A core transcriptional network for early mesoderm development in Drosophila melanogaster

Conserved and novel enhancers in the Aedes aegypti single-minded locus recapitulate embryonic ventral midline gene expression

Transcriptional cis-regulatory modules, e.g., enhancers, control the time and location of metazoan gene expression. While changes in enhancers can provide a powerful force for evolution, there is also significant deep conservation of enhancers for developmentally important genes, with function and sequence characteristics maintained over hundreds of millions of years of divergence. Not well understood, however, is how the overall regulatory composition of a locus evolves, with important outstanding questions such as how many enhancers are conserved vs. novel, and to what extent are the locations of conserved enhancers within a locus maintained? We begin here to address these questions with a comparison of the respective single-minded (sim) loci in the two dipteran species Drosophila melanogaster (fruit fly) and Aedes aegypti (mosquito). sim encodes a highly conserved transcription factor that mediates development of the arthropod embryonic ventral midline. We identify two enhancers in the A. aegypti sim locus and demonstrate that they function equivalently in both transgenic flies and transgenic mosquitoes. One A. aegypti enhancer is highly similar to known Drosophila counterparts in its activity, location, and autoregulatory capability. The other differs from any known Drosophila sim enhancers with a novel location, failure to autoregulate, and regulation of expression in a unique subset of midline cells. Our results suggest that the conserved pattern of sim expression in the two species is the result of both conserved and novel regulatory sequences. Further examination of this locus will help to illuminate how the overall regulatory landscape of a conserved developmental gene evolves.

MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images

Recent breakthrough in spatial transcriptomics has brought great opportunities for exploring gene regulatory networks (GRNs) from a brand-new perspective. Especially, the local expression patterns and spatio-temporal regulation mechanisms captured by spatial expression images allow more delicate delineation of the interplay between transcript factors and their target genes. However, the complexity and size of spatial image collections pose significant challenges to GRN inference using image-based methods. Extracting regulatory information from expression images is difficult due to the lack of supervision and the multi-instance nature of the problem, where a gene often corresponds to multiple images captured from different views. While graph models, particularly graph neural networks, have emerged as a promising method for leveraging underlying structure information from known GRNs, incorporating expression images into graphs is not straightforward. To address these challenges, we propose a two-stage approach, MIGGRI, for capturing comprehensive regulatory patterns from image collections for each gene and known interactions. Our approach involves a multi-instance graph neural network (GNN) model for GRN inference, which first extracts gene regulatory features from spatial expression images via contrastive learning, and then feeds them to a multi-instance GNN for semi-supervised learning. We apply our approach to a large set of Drosophila embryonic spatial gene expression images. MIGGRI achieves outstanding performance in the inference of GRNs for early eye development and mesoderm development of Drosophila, and shows robustness in the scenarios of missing image information. Additionally, we perform interpretable analysis on image reconstruction and functional subgraphs that may reveal potential pathways or coordinate regulations. By leveraging the power of graph neural networks and the information contained in spatial expression images, our approach has the potential to advance our understanding of gene regulation in complex biological systems.

Neofunctionalization of Toll Signaling in Insects: From Immunity to Dorsoventral Patterning

Toll signaling plays a crucial role in pathogen defense throughout the animal kingdom. It was discovered, however, for its function in dorsoventral (DV) axis formation in Drosophila. In all other insects studied so far, but not outside the insects, Toll is also required for DV patterning. However, in insects more distantly related to Drosophila, Toll's patterning role is frequently reduced and substituted by an expanded influence of BMP signaling, the pathway implicated in DV axis formation in all major metazoan lineages. This suggests that Toll was integrated into an ancestral BMP-based patterning system at the base of the insects or during insect evolution. The observation that Toll signaling has an immune function in the extraembryonic serosa, an early differentiating tissue of most insect embryos, suggests a scenario of how Toll was co-opted from an ancestral immune function for its new role in axis formation.

Early mesodermal development in the patellogastropod Lottia goshimai

Mesodermal development is essential to explore the interlineage variations in the development of spiralians. Compared with model mollusks such as Tritia and Crepidula, knowledge about the mesodermal development of other molluscan lineages is limited. Here, we investigated early mesodermal development in the patellogastropod Lottia goshimai, which shows equal cleavage and has a trochophore larva. The endomesoderm derived from the 4d blastomere, that is, the mesodermal bandlets, was situated dorsally and showed a characteristic morphology. Investigations of the potential mesodermal patterning genes revealed that twist1 and snail1 were expressed in a proportion of these endomesodermal tissues, while all of the five genes we investigated (twist1, twist2, snail1, snail2, and mox) were expressed in ventrally located ectomesodermal tissues. Relatively dynamic snail2 expression suggests additional roles in various internalization processes. By tracing snail2 expression in early gastrulae, the 3a²¹¹ and 3b²¹¹ blastomeres were suggested to be the precursors of the ectomesoderm, which elongated to become internalized before division. These results help to understand the variations in the mesodermal development of different spiralians and explore the different mechanisms by which ectomesodermal cells are internalized, which has important evolutionary implications.

Emerging Role of Mechanical Forces in Cell Fate Acquisition

Mechanical forces are now recognized as key cellular effectors that together with genetic and cellular signals physically shape and pattern tissues and organs during development. Increasing efforts are aimed toward understanding the less explored role of mechanical forces in controlling cell fate decisions in embryonic development. Here we discuss recent examples of how differential forces feedback into cell fate specification and tissue patterning. In particular, we focus on the role of actomyosin-contractile force generation and transduction in affecting tissue morphogenesis and cell fate regulation in the embryo.

A non-canonical Raf function is required for dorsal-ventral patterning during Drosophila embryogenesis

Proper embryonic development requires directional axes to pattern cells into embryonic structures. In Drosophila, spatially discrete expression of transcription factors determines the anterior to posterior organization of the early embryo, while the Toll and TGFβ signalling pathways determine the early dorsal to ventral pattern. Embryonic MAPK/ERK signaling contributes to both anterior to posterior patterning in the terminal regions and to dorsal to ventral patterning during oogenesis and embryonic stages. Here we describe a novel loss of function mutation in the Raf kinase gene, which leads to loss of ventral cell fates as seen through the loss of the ventral furrow, the absence of Dorsal/NFκB nuclear localization, the absence of mesoderm determinants Twist and Snail, and the expansion of TGFβ. Gene expression analysis showed cells adopting ectodermal fates much like loss of Toll signaling. Our results combine novel mutants, live imaging, optogenetics and transcriptomics to establish a novel role for Raf, that appears to be independent of the MAPK cascade, in embryonic patterning.

