for 24 and 48 h to identify genes responding to each stress condition in leaves. Analysis revealed both common (35 up regulated, 259 down regulated genes in the three stress conditions) and unique sets of up regulated (1337 genes by drought, 516 genes by heat, and 97 genes by salt stress) and down regulated (2445 genes by drought, 678 genes by heat, and 204 genes by salt stress) differentially expressed genes (DEGs) suggesting common, yet stress‐specific transcriptional responses to these three abiotic stressors. Gene Ontology analysis of down regulated DEGs common to both heat and salt stress revealed enrichment of terms associated with “cell population proliferation” suggestive of an impact on the cell cycle by the two stress conditions. To identify shared and unique gene co‐expression networks under multiple abiotic stress conditions, weighted gene co‐expression network analysis was performed using gene expression profiles from heat, salt, and drought stress treated ‘Beauregard’ leaves yielding 18 co‐expression modules. One module was enriched for “response to water deprivation,” “response to abscisic acid,” and “nitrate transport” indicating synergetic crosstalk between nitrogen, water, and phytohormones with genes encoding osmotin, cell expansion, and cell wall modification proteins present as key hub genes in this drought‐associated module. This research lays the groundwork for exploring to a further degree, mechanisms for abiotic stress tolerance in sweetpotato.

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UniBind: maps of high-confidence direct TF-DNA interactions across nine species

Puig

Boddie

Khan

et al. 2020

Preprint

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Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. Hence, it is critical to locate these TF-DNA interactions to understand transcriptional regulation. The availability of datasets generated by chromatin immunoprecipitation followed by sequencing (ChIP-seq) empowers our efforts to predict the specific locations of TFBSs with greater confidence than previously possible by fusing computational and experimental approaches. In this work, we processed ~10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~44 million TFBSs with experimental and computational evidence for direct TF-DNA interactions for 640 TFs in 1,101 cell lines and tissues. These TFBSs were used to predict >183,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface (https://unibind.uio.no/), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions. UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.

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Global expression and CpG methylation analysis of primary endothelial cells before and after TNFa stimulation reveals gene modules enriched in inflammatory and infectious diseases and associated DMRs

Cited by 12 publications

References 40 publications

Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

Identification of genes associated with abiotic stress tolerance in sweetpotato using weighted gene co‐expression network analysis

UniBind: maps of high-confidence direct TF-DNA interactions across nine species

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