Combinatorial Regulatory Network Research Articles

Chromatin Landscape Governing Murine Epidermal Differentiation

Chromatin landscape and regulatory networks are determinants in lineage specification and differentiation. To define the temporospatial differentiation axis in murine epidermal cells invivo, we generated datasets profiling expression dynamics (RNA sequencing), chromatin accessibility (assay for transposase-accessible chromatin using sequencing), architecture (Hi-C), and histone modifications (chromatin immunoprecipitation followed by sequencing) in the epidermis. We show that many differentially regulated genes are suppressed during the differentiation process, with superenhancers controlling differentiation-specific epigenomic changes. Our data shows the relevance of the Dlx/Klf/Grhl combinatorial regulatory network in maintaining correct temporospatial gene expression during epidermal differentiation. We determined differential open compartments, topologically associating domain score, and looping in the basal cell and suprabasal cell epidermal fractions, with the evolutionarily conserved epidermal differentiation complex region showing distinct suprabasal cell-specific topologically associating domain and loop formation that coincided with superenhancer sites. Overall, our study provides a global genome-wide resource of chromatin dynamics that define unrecognized regulatory networks and the epigenetic control of Dlx3-bound superenhancer elements during epidermal differentiation.

Functional conservation and divergence of miR156 and miR529 during rice development

MicroRNAs (miRNAs) are important regulatory elements involved in the regulation of various plant developmental and physiological processes by blocking the expression of target genes. MiR156 and miR529 are two combinatorial regulators, which cooperatively target the SQUAMOSA PROMOTER BINDING-LIKE (SPL) family genes. However, there has been no report about the functional conservation and divergence of miR156 and miR529 during plant development to date. In this study, the biological function and relationship of miR156, miR529 and their target OsSPL14 in rice were explored. Overexpression of miR156e or miR529a (miR156e-OE and miR529a-OE) increased the grain size and tiller number but decreased the plant height and panicle length, while an opposite phenotype was observed for their target mimicry (miR156-MIMIC and miR529a-MIMIC) transgenic plants. Stem-loop RT-PCR results revealed ubiquitous expression of miR156 in roots, axillary buds and leaves, while miR529 was preferentially expressed in the panicle. Accordingly, OsSPL14 could be preferentially and precisely cleaved by miR529a in young panicle but by miR156 in vegetative tissues. Transgenic plants generated by the target immune strategy exhibited obvious growth defects upon the blocking of miR156 and/or miR529 function in rice, confirming that both miR156 and miR529 play important roles in controlling rice growth and development. Moreover, the miR156/miR529-OsSPL14 module negatively controlled grain size by regulating the genes associated with grain size and cell cycling, and controlled plant height through a more complicated mechanism. Taken together, our results demonstrate that miR156 and miR529 respectively function dominantly in the vegetative stage and reproductive stage to control rice growth and development by regulating the accumulation of OsSPL14. These findings facilitate a better understanding of the functional conservation and divergence of miR156 and miR529 family in the miRNA combinatorial regulatory network of plants.

Decoding brain memory formation by single-cell RNA sequencing.

To understand how distinct memories are formed and stored in the brain is an important and fundamental question in neuroscience and computational biology. A population of neurons, termed engram cells, represents the physiological manifestation of a specific memory trace and is characterized by dynamic changes in gene expression, which in turn alters the synaptic connectivity and excitability of these cells. Recent applications of single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are promising approaches for delineating the dynamic expression profiles in these subsets of neurons, and thus understanding memory-specific genes, their combinatorial patterns and regulatory networks. The aim of this article is to review and discuss the experimental and computational procedures of sc/snRNA-seq, new studies of molecular mechanisms of memory aided by sc/snRNA-seq in human brain diseases and related mouse models, and computational challenges in understanding the regulatory mechanisms underlying long-term memory formation.

