Gene regulation gravitates toward either addition or multiplication when combining the effects of two signals

Signals often ultimately affect the transcription of genes, and often, two different signals can affect the transcription of the same gene. In such cases, it is natural to ask how the combined transcriptional response compares to the individual responses. Mechanistic models can predict a range of combined responses, with the most commonly applied models predicting additive or multiplicative responses, but systematic genome-wide evaluation of these predictions are not available. Here, we performed a comprehensive analysis of the transcriptional response of human MCF-7 cells to two different signals (retinoic acid and TGF-β), applied individually and in combination. We found that the combined responses exhibited a range of behaviors, but clearly favored both additive and multiplicative combined transcriptional responses. We also performed paired chromatin accessibility measurements to measure putative transcription factor occupancy at regulatory elements near these genes. We found that increases in chromatin accessibility were largely additive, meaning that the combined accessibility response was the sum of the accessibility responses to each signal individually. We found some association between super-additivity of accessibility and multiplicative or super-multiplicative combined transcriptional responses, while sub-additivity of accessibility associated with additive transcriptional responses. Our findings suggest that mechanistic models of combined transcriptional regulation must be able to reproduce a range of behaviors.

[1]  J. R. Chubb,et al.  What Is a Transcriptional Burst? , 2020, Trends in genetics : TIG.

[2]  Marius Pachitariu,et al.  Cellpose: a generalist algorithm for cellular segmentation , 2020, Nature Methods.

[3]  Neva C. Durand,et al.  Activity-by-Contact model of enhancer-promoter regulation from thousands of CRISPR perturbations , 2019, Nature Genetics.

[4]  Georg Seelig,et al.  A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation , 2019, Cell.

[5]  Andrey Rzhetsky,et al.  Ultra-multiplexed analysis of single-cell dynamics reveals logic rules in differentiation , 2019, Science Advances.

[6]  A. DePace,et al.  Signal Integration by Shadow Enhancers and Enhancer Duplications Varies across the Drosophila Embryo. , 2019, Cell reports.

[7]  Sandy L. Klemm,et al.  Chromatin accessibility and the regulatory epigenome , 2019, Nature Reviews Genetics.

[8]  Rob Phillips,et al.  Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression. , 2018, Annual review of biophysics.

[9]  Kristofer C. Berrett,et al.  Sufficiency analysis of estrogen responsive enhancers using synthetic activators , 2018, Life Science Alliance.

[10]  A. Raj,et al.  Transcriptional Burst Initiation and Polymerase Pause Release Are Key Control Points of Transcriptional Regulation. , 2019, Molecular cell.

[11]  I. Goldstein,et al.  Synergistic gene expression during the acute phase response is characterized by transcription factor assisted loading , 2017, Nature Communications.

[12]  Nicholas A. Sinnott-Armstrong,et al.  Omni-ATAC-seq: Improved ATAC-seq protocol , 2017 .

[13]  Maozhen Tian,et al.  TGF-β Stimulation of EMT Programs Elicits Non-genomic ER-α Activity and Anti-estrogen Resistance in Breast Cancer Cells. , 2017, Journal of cancer metastasis and treatment.

[14]  Mariko Okada,et al.  Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment , 2017, Bioinform..

[15]  William J. Greenleaf,et al.  chromVAR: Inferring transcription factor-associated accessibility from single-cell epigenomic data , 2017, Nature Methods.

[16]  Clarissa Scholes,et al.  Combinatorial Gene Regulation through Kinetic Control of the Transcription Cycle. , 2017, Cell systems.

[17]  T. Tan,et al.  The immune checkpoint ligand PD-L1 is upregulated in EMT-activated human breast cancer cells by a mechanism involving ZEB-1 and miR-200 , 2017, Oncoimmunology.

[18]  B. Cohen,et al.  Interactions between pluripotency factors specify cis-regulation in embryonic stem cells , 2016, Genome research.

[19]  Gerald Stampfel,et al.  Transcriptional regulators form diverse groups with context-dependent regulatory functions , 2015, Nature.

[20]  Georg Seelig,et al.  Learning the Sequence Determinants of Alternative Splicing from Millions of Random Sequences , 2015, Cell.

[21]  Jinfeng Li,et al.  The effect of TGF-beta-induced epithelial-mesenchymal transition on the expression of intracellular calcium-handling proteins in T47D and MCF-7 human breast cancer cells. , 2015, Archives of biochemistry and biophysics.

[22]  Eric S. Lander,et al.  A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping , 2015, Cell.

[23]  Hernan G. Garcia,et al.  Enhancer additivity and non-additivity are determined by enhancer strength in the Drosophila embryo , 2015, eLife.

[24]  Neva C. Durand,et al.  A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping , 2014, Cell.

[25]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[26]  Kate B. Cook,et al.  Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity , 2014, Cell.

[27]  B. Cohen,et al.  High-throughput functional testing of ENCODE segmentation predictions , 2014, Genome research.

[28]  Uri Alon,et al.  Linear Superposition and Prediction of Bacterial Promoter Activity Dynamics in Complex Conditions , 2014, PLoS Comput. Biol..

[29]  Lauren M McIntyre,et al.  Leveraging biological replicates to improve analysis in ChIP-seq experiments , 2014, Computational and structural biotechnology journal.

[30]  Yan Li,et al.  A high-resolution map of three-dimensional chromatin interactome in human cells , 2013, Nature.

[31]  Howard Y. Chang,et al.  Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position , 2013, Nature Methods.

[32]  Nathan C. Sheffield,et al.  The accessible chromatin landscape of the human genome , 2012, Nature.

[33]  Mark Groudine,et al.  The hypersensitive sites of the murine β-globin locus control region act independently to affect nuclear localization and transcriptional elongation. , 2012, Blood.

[34]  Marc S. Sherman,et al.  Thermodynamic State Ensemble Models of cis-Regulation , 2012, PLoS Comput. Biol..

[35]  Joseph B Hiatt,et al.  Massively parallel functional dissection of mammalian enhancers in vivo , 2012, Nature Biotechnology.

[36]  J. Carroll,et al.  Pioneer transcription factors: establishing competence for gene expression. , 2011, Genes & development.

[37]  Laurence Ettwiller,et al.  Large-scale analysis of the regulatory architecture of the mouse genome with a transposon-associated sensor , 2011, Nature Genetics.

[38]  S. Nechaev,et al.  Pol II waiting in the starting gates: Regulating the transition from transcription initiation into productive elongation. , 2011, Biochimica et biophysica acta.

[39]  A. Visel,et al.  Homotypic clusters of transcription factor binding sites are a key component of human promoters and enhancers. , 2010, Genome research.

[40]  U. Alon,et al.  Protein Dynamics in Drug Combinations: a Linear Superposition of Individual-Drug Responses , 2010, Cell.

[41]  John T. Lis,et al.  Defining mechanisms that regulate RNA polymerase II transcription in vivo , 2009, Nature.

[42]  K. White,et al.  Genomic Antagonism between Retinoic Acid and Estrogen Signaling in Breast Cancer , 2009, Cell.

[43]  E. Siggia,et al.  Analysis of Combinatorial cis-Regulation in Synthetic and Genomic Promoters , 2008, Nature.

[44]  U. Alon,et al.  Diverse two-dimensional input functions control bacterial sugar genes. , 2008, Molecular cell.

[45]  Terence Hwa,et al.  Transcriptional regulation by the numbers: models. , 2005, Current opinion in genetics & development.

[46]  Sven Bilke,et al.  The gene expression response of breast cancer to growth regulators: patterns and correlation with tumor expression profiles. , 2003, Cancer research.

[47]  R. Horst,et al.  Quantitative analyses of naturally occurring retinoids. , 1998, Methods in molecular biology.

[48]  J. Greenblatt,et al.  Three functional classes of transcriptional activation domain , 1996, Molecular and cellular biology.

[49]  R. Evans,et al.  The RXR heterodimers and orphan receptors , 1995, Cell.

[50]  O. Jardetzky,et al.  Protein dynamics , 1994, FEBS letters.

[51]  J. Joung,et al.  Synergistic activation of transcription by bacteriophage lambda cI protein and E. coli cAMP receptor protein. , 1994, Science.

[52]  Michael R. Green,et al.  Gene Expression , 1993, Progress in Gene Expression.

[53]  J. L. Napoli.  [13] Quantification of physiological levels of retinoic acid , 1986 .

[54]  J. L. Napoli.  Quantification of physiological levels of retinoic acid. , 1986, Methods in enzymology.

[55]  G. K. Ackers,et al.  Quantitative model for gene regulation by lambda phage repressor. , 1982, Proceedings of the National Academy of Sciences of the United States of America.