Thermodynamics-based modeling reveals regulatory effects of indirect transcription factor-DNA binding

Transcription factors (TFs) influence gene expression by binding to DNA, yet experimental data suggests that they also frequently bind regulatory DNA indirectly by interacting with other DNA-bound proteins. Here, we used a data modeling approach to test if such indirect binding by TFs plays a significant role in gene regulation. We first incorporated regulatory function of indirectly bound TFs into a thermodynamics-based model for predicting enhancer-driven expression from its sequence. We then fit the new model to a rich data set comprising hundreds of enhancers and their regulatory activities during mesoderm specification in Drosophila embryogenesis and showed that the newly incorporated mechanism results in significantly better agreement with data. In the process, we derived the first sequence-level model of this extensively characterized regulatory program. We further showed that allowing indirect binding of a TF explains its localization at enhancers more accurately than with direct binding only. Our model also provided a simple explanation of how a TF may switch between activating and repressive roles depending on context.

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Inclusion of indirect DNA binding of transcription factor improves enhancer function prediction

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Context specific activating or repressive roles of TFs

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Indirect binding improves fits to experimental TF-DNA binding data

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Role of Tinman depends on its DNA-binding mode (direct or indirect)

Simultaneous cellular and molecular phenotyping of embryonic mutants using single-cell regulatory trajectories

Developmental progression and cellular diversity are largely driven by transcription factors (TFs); yet, characterizing their loss-of-function phenotypes remains challenging and often disconnected from their underlying molecular mechanisms. Here, we combine single-cell regulatory genomics with loss-of-function mutants to jointly assess both cellular and molecular phenotypes. Performing sci-ATAC-seq at eight overlapping time points during Drosophila mesoderm development could reconstruct the developmental trajectories of all major muscle types and reveal the TFs and enhancers involved. To systematically assess mutant phenotypes, we developed a single-nucleus genotyping strategy to process embryo pools of mixed genotypes. Applying this to four TF mutants could identify and quantify their characterized phenotypes de novo and discover new ones, while simultaneously revealing their regulatory input and mode of action. Our approach is a general framework to dissect the functional input of TFs in a systematic, unbiased manner, identifying both cellular and molecular phenotypes at a scale and resolution that has not been feasible before.

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scATAC time course constructs regulatory trajectories of Drosophila muscle lineages

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Combining a wild-type trajectory with mutants identifies and quantifies phenotypes de novo

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Digital nuclear genotyping enables the processing of pooled embryos of mixed genotypes

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This framework simultaneously uncovers cellular and molecular phenotypes in embryos

Expression and Function of Toll Pathway Components in the Early Development of the Wasp Nasonia vitripennis

The Toll signaling pathway is the main source of embryonic DV polarity in the fly Drosophila melanogaster. This pathway appears to have been co-opted from an ancestral innate immunity system within the insects and has been deployed in different ways among insect taxa. Here we report the expression and function of homologs of the important components of the D. melanogaster Toll pathway in the wasp Nasonia vitripennis. We found homologs for all the components; many components had one or more additional paralogs in the wasp relative the fly. We also found significant deviations in expression patterns of N. vitripennis homologs. Finally, we provide some preliminary functional analyses of the N. vitripennis homologs, where we find a mixture of conservation and divergence of function.

Evolution of Somite Compartmentalization: A View From Xenopus

Somites are transitory metameric structures at the basis of the axial organization of vertebrate musculoskeletal system. During evolution, somites appear in the chordate phylum and compartmentalize mainly into the dermomyotome, the myotome, and the sclerotome in vertebrates. In this review, we summarized the existing literature about somite compartmentalization in Xenopus and compared it with other anamniote and amniote vertebrates. We also present and discuss a model that describes the evolutionary history of somite compartmentalization from ancestral chordates to amniote vertebrates. We propose that the ancestral organization of chordate somite, subdivided into a lateral compartment of multipotent somitic cells (MSCs) and a medial primitive myotome, evolves through two major transitions. From ancestral chordates to vertebrates, the cell potency of MSCs may have evolved and gave rise to all new vertebrate compartments, i.e., the dermomyome, its hypaxial region, and the sclerotome. From anamniote to amniote vertebrates, the lateral MSC territory may expand to the whole somite at the expense of primitive myotome and may probably facilitate sclerotome formation. We propose that successive modifications of the cell potency of some type of embryonic progenitors could be one of major processes of the vertebrate evolution.

Accurate inference of gene regulatory interactions from spatial gene expression with deep contrastive learning

MOTIVATION

Reverse engineering of gene regulatory networks (GRNs) has long been an attractive research topic in system biology. Computational prediction of gene regulatory interactions has remained a challenging problem due to the complexity of gene expression and scarce information resources. The high-throughput spatial gene expression data, like in situ hybridization images that exhibit temporal and spatial expression patterns, has provided abundant and reliable information for the inference of GRNs. However, computational tools for analyzing the spatial gene expression data are highly underdeveloped.

RESULTS

In this study, we develop a new method for identifying gene regulatory interactions from gene expression images, called ConGRI. The method is featured by a contrastive learning scheme and deep Siamese convolutional neural network architecture, which automatically learns high-level feature embeddings for the expression images and then feeds the embeddings to an artificial neural network to determine whether or not the interaction exists. We apply the method to a Drosophila embryogenesis dataset and identify GRNs of eye development and mesoderm development. Experimental results show that ConGRI outperforms previous traditional and deep learning methods by a large margin, which achieves accuracies of 76.7% and 68.7% for the GRNs of early eye development and mesoderm development, respectively. It also reveals some master regulators for Drosophila eye development.

