Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo

Sayal, Rupinder; Dresch, Jacqueline M.; Pushel, Irina; Taylor, Benjamin; Arnosti, David N.

doi:10.7554/elife.08445

Cited by 41 publications

(95 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, even dynamic chromatin features may reflect off-target effects of transcription factors, rather than functional interactions (Kok et al, 2015). Although likely to be incomplete, our identification of the cisregulatory circuitry of InR represents a first step in enabling the construction of computational models, which can be tested in physiological settings to understand the impact of regulatory sequence variation and signaling (Samee et al, 2015;Sayal et al, 2016).…”

Section: Regulation Of Insulin Receptor Expression In Development Andmentioning

confidence: 99%

Complex cis-regulatory landscape of the insulin receptor gene underlies the broad expression of a central signaling regulator

et al. 2016

Self Cite

View full text Add to dashboard Cite

Insulin signaling plays key roles in development, growth and metabolism through dynamic control of glucose uptake, global protein translation and transcriptional regulation. Altered levels of insulin signaling are known to play key roles in development and disease, yet the molecular basis of such differential signaling remains obscure. Expression of the insulin receptor (InR) gene itself appears to play an important role, but the nature of the molecular wiring controlling InR transcription has not been elucidated. We characterized the regulatory elements driving Drosophila InR expression and found that the generally broad expression of this gene is belied by complex individual switch elements, the dynamic regulation of which reflects direct and indirect contributions of FOXO, EcR, Rbf and additional transcription factors through redundant elements dispersed throughout ∼40 kb of non-coding regions. The control of InR transcription in response to nutritional and tissuespecific inputs represents an integration of multiple cis-regulatory elements, the structure and function of which may have been sculpted by evolutionary selection to provide a highly tailored set of signaling responses on developmental and tissue-specific levels.

show abstract

Section: Regulation Of Insulin Receptor Expression In Development Andmentioning

confidence: 99%

Complex cis-regulatory landscape of the insulin receptor gene underlies the broad expression of a central signaling regulator

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is believed that a common mechanism of variant impact on cellular function is by affecting TF binding site strength, and consequently the gene expression level driven by an enhancer (14,15). Thus, to investigate such mechanisms we need a precise quantitative method to predict expression level from enhancer sequence, i.e., a "sequence-toexpression" model (16)(17)(18)(19)(20)(21)(22); such methods must be sensitive enough to predict the regulatory effect of relatively minor changes in enhancer sequence, as is often the case with individual variations.…”

Section: Introductionmentioning

confidence: 99%

Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms

Khajouei

Samper

Djabrayan

et al. 2020

Preprint

View full text Add to dashboard Cite

It is challenging to predict the impact of small genetic changes such as single nucleotide polymorphisms on gene expression, since mechanisms involved in gene regulation and their cis-regulatory encoding are not well-understood. Recent studies have attempted to predict the functional impact of non-coding variants based on available knowledge of cis-regulatory encoding, e.g., transcription factor (TF) motifs. In this work, we explore the relationship between regulatory variants and cis-regulatory encoding from the opposite angle, using the former to inform the latter. We employ sequence-to-expression modeling to resolve ambiguities regarding gene regulatory mechanisms using information about effects of single nucleotide variations in an enhancer. We demonstrate our methodology using a well-studied enhancer of the developmental gene intermediate neuroblasts defective (ind) in D. melanogaster. We first trained the thermodynamics-based model GEMSTAT to relate the neuroectodermal expression pattern of ind to its enhancer's sequence, and constructed an ensemble of models that represent different parameter settings consistent with available data for this gene. We then predicted the effects of every possible single nucleotide variation within this enhancer, and compared these to SNP data recorded in the Drosophila Genome Reference Panel. We chose specific SNPs for which different models in the ensemble made conflicting predictions, and tested their effect in vivo. These experiments narrowed in on one mechanistic model as capable of explaining the observed effects. We further confirmed the generalizability of this model to orthologous enhancers and other related developmental enhancers. In conclusion, mechanistic models of cis-regulatory function not only help make specific predictions of variant impact, they may also be learned more accurately using data on variants.A central issue in analyzing variations in the non-coding genome is to interpret their functional impact, and their connections to phenotype differences and disease etiology. Machine learning methods based on statistical modeling have been developed to associate genetic variants to expression changes. However, associations predicted by these models may not be functionally relevant, despite being statisticaly significant. We describe how mathematical modeling of gene expression can be employed to systematically study the non-coding sequence and its relationship to gene expression. We demonstrate our method in a well studied developmental enhancer of the fruitfly. We establish the efficacy of mathematical models in combination with the polymorphism data to reveal new mechanistic insights.

show abstract

“…In this paper, we show that DNNs can be used to generate a predictive model of gene expression. Our starting point is a previously published model of transcriptional control [23], which is one of a family of so-called thermodynamic transcription models [38,21,42,40,22,16,23,32,41,4,5]. In these models, occupancy of DNA by TFs is calculated using thermodynamics, and phenomenological rules are used to calculate the transcription rate from the configuration of bound factors.…”

Section: Introductionmentioning

confidence: 99%

Fully Interpretable Deep Learning Model of Transcriptional Control

Liu

Barr²,

Reinitz

2019

Preprint

View full text Add to dashboard Cite

The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent work in the system biology community to employ DNNs to solve important problems in functional genomics and molecular genetics. Because of the black box nature of DNNs, such assumptions, while useful in practice, are unsatisfactory for scientific analysis. In this paper, we give an example of a DNN in which every layer is interpretable. Moreover, this DNN is biologically validated and predictive. We derive our DNN from a systems biology model that was not previously recognized as having a DNN structure. This DNN is concerned with a key unsolved biological problem, which is to understand the DNA regulatory code which controls how genes in multicellular organisms are turned on and off. Although we apply our DNN to data from the early embryo of the fruit fly Drosophila, this system serves as a testbed for analysis of much larger data sets obtained by systems biology studies on a genomic scale.

show abstract

Quantitative perturbation-based analysis of gene expression predicts enhancer activity in early Drosophila embryo

Cited by 41 publications

References 62 publications

Complex cis-regulatory landscape of the insulin receptor gene underlies the broad expression of a central signaling regulator

Complex cis-regulatory landscape of the insulin receptor gene underlies the broad expression of a central signaling regulator

Model-based analysis of polymorphisms in an enhancer reveals cis-regulatory mechanisms

Fully Interpretable Deep Learning Model of Transcriptional Control

Contact Info

Product

Resources

About