A new suite of reporter vectors and a novel landing site survey system to study cis-regulatory elements in diverse insect species.

Comparative analyses between traditional model organisms, such as the fruit fly Drosophila melanogaster, and more recent model organisms, such as the red flour beetle Tribolium castaneum, have provided a wealth of insight into conserved and diverged aspects of gene regulation. While the study of trans-regulatory components is relatively straightforward, the study of cis-regulatory elements (CREs, or enhancers) remains challenging outside of Drosophila. A central component of this challenge has been finding a core promoter suitable for enhancer-reporter assays in diverse insect species. Previously, we demonstrated that a Drosophila Synthetic Core Promoter (DSCP) functions in a cross-species manner in Drosophila and Tribolium. