A predictive model of bifunctional transcription factor signaling during embryonic tissue patterning

Dual-role transcription factors stabilize intermediate expression levels

Precise control of gene expression levels is essential for normal cell functions, yet how they are defined and tightly maintained, particularly at intermediate levels, remains elusive. Here, using a series of newly developed sequencing, imaging, and functional assays, we uncover a class of transcription factors with dual roles as activators and repressors, referred to as condensate-forming level-regulating dual-action transcription factors (TFs). They reduce high expression but increase low expression to achieve stable intermediate levels. Dual-action TFs directly exert activating and repressing functions via condensate-forming domains that compartmentalize core transcriptional unit selectively. Clinically relevant mutations in these domains, which are linked to a range of developmental disorders, impair condensate selectivity and dual-action TF activity. These results collectively address a fundamental question in expression regulation and demonstrate the potential of level-regulating dual-action TFs as powerful effectors for engineering controlled expression levels.

Optimal control of gene regulatory networks for morphogen-driven tissue patterning

The generation of distinct cell types in developing tissues depends on establishing spatial patterns of gene expression. Often, this is directed by spatially graded chemical signals-known as morphogens. In the "French Flag model," morphogen concentration instructs cells to acquire specific fates. How this mechanism produces timely and organized cell-fate decisions, despite the presence of changing morphogen levels, molecular noise, and individual variability, is unclear. Moreover, feedback is present at various levels in developing tissues, breaking the link between morphogen concentration, signaling activity, and position. Here, we develop an alternative framework using optimal control theory to tackle the problem of morphogen-driven patterning: intracellular signaling is derived as the control strategy that guides cells to the correct fate while minimizing a combination of signaling levels and time. This approach recovers experimentally observed properties of patterning strategies and offers insight into design principles that produce timely, precise, and reproducible morphogen patterning.

The Drosophila ecdysone receptor promotes or suppresses proliferation according to ligand level

The steroid hormone 20-hydroxy-ecdysone (20E) promotes proliferation in Drosophila wing precursors at low titer but triggers proliferation arrest at high doses. Remarkably, wing precursors proliferate normally in the complete absence of the 20E receptor, suggesting that low-level 20E promotes proliferation by overriding the default anti-proliferative activity of the receptor. By contrast, 20E needs its receptor to arrest proliferation. Dose-response RNA sequencing (RNA-seq) analysis of ex vivo cultured wing precursors identifies genes that are quantitatively activated by 20E across the physiological range, likely comprising positive modulators of proliferation and other genes that are only activated at high doses. We suggest that some of these "high-threshold" genes dominantly suppress the activity of the pro-proliferation genes. We then show mathematically and with synthetic reporters that combinations of basic regulatory elements can recapitulate the behavior of both types of target genes. Thus, a relatively simple genetic circuit can account for the bimodal activity of this hormone.

Thermodynamic Modelling of Transcriptional Control: A Sensitivity Analysis

Modelling is a tool used to decipher the biochemical mechanisms involved in transcriptional control. Experimental evidence in genetics is usually supported by theoretical models in order to evaluate the effects of all the possible interactions that can occur in these complicated processes. Models derived from the thermodynamic method are critical in this labour because they are able to take into account multiple mechanisms operating simultaneously at the molecular micro-scale and relate them to transcriptional initiation at the tissular macro-scale. This work is devoted to adapting computational techniques to this context in order to theoretically evaluate the role played by several biochemical mechanisms. The interest of this theoretical analysis relies on the fact that it can be contrasted against those biological experiments where the response to perturbations in the transcriptional machinery environment is evaluated in terms of genetically activated/repressed regions. The theoretical reproduction of these experiments leads to a sensitivity analysis whose results are expressed in terms of the elasticity of a threshold function determining those activated/repressed regions. The study of this elasticity function in thermodynamic models already proposed in the literature reveals that certain modelling approaches can alter the balance between the biochemical mechanisms considered, and this can cause false/misleading outcomes. The reevaluation of classical thermodynamic models gives us a more accurate and complete picture of the interactions involved in gene regulation and transcriptional control, which enables more specific predictions. This sensitivity approach provides a definite advantage in the interpretation of a wide range of genetic experimental results.

Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch

The formation of spatiotemporal patterns of gene expression is frequently guided by gradients of diffusible signaling molecules. The toggle switch subnetwork, composed of two cross-repressing transcription factors, is a common component of gene regulatory networks in charge of patterning, converting the continuous information provided by the gradient into discrete abutting stripes of gene expression. We present a synthetic biology framework to understand and characterize the spatiotemporal patterning properties of the toggle switch. To this end, we built a synthetic toggle switch controllable by diffusible molecules in Escherichia coli. We analyzed the patterning capabilities of the circuit by combining quantitative measurements with a mathematical reconstruction of the underlying dynamical system. The toggle switch can produce robust patterns with sharp boundaries, governed by bistability and hysteresis. We further demonstrate how the hysteresis, position, timing, and precision of the boundary can be controlled, highlighting the dynamical flexibility of the circuit.

Regulation of size and scale in vertebrate spinal cord development

All vertebrates have a spinal cord with dimensions and shape specific to their species. Yet how species‐specific organ size and shape are achieved is a fundamental unresolved question in biology. The formation and sculpting of organs begins during embryonic development. As it develops, the spinal cord extends in anterior–posterior direction in synchrony with the overall growth of the body. The dorsoventral (DV) and apicobasal lengths of the spinal cord neuroepithelium also change, while at the same time a characteristic pattern of neural progenitor subtypes along the DV axis is established and elaborated. At the basis of these changes in tissue size and shape are biophysical determinants, such as the change in cell number, cell size and shape, and anisotropic tissue growth. These processes are controlled by global tissue‐scale regulators, such as morphogen signaling gradients as well as mechanical forces. Current challenges in the field are to uncover how these tissue‐scale regulatory mechanisms are translated to the cellular and molecular level, and how regulation of distinct cellular processes gives rise to an overall defined size. Addressing these questions will help not only to achieve a better understanding of how size is controlled, but also of how tissue size is coordinated with the specification of pattern.

This article is categorized under:

Establishment of Spatial and Temporal Patterns > Regulation of Size, Proportion, and Timing

Signaling Pathways > Global Signaling Mechanisms

Nervous System Development > Vertebrates: General Principles

Measuring Dorsoventral Pattern and Morphogen Signaling Profiles in the Growing Neural Tube

Developmental processes are inherently dynamic and understanding them requires quantitative measurements of gene and protein expression levels in space and time. While live imaging is a powerful approach for obtaining such data, it is still a challenge to apply it over long periods of time to large tissues, such as the embryonic spinal cord in mouse and chick. Nevertheless, dynamics of gene expression and signaling activity patterns in this organ can be studied by collecting tissue sections at different developmental stages. In combination with immunohistochemistry, this allows for measuring the levels of multiple developmental regulators in a quantitative manner with high spatiotemporal resolution. The mean protein expression levels over time, as well as embryo-to-embryo variability can be analyzed. A key aspect of the approach is the ability to compare protein levels across different samples. This requires a number of considerations in sample preparation, imaging and data analysis. Here we present a protocol for obtaining time course data of dorsoventral expression patterns from mouse and chick neural tube in the first 3 days of neural tube development. The described workflow starts from embryo dissection and ends with a processed dataset. Software scripts for data analysis are included. The protocol is adaptable and instructions that allow the user to modify different steps are provided. Thus, the procedure can be altered for analysis of time-lapse images and applied to systems other than the neural tube.

Understanding Pattern Formation in Embryos: Experiment, Theory, and Simulation

Twenty-five years ago, Lewis Wolpert, the eminent developmental biologist, asked the question, "Do We Understand Development?" He concluded that such rapid progress had been made in the preceding two decades that "It is not unreasonable to think that enough will eventually be known to program a computer and simulate some aspects of development." This prediction has been fulfilled, at least partially, with data-driven simulations of several different developmental processes being developed in the intervening years. Nevertheless, the question remains of whether we "understand" development and if simulations are sufficient to provide an explanation of development. While in silico replications and models are undoubtedly an important tool in the investigation and dissection of developmental processes, which complement traditional experimental methods, these need to be supplemented by theory that identifies principles and provides coherent explanations. Here, I use the example of pattern formation in the vertebrate neural tube to illustrate this idea.

