Computational Promoter Modeling Identifies the Modes of Transcriptional Regulation in Hematopoietic Stem Cells

Park, Sung‐Joon; Umemoto, Terumasa; Saito‐Adachi, Mihoko; Shiratsuchi, Yoshiko; Yamato, Masayuki; Nakai, Kenta

doi:10.1371/journal.pone.0093853

Cited by 9 publications

(19 citation statements)

References 63 publications

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“…This complements other recent studies on the use of computational modeling to elucidate cellular behavior such as genetic expression in individual stem cells, 21,22 the role of stem cells in tumor development and metastasis, 23,24 and embryogenesis in the pancreas. 25 …”

Section: Discussionsupporting

confidence: 73%

A Computational Model of Cellular Engraftment on Lung Scaffolds

Pothen

Rajendran

Wagner

et al. 2016

BioResearch Open Access

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The possibility that stem cells might be used to regenerate tissue is now being investigated for a variety of organs, but these investigations are still essentially exploratory and have few predictive tools available to guide experimentation. We propose, in this study, that the field of lung tissue regeneration might be better served by predictive tools that treat stem cells as agents that obey certain rules of behavior governed by both their phenotype and their environment. Sufficient knowledge of these rules of behavior would then, in principle, allow lung tissue development to be simulated computationally. Toward this end, we developed a simple agent-based computational model to simulate geographic patterns of cells seeded onto a lung scaffold. Comparison of the simulated patterns to those observed experimentally supports the hypothesis that mesenchymal stem cells proliferate preferentially toward the scaffold boundary, whereas alveolar epithelial cells do not. This demonstrates that a computational model of this type has the potential to assist in the discovery of rules of cellular behavior.

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Section: Discussionsupporting

confidence: 73%

A Computational Model of Cellular Engraftment on Lung Scaffolds

Pothen

Rajendran

Wagner

et al. 2016

BioResearch Open Access

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“…A total of 2,234 genes were found: 1,239 upregulated and 995 downregulated. DEGs analysis by RNA-seq carried out on hematopoietic stem cells detected a higher number of DEGs than reported previously, which may have been related to the more comprehensive transcriptome discovery method ( Park et al, 2014 ). Herein, GO functional enrichment of the gene expression was performed.…”

Section: Discussionmentioning

confidence: 69%

“…These genes profiles are very attractive, because difference in the expression levels of genes between the two chicken lines may provide clues for underlying protective immune response mechanisms in NE. Recently, several groups were used RNA-Seq to analysis of gene profiles and they show that the minimum 70% of reads were aligned to reference genome and 40% reads were mapped uniquely to the reference genome ( Park et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

RNA-seq Profiles of Immune Related Genes in the Spleen of Necrotic Enteritis-afflicted Chicken Lines

Truong

Hong

Lillehoj

2015

Asian Australas. J. Anim. Sci

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The study aimed to compare the necrotic enteritis (NE)-induced transcriptome differences between the spleens of Marek’s disease resistant chicken line 6.3 and susceptible line 7.2 co-infected with Eimeria maxima/Clostridium perfringens using RNA-Seq. Total RNA from the spleens of two chicken lines were used to make libraries, generating 42,736,296 and 42,617,720 usable reads, which were assembled into groups of 29,897 and 29,833 mRNA genes, respectively. The transcriptome changes were investigated using the differentially expressed genes (DEGs) package, which indicated 3,255, 2,468 and 2,234 DEGs of line 6.3, line 7.2, and comparison between two lines, respectively (fold change ≥2, p<0.01). The transcription levels of 14 genes identified were further examined using qRT-PCR. The results of qRT-PCR were consistent with the RNA-seq data. All of the DEGs were analysed using gene ontology terms, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the DEGs in each term were found to be more highly expressed in line 6.3 than in line 7.2. RNA-seq analysis indicated 139 immune related genes, 44 CD molecular genes and 150 cytokines genes which were differentially expressed among chicken lines 6.3 and 7.2 (fold change ≥2, p<0.01). Novel mRNA analysis indicated 15,518 novel genes, for which the expression was shown to be higher in line 6.3 than in line 7.2 including some immune-related targets. These findings will help to understand host-pathogen interaction in the spleen and elucidate the mechanism of host genetic control of NE, and provide basis for future studies that can lead to the development of marker-based selection of highly disease-resistant chickens.

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“…The second approach to predict TF cooperativity in a cell-type specific manner combines gene expression measurements with the investigation of the regulatory regions of co-expressed genes for overrepresentation of TF motifs [ 5 , 14 ]. For example, one previous study used a combination of DNA accessibility and gene expression data to build regulatory maps of Drosophila embryonic development [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

coTRaCTE predicts co-occurring transcription factors within cell-type specific enhancers

et al. 2018

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Cell-type specific gene expression is regulated by the combinatorial action of transcription factors (TFs). In this study, we predict transcription factor (TF) combinations that cooperatively bind in a cell-type specific manner. We first divide DNase hypersensitive sites into cell-type specifically open vs. ubiquitously open sites in 64 cell types to describe possible cell-type specific enhancers. Based on the pattern contrast between these two groups of sequences we develop “co-occurring TF predictor on Cell-Type specific Enhancers” (coTRaCTE) - a novel statistical method to determine regulatory TF co-occurrences. Contrasting the co-binding of TF pairs between cell-type specific and ubiquitously open chromatin guarantees the high cell-type specificity of the predictions. coTRaCTE predicts more than 2000 co-occurring TF pairs in 64 cell types. The large majority (70%) of these TF pairs is highly cell-type specific and overlaps in TF pair co-occurrence are highly consistent among related cell types. Furthermore, independently validated co-occurring and directly interacting TFs are significantly enriched in our predictions. Focusing on the regulatory network derived from the predicted co-occurring TF pairs in embryonic stem cells (ESCs) we find that it consists of three subnetworks with distinct functions: maintenance of pluripotency governed by OCT4, SOX2 and NANOG, regulation of early development governed by KLF4, STAT3, ZIC3 and ZNF148 and general functions governed by MYC, TCF3 and YY1. In summary, coTRaCTE predicts highly cell-type specific co-occurring TFs which reveal new insights into transcriptional regulatory mechanisms.

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Computational Promoter Modeling Identifies the Modes of Transcriptional Regulation in Hematopoietic Stem Cells

Cited by 9 publications

References 63 publications

A Computational Model of Cellular Engraftment on Lung Scaffolds

A Computational Model of Cellular Engraftment on Lung Scaffolds

RNA-seq Profiles of Immune Related Genes in the Spleen of Necrotic Enteritis-afflicted Chicken Lines

coTRaCTE predicts co-occurring transcription factors within cell-type specific enhancers

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