The complexity of transferring genetic information.

The Central Dogma has been a useful conceptualization of the transfer of genetic information, and our understanding of the detailed mechanisms involved in that transfer continues to evolve. Here, we speak to several scientists about their research, how it influences our understanding of information transfer, and questions for the future.

Identification of plant transcriptional activation domains.

Gene expression in Arabidopsis is regulated by more than 1,900 transcription factors (TFs), which have been identified genome-wide by the presence of well-conserved DNA-binding domains. Activator TFs contain activation domains (ADs) that recruit coactivator complexes; however, for nearly all Arabidopsis TFs, we lack knowledge about the presence, location and transcriptional strength of their ADs1. To address this gap, here we use a yeast library approach to experimentally identify Arabidopsis ADs on a proteome-wide scale, and find that more than half of the Arabidopsis TFs contain an AD. We annotate 1,553 ADs, the vast majority of which are, to our knowledge, previously unknown. Using the dataset generated, we develop a neural network to accurately predict ADs and to identify sequence features that are necessary to recruit coactivator complexes. We uncover six distinct combinations of sequence features that result in activation activity, providing a framework to interrogate the subfunctionalization of ADs. Furthermore, we identify ADs in the ancient AUXIN RESPONSE FACTOR family of TFs, revealing that AD positioning is conserved in distinct clades. Our findings provide a deep resource for understanding transcriptional activation, a framework for examining function in intrinsically disordered regions and a predictive model of ADs.

Yearly planning meetings: individualized development plans aren't just more paperwork.

The National Institutes of Health (NIH) encourages trainees to make Individualized Development Plans to help them prepare for academic and nonacademic careers. We describe our approach to building an Individualized Development Plan, the reasons we find them useful and empowering for both PIs and trainees, and resources to help other labs implement them constructively.

Hunchback is counter-repressed to regulate even-skipped stripe 2 expression in Drosophila embryos.

Hunchback is a bifunctional transcription factor that can activate and repress gene expression in Drosophila development. We investigated the regulatory DNA sequence features that control Hunchback function by perturbing enhancers for one of its target genes, even-skipped (eve). While Hunchback directly represses the eve stripe 3+7 enhancer, we found that in the eve stripe 2+7 enhancer, Hunchback repression is prevented by nearby sequences-this phenomenon is called counter-repression. We also found evidence that Caudal binding sites are responsible for counter-repression, and that this interaction may be a conserved feature of eve stripe 2 enhancers. Our results alter the textbook view of eve stripe 2 regulation wherein Hb is described as a direct activator. Instead, to generate stripe 2, Hunchback repression must be counteracted. We discuss how counter-repression may influence eve stripe 2 regulation and evolution.

A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate.

In developing embryos, gene regulatory networks drive cells towards discrete terminal fates, a process called canalization. We studied the behavior of the anterior-posterior segmentation network in Drosophila melanogaster embryos by depleting a key maternal input, bicoid (bcd), and measuring gene expression patterns of the network at cellular resolution. This method results in a gene expression atlas containing the levels of mRNA or protein expression of 13 core patterning genes over six time points for every cell of the blastoderm embryo. This is the first cellular resolution dataset of a genetically perturbed Drosophila embryo that captures all cells in 3D. We describe the technical developments required to build this atlas and how the method can be employed and extended by others. We also analyze this novel dataset to characterize the degree and timing of cell fate canalization in the segmentation network. We find that in two layers of this gene regulatory network, following depletion of bcd, individual cells rapidly canalize towards normal cell fates. This result supports the hypothesis that the segmentation network directly canalizes cell fate, rather than an alternative hypothesis whereby cells are initially mis-specified and later eliminated by apoptosis. Our gene expression atlas provides a high resolution picture of a classic perturbation and will enable further computational modeling of canalization and gene regulation in this transcriptional network.

Shadow enhancers enable Hunchback bifunctionality in the Drosophila embryo.

Hunchback (Hb) is a bifunctional transcription factor that activates and represses distinct enhancers. Here, we investigate the hypothesis that Hb can activate and repress the same enhancer. Computational models predicted that Hb bifunctionally regulates the even-skipped (eve) stripe 3+7 enhancer (eve3+7) in Drosophila blastoderm embryos. We measured and modeled eve expression at cellular resolution under multiple genetic perturbations and found that the eve3+7 enhancer could not explain endogenous eve stripe 7 behavior. Instead, we found that eve stripe 7 is controlled by two enhancers: the canonical eve3+7 and a sequence encompassing the minimal eve stripe 2 enhancer (eve2+7). Hb bifunctionally regulates eve stripe 7, but it executes these two activities on different pieces of regulatory DNA--it activates the eve2+7 enhancer and represses the eve3+7 enhancer. These two "shadow enhancers" use different regulatory logic to create the same pattern.

Commonly asked questions about transcriptional activation domains.

Eukaryotic transcription factors activate gene expression with their DNA-binding domains and activation domains. DNA-binding domains bind the genome by recognizing structurally related DNA sequences; they are structured, conserved, and predictable from protein sequences. Activation domains recruit chromatin modifiers, coactivator complexes, or basal transcriptional machinery via structurally diverse protein-protein interactions. Activation domains and DNA-binding domains have been called independent, modular units, but there are many departures from modularity, including interactions between these regions and overlap in function. Compared to DNA-binding domains, activation domains are poorly understood because they are poorly conserved, intrinsically disordered, and difficult to predict from protein sequences. This review, organized around commonly asked questions, describes recent progress that the field has made in understanding the sequence features that control activation domains and predicting them from sequence.

Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast.

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

Correlating Disordered Activation Domain Ensembles with Gene Expression Levels.

Transcription factor proteins bind to specific DNA promoter sequences and initiate gene transcription. In eukaryotes, most transcription factors contain intrinsically disordered activation domains (ADs) that regulate their transcriptional activity. Like other disordered protein regions, ADs do not have a fixed three-dimensional structure and instead exist in an ensemble of conformations. Disordered ensembles contain sequence-encoded structural preferences which are often linked to their function. We hypothesize this link exists between the structural preferences of disordered AD ensembles and their ability to induce gene expression. To test this, we used FRET microscopy to measure the ensemble dimensions of two activation domains, HIF-1α and CITED2, in live cells, and correlate this structural information with transcriptional activity. We find that point mutations that expanded the HIF-1α ensemble increased transcriptional activity, while those that compacted it reduced activity. Conversely, CITED2 showed no correlation between ensemble dimensions and activity. Our results reveal a sequence-dependent relationship between AD ensemble dimensions and their transcriptional activity. WHY IT MATTERS: Transcription factors have activation domains (ADs) that bind to coactivator complexes to initiate gene transcription. Despite their key role, a comprehensive understanding of what drives their transcriptional activity has remained elusive. Efforts to understand AD activity have largely focused on their amino acid composition. In recent years, it is increasingly realized that the structural ensembles of disordered proteins contain biases that dictate their structural properties. For ADs, ensemble structures remain poorly explored, especially in relation to their activity. Here we report a mutational study of two ADs, HIF-1α and CITED2, that examines how ensemble dimensions correlate with activity. Our findings suggest that ensemble dimensions may drive activity in some ADs, and that AD ensemble dimensions can be modulated not only through mutations, but also through changes in the cellular environment.

Systematic identification of transcriptional activator domains from non-transcription factor proteins in plants and yeast.

Transcription factors promote gene expression via trans-regulatory activation domains. Although whole genome scale screens in model organisms (e.g. human, yeast, fly) have helped identify activation domains from transcription factors, such screens have been less extensively used to explore the occurrence of activation domains in non-transcription factor proteins, such as transcriptional coactivators, chromatin regulators and some cytosolic proteins, leaving a blind spot on what role activation domains in these proteins could play in regulating transcription. We utilized the activation domain predictor PADDLE to mine the entire proteomes of two model eukaryotes, Arabidopsis thaliana and Saccharomyces cerevisiae ( 1 ). We characterized 18,000 fragments covering predicted activation domains from >800 non-transcription factor genes in both species, and experimentally validated that 89% of proteins contained fragments capable of activating transcription in yeast. Peptides with similar sequence composition show a broad range of activities, which is explained by the arrangement of key amino acids. We also annotated hundreds of nuclear proteins with activation domains as putative coactivators; many of which have never been ascribed any function in plants. Furthermore, our library contains >250 non-nuclear proteins containing peptides with activation domain function across both eukaryotic lineages, suggesting that there are unknown biological roles of these peptides beyond transcription. Finally, we identify and validate short, 'universal' eukaryotic activation domains that activate transcription in both yeast and plants with comparable or stronger performance to state-of-the-art activation domains. Overall, our dual host screen provides a blueprint on how to systematically discover novel genetic parts for synthetic biology that function across a wide diversity of eukaryotes. Significance Statement: Activation domains promote transcription and play a critical role in regulating gene expression. Although the mapping of activation domains from transcription factors has been carried out in previous genome-wide screens, their occurrence in non-transcription factors has been less explored. We utilize an activation domain predictor to mine the entire proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae for new activation domains on non-transcription factor proteins. We validate peptides derived from >750 non-transcription factor proteins capable of activating transcription, discovering many potentially new coactivators in plants. Importantly, we identify novel genetic parts that can function across both species, representing unique synthetic biology tools.

Activation domains can decouple the mean and noise of gene expression.

Regulatory mechanisms set a gene's average level of expression, but a gene's expression constantly fluctuates around that average. These stochastic fluctuations, or expression noise, play a role in cell-fate transitions, bet hedging in microbes, and the development of chemotherapeutic resistance in cancer. An outstanding question is what regulatory mechanisms contribute to noise. Here, we demonstrate that, for a fixed mean level of expression, strong activation domains (ADs) at low abundance produce high expression noise, while weak ADs at high abundance generate lower expression noise. We conclude that differences in noise can be explained by the interplay between a TF's nuclear concentration and the strength of its AD's effect on mean expression, without invoking differences between classes of ADs. These results raise the possibility of engineering gene expression noise independently of mean levels in synthetic biology contexts and provide a potential mechanism for natural selection to tune the noisiness of gene expression.

Directed mutational scanning reveals a balance between acidic and hydrophobic residues in strong human activation domains.

Acidic activation domains are intrinsically disordered regions of the transcription factors that bind coactivators. The intrinsic disorder and low evolutionary conservation of activation domains have made it difficult to identify the sequence features that control activity. To address this problem, we designed thousands of variants in seven acidic activation domains and measured their activities with a high-throughput assay in human cell culture. We found that strong activation domain activity requires a balance between the number of acidic residues and aromatic and leucine residues. These findings motivated a predictor of acidic activation domains that scans the human proteome for clusters of aromatic and leucine residues embedded in regions of high acidity. This predictor identifies known activation domains and accurately predicts previously unidentified ones. Our results support a flexible acidic exposure model of activation domains in which the acidic residues solubilize hydrophobic motifs so that they can interact with coactivators. A record of this paper's transparent peer review process is included in the supplemental information.

Sex- and mutation-specific p53 gain-of-function activity in gliomagenesis.

