Specific Gene Regulatory Networks Research Articles

COFFEE: consensus single cell-type specific inference for gene regulatory networks.

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

Abstract We086: Identifying Gene Regulatory Subnetwork Drivers of Cardiomyocyte Chamber Specification

Introduction: Fetal heart development is a highly coordinated process directed by complex gene regulatory networks (GRN). Imbalances in such networks can lead to congenital heart defects, a leading cause of morbidity and mortality in children. Many of these defects are related to the improper specification of cardiac progenitor cells to chamber-specific (i.e., atrial, or ventricular) cardiomyocytes, but the GRNs involved in this process are not well characterized. Hypothesis: We hypothesize that cardiomyocyte chamber specification depends on discordant atrial and ventricular regulatory subnetworks within heart field cardiac progenitor cells. Approach: We used CellOracle, a GRN inference software, and publicly available fetal mouse heart single cell RNA and ATAC sequencing data to infer cell type specific GRNs during cardiomyocyte differentiation. Each of the first heart field and two second heart field networks included 4,000 directed interactions and more than 600 genes. We performed a systematic in silico knockdown study with these networks to identify TFs that differentially modulate atrial and ventricular gene expression. Correlations in the downstream responses to TF perturbations were used to extract chamber specification subnetworks. Results: The most central genes within the heart field networks included several previously identified cardiac developmental TFs (Nkx2-5, Mef2c, Tbx5). Perturbations of these heart field GRNs identified 12 TFs that increased ventricular gene expression while also decreasing atrial gene expression (ventricular program), as well as 20 genes with the opposite effect (atrial program). Previous in vitro knockdown studies during iPSC-CM differentiation validated one of the ventricular (Hey2) and two of the atrial programming TF (Nr2f2, Tead2) predictions. Discordant atrial and ventricular regulatory subnetworks were identified, including one network with opposing effects on the expression of Wnt2 by ventricular (repressing) and atrial (activating) TFs. Conclusions: We identified a set of TFs organized within regulatory subnetworks that drive discord specification of ventricular vs. atrial cardiomyocyte gene expression within cardiac progenitor cells in mouse development. These predictions will be validated through in vitro and in vivo studies.

Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma.

Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in the regulation of key cellular processes might affect LUAD risk and survival outcomes, we built individual (person)-specific gene regulatory networks integrating gene expression, transcription factor protein-protein interaction, and sequence motif data, using PANDA/LIONESS algorithms, for both non-cancerous lung tissue samples from the Genotype Tissue Expression (GTEx) project and LUAD samples from The Cancer Genome Atlas (TCGA). In GTEx, we found that pathways involved in cell proliferation and immune response are increasingly targeted by regulatory transcription factors with age; these aging-associated alterations are accelerated by tobacco smoking and resemble oncogenic shifts in the regulatory landscape observed in LUAD and suggests that dysregulation of aging pathways might be associated with an increased risk of LUAD. Comparing normal adjacent samples from individuals with LUAD with healthy lung tissue samples from those without LUAD, we found that aging-associated genes show greater aging-biased targeting patterns in younger individuals with LUAD compared to their healthy counterparts of similar age, a pattern suggestive of age acceleration. This implies that an accelerated aging process may be responsible for tumor incidence in younger individuals. Using drug repurposing tool CLUEreg, we found small molecule drugs with potential geroprotective effects that may alter the accelerating aging profiles we found. We also observed that, in contrast to chronological age, a network-informed aging signature was associated with survival and response to chemotherapy in LUAD.

Abstract 151: Single Cell Transcriptional And Epigenetic Map Of Human Coronary Artery Disease In A Multi-ethnic Population

Identifying target genes that are causally linked to coronary artery disease (CAD) remains an elusive problem despite the plethora of CAD genome-wide association study (GWAS) risk loci. Here, we performed single nucleus multiomic (RNA + ATAC) sequencing in 44 human coronary arteries and bulk Hi-C in 16 coronaries from multi-ethnic participants. From sequencing 126,804 nuclei, we identified cell type specific transcriptional signatures, chromatin peaks, and gene regulatory networks through peak to gene linkage analysis. Single-cell quantitative trait loci (QTL) analysis identified cell-specific caQTL’s including cell-type specific caQTLs within CAD GWAS loci. The overlap of CAD GWAS loci with ATAC peaks demonstrated that vascular smooth muscle and endothelial cells harbor the greatest genetic risk. To identify proximal and distal putative target genes for CAD loci in a cell type specific manner, we built a enhancer-gene map by leveraging coronary artery ChiP-seq, cell specific RNA/ATAC-seq, and Hi-C based chromatin contact loops. Using this map, we identified distal target genes linked to CAD GWAS loci. Finally, given the effect of ancestry on CAD GWAS risk interpretation, we leveraged Hi-C data to compare the chromatin architecture in coronary tissue from 8 European Americans vs 8 African Americans. A meta-map, sample level, and local ancestry analysis identified ancestry-specific differences in chromatin looping and topologically associated domains. Collectively, our results provide a cell-type specific transcriptomic and epigenetic map which along with bulk chromatin contacts can be used to interpret and prioritize CAD GWAS candidates and nominate cell specific causal target genes.

Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.

There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.

Gene Regulatory Networks Controlling Sinusoidal Hematopoietic Niche Function

The bone marrow sinusoidal vascular niche is important for HSC engraftment and regeneration of hematopoiesis after transplantation, but the epigenetic factors that control these biological functions are undefined. Here, we show that sinusoidal hematopoietic niche functions are controlled by gene regulatory networks (GRNs), involving transcription factors (TFs) that bind to enhancers to regulate corresponding target genes. To characterize sinusoidal niche specific transcriptional and chromatin accessibility states, we performed multimodal single nuclei RNA and ATAC-seq analysis on unsorted zebrafish embryos. We identified 4 niche clusters, including mesenchymal stem cells, fibroblasts, osteoblasts and sinusoidal endothelial cells. Differential RNA transcriptome analysis confirmed the identity of each niche cluster with canonical gene markers for each niche cluster. To examine differential transcriptional accessibility in zebrafish niche clusters, we aggregated co-accessible regions in cells from these clusters. Each cluster demonstrated a unique chromatin accessibility profile with differentially accessible regions. Differential motif activity based on chromatin accessibility analysis further exhibited unique TF motifs activity in sinusoidal endothelial cells. To identify potential regulatory TFs in sinusoidal vascular niche, we predicted TFs based on the TF binding sites accessibility and TF expression. Thus, we identified 3 specific expressed and enriched TFs for sinusoidal vascular niche, including Fli1 (Friend leukemia integration 1), Mef2a (Myocyte enhancer factor 2A) and Mef2c. Next, to investigate genomic regulatory regions that interact with TFs, in which control the transcription of their target genes, we performed random forests and regression trees machine learning methods to infer GRNs. We observed multiple active GRNs in sinusoidal vascular niche, which were predicted to essential for regulating hematopoietic niche development and HSC maintenance, including klf12, egfl7, ebf3 and ldb2. Finally, we examined whether these putative gene regulatory networks were conserved across species and tissue types. We used same approach (single-cell multiome sequencing) on E4-HUVEC cells, a transduced human umbilical vein endothelial cells (HUVEC) with the adenovirus E4ORF1 gene, which was a well-defined model of the human sinusoidal vascular niche. We likewise observed regulatory networks in HUVEC cells. As in zebrafish, GRNs in human sinusoidal cluster consisted of FLI1 and MEF2 family, regulatory regions and their target genes. Our findings reveal that unique sinusoidal vascular niche specific gene regulatory networks, including TFs, regulatory regions and target genes indicate the treatments targeting GRNs may help to facilitate reprogramming of sinusoidal endothelial niche to support HSC homing and regeneration after transplantation.

EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors.

Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.

Human microglia maturation is underpinned by specific gene regulatory networks

Microglia phenotypes are highly regulated by the brain environment, but the transcriptional networks that specify the maturation of human microglia are poorly understood. Here, we characterized stage-specific transcriptomes and epigenetic landscapes of fetal and postnatal human microglia and acquired corresponding data in induced pluripotent stem cell (iPSC)-derived microglia, in cerebral organoids, and following engraftment into humanized mice. Parallel development of computational approaches that considered transcription factor (TF) co-occurrence and enhancer activity allowed prediction of shared and state-specific gene regulatory networks associated with fetal and postnatal microglia. Additionally, many features of the human fetal-to-postnatal transition were recapitulated in a time-dependent manner following the engraftment of iPSC cells into humanized mice. These data and accompanying computational approaches will facilitate further efforts to elucidate mechanisms by which human microglia acquire stage- and disease-specific phenotypes.

MANGO: Inferring gene regulatory networks from single cell multiomics

Abstract Transcription factors (TFs) and their target gene regulatory networks (GRNs) control immune cell fate decisions during differentiation and responses to environmental cues and signals. To better understand how GRN control immune outcomes and are dysregulated in disease settings, we developed MANGO (Multiomics Aided Neural Graph Ontology), a computational method that integrates single cell gene expression and chromatin accessibility data to infer genome-wide, cell-type specific GRN. MANGO harnesses graph neural networks to generate representations of gene and regulatory link behavior and uses existing interpretable methods, like UMAPs, to visualize the GRN and gather insights about functional neighborhoods of regulators or programs of regulation. By scoring each regulatory interaction and aggregating those with common sources, TF activity can be quantified and compared between networks. We applied MANGO to a human PBMC dataset to define GRN and identify key regulatory, cell-type specific TFs, despite sometimes having low changes in expression. Additionally, we show that MANGO can distinguish cells by regulation network at single cell resolution by feeding each cell's expression and accessibility through its respective cell type's regulation network and projecting the resulting embedding in lower dimensions with a UMAP. Finally, MANGO was used to uncover memory B and T cell GRNs that prime rapid secondary immune responses. Given the increase in single cell multiome datasets, we anticipate MANGO will provide unique insights into GRN in normal and diseased immune responses, and ultimately give rise to patient specific understanding of the immune system and precision medicine. Supported by grants from NIH/NIAID (R01 AI148471) to CDS

SRGS: sparse partial least squares-based recursive gene selection for gene regulatory network inference

BackgroundThe identification of gene regulatory networks (GRNs) facilitates the understanding of the underlying molecular mechanism of various biological processes and complex diseases. With the availability of single-cell RNA sequencing data, it is essential to infer GRNs from single-cell expression. Although some GRN methods originally developed for bulk expression data can be applicable to single-cell data and several single-cell specific GRN algorithms were developed, recent benchmarking studies have emphasized the need of developing more accurate and robust GRN modeling methods that are compatible for single-cell expression data.ResultsWe present SRGS, SPLS (sparse partial least squares)-based recursive gene selection, to infer GRNs from bulk or single-cell expression data. SRGS recursively selects and scores the genes which may have regulations on the considered target gene based on SPLS. When dealing with gene expression data with dropouts, we randomly scramble samples, set some values in the expression matrix to zeroes, and generate multiple copies of data through multiple iterations to make SRGS more robust. We test SRGS on different kinds of expression data, including simulated bulk data, simulated single-cell data without and with dropouts, and experimental single-cell data, and also compared with the existing GRN methods, including the ones originally developed for bulk data, the ones developed specifically for single-cell data, and even the ones recommended by recent benchmarking studies.ConclusionsIt has been shown that SRGS is competitive with the existing GRN methods and effective in the gene regulatory network inference from bulk or single-cell gene expression data. SRGS is available at: https://github.com/JGuan-lab/SRGS.

Computational Tactics for Precision Cancer Network Biology.

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.

Comparative Analysis of Genome-Scale Gene Regulatory Networks in Human Hematopoietic Stem and Myeloid Progenitor Fractions

Background: Dynamic remodelling of gene regulatory networks (GRN) guides cell state changes during differentiation to establish and maintain cell identity and gene expression. We previously described combinatorial binding of 7 transcription factors (TF (heptad: FLI1, ERG, GATA2, RUNX1, TAL1, LYL1 and LMO2)) which form an autoregulatory network in human hematopoietic stem and progenitor cells (HSPC: Beck et al Blood 2013, Thoms et al Blood 2021). Here we mapped genome-wide binding of TFs and chromatin regulators and explored their roles in establishing and maintaining cell type specific GRNs across human hematopoietic stem cell (HSC) differentiation. Methods: We flow sorted cryopreserved CD34+ LIN- cells collected by apheresis into HSCs (CD38loCD45RA-), common myeloid progenitors (CMP: CD38hiCD45RA-CD123+), granulocyte monocyte progenitors (GMP: CD38hiCD45RA+CD123+) and megakaryocyte erythroid progenitors (MEP: CD38hiCD45RA-CD123-). Active regulatory regions (Fig A) were identified by a) chromatin immunoprecipitation (ChIP)/ChIPmentation for genome wide identification of active (H3K27ac, H3K4me3) and inactive (H3K27me3) histone marks, and TF and co-factor (heptad, PU.1, STAG2, CTCF) binding, b) HiC to broadly classify high order 3D genome structures and c) H3K27ac HiChIP at 5kb. Results: Combinatorial binding of heptad TFs in all cell types was confirmed, with the most overrepresented pattern being co-binding of all 7 factors. However, certain multifactor combinations were identified in restricted cell types or associated with lineage specific gene promoters. For example, the master erythroid TF GATA1 showed combinatorial binding in CMPs and MEPs, while the myeloid TF MPO showed combinatorial binding in CMPs and GMPs. Chromatin looping was highly variable across the four cell types, although high order genome architecture was conserved. We first focussed on HiChIP interactions involving promoters of the heptad genes and observed chromatin looping patterns consistent with an interconnected circuit. Promoter looping patterns were dynamic across cell types, and we found multiple distal regions, each with specific patterns of TF binding, connected to each heptad promoter (Fig B). Many of the distal regions were previously undescribed and may constitute novel regulators of heptad genes. To further understand the role of heptad TFs in regulating cell type specific gene expression, we used our HiChIP data to link promoters to candidate distal regulatory regions and focussed on promoter-regulator pairs with differential heptad binding across the cell types. In general, differential binding was accompanied by differential gene expression. Genes with high heptad binding in MEPs were enriched for pathways related to erythroid cells (e.g., ferroptosis signalling) while genes with high heptad binding in GMPs were enriched for pathways related to myeloid cells (e.g., TREM1 signalling). We then focussed on promoter-regulator pairs corresponding to genes expressed in mature erythroid and myeloid cells and observed cell specific patterns of TF binding. At erythroid genes we saw increasing GATA2/TAL1/LYL1/LMO2 binding and decreasing ERG binding along the HSC to MEP trajectory, while at myeloid genes we saw high GATA2/LYL1/LMO2 binding and low TAL1 binding along the HSC to GMP trajectory. To conduct an unbiased genome wide analysis of HSPC GRNs we used our data to annotate and cluster the 85,100 accessible chromatin regions present in HSPCs (Corces et al Nature Genetics 2016). Accessible regions clustered broadly as promoters, regulatory regions, or CTCF-bound. Regions with high heptad binding in either MEPs or GMPs were located in different subclusters, suggesting dynamic reorganisation of heptad TFs across the genome as HSCs proceed along differentiation trajectories. Taken together our data strongly supports a mechanistic link between differential heptad TF binding and cell type specific expression patterns and provides insight into how expression of multiple genes is coordinated during cell state transitions. Conclusion: This study provides a comprehensive characterisation of the gene regulatory landscape in rare subpopulations of human HSPCs and will be a key resource for understanding adult hematopoiesis. Our work provides a framework for analysing aberrant regulatory networks in leukemic cells and leveraging these to devise novel therapeutic strategies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

RNA-binding protein Elavl1/HuR is required for maintenance of cranial neural crest specification.

While neural crest development is known to be transcriptionally controlled via sequential activation of gene regulatory networks (GRNs), recent evidence increasingly implicates a role for post-transcriptional regulation in modulating the output of these regulatory circuits. Using available single-cell RNA-sequencing datasets from avian embryos to identify potential post-transcriptional regulators, we found that Elavl1, which encodes for an RNA-binding protein with roles in transcript stability, was enriched in the premigratory cranial neural crest. Perturbation of Elavl1 resulted in premature neural crest delamination from the neural tube as well as significant reduction in transcripts associated with the neural crest specification GRN, phenotypes that are also observed with downregulation of the canonical Wnt inhibitor Draxin. That Draxin is the primary target for stabilization by Elavl1 during cranial neural crest specification was shown by RNA-sequencing, RNA immunoprecipitation, RNA decay measurement, and proximity ligation assays, further supporting the idea that the downregulation of neural crest specifier expression upon Elavl1 knockdown was largely due to loss of Draxin. Importantly, exogenous Draxin rescued cranial neural crest specification defects observed with Elavl1 knockdown. Thus, Elavl1 plays a critical a role in the maintenance of cranial neural crest specification via Draxin mRNA stabilization. Together, these data highlight an important intersection of post-transcriptional regulation with modulation of the neural crest specification GRN.

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.

Gene Regulatory Network Dynamical Logical Models for Plant Development.

Mathematical and computational approaches that integrate and model the concerted action of multiple genetic and nongenetic components holding highly nonlinear interactions are fundamental for the study of developmental processes. Among these, gene regulatory network (GRN) dynamical models are very useful to understand how diverse types of regulatory constraints restrict the multigene expression patterns that characterize different cell fates. In this chapter we present a hands-on approach to model GRN dynamics, taking as a working example a well-curated and experimentally grounded GRN developmental module proposed by our group: the flower organ specification gene regulatory network (FOS-GRN). We demonstrate how to build and analyze a GRN model according to the following steps: (1) integration of molecular genetic data and formulation of logical rules specifying the dynamic behavior of each gene; (2) determination of steady states (attractors) corresponding to each cell type; (3) validation of the GRN model; and (4) extension of the deterministic model with the inclusion of stochasticity in order to model cell-state transitions dependent on noise due to fluctuations of the involved gen products. The methodologies explained here in detail can be applied to any other developmental module.

RNA‐binding protein Elavl1/HuR is required for maintenance of cranial neural crest specification

Neural crest (NC) cells are a multipotent cell population, unique to the vertebrate species, which gather at the dorsal neural tube and undergo an epithelial-to-mesenchymal transition (EMT). NC then migrate throughout the developing embryo contributing to various cell types such as craniofacial cartilage, neurons, and elements of the peripheral nervous system. While a gene regulatory network (GRN) underlies establishment of NC formation, recent studies suggest a role for post-transcriptional regulation in modulating the output of these regulatory circuits. In this study, we examined RNA-sequencing data from avian embryos at different stages of NC development and found enrichment of Elav1, a transcript encoding for the RNA-binding protein HuR. Using immunohistochemical analyses we characterized the spatiotemporal expression of HuR. We observed HuR expression following the establishment of the neural plate border and upon closure of the neural tube, with an enrichment in expression coinciding with NC formation. Using antisense morpholino oligo to perturb HuR expression, we observed loss of early neural crest specifier FoxD3 and its activator Axud1, as well as the EMT regulator Draxin. In addition, we identified several putative HuR binding sites within the predicted secondary structure of Draxin 3’UTR. RNA pulldown further showed Draxin is a specific target of HuR. Importantly, overexpression of exogenous Draxin was able to rescue NC specification defects observed in HuR knockdowns. Our results demonstrate a critical role for HuR in maintenance of cranial NC specification, at least partially via Draxin mRNA stabilization. Together, these data highlight an important intersection of post-transcriptional regulation with modulation of the neural crest specification GRN.

Temporal flexibility of gene regulatory network underlies a novel wing pattern in flies

Organisms have evolved endless morphological, physiological, and behavioral novel traits during the course of evolution. Novel traits were proposed to evolve mainly by orchestration of preexisting genes. Over the past two decades, biologists have shown that cooption of gene regulatory networks (GRNs) indeed underlies numerous evolutionary novelties. However, very little is known about the actual GRN properties that allow such redeployment. Here we have investigated the generation and evolution of the complex wing pattern of the fly Samoaia leonensis We show that the transcription factor Engrailed is recruited independently from the other players of the anterior-posterior specification network to generate a new wing pattern. We argue that partial cooption is made possible because 1) the anterior-posterior specification GRN is flexible over time in the developing wing and 2) this flexibility results from the fact that every single gene of the GRN possesses its own functional time window. We propose that the temporal flexibility of a GRN is a general prerequisite for its possible cooption during the course of evolution.

OTX2 represses sister cell fate choices in the developing retina to promote photoreceptor specification.

During vertebrate retinal development, subsets of progenitor cells generate progeny in a non-stochastic manner, suggesting that these decisions are tightly regulated. However, the gene-regulatory network components that are functionally important in these progenitor cells are largely unknown. Here we identify a functional role for the OTX2 transcription factor in this process. CRISPR/Cas9 gene editing was used to produce somatic mutations of OTX2 in the chick retina and identified similar phenotypes to those observed in human patients. Single cell RNA sequencing was used to determine the functional consequences OTX2 gene editing on the population of cells derived from OTX2-expressing retinal progenitor cells. This confirmed that OTX2 is required for the generation of photoreceptors, but also for repression of specific retinal fates and alternative gene regulatory networks. These include specific subtypes of retinal ganglion and horizontal cells, suggesting that in this context, OTX2 functions to repress sister cell fate choices.

Automated generation of context specific Gene Regulatory Networks from epigenetic and genomic data

Automated generation of context specific Gene Regulatory Networks from epigenetic and genomic data

Early expression of Tubulin Beta-III in avian cranial neural crest cells

Neural crest cells are a transient stem-like cell population that forms in the dorsal neural tube of vertebrate embryos and then migrates to various locations to differentiate into diverse derivatives such as craniofacial bone, cartilage, and the enteric and peripheral nervous systems. The current dogma of neural crest cell development suggests that there is a specific hierarchical gene regulatory network (GRN) that controls the induction, specification, and differentiation of these cells at specific developmental times. Our lab has identified that a marker of differentiated neurons, Tubulin Beta-III (TUBB3), is expressed in premigratory neural crest cells. TUBB3 has previously been identified as a major constituent of microtubules and is required for the proper guidance and maintenance of axons during development. Using the model organism, Gallus gallus, we have characterized the spatiotemporal localization of TUBB3 in early stages of development. Here we show TUBB3 is expressed in the developing neural plate, is upregulated in the pre-migratory cranial neural crest prior to cell delamination and migration, and it is maintained or upregulated in neurons in later developmental stages. We believe that TUBB3 likely has a role in early neural crest formation and migration separate from its role in neurogenesis.