Analysis of the transcriptional logic governing differential spatial expression in Hh target genes

This work provides theoretical tools to analyse the transcriptional effects of certain biochemical mechanisms (i.e. affinity and cooperativity) that have been proposed in previous literature to explain the proper spatial expression of Hedgehog target genes involved in Drosophila development. Specifically we have focused on the expression of decapentaplegic, wingless, stripe and patched. The transcription of these genes is believed to be controlled by enhancer modules able to interpret opposing gradients of the activator and repressor forms of the transcription factor Cubitus interruptus (Ci). This study is based on a thermodynamic approach, which provides expression rates for these genes. These expression rates are controlled by transcription factors which are competing and cooperating for common binding sites. We have made mathematical representations of the different expression rates which depend on multiple factors and variables. The expressions obtained with the model have been refined to produce simpler equivalent formulae which allow for their mathematical analysis. Thanks to this, we can evaluate the correlation between the different interactions involved in transcription and the biological features observed at tissular level. These mathematical models can be applied to other morphogenes to help understand the complex transcriptional logic of opposing activator and repressor gradients.

GLI3 knockdown decreases stemness, cell proliferation and invasion in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is an extremely aggressive disease associated with a poor prognosis. Previous studies have established that cancer stem cells (CSCs) actively participate in OSCC development, progression and resistance to conventional treatments. Furthermore, CSCs frequently exhibit a deregulated expression of normal stem cell signalling pathways, thereby acquiring their distinctive abilities, of which self-renewal is an example. In this study, we examined the effects of GLI3 knockdown in OSCC, as well as the differentially expressed genes in CSC-like cells (CSCLCs) expressing high (CD44^high) or low (CD44^low) levels of CD44. The prognostic value of GLI3 in OSCC was also evaluated. The OSCC cell lines were sorted based on CD44 expression; gene expression was evaluated using a PCR array. Following this, we examined the effects of GLI3 knockdown on CD44 and ESA expression, colony and sphere formation capability, stem-related gene expression, proliferation and invasion. The overexpression of genes related to the Notch, transforming growth factor (TGF)β, FGF, Hedgehog, Wnt and pluripotency maintenance pathways was observed in the CD44^high cells. GLI3 knockdown was associated with a significant decrease in different CSCLC fractions, spheres and colonies in addition to the downregulation of the CD44, Octamer-binding transcription factor 4 (OCT4; also known as POU5F1) and BMI1 genes. This downregulation was accompanied by an increase in the expression of the Involucrin (IVL) and S100A9 genes. Cellular proliferation and invasion were inhibited following GLI3 knockdown. In OSCC samples, a high GLI3 expression was associated with tumour size but not with prognosis. On the whole, the findings of this study demonstrate for the first time, at least to the best of our knowledge, that GLI3 contributes to OSCC stemness and malignant behaviour. These findings suggest the potential for the development of novel therapies, either in isolation or in combination with other drugs, based on CSCs in OSCC.

Morphogen gradient reconstitution reveals Hedgehog pathway design principles

In developing tissues, cells estimate their spatial position by sensing graded concentrations of diffusible signaling proteins called morphogens. Morphogen-sensing pathways exhibit diverse molecular architectures, whose roles in controlling patterning dynamics and precision have been unclear. In this work, combining cell-based in vitro gradient reconstitution, genetic rewiring, and mathematical modeling, we systematically analyzed the distinctive architectural features of the Sonic Hedgehog pathway. We found that the combination of double-negative regulatory logic and negative feedback through the PTCH receptor accelerates gradient formation and improves robustness to variation in the morphogen production rate compared with alternative designs. The ability to isolate morphogen patterning from concurrent developmental processes and to compare the patterning behaviors of alternative, rewired pathway architectures offers a powerful way to understand and engineer multicellular patterning.

G protein-coupled receptors control the sensitivity of cells to the morphogen Sonic Hedgehog

The morphogen Sonic Hedgehog (SHH) patterns tissues during development by directing cell fates in a concentration-dependent manner. The SHH signal is transmitted across the membrane of target cells by the heptahelical transmembrane protein Smoothened (SMO), which activates the GLI family of transcription factors through a mechanism that is undefined in vertebrates. Using CRISPR-edited null alleles and small-molecule inhibitors, we systematically analyzed the epistatic interactions between SMO and three proteins implicated in SMO signaling: the heterotrimeric G protein subunit Gα_S, the G protein-coupled receptor kinase 2 (GRK2), and the Gα_S-coupled receptor GPR161. Our experiments uncovered a signaling mechanism that modifies the sensitivity of target cells to SHH and consequently changes the shape of the SHH dose-response curve. In both fibroblasts and spinal neural progenitors, the loss of GPR161, previously implicated as an inhibitor of basal SHH signaling, increased the sensitivity of target cells across the entire spectrum of SHH concentrations. Even in cells lacking GPR161, GRK2 was required for SHH signaling, and Gα_s, which promotes the activation of protein Kinase A (PKA), antagonized SHH signaling. We propose that the sensitivity of target cells to Hedgehog morphogens, and the consequent effects on gene expression and differentiation outcomes, can be controlled by signals from G protein-coupled receptors that converge on Gα_s and PKA.

Ventral neural patterning in the absence of a Shh activity gradient from the floorplate

BACKGROUND

Vertebrate spinal cord development requires Sonic Hedgehog (Shh) signaling from the floorplate and notochord, where it is thought to act in concentration dependent manner to pattern distinct cell identities along the ventral-to-dorsal axis. While in vitro experiments demonstrate naïve neural tissues are sensitive to small changes in Shh levels, genetic studies illustrate that some degree of ventral patterning can occur despite significant perturbations in Shh signaling. Consequently, the mechanistic relationship between Shh morphogen levels and acquisition of distinct cell identities remains unclear.

RESULTS

We addressed this using Hedgehog acetyltransferase (Hhat^Creface ) and Wiggable mouse mutants. Hhat encodes a palmitoylase required for the secretion of Hedgehog proteins and formation of the Shh gradient. In its absence, the spinal cord develops without floorplate cells and V3 interneurons. Wiggable is an allele of the Shh receptor Patched1 (Ptch1^Wig ) that is unable to inhibit Shh signal transduction, resulting in expanded ventral progenitor domains. Surprisingly, Hhat^{Creface/Creface} ; Ptch1^Wig/Wig double mutants displayed fully restored ventral patterning despite an absence of Shh secretion from the floorplate.

CONCLUSIONS

The full range of neuronal progenitor types can be generated in the absence of a Shh gradient provided pathway repression is dampened, illustrating the complexity of morphogen dynamics in vertebrate patterning. Developmental Dynamics 247:170-184, 2018. © 2017 Wiley Periodicals, Inc.

Hedgehog Signaling: From Basic Biology to Cancer Therapy

The Hedgehog (HH) signaling pathway was discovered originally as a key pathway in embryonic patterning and development. Since its discovery, it has become increasingly clear that the HH pathway also plays important roles in a multitude of cancers. Therefore, HH signaling has emerged as a therapeutic target of interest for cancer therapy. In this review, we provide a brief overview of HH signaling and the key molecular players involved and offer an up-to-date summary of our current knowledge of endogenous and exogenous small molecules that modulate HH signaling. We discuss experiences and lessons learned from the decades-long efforts toward the development of cancer therapies targeting the HH pathway. Challenges to develop next-generation cancer therapies are highlighted.

Inference of gene regulation functions from dynamic transcriptome data

To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a ‘gene regulation function’ (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast S. cerevisiae. We develop this inference approach based on a time series of mRNA synthesis rates from a synchronized population of cells observed over three cell cycles. We first estimate the functional form of how input transcription factors determine mRNA output and then derive GRFs for target genes in the CLB2 gene cluster that are expressed during G2/M phase. Systematic analysis of additional GRFs suggests a network architecture that rationalizes transcriptional cell cycle oscillations. We find that a transcription factor network alone can produce oscillations in mRNA expression, but that additional input from cyclin oscillations is required to arrive at the native behaviour of the cell cycle oscillator.

DOI:http://dx.doi.org/10.7554/eLife.12188.001

Living cells rely on networks of genes to control their behavior, including how they grow, develop and respond to stress. Genes encode instructions needed to make proteins and other molecules, and much of the control is exerted at the first stage of protein production, known as transcription. During this process, a gene is copied to make molecules known as transcripts that may later be used as templates to make proteins.