Extending Combinatorial Regulatory Network Modeling to Include Activity Control and Decay Modulation

Understanding how the structure of within-system interactions affects the dynamics of the system is important in many areas of science. We extend a network dynamics modeling platform DSGRN, which combinatorializes both dynamics and parameter space to construct finite but accurate summaries of network dynamics, to new types of interactions. While the standard DSGRN assumes that each network edge controls the rate of abundance of the target node, the new edges may control either activity level or a decay rate of its target. While motivated by processes of post-transcriptional modification and ubiquitination in systems biology, our extension is applicable to the dynamics of any signed directed network.

Combinatorial Regulatory Networks Provide Novel Biomarkers for Diagnosis and Therapeutics

Combinatorial Regulatory Networks Provide Novel Biomarkers for Diagnosis and Therapeutics

Abstract 07: An oncogenic transcriptional network anchored by ETS1, p63 and subtype specific drivers of HNSCC revealed by epigenomic and genomic interrogation

Abstract Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease of significant mortality and morbidity, with a 5-year survival rate that has shown only modest improvement. This persistent patient mortality and morbidity can be attributed, in part, to an overall lack of novel targeted treatment options. Indeed, recent genome sequencing studies have highlighted the striking heterogeneity of HNSCC tumors and have failed to identify pervasive and broadly actionable driver mutations and/or other genetic alterations. Despite this genetic heterogeneity, HNSCC tumors can be classified into gene-expression defined subtypes, raising the possibility that such distinct gene expression programs and regulatory networks are closely interwoven with an intrinsic heritable and signature epigenetic state. We hypothesized that there exist key oncogenic transcriptional regulators of subtype dependency and that defining the epigenetic landscape of HNSCC tumors will reveal the identity of these crucial factors. This will be impactful since targeting of tumor addiction to key oncogenic Transcription Factors (TFs), although successful in a number of other tumor types, remains largely unexplored in HNSCC due to limited knowledge of the identity of these regulators in this cancer. To decipher the genomic and epigenomic drivers of HNSCC tumor subtypes, we examined a large cohort of cell lines that represent the Atypical, Basal and Mesenchymal subtypes. By using global H3K27 acetylation levels as a surrogate marker, we first generated a consensus genome-wide regulatory enhancer and Super-Enhancer map. Interestingly, we observed a differential enrichment pattern of H3K27Ac signals that recapitulated the segregation of cell-lines according to subtype based on differential gene expression profiles. We leveraged the differential enhancer map to identify enriched DNA-Binding Motifs to generate a Transcription Factor Combinatorial Regulatory Network, based on the co-occurrence frequency of TFs relative to genomic background. Our analysis highlighted a Cis-Regulatory Module (CRM) Network, specific to each molecular subtype of HNSCC. Furthermore, we identified key TF motifs that are embedded within these CRMs and enriched for each subtype-specific network, which we posit represent the pertinent TF drivers of the distinct gene-expression network found in various HNSCC tumors. Here, as proof of principle, we have examined the potential interaction between TP63 and ETS1, two oncogenic TFs revealed by our epigenomic and genomic analysis to be crucial mediators of HPV-negative HNSCC tumors. To better understand the TP63-ETS1 driven molecular processes in HPV-negative tumors, we performed transcriptomic analysis of the effects of knockdown of TP63 and ETS1 by RNA-Seq and identified their global targets by ChIP-Seq. Interestingly, our comprehensive analysis showed that TP63 and ETS1 share a significant number of genomic binding sites, exhibit a marked preference for binding to Super-Enhancers and together act in a concerted fashion to drive expression of unique set of pathways and processes. By integrating our findings with meta-analysis of HNSCC patient datasets, we have uncovered a core TP63-ETS1 driven signature network that serves as a regulatory anchor of common facets of cancer biology, such as proliferation and angiogenesis, as well as drug-targetable tumor-specific processes involving cancer stem cells. Our study has harnessed the power of an integrative genomic/epigenomic approach to characterize HNSCC subtypes and heterogeneity and to provide a robust framework for the discovery of novel tumor drivers that can be leveraged for targeted therapeutics. Citation Format: Christian Gluck, Isha Sethi, Maria Tsompana, Satrajit Sinha. An oncogenic transcriptional network anchored by ETS1, p63 and subtype specific drivers of HNSCC revealed by epigenomic and genomic interrogation [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; April 23-25, 2017; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(23_Suppl):Abstract nr 07.

Differential regulation analysis reveals dysfunctional regulatory mechanism involving transcription factors and microRNAs in gastric carcinogenesis.

Gastric cancer (GC) is one of the most incident malignancies in the world. Although lots of featured genes and microRNAs (miRNAs) have been identified to be associated with gastric carcinogenesis, underlying regulatory mechanisms still remain unclear. In order to explore the dysfunctional mechanisms of GC, we developed a novel approach to identify carcinogenesis relevant regulatory relationships, which is characterized by quantifying the difference of regulatory relationships between stages. Firstly, we applied the strategy of differential coexpression analysis (DCEA) to transcriptomic datasets including paired mRNA and miRNA of gastric samples to identify a set of genes/miRNAs related to gastric cancer progression. Based on these genes/miRNAs, we constructed conditional combinatorial gene regulatory networks (cGRNs) involving both transcription factors (TFs) and miRNAs. Enrichment of known cancer genes/miRNAs and predicted prognostic genes/miRNAs was observed in each cGRN. Then we designed a quantitative method to measure differential regulation level of every regulatory relationship between normal and cancer, and the known cancer genes/miRNAs proved to be ranked significantly higher. Meanwhile, we defined differentially regulated link (DRL) by combining differential regulation, differential expression and the regulation contribution of the regulator to the target. By integrating survival analysis and DRL identification, three master regulators TCF7L1, TCF4, and MEIS1 were identified and testable hypotheses of dysfunctional mechanisms underlying gastric carcinogenesis related to them were generated. The fine-tuning effects of miRNAs were also observed. We propose that this differential regulation network analysis framework is feasible to gain insights into dysregulated mechanisms underlying tumorigenesis and other phenotypic changes.

Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer.

While previous studies reported aberrant expression of microRNAs (miRNAs) in non-small cell lung cancer (NSCLC), little is known about which miRNAs play central roles in NSCLC's pathogenesis and its regulatory mechanisms. To address this issue, we presented a robust computational framework that integrated matched miRNA and mRNA expression profiles in NSCLC using feed-forward loops. The network consists of miRNAs, transcription factors (TFs), and their common predicted target genes. To discern the biological meaning of their associations, we introduced the direction of regulation. A network edge validation strategy using three independent NSCLC expression profiling data sets pinpointed reproducible biological regulations. Reproducible regulation, which may reflect the true molecular interaction, has not been applied to miRNA-TF co-regulatory network analyses in cancer or other diseases yet. We revealed eight hub miRNAs that connected to a higher proportion of targets validated by independent data sets. Network analyses showed that these miRNAs might have strong oncogenic characteristics. Furthermore, we identified a novel miRNA-TF co-regulatory module that potentially suppresses the tumor suppressor activity of the TGF-β pathway by targeting a core pathway molecule (TGFBR2). Follow-up experiments showed two miRNAs (miR-9-5p and miR-130b-3p) in this module had increased expression while their target gene TGFBR2 had decreased expression in a cohort of human NSCLC. Moreover, we demonstrated these two miRNAs directly bind to the 3' untranslated region of TGFBR2. This study enhanced our understanding of miRNA-TF co-regulatory mechanisms in NSCLC. The combined bioinformatics and validation approach we described can be applied to study other types of diseases.

Using graphical adaptive lasso approach to construct transcription factor and microRNA's combinatorial regulatory network in breast cancer.

Discovering the regulation of cancer-related gene is of great importance in cancer biology. Transcription factors and microRNAs are two kinds of crucial regulators in gene expression, and they compose a combinatorial regulatory network with their target genes. Revealing the structure of this network could improve the authors' understanding of gene regulation, and further explore the molecular pathway in cancer. In this article, the authors propose a novel approach graphical adaptive lasso (GALASSO) to construct the regulatory network in breast cancer. GALASSO use a Gaussian graphical model with adaptive lasso penalties to integrate the sequence information as well as gene expression profiles. The simulation study and the experimental profiles verify the accuracy of the authors' approach. The authors further reveal the structure of the regulatory network, and explore the role of feedforward loops in gene regulation. In addition, the authors discuss the combinatorial regulatory effect between transcription factors and microRNAs, and select miR-155 for detailed analysis of microRNA's role in cancer. The proposed GALASSO approach is an efficient method to construct the combinatorial regulatory network. It also provides a new way to integrate different data sources and could find more applications in meta-analysis problem.

Studying Weak and Dynamic Interactions of Posttranslationally Modified Proteins using Expressed Protein Ligation

Many cellular processes are regulated by posttranslational modifications that are recognized by specific domains in protein binding partners. These interactions are often weak, thus allowing a highly dynamic and combinatorial regulatory network of protein-protein interactions. We report an efficient strategy that overcomes challenges in structural analysis of such a weak transient interaction between the Tudor domain of the Survival of Motor Neuron (SMN) protein and symmetrically dimethylated arginine (sDMA). The posttranslational modification is chemically introduced and covalently linked to the effector module by a one-pot expressed protein ligation (EPL) procedure also enabling segmental incorporation of NMR-active isotopes for structural analysis. Covalent coupling of the two interacting moieties shifts the equilibrium to the bound state, and stoichiometric interactions are formed even for low affinity interactions. Our approach should enable the structural analysis of weak interactions by NMR or X-ray crystallography to better understand the role of posttranslational modifications in dynamic biological processes.

CGRNB: a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets

BackgroundWe are witnessing rapid progress in the development of methodologies for building the combinatorial gene regulatory networks involving both TFs (Transcription Factors) and miRNAs (microRNAs). There are a few tools available to do these jobs but most of them are not easy to use and not accessible online. A web server is especially needed in order to allow users to upload experimental expression datasets and build combinatorial regulatory networks corresponding to their particular contexts.MethodsIn this work, we compiled putative TF-gene, miRNA-gene and TF-miRNA regulatory relationships from forward-engineering pipelines and curated them as built-in data libraries. We streamlined the R codes of our two separate forward-and-reverse engineering algorithms for combinatorial gene regulatory network construction and formalized them as two major functional modules. As a result, we released the cGRNB (combinatorial Gene Regulatory Networks Builder): a web server for constructing combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. The cGRNB enables two major network-building modules, one for MPGE (miRNA-perturbed gene expression) datasets and the other for parallel miRNA/mRNA expression datasets. A miRNA-centered two-layer combinatorial regulatory cascade is the output of the first module and a comprehensive genome-wide network involving all three types of combinatorial regulations (TF-gene, TF-miRNA, and miRNA-gene) are the output of the second module.ConclusionsIn this article we propose cGRNB, a web server for building combinatorial gene regulatory networks through integrated engineering of seed-matching sequence information and gene expression datasets. Since parallel miRNA/mRNA expression datasets are rapidly accumulated by the advance of next-generation sequencing techniques, cGRNB will be very useful tool for researchers to build combinatorial gene regulatory networks based on expression datasets. The cGRNB web-server is free and available online at http://www.scbit.org/cgrnb.

Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is one of the most fatal cancers in the world, and metastasis is a significant cause to the high mortality in patients with HCC. However, the molecular mechanism behind HCC metastasis is not fully understood. Study of regulatory networks may help investigate HCC metastasis in the way of systems biology profiling.MethodsBy utilizing both sequence information and parallel microRNA(miRNA) and mRNA expression data on the same cohort of HBV related HCC patients without or with venous metastasis, we constructed combinatorial regulatory networks of non-metastatic and metastatic HCC which contain transcription factor(TF) regulation and miRNA regulation. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks.ResultsGlobally TFs accounted for the main part of regulation while miRNAs for the minor part of regulation. However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one. Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. MiR-16, miR-30a, Let-7e and miR-204 were identified as key miRNA regulators contributed to HCC metastasis.ConclusionIn this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. The workflow in this study can be applied in similar context of cancer research and could also be extended to other clinical topics.

BSRD: a repository for bacterial small regulatory RNA

In bacteria, small regulatory non-coding RNAs (sRNAs) are the most abundant class of post-transcriptional regulators. They are involved in diverse processes including quorum sensing, stress response, virulence and carbon metabolism. Recent developments in high-throughput techniques, such as genomic tiling arrays and RNA-Seq, have allowed efficient detection and characterization of bacterial sRNAs. However, a comprehensive repository to host sRNAs and their annotations is not available. Existing databases suffer from a limited number of bacterial species or sRNAs included. In addition, these databases do not have tools to integrate or analyse high-throughput sequencing data. Here, we have developed BSRD (http://kwanlab.bio.cuhk.edu.hk/BSRD), a comprehensive bacterial sRNAs database, as a repository for published bacterial sRNA sequences with annotations and expression profiles. BSRD contains over nine times more experimentally validated sRNAs than any other available databases. BSRD also provides combinatorial regulatory networks of transcription factors and sRNAs with their common targets. We have built and implemented in BSRD a novel RNA-Seq analysis platform, sRNADeep, to characterize sRNAs in large-scale transcriptome sequencing projects. We will update BSRD regularly.

Combinatorial network of transcriptional regulation and microRNA regulation in human cancer.

BackgroundBoth transcriptional control and microRNA (miRNA) control are critical regulatory mechanisms for cells to direct their destinies. At present, the combinatorial regulatory network composed of transcriptional regulations and post-transcriptional regulations is often constructed through a forward engineering strategy that is based solely on searching of transcriptional factor binding sites or miRNA seed regions in the putative target sequences. If the reverse engineering strategy is integrated with the forward engineering strategy, a more accurate and more specific combinatorial regulatory network will be obtained.ResultsIn this work, utilizing both sequence-matching information and parallel expression datasets of miRNAs and mRNAs, we integrated forward engineering with reverse engineering strategies and as a result built a hypothetical combinatorial gene regulatory network in human cancer. The credibility of the regulatory relationships in the network was validated by random permutation procedures and supported by authoritative experimental evidence-based databases. The global and local architecture properties of the combinatorial regulatory network were explored, and the most important tumor-regulating miRNAs and TFs were highlighted from a topological point of view.ConclusionsBy integrating the forward engineering and reverse engineering strategies, we manage to sketch a genome-scale combinatorial gene regulatory network in human cancer, which includes transcriptional regulations and miRNA regulations, allowing systematic study of cancer gene regulation. Our work establishes a pipeline that can be extended to reveal conditional combinatorial regulatory landscapes correlating to specific cellular contexts.

Combinatorial regulation of transcription factors and microRNAs

BackgroundGene regulation is a key factor in gaining a full understanding of molecular biology. Cis-regulatory modules (CRMs), consisting of multiple transcription factor binding sites, have been confirmed as the main regulators in gene expression. In recent years, a novel regulator known as microRNA (miRNA) has been found to play an important role in gene regulation. Meanwhile, transcription factor and microRNA co-regulation has been widely identified. Thus, the relationships between CRMs and microRNAs have generated interest among biologists.ResultsWe constructed new combinatorial regulatory modules based on CRMs and miRNAs. By analyzing their effect on gene expression profiles, we found that genes targeted by both CRMs and miRNAs express in a significantly similar way. Furthermore, we constructed a regulatory network composed of CRMs, miRNAs, and their target genes. Investigating its structure, we found that the feed forward loop is a significant network motif, which plays an important role in gene regulation. In addition, we further analyzed the effect of miRNAs in embryonic cells, and we found that mir-154, as well as some other miRNAs, have significant co-regulation effect with CRMs in embryonic development.ConclusionsBased on the co-regulation of CRMs and miRNAs, we constructed a novel combinatorial regulatory network which was found to play an important role in gene regulation, particularly during embryonic development.

Combinatorial network of primary and secondary microRNA-driven regulatory mechanisms

Recent miRNA transfection experiments show strong evidence that miRNAs influence not only their target but also non-target genes; the precise mechanism of the extended regulatory effects of miRNAs remains to be elucidated. A hypothetical two-layer regulatory network in which transcription factors (TFs) function as important mediators of miRNA-initiated regulatory effects was envisioned, and a comprehensive strategy was developed to map such miRNA-centered regulatory cascades. Given gene expression profiles after miRNA-perturbation, along with putative miRNA–gene and TF–gene regulatory relationships, highly likely degraded targets were fetched by a non-parametric statistical test; miRNA-regulated TFs and their downstream targets were mined out through linear regression modeling. When applied to 53 expression datasets, this strategy discovered combinatorial regulatory networks centered around 19 miRNAs. A tumor-related regulatory network was diagrammed as an example, with the important tumor-related regulators TP53 and MYC playing hub connector roles. A web server is provided for query and analysis of all reported data in this article. Our results reinforce the growing awareness that non-coding RNAs may play key roles in the transcription regulatory network. Our strategy could be applied to reveal conditional regulatory pathways in many more cellular contexts.

Genome-wide analysis of ABA-responsive elements ABRE and CE3 reveals divergent patterns in Arabidopsis and rice

BackgroundIn plants, complex regulatory mechanisms are at the core of physiological and developmental processes. The phytohormone abscisic acid (ABA) is involved in the regulation of various such processes, including stomatal closure, seed and bud dormancy, and physiological responses to cold, drought and salinity stress. The underlying tissue or plant-wide control circuits often include combinatorial gene regulatory mechanisms and networks that we are only beginning to unravel with the help of new molecular tools. The increasing availability of genomic sequences and gene expression data enables us to dissect ABA regulatory mechanisms at the individual gene expression level. In this paper we used an in-silico-based approach directed towards genome-wide prediction and identification of specific features of ABA-responsive elements. In particular we analysed the genome-wide occurrence and positional arrangements of two well-described ABA-responsive cis-regulatory elements (CREs), ABRE and CE3, in thale cress (Arabidopsis thaliana) and rice (Oryza sativa).ResultsOur results show that Arabidopsis and rice use the ABA-responsive elements ABRE and CE3 distinctively. Earlier reports for various monocots have identified CE3 as a coupling element (CE) associated with ABRE. Surprisingly, we found that while ABRE is equally abundant in both species, CE3 is practically absent in Arabidopsis. ABRE-ABRE pairs are common in both genomes, suggesting that these can form functional ABA-responsive complexes (ABRCs) in Arabidopsis and rice. Furthermore, we detected distinct combinations, orientation patterns and DNA strand preferences of ABRE and CE3 motifs in rice gene promoters.ConclusionOur computational analyses revealed distinct recruitment patterns of ABA-responsive CREs in upstream sequences of Arabidopsis and rice. The apparent absence of CE3s in Arabidopsis suggests that another CE pairs with ABRE to establish a functional ABRC capable of interacting with transcription factors. Further studies will be needed to test whether the observed differences are extrapolatable to monocots and dicots in general, and to understand how they contribute to the fine-tuning of the hormonal response. The outcome of our investigation can now be used to direct future experimentation designed to further dissect the ABA-dependent regulatory networks.

An evolutionarily conserved palindrome in the Drosophila Gld promoter directs tissue-specific expression.

A conserved palindromic sequence (Gpal) in the promoter region of the Drosophila Gld directs expression of a heterologous reporter gene in transgenic flies to the anterior spiracular glands of third instar larvae and to the ejaculatory bulb of adult males. The Gld gene is normally expressed at high levels in the anterior spiracular glands but is not expressed in the ejaculatory bulb of Drosophila melanogaster. However, Gld promoters from other Drosophila species contain the Gpal element and express glucose dehydrogenase (GLD) in the adult male ejaculatory bulb. A gene fusion composed of the D. melanogaster Gld promoter and the lacZ gene is expressed in the anterior spiracular glands of transgenic larvae. Mutations of the Gpal sequence element in this gene fusion block expression of beta-galactosidase in the anterior spiracular gland. Together these experiments demonstrate that Gpal is necessary and sufficient for tissue-specific expression in the anterior spiracular glands. Based upon the tissue distribution and function of GLD, it is speculated that expression of GLD in the anterior spiracular glands represents the ancestral state and that GLD expression in other tissues arose as a fortuitous consequence of a shared combinatorial regulatory network.