Sp3 is essential for normal lung morphogenesis and cell cycle progression during mouse embryonic development

McCoy, Alyssa M.; Lakhdari, Omar; Shome, Sayane; Caoili, Kaitlin; Hernandez, Gilberto E.; Aghaeepour, Nima; Butcher, Lindsay D.; Fisch, Kathleen; Prince, Lawrence S.

doi:10.1242/dev.200839

Cited by 4 publications

(4 citation statements)

References 68 publications

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“…2 c). These database motifs include NFYA 22 , SNAI2 23 , POU2F2 24 , KLF9 25 and SP3 26 , which are well known to be active in developmental processes. Not surprisingly, we also identified the Dux motif as highly specific for the Dux positive condition.…”

Section: Resultsmentioning

confidence: 99%

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Schultheis,

Bentsen,

Heger

et al. 2024

Sci Rep

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Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only ~ 700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality (1) to isolate FPs without binding motifs, (2) to perform de novo motif generation and (3) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

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Section: Resultsmentioning

confidence: 99%

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Schultheis,

Bentsen,

Heger

et al. 2024

Sci Rep

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show abstract

“…They are closely related to cell proliferation and self-renewal, and are crucial contributors to the early embryonic development and cell lineage specification. For GSE67310 data, the absence of CHCHD3 expression can lead to tissue undergrowth and cell proliferation defects [ 16 ], VEZF1 can regulate cell differentiation and proliferation and participate in the early vascular differentiation process [ 17 ], SP3 is required for perinatal survival in mice [ 18 ]. GTF2I indirectly contributes to the transcriptional regulation of genes controlling cell proliferation and cell cycle through encoding transcription factor TFII-I [ 19 ].…”

Section: Resultsmentioning

confidence: 99%

MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy

Xie,

Peng,

2024

BMC Bioinformatics

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Complex biological systems often undergo sudden qualitative changes during their dynamic evolution. These critical transitions are typically characterized by a catastrophic progression of the system. Identifying the critical point is critical to uncovering the underlying mechanisms of complex biological systems. However, the system may exhibit minimal changes in its state until the critical point is reached, and in the face of high throughput and strong noise data, traditional biomarkers may not be effective in distinguishing the critical state. In this study, we propose a novel approach, mutual information weighted entropy (MIWE), which uses mutual information between genes to build networks and identifies critical states by quantifying molecular dynamic differences at each stage through weighted differential entropy. The method is applied to one numerical simulation dataset and four real datasets, including bulk and single-cell expression datasets. The critical states of the system can be recognized and the robustness of MIWE method is verified by numerical simulation under the influence of different noises. Moreover, we identify two key transcription factors (TFs), CREB1 and CREB3, that regulate downstream signaling genes to coordinate cell fate commitment. The dark genes in the single-cell expression datasets are mined to reveal the potential pathway regulation mechanism.

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“…Figure 2c). These database motifs include NFYA 18 , SNAI2 19 , POU2F2 20 , KLF9 21 and SP3 22 , which are well known to be active in developmental processes. Not surprisingly, we also identified the Dux motif as highly specific for the Dux positive condition.…”

Section: Resultsmentioning

confidence: 99%

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Schultheis,

Bentsen,

Heger

et al. 2023

Preprint

View full text Add to dashboard Cite

Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only ∼700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality i) to isolate FPs without binding motifs, ii) to perform de novo motif generation and iii) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

show abstract

Sp3 is essential for normal lung morphogenesis and cell cycle progression during mouse embryonic development

Cited by 4 publications

References 68 publications

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

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