Many genes encode proteins that act to regulate transcription. Therefore, an individual gene may receive inputs from other genes, and these inputs affect how much transcript the gene produces, which can be considered as the gene’s output. While these inputs and outputs can often be wired together to form a network, it is less clear exactly how all the different inputs at a gene interact to determine its output. These interactions are known as “gene regulation functions”, and knowing them would be an important step towards understanding gene networks, which would help us to predict how cells will behave in different situations.

Gene regulation functions are difficult to measure directly, so researchers would like to find other ways to assess them indirectly. A recently developed experimental technique called “dynamic transcriptome analysis” seemed promising as it measures both the inputs and outputs of all genes in a cell over time. Hillenbrand et al. used this technique to infer gene regulation functions with one or two inputs in yeast cells.

Comparing these estimates with experimental data from previous studies showed that these inferred gene regulation functions could successfully predict the output of a gene based on its inputs. Hillenbrand et al. then used these estimates to search and model a well-known genetic network that is thought to be part of the molecular clockwork that controls the timing of events that cause a cell to divide.

Currently, the approach used by Hillenbrand et al. treats gene regulation functions like “black boxes”. This means that, while an output can be predicted if the inputs are known, it cannot reveal all of the detailed mechanisms behind it. Gaining insights into the inner workings of these black boxes will require taking more data into account, such as how abundant the proteins that regulate transcription are, where they are located within cells or whether they are active or not. Therefore, the next challenge is to incorporate these kinds of data to gain a fuller picture of how gene networks operate within cells.

DOI:http://dx.doi.org/10.7554/eLife.12188.002

Patched Receptors Sense, Interpret, and Establish an Epidermal Hedgehog Signaling Gradient

By using the sensitivity of single-molecule fluorescent in situ hybridization, we have precisely quantified the levels and defined the temporal and spatial distribution of Hedgehog signaling activity during embryonic skin development and discovered that there is a Hedgehog signaling gradient along the proximal-distal axis of developing hair follicles. To explore the contribution of Hedgehog receptors Ptch1 and Ptch2 in establishing the epidermal signaling gradient, we quantitated the level of pathway activity generated in Ptch1- and Ptch1;Ptch2-deficient skin and defined the contribution of each receptor to regulation of the levels of Hedgehog signaling identified in wild-type skin. Moreover, we show that both the cellular phenotype and level of pathway activity featured in Ptch1;Ptch2-deficient cells faithfully recapitulates the Peak level of endogenous Hedgehog signaling detected at the base of developing follicles, where the concentration of endogenous Shh is predicted to be highest. Taken together, these data show that both Ptch1 and Ptch2 play a crucial role in sensing the concentration of Hedgehog ligand and regulating the appropriate dose-dependent response.

Developmental Pattern Formation in Phases

Cells in developing organs undergo a series of changes in their transcriptional state until a complete repertoire of cell types is specified. These changes in cell identity, together with the control of tissue growth, determine the pattern of gene expression in the tissue. Recent studies explore the dynamics of pattern formation during development and provide new insights into the control mechanisms. Changes in morphogen signalling and transcriptional networks control the specification of cell types. This is often followed by a distinct second phase, where pattern is elaborated by tissue growth. Here, we discuss the transitions between distinct phases in pattern formation. We consider the implications of the underlying mechanisms for understanding how reproducible patterns form during development.

Emerging Modeling Concepts and Solutions in Stem Cell Research

Modern stem cell research, as well as other fields of contemporary biology involves quantitative sciences in many ways. Identifying candidates for key differentiation or reprogramming factors, tracing global transcriptome changes, or finding drugs is now broadly involves bioinformatics and biostatistics. However, the next key step, understanding the underlying reasons and establishing causal links leading to differentiation or reprogramming requires qualitative and quantitative biological models describing complex biological systems. Currently, quantitative modeling is a challenging science, capable to deliver rather modest results or predictions. What model types are the most popular and what features of stem cell behavior they are capturing? What new insights do we expect from the computational modeling of stem cells in the foreseeable future? Current review attempts to approach these essential questions by considering published quantitative models and solutions emerging in the area of stem cell research.

Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos

Analysis of differential gene expression is crucial for the study of cell fate and behavior during embryonic development. However, automated methods for the sensitive detection and quantification of RNAs at cellular resolution in embryos are lacking. With the advent of single-molecule fluorescence in situ hybridization (smFISH), gene expression can be analyzed at single-molecule resolution. However, the limited availability of protocols for smFISH in embryos and the lack of efficient image analysis pipelines have hampered quantification at the (sub)cellular level in complex samples such as tissues and embryos. Here, we present a protocol for smFISH on zebrafish embryo sections in combination with an image analysis pipeline for automated transcript detection and cell segmentation. We use this strategy to quantify gene expression differences between different cell types and identify differences in subcellular transcript localization between genes. The combination of our smFISH protocol and custom-made, freely available, analysis pipeline will enable researchers to fully exploit the benefits of quantitative transcript analysis at cellular and subcellular resolution in tissues and embryos.

Morphogen rules: design principles of gradient-mediated embryo patterning

The Drosophila blastoderm and the vertebrate neural tube are archetypal examples of morphogen-patterned tissues that create precise spatial patterns of different cell types. In both tissues, pattern formation is dependent on molecular gradients that emanate from opposite poles. Despite distinct evolutionary origins and differences in time scales, cell biology and molecular players, both tissues exhibit striking similarities in the regulatory systems that establish gene expression patterns that foreshadow the arrangement of cell types. First, signaling gradients establish initial conditions that polarize the tissue, but there is no strict correspondence between specific morphogen thresholds and boundary positions. Second, gradients initiate transcriptional networks that integrate broadly distributed activators and localized repressors to generate patterns of gene expression. Third, the correct positioning of boundaries depends on the temporal and spatial dynamics of the transcriptional networks. These similarities reveal design principles that are likely to be broadly applicable to morphogen-patterned tissues.

Ptch1 and Gli regulate Shh signalling dynamics via multiple mechanisms

In the vertebrate neural tube, the morphogen Sonic Hedgehog (Shh) establishes a characteristic pattern of gene expression. Here we quantify the Shh gradient in the developing mouse neural tube and show that while the amplitude of the gradient increases over time, the activity of the pathway transcriptional effectors, Gli proteins, initially increases but later decreases. Computational analysis of the pathway suggests three mechanisms that could contribute to this adaptation: transcriptional upregulation of the inhibitory receptor Ptch1, transcriptional downregulation of Gli and the differential stability of active and inactive Gli isoforms. Consistent with this, Gli2 protein expression is downregulated during neural tube patterning and adaptation continues when the pathway is stimulated downstream of Ptch1. Moreover, the Shh-induced upregulation of Gli2 transcription prevents Gli activity levels from adapting in a different cell type, NIH3T3 fibroblasts, despite the upregulation of Ptch1. Multiple mechanisms therefore contribute to the intracellular dynamics of Shh signalling, resulting in different signalling dynamics in different cell types.

The route to spinal cord cell types: a tale of signals and switches

Understanding the mechanisms that control induction and elaboration of the vertebrate central nervous system (CNS) requires an analysis of the extrinsic signals and downstream transcriptional networks that assign cell fates in the correct space and time. We focus on the generation and patterning of the spinal cord. We summarize evidence that the origin of the spinal cord is distinct from the anterior regions of the CNS. We discuss how this affects the gene regulatory networks and cell state transitions that specify spinal cord cell subtypes, and we highlight how the timing of extracellular signals and dynamic control of transcriptional networks contribute to the correct spatiotemporal generation of different neural cell types.

The Eya1 phosphatase promotes Shh signaling during hindbrain development and oncogenesis

Sonic hedgehog (Shh) signaling is critical in development and oncogenesis, but the mechanisms regulating this pathway remain unclear. Although protein phosphorylation clearly affects Shh signaling, little is known about phosphatases governing the pathway. Here, we conducted a small hairpin RNA (shRNA) screen of the phosphatome and identified Eya1 as a positive regulator of Shh signaling. We find that the catalytically active phosphatase Eya1 cooperates with the DNA-binding protein Six1 to promote gene induction in response to Shh and that Eya1/Six1 together regulate Gli transcriptional activators. We show that Eya1, which is mutated in a human deafness disorder, branchio-oto-renal syndrome, is critical for Shh-dependent hindbrain growth and development. Moreover, Eya1 drives the growth of medulloblastoma, a Shh-dependent hindbrain tumor. Together, these results identify Eya1 and Six1 as key components of the Shh transcriptional network in normal development and in oncogenesis.