AVAILABILITYAND IMPLEMENTATION

https://github.com/lugimzheng/ConGRI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Diversification of muscle types in Drosophila embryos

Drosophila embryonic somatic muscles represent a simple and tractable model system to study the gene regulatory networks that control diversification of cell types. Somatic myogenesis in Drosophila is initiated by intrinsic action of the mesodermal master gene twist, which activates a cascade of transcriptional outputs including myogenic differentiation factor Mef2, which triggers all aspects of the myogenic differentiation program. In parallel, the expression of a combinatorial code of identity transcription factors (iTFs) defines discrete particular features of each muscle fiber, such as number of fusion events, and specific attachment to tendon cells or innervation, thus ensuring diversification of muscle types. Here, we take the example of a subset of lateral transverse (LT) muscles and discuss how the iTF code and downstream effector genes progressively define individual LT properties such as fusion program, attachment and innervation. We discuss new challenges in the field including the contribution of posttranscriptional and epitranscriptomic regulation of gene expression in the diversification of cell types.

Methods of massive parallel reporter assays for investigation of enhancers

The correct deployment of genetic programs for development and differentiation relies on finely coordinated regulation of specific gene sets. Genomic regulatory elements play an exceptional role in this process. There are few types of gene regulatory elements, including promoters, enhancers, insulators and silencers. Alterations of gene regulatory elements may cause various pathologies, including cancer, congenital disorders and autoimmune diseases. The development of high-throughput genomic assays has made it possible to significantly accelerate the accumulation of information about the characteristic epigenetic properties of regulatory elements. In combination with high-throughput studies focused on the genome-wide distribution of epigenetic marks, regulatory proteins and the spatial structure of chromatin, this significantly expands the understanding of the principles of epigenetic regulation of genes and allows potential regulatory elements to be searched for in silico. However, common experimental approaches used to study the local characteristics of chromatin have a number of technical limitations that may reduce the reliability of computational identification of genomic regulatory sequences. Taking into account the variability of the functions of epigenetic determinants and complex multicomponent regulation of genomic elements activity, their functional verification is often required. A plethora of methods have been developed to study the functional role of regulatory elements on the genome scale. Common experimental approaches for in silico identification of regulatory elements and their inherent technical limitations will be described. The present review is focused on original high-throughput methods of enhancer activity reporter analysis that are currently used to validate predicted regulatory elements and to perform de novo searches. The methods described allow assessing the functional role of the nucleotide sequence of a regulatory element, to determine its exact boundaries and to assess the influence of the local state of chromatin on the activity of enhancers and gene expression. These approaches have contributed substantially to the understanding of the fundamental principles of gene regulation.

An Evolutionary Perspective on Hox Binding Site Preferences in Two Different Tissues

Transcription factor (TF) networks define the precise development of multicellular organisms. While many studies focused on TFs expressed in specific cell types to elucidate their contribution to cell specification and differentiation, it is less understood how broadly expressed TFs perform their precise functions in the different cellular contexts. To uncover differences that could explain tissue-specific functions of such TFs, we analyzed here genomic chromatin interactions of the broadly expressed Drosophila Hox TF Ultrabithorax (Ubx) in the mesodermal and neuronal tissues using bioinformatics. Our investigations showed that Ubx preferentially interacts with multiple yet tissue-specific chromatin sites in putative regulatory regions of genes in both tissues. Importantly, we found the classical Hox/Ubx DNA binding motif to be enriched only among the neuronal Ubx chromatin interactions, whereas a novel Ubx-like motif with rather low predicted Hox affinities was identified among the regions bound by Ubx in the mesoderm. Finally, our analysis revealed that tissues-specific Ubx chromatin sites are also different with regards to the distribution of active and repressive histone marks. Based on our data, we propose that the tissue-related differences in Ubx binding behavior could be a result of the emergence of the mesoderm as a new germ layer in triploblastic animals, which might have required the Hox TFs to relax their binding specificity.

Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm

A detailed comprehension of transcriptional regulation is critical to understanding the genetic control of development and disease across many different organisms. To more fully investigate the complex molecular interactions controlling the precise expression of genes, many groups have constructed mathematical models to complement their experimental approaches. A critical step in such studies is choosing the most appropriate parameter estimation algorithm to enable detailed analysis of the parameters that contribute to the models. In this study, we develop a novel set of evolutionary algorithms that use a pseudo-random Sobol Set to construct the initial population and incorporate parameter sensitivities into the adaptation of mutation rates, using local, global, and hybrid strategies. Comparison of the performance of these new algorithms to a number of current state-of-the-art global parameter estimation algorithms on a range of continuous test functions, as well as synthetic biological data representing models of gene regulatory systems, reveals improved performance of the new algorithms in terms of runtime, error and reproducibility. In addition, by analyzing the ability of these algorithms to fit datasets of varying quality, we provide the experimentalist with a guide to how the algorithms perform across a range of noisy data. These results demonstrate the improved performance of the new set of parameter estimation algorithms and facilitate meaningful integration of model parameters and predictions in our understanding of the molecular mechanisms of gene regulation.

Dynamic Interactions of Transcription Factors and Enhancer Reprogramming in Cancer Progression

While improved tumor treatment has significantly reduced the overall mortality rates, invasive progression including recurrence, therapy resistance and metastasis contributes to the majority of deaths caused by cancer. Enhancers are essential distal DNA regulatory elements that control temporal- or spatial-specific gene expression patterns during development and other biological processes. Genome-wide sequencing has revealed frequent alterations of enhancers in cancers and reprogramming of distal enhancers has emerged as one of the important features for tumors. In this review, we will discuss tumor progression-associated enhancer dynamics, its transcription factor (TF) drivers and how enhancer reprogramming modulates gene expression during cancer invasive progression. Additionally, we will explore recent advancements in contemporary technology including single-cell sequencing, spatial transcriptomics and CUT&RUN, which have permitted integrated studies of enhancer reprogramming in vivo. Given the essential roles of enhancer dynamics and its drivers in controlling cancer progression and treatment outcome, understanding these changes will be paramount in mitigating invasive events and discovering novel therapeutic targets.

Extremely rapid and reversible optogenetic perturbation of nuclear proteins in living embryos

Many developmental regulators have complex and context-specific roles in different tissues and stages, making the dissection of their function extremely challenging. As regulatory processes often occur within minutes, perturbation methods that match these dynamics are needed. Here, we present the improved light-inducible nuclear export system (iLEXY), an optogenetic loss-of-function approach that triggers translocation of proteins from the nucleus to the cytoplasm. By introducing a series of mutations, we substantially increased LEXY's efficiency and generated variants with different recovery times. iLEXY enables rapid (t1/2 < 30 s), efficient, and reversible nuclear protein depletion in embryos, and is generalizable to proteins of diverse sizes and functions. Applying iLEXY to the Drosophila master regulator Twist, we phenocopy loss-of-function mutants, precisely map the Twist-sensitive embryonic stages, and investigate the effects of timed Twist depletions. Our results demonstrate the power of iLEXY to dissect the function of pleiotropic factors during embryogenesis with unprecedented temporal precision.

FGF signaling acts on different levels of mesoderm development within Spiralia

FGF signaling is involved in mesoderm induction in members of deuterostomes (e.g. tunicates, hemichordates), but not in flies and nematodes, in which it has a role in mesoderm patterning and migration. However, we need comparable studies in other protostome taxa in order to decipher whether this mesoderm-inducing function of FGF extends beyond the lineage of deuterostomes. Here, we investigated the role of FGF signaling in mesoderm development in three species of lophophorates, a clade within the protostome group Spiralia. Our gene expression analyses show that the mesodermal molecular patterning is conserved between brachiopods and phoronids, but the spatial and temporal recruitment of transcription factors differs significantly. Moreover, the use of the inhibitor SU5402 demonstrates that FGF signaling is involved in different steps of mesoderm development, as well as in morphogenetic movements of gastrulation and axial elongation. Our findings suggest that the mesoderm-inducing role of FGF extends beyond the group of deuterostomes.

Drosophila models of metastasis

An important goal in the fight against cancer is to understand how tumors become invasive and metastatic. A crucial early step in metastasis is thought to be the epithelial mesenchymal transition (EMT), the process in which epithelial cells transition into a more migratory and invasive, mesenchymal state. Since the genetic regulatory networks driving EMT in tumors derive from those used in development, analysis of EMTs in genetic model organisms such as the vinegar fly, Drosophila melanogaster, can provide great insight into cancer. In this review I highlight the many ways in which studies in the fly are shedding light on cancer metastasis. The review covers both normal developmental events in which epithelial cells become migratory, as well as induced events, whereby normal epithelial cells become metastatic due to genetic manipulations. The ability to make such precise genetic perturbations in the context of a normal, in vivo environment, complete with a working innate immune system, is making the fly increasingly important in understanding metastasis.

Imaging translation dynamics in live embryos reveals spatial heterogeneities

Much is known about the factors involved in the translation of messenger RNA (mRNA) into protein; however, this multistep process has not been imaged in living multicellular organisms. Here, we deploy the SunTag method to visualize and quantify the timing, location, and kinetics of the translation of single mRNAs in living Drosophila embryos. By focusing on the translation of the conserved major epithelial-mesenchymal transition–inducing transcription factor Twist, we identify spatial heterogeneity in mRNA translation efficiency and reveal the existence of translation factories, where clustered mRNAs are cotranslated preferentially at basal perinuclear regions. Observing the location and dynamics of mRNA translation in a living multicellular organism opens avenues for understanding gene regulation during development.

Inference of dynamic spatial GRN models with multi-GPU evolutionary computation

Reverse engineering mechanistic gene regulatory network (GRN) models with a specific dynamic spatial behavior is an inverse problem without analytical solutions in general. Instead, heuristic machine learning algorithms have been proposed to infer the structure and parameters of a system of equations able to recapitulate a given gene expression pattern. However, these algorithms are computationally intensive as they need to simulate millions of candidate models, which limits their applicability and requires high computational resources. Graphics processing unit (GPU) computing is an affordable alternative for accelerating large-scale scientific computation, yet no method is currently available to exploit GPU technology for the reverse engineering of mechanistic GRNs from spatial phenotypes. Here we present an efficient methodology to parallelize evolutionary algorithms using GPU computing for the inference of mechanistic GRNs that can develop a given gene expression pattern in a multicellular tissue area or cell culture. The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation and selection operators and launching GPU kernels asynchronously. Kernels can run in parallel in a single or multiple GPUs and each kernel simulates and scores the error of a model using the thread parallelism of the GPU. We tested this methodology for the inference of spatiotemporal mechanistic gene regulatory networks (GRNs)-including topology and parameters-that can develop a given 2D gene expression pattern. The results show a 700-fold speedup with respect to a single CPU implementation. This approach can streamline the extraction of knowledge from biological and medical datasets and accelerate the automatic design of GRNs for synthetic biology applications.

Transcriptional Silencers: Driving Gene Expression with the Brakes On

Silencers are regulatory DNA elements that reduce transcription from their target promoters; they are the repressive counterparts of enhancers. Although discovered decades ago, and despite evidence of their importance in development and disease, silencers have been much less studied than enhancers. Recently, however, a series of papers have reported systematic studies of silencers in various model systems. Silencers are often bifunctional regulatory elements that can also act as enhancers, depending on cellular context, and are enriched for expression quantitative trait loci (eQTLs) and disease-associated variants. There is not yet evidence of a 'silencer chromatin signature', in the distribution of histone modifications or associated proteins, that is common to all silencers; instead, silencers may fall into various subclasses, acting by distinct (and possibly overlapping) mechanisms.

Dynamic patterning by morphogens illuminated by cis-regulatory studies

Morphogen concentration changes in space as well as over time during development. However, how these dynamics are interpreted by cells to specify fate is not well understood. Here, we focus on two morphogens: the maternal transcription factors Bicoid and Dorsal, which directly regulate target genes to pattern Drosophila embryos. The actions of these factors at enhancers has been thoroughly dissected and provides a rich platform for understanding direct input by morphogens and their changing roles over time. Importantly, Bicoid and Dorsal do not work alone; we also discuss additional inputs that work with morphogens to control spatiotemporal gene expression in embryos.

Internetwork connectivity of molecular networks across species of life

Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene and protein-protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN-PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

The Hox Transcription Factor Ubx Ensures Somatic Myogenesis by Suppressing the Mesodermal Master Regulator Twist

Early lineage-specific master regulators are essential for the specification of cell types. However, once cells are committed to a specific fate, it is critical to restrict the activity of such factors to enable differentiation. To date, it remains unclear how these factors are silenced. Using the Drosophila mesoderm as a model and a comparative genomic approach, we identify the Hox transcription factor Ultrabithorax (Ubx) to be critical for the repression of the master regulator Twist. Mesoderm-specific Ubx loss-of-function experiments using CRISPR-Cas9 and overexpression studies demonstrate that Ubx majorly impacts twist transcription. A mechanistic analysis reveals that Ubx requires the NK-homeodomain protein Tinman to bind to the twist promoter. Furthermore, we find these factor interactions to be critical for silencing by recruiting the Polycomb DNA binding protein Pleiohomeotic. Altogether, our data reveal that Ubx is a critical player in mediating the silencing of Twist, which is crucial for coordinated muscle differentiation.

Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling

Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in development, tumour progression and fibrosis. Predicted "neutral" (non-functional) TF binding always accounted for the majority (50% to 95%) of candidate target genes from statistically significant peaks and HOT regions had higher functional binding than most of the Snail and Twist datasets examined. Our results illuminated conserved gene networks that control epithelial plasticity in development and disease. We identified new gene functions and network modules including crosstalk with notch signalling and regulation of chromatin organisation, evidencing networks that reshape Waddington's epigenetic landscape during epithelial remodelling. Expression of orthologous functional TF targets discriminated breast cancer molecular subtypes and predicted novel tumour biology, with implications for precision medicine. Predicted invasion roles were validated using a tractable cell model, supporting our approach.

Intrinsic control of muscle attachment sites matching

Myogenesis is an evolutionarily conserved process. Little known, however, is how the morphology of each muscle is determined, such that movements relying upon contraction of many muscles are both precise and coordinated. Each Drosophila larval muscle is a single multinucleated fibre whose morphology reflects expression of distinctive identity Transcription Factors (iTFs). By deleting transcription cis-regulatory modules of one iTF, Collier, we generated viable muscle identity mutants, allowing live imaging and locomotion assays. We show that both selection of muscle attachment sites and muscle/muscle matching is intrinsic to muscle identity and requires transcriptional reprogramming of syncytial nuclei. Live-imaging shows that the staggered muscle pattern involves attraction to tendon cells and heterotypic muscle-muscle adhesion. Unbalance leads to formation of branched muscles, and this correlates with locomotor behavior deficit. Thus, engineering Drosophila muscle identity mutants allows to investigate, in vivo, physiological and mechanical properties of abnormal muscles.

The regulatory landscape of early maize inflorescence development

BACKGROUND

The functional genome of agronomically important plant species remains largely unexplored, yet presents a virtually untapped resource for targeted crop improvement. Functional elements of regulatory DNA revealed through profiles of chromatin accessibility can be harnessed for fine-tuning gene expression to optimal phenotypes in specific environments.

RESULT

Here, we investigate the non-coding regulatory space in the maize (Zea mays) genome during early reproductive development of pollen- and grain-bearing inflorescences. Using an assay for differential sensitivity of chromatin to micrococcal nuclease (MNase) digestion, we profile accessible chromatin and nucleosome occupancy in these largely undifferentiated tissues and classify at least 1.6% of the genome as accessible, with the majority of MNase hypersensitive sites marking proximal promoters, but also 3' ends of maize genes. This approach maps regulatory elements to footprint-level resolution. Integration of complementary transcriptome profiles and transcription factor occupancy data are used to annotate regulatory factors, such as combinatorial transcription factor binding motifs and long non-coding RNAs, that potentially contribute to organogenesis, including tissue-specific regulation between male and female inflorescence structures. Finally, genome-wide association studies for inflorescence architecture traits based solely on functional regions delineated by MNase hypersensitivity reveals new SNP-trait associations in known regulators of inflorescence development as well as new candidates.

CONCLUSIONS

These analyses provide a comprehensive look into the cis-regulatory landscape during inflorescence differentiation in a major cereal crop, which ultimately shapes architecture and influences yield potential.

Gastrulation in Drosophila melanogaster: Genetic control, cellular basis and biomechanics

Gastrulation is generally understood as the morphogenetic processes that result in the spatial organization of the blastomere into the three germ layers, ectoderm, mesoderm and endoderm. This review summarizes our current knowledge of the morphogenetic mechanisms in Drosophila gastrulation. In addition to the events that drive mesoderm invagination and germband elongation, we pay particular attention to other, less well-known mechanisms including midgut invagination, cephalic furrow formation, dorsal fold formation, and mesoderm layer formation. This review covers topics ranging from the identification and functional characterization of developmental and morphogenetic control genes to the analysis of the physical properties of cells and tissues and the control of cell and tissue mechanics of the morphogenetic movements in the gastrula.

Interleukin-31 Receptor α Is Required for Basal-Like Breast Cancer Progression

Purpose: Interleukin-31 receptor α (IL31RA) usually mediates IL-31 induced inflammation and allergic diseases. However, the functional roles of IL-31/IL31RA signaling in basal-like breast cancer (BLBC) progression remain totally unclear.

Methods: Tumorsphere formation, transwell, and wound healing assays were used to measure the BLBC progression. We implanted tumor cells in mammary fat pad and tail vein of nude mice to detect the growth and metastasis of BLBC cells. Luciferase and ChIP assays were employed to measure the transcriptional regulation. Western blot and real-time PCR assays as well as bio-informatics analyses were conducted to observe the expression of IL31RA.

Results: We found that silencing of IL31RA suppresses the cancer stem cell-like properties, migration and invasion of BLBC cells in vitro as well as tumor growth and metastasis in vivo. Knockdown of IL31RA ameliorates IL-31-mediated pro-oncogenic functions. Overexpression of IL31RA in luminal breast cancer cells enhances the cancer stem cell-like properties and cell motility. Our data further identified IL31RA as a target gene of Twist/BRD4 transcription complex.

Conclusion: Overall, these data indicate that IL31RA promotes basal-like breast cancer progression and metastasis, suggesting that targeting of IL-31/IL31RA axis might be beneficial to treatment of BLBC.

REDfly: the transcriptional regulatory element database for Drosophila

The REDfly database provides a comprehensive curation of experimentally-validated Drosophila transcriptional cis-regulatory elements and includes information on DNA sequence, experimental evidence, patterns of regulated gene expression, and more. Now in its thirteenth year, REDfly has grown to over 23 000 records of tested reporter gene constructs and 2200 tested transcription factor binding sites. Recent developments include the start of curation of predicted cis-regulatory modules in addition to experimentally-verified ones, improved search and filtering, and increased interaction with the authors of curated papers. An expanded data model that will capture information on temporal aspects of gene regulation, regulation in response to environmental and other non-developmental cues, sexually dimorphic gene regulation, and non-endogenous (ectopic) aspects of reporter gene expression is under development and expected to be in place within the coming year. REDfly is freely accessible at http://redfly.ccr.buffalo.edu, and news about database updates and new features can be followed on Twitter at @REDfly_database.

Mechanochemical Feedback Loops in Development and Disease

There is increasing evidence that both mechanical and biochemical signals play important roles in development and disease. The development of complex organisms, in particular, has been proposed to rely on the feedback between mechanical and biochemical patterning events. This feedback occurs at the molecular level via mechanosensation but can also arise as an emergent property of the system at the cellular and tissue level. In recent years, dynamic changes in tissue geometry, flow, rheology, and cell fate specification have emerged as key platforms of mechanochemical feedback loops in multiple processes. Here, we review recent experimental and theoretical advances in understanding how these feedbacks function in development and disease.

Functional genomic approaches to elucidate the role of enhancers during development

Successful development depends on the precise tissue-specific regulation of genes by enhancers, genetic elements that act as switches to control when and where genes are expressed. Because enhancers are critical for development, and the majority of disease-associated mutations reside within enhancers, it is essential to understand which sequences within enhancers are important for function. Advances in sequencing technology have enabled the rapid generation of genomic data that predict putative active enhancers, but functionally validating these sequences at scale remains a fundamental challenge. Herein, we discuss the power of genome-wide strategies used to identify candidate enhancers, and also highlight limitations and misconceptions that have arisen from these data. We discuss the use of massively parallel reporter assays to test enhancers for function at scale. We also review recent advances in our ability to study gene regulation during development, including CRISPR-based tools to manipulate genomes and single-cell transcriptomics to finely map gene expression. Finally, we look ahead to a synthesis of complementary genomic approaches that will advance our understanding of enhancer function during development. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Developmental Biology > Developmental Processes in Health and Disease Laboratory Methods and Technologies > Genetic/Genomic Methods.

The Role of Chromatin Accessibility in cis-Regulatory Evolution

Transcription factor (TF) binding is determined by sequence as well as chromatin accessibility. Although the role of accessibility in shaping TF-binding landscapes is well recorded, its role in evolutionary divergence of TF binding, which in turn can alter cis-regulatory activities, is not well understood. In this work, we studied the evolution of genome-wide binding landscapes of five major TFs in the core network of mesoderm specification, between Drosophila melanogaster and Drosophila virilis, and examined its relationship to accessibility and sequence-level changes. We generated chromatin accessibility data from three important stages of embryogenesis in both Drosophila melanogaster and Drosophila virilis and recorded conservation and divergence patterns. We then used multivariable models to correlate accessibility and sequence changes to TF-binding divergence. We found that accessibility changes can in some cases, for example, for the master regulator Twist and for earlier developmental stages, more accurately predict binding change than is possible using TF-binding motif changes between orthologous enhancers. Accessibility changes also explain a significant portion of the codivergence of TF pairs. We noted that accessibility and motif changes offer complementary views of the evolution of TF binding and developed a combined model that captures the evolutionary data much more accurately than either view alone. Finally, we trained machine learning models to predict enhancer activity from TF binding and used these functional models to argue that motif and accessibility-based predictors of TF-binding change can substitute for experimentally measured binding change, for the purpose of predicting evolutionary changes in enhancer activity.

Spatiotemporal control of gene expression boundaries using a feedforward loop

BACKGROUND

A feedforward loop (FFL) is commonly observed in several biological networks. The FFL network motif has been mostly studied with respect to variation of the input signal in time, with only a few studies of FFL activity in a spatially distributed system such as morphogen-mediated tissue patterning. However, most morphogen gradients also evolve in time.

RESULTS

We studied the spatiotemporal behavior of a coherent FFL in two contexts: (a) a generic, oscillating morphogen gradient and (b) the dorsal-ventral patterning of the early Drosophila embryo by a gradient of the NF-κB homolog dorsal with its early target Twist. In both models, we found features in the dynamics of the intermediate node-phase difference and noise filtering-that were largely independent of the parameterization of the models, and thus were functions of the structure of the FFL itself. In the dorsal gradient model, we also found that proper target gene expression was not possible without including the effect of maternal pioneer factor Zelda.

CONCLUSIONS

An FFL buffers fluctuation to changes in the morphogen signal ensuring stable gene expression boundaries.

Formation, interpretation, and regulation of the Drosophila Dorsal/NF-κB gradient

The morphogen gradient of the transcription factor Dorsal in the early Drosophila embryo has become one of the most widely studied tissue patterning systems. Dorsal is a Drosophila homolog of mammalian NF-κB and patterns the dorsal-ventral axis of the blastoderm embryo into several tissue types by spatially regulating upwards of 100 zygotic genes. Recent studies using fluorescence microscopy and live imaging have quantified the Dorsal gradient and its target genes, which has paved the way for mechanistic modeling of the gradient. In this review, we describe the mechanisms behind the initiation of the Dorsal gradient and its regulation of target genes. The main focus of the review is a discussion of quantitative and computational studies of the Dl gradient system, including regulation of the Dl gradient. We conclude with a discussion of potential future directions.

Coacting enhancers can have complementary functions within gene regulatory networks and promote canalization

Developmental genes are often regulated by multiple enhancers exhibiting similar spatiotemporal outputs, which are generally considered redundantly acting though few have been studied functionally. Using CRISPR-Cas9, we created deletions of two enhancers, brk5' and brk3', that drive similar but not identical expression of the gene brinker (brk) in early Drosophila embryos. Utilizing both in situ hybridization and quantitative mRNA analysis, we investigated the changes in the gene network state caused by the removal of one or both of the early acting enhancers. brk5' deletion generally phenocopied the gene mutant, including expansion of the BMP ligand decapentaplegic (dpp) as well as inducing variability in amnioserosa tissue cell number suggesting a loss of canalization. In contrast, brk3' deletion presented unique phenotypes including dorsal expansion of several ventrally expressed genes and a decrease in amnioserosa cell number. Similarly, deletions were made for two enhancers associated with the gene short-gastrulation (sog), sog.int and sog.dist, demonstrating that they also exhibit distinct patterning phenotypes and affect canalization. In summary, this study shows that similar gene expression driven by coacting enhancers can support distinct, and sometimes complementary, functions within gene regulatory networks and, moreover, that phenotypes associated with individual enhancer deletion mutants can provide insight into new gene functions.

Reconstruction of the Global Neural Crest Gene Regulatory Network In Vivo

Precise control of developmental processes is encoded in the genome in the form of gene regulatory networks (GRNs). Such multi-factorial systems are difficult to decode in vertebrates owing to their complex gene hierarchies and dynamic molecular interactions. Here we present a genome-wide in vivo reconstruction of the GRN underlying development of the multipotent neural crest (NC) embryonic cell population. By coupling NC-specific epigenomic and transcriptional profiling at population and single-cell levels with genome/epigenome engineering in vivo, we identify multiple regulatory layers governing NC ontogeny, including NC-specific enhancers and super-enhancers, novel trans-factors, and cis-signatures allowing reverse engineering of the NC-GRN at unprecedented resolution. Furthermore, identification and dissection of divergent upstream combinatorial regulatory codes has afforded new insights into opposing gene circuits that define canonical and neural NC fates early during NC ontogeny. Our integrated approach, allowing dissection of cell-type-specific regulatory circuits in vivo, has broad implications for GRN discovery and investigation.

Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development

Comprehensive information on the timing and location of gene expression is fundamental to our understanding of embryonic development and tissue formation. While high-throughput in situ hybridization projects provide invaluable information about developmental gene expression patterns for model organisms like Drosophila, the output of these experiments is primarily qualitative, and a high proportion of protein coding genes and most non-coding genes lack any annotation. Accurate data-centric predictions of spatio-temporal gene expression will therefore complement current in situ hybridization efforts. Here, we applied a machine learning approach by training models on all public gene expression and chromatin data, even from whole-organism experiments, to provide genome-wide, quantitative spatio-temporal predictions for all genes. We developed structured in silico nano-dissection, a computational approach that predicts gene expression in >200 tissue-developmental stages. The algorithm integrates expression signals from a compendium of 6,378 genome-wide expression and chromatin profiling experiments in a cell lineage-aware fashion. We systematically evaluated our performance via cross-validation and experimentally confirmed 22 new predictions for four different embryonic tissues. The model also predicts complex, multi-tissue expression and developmental regulation with high accuracy. We further show the potential of applying these genome-wide predictions to extract tissue specificity signals from non-tissue-dissected experiments, and to prioritize tissues and stages for disease modeling. This resource, together with the exploratory tools are freely available at our webserver http://find.princeton.edu, which provides a valuable tool for a range of applications, from predicting spatio-temporal expression patterns to recognizing tissue signatures from differential gene expression profiles.

Predicting gene regulatory interactions based on spatial gene expression data and deep learning

Reverse engineering of gene regulatory networks (GRNs) is a central task in systems biology. Most of the existing methods for GRN inference rely on gene co-expression analysis or TF-target binding information, where the determination of co-expression is often unreliable merely based on gene expression levels, and the TF-target binding data from high-throughput experiments may be noisy, leading to a high ratio of false links and missed links, especially for large-scale networks. In recent years, the microscopy images recording spatial gene expression have become a new resource in GRN reconstruction, as the spatial and temporal expression patterns contain much abundant gene interaction information. Till now, the spatial expression resources have been largely underexploited, and only a few traditional image processing methods have been employed in the image-based GRN reconstruction. Moreover, co-expression analysis using conventional measurements based on image similarity may be inaccurate, because it is the local-pattern consistency rather than global-image-similarity that determines gene-gene interactions. Here we present GripDL (Gene regulatory interaction prediction via Deep Learning), which incorporates high-confidence TF-gene regulation knowledge from previous studies, and constructs GRNs for Drosophila eye development based on Drosophila embryonic gene expression images. Benefitting from the powerful representation ability of deep neural networks and the supervision information of known interactions, the new method outperforms traditional methods with a large margin and reveals new intriguing knowledge about Drosophila eye development.

[Overexpression of histone deacetylase 11 suppresses basal-like breast cancer cell invasion and metastasis]

OBJECTIVE

Histone deacetylase 11 (HDAC11) is a class Ⅳ member of histone deacetylase family, and its role in regulating cancer cell invasion and metastasis remains unclear. We aimed to investigate the role of HDAC11 in regulating the biological behaviors of basal-like breast cancer (BLBC) cells.

METHODS

We analyzed the expression of HDAC11 based on Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). The effects of HDAC11 on the cell invasion and metastasis were examined using Transwell assay and in a mouse model. The interaction between HDAC11 and Twist was detected with immunoprecipitation. We identified HAS2 as a target gene of Twist using promoter luciferase assay and chromatin immunoprecipitation assay.

RESULTS

HDAC11 was lowly expressed in BLBC cells. HDAC11 overexpression suppressed BLBC cell invasion in vitro and their metastasis in nude mice. Mechanistically, HDAC11 directly interacted with Twist protein, antagonized its pro-invasive function and repressed Twist-induced HAS2 gene transcription.

CONCLUSIONS

Our data suggest that HDAC11 acts as a negative modulator of invasion and metastasis of BLBC cells.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes.

The Hox transcription factor Ubx stabilizes lineage commitment by suppressing cellular plasticity in Drosophila

During development cells become restricted in their differentiation potential by repressing alternative cell fates, and the Polycomb complex plays a crucial role in this process. However, how alternative fate genes are lineage-specifically silenced is unclear. We studied Ultrabithorax (Ubx), a multi-lineage transcription factor of the Hox class, in two tissue lineages using sorted nuclei and interfered with Ubx in mesodermal cells. We find that depletion of Ubx leads to the de-repression of genes normally expressed in other lineages. Ubx silences expression of alternative fate genes by retaining the Polycomb Group protein Pleiohomeotic at Ubx targeted genomic regions, thereby stabilizing repressive chromatin marks in a lineage-dependent manner. Our study demonstrates that Ubx stabilizes lineage choice by suppressing the multipotency encoded in the genome via its interaction with Pho. This mechanism may explain why the Hox code is maintained throughout the lifecycle, since it could set a block to transdifferentiation in adult cells.

