A Systematic Approach to Identify Candidate Transcription Factors that Control Cell Identity

The rapid increase of genome-wide datasets on gene expression, chromatin states, and transcription factor (TF) binding locations offers an exciting opportunity to interpret the information encoded in genomes and epigenomes. This task can be challenging as it requires joint modeling of context-specific activation of cis-regulatory elements (REs) and the effects on transcription of associated regulatory factors. To meet this challenge, we propose a statistical approach based on paired expression and chromatin accessibility (PECA) data across diverse cellular contexts. In our approach, we model (i) the localization to REs of chromatin regulators (CRs) based on their interaction with sequence-specific TFs, (ii) the activation of REs due to CRs that are localized to them, and (iii) the effect of TFs bound to activated REs on the transcription of target genes (TGs). The transcriptional regulatory network inferred by PECA provides a detailed view of how trans-and cis-regulatory elements work together to affect gene expression in a context-specific manner. We illustrate the feasibility of this approach by analyzing paired expression and accessibility data from the mouse Encyclopedia of DNA Elements (ENCODE) and explore various applications of the resulting model. gene regulation | transcription factor | regulatory element | chromatin regulator | chromatin activity E ver since the emergence of high-throughput gene expression experiments (1), computational biologists have been interested in the inference of gene regulatory relationships from gene expression data across diverse cellular contexts corresponding to diverse cell types and experimental conditions ( Fig. 1, red boxes). However, progress has been hindered by the fact that gene expression measurements provide little information on underlying regulatory mechanisms such as transcription factor binding and chromatin modification. To fill this gap, chromatin immunoprecipitationbased methods (2, 3) have been developed for the genome-wide mapping of transcriptional regulator binding locations and the detection of epigenetic marks characteristic of specific chromatin states. For example, by performing thousands of ChIP-seq experiments, the Encyclopedia of DNA Elements (ENCODE) consortium has generated such data for many chromatin marks and transcriptional regulators on a small number of cell lines (Fig. 1, green boxes). However, because a large number of transcriptional regulators and chromatin marks have to be analyzed one by one, it is unlikely that such comprehensive data will become available for many other cell lines. For most cellular contexts, the desired data will remain missing in the foreseeable future (Fig. 1, gray boxes).On the other hand, it is known that many of the protein-DNA interactions important for gene regulation occur in regulatory elements (REs) such as enhancers and insulators, which compose only a small portion of the noncoding sequences in a genome. The REs active in gene regulation in a given cellular state tend to have an open chromatin struc...

show abstract

“…The set of core TFs was studied in ref. 13, and details of enhancer mapping by ENCODE on various tissues are given in ref. 12.…”

Section: Resultsmentioning

confidence: 99%

Modeling gene regulation from paired expression and chromatin accessibility data

Duren

Chen

Jiang

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

182

198

View full text Add to dashboard Cite

show abstract

“…In the past year, several algorithms have been proposed for recommending TFs to overexpress to convert mammalian cells from one cell type to another (8,11,12). These are important contributions to regenerative medicine, but they are not general algorithms for moving cells from any transcriptional state to any other transcriptional state.…”

mentioning

confidence: 99%

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

Michael

Maier

Brown

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genomewide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.gene-regulatory networks | regulatory systems biology | transcriptome | engineering | Saccharomyces cerevisiae T he central premise of regulatory systems biology is that a systematic map of a cell's regulatory machinery will enable us to understand, predict, and rationally manipulate the cell's state or behavior. Manipulation of cellular state has many promising applications, including stem cell biology and regenerative medicine, biofuel production, and gene therapy. Progress toward cellular state control has been driven by both the systems biology and the synthetic biology research communities. Systems biology has produced whole-genome regulatory network maps (1), but relatively little research has focused on using these maps for predicting and manipulating cellular behavior (2). Regulatory synthetic biology has focused on creating molecular circuits that can be placed into a cell to control the transcription of a small number of transgenes, but genome-scale engineering of the cell's native regulatory apparatus is still rare, with most systems restricted to a limited set of controlled targets (3). Here, we demonstrate that transcription factor (TF) network mapping, gene expression profiling, and computational modeling can be integrated to rationally engineer transcriptional state. We call this activity, which bridges the gap between systems biology and synthetic biology, "transcriptome engineering

show abstract

“…Thus, these observations suggest that GLIS2 may have a role in maintaining the pluripotent state of hESCs. GLIS3, together with several other transcription factors, also promotes the reprogramming of human fibroblasts into retinal pigmented epithelial cells [108]. …”

Section: Molecular and Physiological Functions Of Glis1–3mentioning

confidence: 99%

GLIS1–3 transcription factors: critical roles in the regulation of multiple physiological processes and diseases

Jetten

2018

Cell. Mol. Life Sci.

View full text Add to dashboard Cite

Krüppel-like zinc finger proteins form one of the largest families of transcription factors. They function as key regulators of embryonic development and a wide range of other physiological processes, and are implicated in a variety of pathologies. GLI-Similar 1–3 (GLIS1–3) constitute a subfamily of Krüppel-like zinc finger proteins that act either as activators or repressors of gene transcription. GLIS3 plays a critical role in the regulation of multiple biological processes and is a key regulator of pancreatic β cell generation and maturation, insulin gene expression, thyroid hormone biosynthesis, spermatogenesis, and the maintenance of normal kidney functions. Loss of GLIS3 function in humans and mice leads to the development of several pathologies, including neonatal diabetes and congenital hypothyroidism, polycystic kidney disease, and infertility. Single nucleotide polymorphisms in GLIS3 genes have been associated with increased risk of several diseases, including type 1 and type 2 diabetes, glaucoma, and neurological disorders. GLIS2 plays a critical role in the kidney and GLIS2 dysfunction leads to nephronophthisis, an end-stage, cystic renal disease. In addition, GLIS1–3 have regulatory functions in several stem/progenitor cell populations. GLIS1 and GLIS3 greatly enhance reprogramming efficiency of somatic cells into induced embryonic stem cells, while GLIS2 inhibits reprogramming. Recent studies have obtained substantial mechanistic insights into several physiological processes regulated by GLIS2 and GLIS3, while little is still known about the physiological functions of GLIS1. The localization of some GLIS proteins to the primary cilium suggests that their activity may be regulated by a downstream primary cilium-associated signaling pathway. Insights into the upstream GLIS signaling pathway may provide opportunities for the development of new therapeutic strategies for diabetes, hypothyroidism, and other diseases.

show abstract

A Systematic Approach to Identify Candidate Transcription Factors that Control Cell Identity

Cited by 154 publications

References 53 publications

Modeling gene regulation from paired expression and chromatin accessibility data

Modeling gene regulation from paired expression and chromatin accessibility data

Model-based transcriptome engineering promotes a fermentative transcriptional state in yeast

GLIS1–3 transcription factors: critical roles in the regulation of multiple physiological processes and diseases

Contact Info

Product

Resources

About