In cancer, missense mutations in the DNA-binding domain of TP53 are common. They abrogate canonical p53 activity and frequently confer gain-of-oncogenic function (GOF) through localization of transcriptionally active mutant p53 to non-canonical genes. We found that several recurring p53 mutations exhibit a sex difference in frequency in patients with glioblastoma (GBM). In vitro and in vivo analysis of three mutations, p53R172H, p53Y202C, and p53Y217C revealed unique interactions between cellular sex and p53 GOF mutations that determined each mutation's ability to transform male versus female primary mouse astrocytes. These phenotypic differences were correlated with sex- and p53 mutation- specific patterns of genomic localization to the transcriptional start sites of upregulated genes belonging to core cancer pathways. The promoter regions of these genes exhibited a sex difference in enrichment for different transcription factor DNA-binding motifs. Together, our data establish a novel mechanism for sex specific mutant p53 GOF activity in GBM with implications for all cancer.

A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain.

Transcriptional activation domains are essential for gene regulation, but their intrinsic disorder and low primary sequence conservation have made it difficult to identify the amino acid composition features that underlie their activity. Here, we describe a rational mutagenesis scheme that deconvolves the function of four activation domain sequence features-acidity, hydrophobicity, intrinsic disorder, and short linear motifs-by quantifying the activity of thousands of variants in vivo and simulating their conformational ensembles using an all-atom Monte Carlo approach. Our results with a canonical activation domain from the Saccharomyces cerevisiae transcription factor Gcn4 reconcile existing observations into a unified model of its function: the intrinsic disorder and acidic residues keep two hydrophobic motifs from driving collapse. Instead, the most-active variants keep their aromatic residues exposed to the solvent. Our results illustrate how the function of intrinsically disordered proteins can be revealed by high-throughput rational mutagenesis.

Causal Genetic Variation Underlying Metabolome Differences.

An ongoing challenge in biology is to predict the phenotypes of individuals from their genotypes. Genetic variants that cause disease often change an individual's total metabolite profile, or metabolome. In light of our extensive knowledge of metabolic pathways, genetic variants that alter the metabolome may help predict novel phenotypes. To link genetic variants to changes in the metabolome, we studied natural variation in the yeast Saccharomyces cerevisiae We used an untargeted mass spectrometry method to identify dozens of metabolite Quantitative Trait Loci (mQTL), genomic regions containing genetic variation that control differences in metabolite levels between individuals. We mapped differences in urea cycle metabolites to genetic variation in specific genes known to regulate amino acid biosynthesis. Our functional assays reveal that genetic variation in two genes, AUA1 and ARG81, cause the differences in the abundance of several urea cycle metabolites. Based on knowledge of the urea cycle, we predicted and then validated a new phenotype: sensitivity to a particular class of amino acid isomers. Our results are a proof-of-concept that untargeted mass spectrometry can reveal links between natural genetic variants and metabolome diversity. The interpretability of our results demonstrates the promise of using genetic variants underlying natural differences in the metabolome to predict novel phenotypes from genotype.

Scaling-Up Signaling Pathway Analysis.

A new technique for simultaneously measuring the activities of many signaling pathways unravels interconnected signaling networks.

Depleting gene activities in early Drosophila embryos with the "maternal-Gal4-shRNA" system.

In a developing Drosophila melanogaster embryo, mRNAs have a maternal origin, a zygotic origin, or both. During the maternal-zygotic transition, maternal products are degraded and gene expression comes under the control of the zygotic genome. To interrogate the function of mRNAs that are both maternally and zygotically expressed, it is common to examine the embryonic phenotypes derived from female germline mosaics. Recently, the development of RNAi vectors based on short hairpin RNAs (shRNAs) effective during oogenesis has provided an alternative to producing germline clones. Here, we evaluate the efficacies of: (1) maternally loaded shRNAs to knockdown zygotic transcripts and (2) maternally loaded Gal4 protein to drive zygotic shRNA expression. We show that, while Gal4-driven shRNAs in the female germline very effectively generate phenotypes for genes expressed maternally, maternally loaded shRNAs are not very effective at generating phenotypes for early zygotic genes. However, maternally loaded Gal4 protein is very efficient at generating phenotypes for zygotic genes expressed during mid-embryogenesis. We apply this powerful and simple method to unravel the embryonic functions of a number of pleiotropic genes.