Transcription Factors

Caramori, Gaetano; Nucera, Francesco; Coppolino, Irene; Bello, Federica Lo; Ruggeri, Paolo; Ito, Kazuhiro; Stefano, Antonino Di; Adcock, Ian M.

doi:10.1016/b978-0-12-801238-3.11699-x

“…scREMOTE, like any computational model, is based on a set of assumptions that, to varying degrees, reflect the underlying biology. Here, we approximate the regulatory effect of each TF to be linear and additive, whereas in reality, TFs often work in combinations and in complex relationships ( 55 ). Although, in our current implementation, we used ordinary least squares linear regression, the scREMOTE framework could be easily extended to include regularization like LASSO if there are too many TFs, or if multicollinearity is a concern.…”

Section: Discussionmentioning

confidence: 99%

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

Tran

¹

,

Yang

²

,

Yang

³

et al. 2022

NAR Genomics and Bioinformatics

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Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

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“…scREMOTE, like any computational model, is based on a set of assumptions that, to varying degrees, reflect the underlying biology. Here, we approximate the regulatory effect of each TF to be linear and additive, whereas in reality, TFs often work in combinations and in complex relationships [51]. Although, in our current implementation, we used ordinary least squares linear regression, the scREMOTE framework could be easily extended to more advanced models, possibly capturing non-linear effects or it could be extended to include regularization like LASSO if there are too many TFs, or if multicollinearity is a concern.…”

Section: Discussionmentioning

confidence: 99%

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

Tran

¹

,

Yang

²

,

Ormerod

³

2021

Preprint

0

View full text Add to dashboard Cite

Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

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“…Transcription factors are key players during gene expression as they facilitate when and what gene is "turn on or off" by binding to DNA sequences, acting as coactivator or co-repressor of the gene response to various signals [31] . Structurally they comprise of two domains: a) DNA binding domain that recognizes and bind to DNA sequences, b) activation (effector) domain which interacts with cofactors or other transcription factors [32][33][34] . Some of them also have an additional domain (signalsensing domain) that binds to ligands to modulate their activity response to environmental cues [32,33] .…”

Section: Interferon Regulatory Factors (Irfs) Familymentioning

confidence: 99%

“…Transcription factors are categorized into several groups based on their DNA binding domain structures and their interaction with DNA sequences [33][34][35] . Some of these transcription factors are known as general transcription factors (GTFs), which are ubiquitous and essential for initiating transcription in the protein-coding gene [31,34] . Other transcription factors are either constitutive or inducible and are specific to certain cell types and stages of organism development [31,34] .…”

Section: Interferon Regulatory Factors (Irfs) Familymentioning

confidence: 99%

“…Some of these transcription factors are known as general transcription factors (GTFs), which are ubiquitous and essential for initiating transcription in the protein-coding gene [31,34] . Other transcription factors are either constitutive or inducible and are specific to certain cell types and stages of organism development [31,34] . Several of these transcription factors function as master transcriptional regulators in controlling signal responses and specifying cells' lineage [31,36] .…”

Section: Interferon Regulatory Factors (Irfs) Familymentioning

confidence: 99%

See 1 more Smart Citation

An overview of the human immune system and the role of interferon regulatory factors (IRFs)

Kaur¹,

Fang²

2020

Prog Micobes Mol Biol

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The immune system consists of a dynamic network of cells, proteins, tissues, and organs that communicate to provide adequate defense responses against pathogenic agents. The immune system divide into the non-specific (innate) and the specific (adaptive) components, where the interactions between these two arms are intricately regulated. To deploy effective immune responses, immune systems comprise various cells and molecules that communicate with each other via signaling pathways coordinated by gene regulatory networks. The interferon regulatory factors (IRFs) are critical regulators of both the immune system’s development and activation of different cells. To better understand the essential components of the normal immune system, this review essentially aims to cover the current knowledge of individual components of the immune system and the important role of IRFs in regulating the immune system.

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Transcription Factors

Cited by 3 publications

References 119 publications

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

An overview of the human immune system and the role of interferon regulatory factors (IRFs)

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