Inference of Developmental Gene Regulatory Networks Beyond Classical Model Systems: New Approaches in the Post-genomic Era

The advent of high-throughput sequencing (HTS) technologies has revolutionized the way we understand the transformation of genetic information into morphological traits. Elucidating the network of interactions between genes that govern cell differentiation through development is one of the core challenges in genome research. These networks are known as developmental gene regulatory networks (dGRNs) and consist largely of the functional linkage between developmental control genes, cis-regulatory modules, and differentiation genes, which generate spatially and temporally refined patterns of gene expression. Over the last 20 years, great advances have been made in determining these gene interactions mainly in classical model systems, including human, mouse, sea urchin, fruit fly, and worm. This has brought about a radical transformation in the fields of developmental biology and evolutionary biology, allowing the generation of high-resolution gene regulatory maps to analyze cell differentiation during animal development. Such maps have enabled the identification of gene regulatory circuits and have led to the development of network inference methods that can recapitulate the differentiation of specific cell-types or developmental stages. In contrast, dGRN research in non-classical model systems has been limited to the identification of developmental control genes via the candidate gene approach and the characterization of their spatiotemporal expression patterns, as well as to the discovery of cis-regulatory modules via patterns of sequence conservation and/or predicted transcription-factor binding sites. However, thanks to the continuous advances in HTS technologies, this scenario is rapidly changing. Here, we give a historical overview on the architecture and elucidation of the dGRNs. Subsequently, we summarize the approaches available to unravel these regulatory networks, highlighting the vast range of possibilities of integrating multiple technical advances and theoretical approaches to expand our understanding on the global gene regulation during animal development in non-classical model systems. Such new knowledge will not only lead to greater insights into the evolution of molecular mechanisms underlying cell identity and animal body plans, but also into the evolution of morphological key innovations in animals.

CDX4 regulates the progression of neural maturation in the spinal cord

The progression of cells down different lineage pathways is a collaborative effort between networks of extracellular signals and intracellular transcription factors. In the vertebrate spinal cord, FGF, Wnt and Retinoic Acid signaling pathways regulate the progressive caudal-to-rostral maturation of neural progenitors by regulating a poorly understood gene regulatory network of transcription factors. We have mapped out this gene regulatory network in the chicken pre-neural tube, identifying CDX4 as a dual-function core component that simultaneously regulates gradual loss of cell potency and acquisition of differentiation states: in a caudal-to-rostral direction, CDX4 represses the early neural differentiation marker Nkx1.2 and promotes the late neural differentiation marker Pax6. Significantly, CDX4 prevents premature PAX6-dependent neural differentiation by blocking Ngn2 activation. This regulation of CDX4 over Pax6 is restricted to the rostral pre-neural tube by Retinoic Acid signaling. Together, our results show that in the spinal cord, CDX4 is part of the gene regulatory network controlling the sequential and progressive transition of states from high to low potency during neural progenitor maturation. Given CDX well-known involvement in Hox gene regulation, we propose that CDX factors coordinate the maturation and axial specification of neural progenitor cells during spinal cord development.

Inference of Transcription Factor Regulation Patterns Using Gene Expression Covariation in Natural Populations of Drosophila melanogaster

Gene regulatory networks control the complex programs that drive development. Deciphering the connections between transcription factors (TFs) and target genes is challenging, in part because TFs bind to thousands of places in the genome but control expression through a subset of these binding events. We hypothesize that we can combine natural variation of expression levels and predictions of TF binding sites to identify TF targets. We gather RNA-seq data from 71 genetically distinct F1 Drosophila melanogaster embryos and calculate the correlations between TF and potential target genes' expression levels, which we call "regulatory strength." To separate direct and indirect TF targets, we hypothesize that direct TF targets will have a preponderance of binding sites in their upstream regions. Using 14 TFs active during embryogenesis, we find that 12 TFs showed a significant correlation between their binding strength and regulatory strength on downstream targets, and 10 TFs showed a significant correlation between the number of binding sites and the regulatory effect on target genes. The general roles, e.g. bicoid's role as an activator, and the particular interactions we observed between our TFs, e.g. twist's role as a repressor of sloppy paired and odd paired, generally coincide with the literature.

Discoidin-domain receptor coordinates cell-matrix adhesion and collective polarity in migratory cardiopharyngeal progenitors

Integrated analyses of regulated effector genes, cellular processes, and extrinsic signals are required to understand how transcriptional networks coordinate fate specification and cell behavior during embryogenesis. Ciona cardiopharyngeal progenitors, the trunk ventral cells (TVCs), polarize as leader and trailer cells that migrate between the ventral epidermis and trunk endoderm. We show that the TVC-specific collagen-binding Discoidin-domain receptor (Ddr) cooperates with Integrin-β1 to promote cell-matrix adhesion. We find that endodermal cells secrete a collagen, Col9-a1, that is deposited in the basal epidermal matrix and promotes Ddr activation at the ventral membrane of migrating TVCs. A functional antagonism between Ddr/Intβ1-mediated cell-matrix adhesion and Vegfr signaling appears to modulate the position of cardiopharyngeal progenitors between the endoderm and epidermis. We show that Ddr promotes leader-trailer-polarized BMP-Smad signaling independently of its role in cell-matrix adhesion. We propose that dual functions of Ddr integrate transcriptional inputs to coordinate subcellular processes underlying collective polarity and migration.

Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy

Identifying functional enhancer elements in metazoan systems is a major challenge. Large-scale validation of enhancers predicted by ENCODE reveal false-positive rates of at least 70%. We used the pregrastrula-patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held-out data results from heterogeneity of functional signatures in enhancer elements. We show that at least two classes of enhancers are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, greater than 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well-predicted elements is composed predominantly of enhancers driving multistage segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome. An analysis of 32 SDEs using whole-mount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.

Spatially varying cis-regulatory divergence in Drosophila embryos elucidates cis-regulatory logic

Spatial patterning of gene expression is a key process in development, yet how it evolves is still poorly understood. Both cis- and trans-acting changes could participate in complex interactions, so to isolate the cis-regulatory component of patterning evolution, we measured allele-specific spatial gene expression patterns in D. melanogaster × simulans hybrid embryos. RNA-seq of cryo-sectioned slices revealed 66 genes with strong spatially varying allele-specific expression. We found that hunchback, a major regulator of developmental patterning, had reduced expression of the D. simulans allele specifically in the anterior tip of hybrid embryos. Mathematical modeling of hunchback cis-regulation suggested a candidate transcription factor binding site variant, which we verified as causal using CRISPR-Cas9 genome editing. In sum, even comparing morphologically near-identical species we identified surprisingly extensive spatial variation in gene expression, suggesting not only that development is robust to many such changes, but also that natural selection may have ample raw material for evolving new body plans via changes in spatial patterning.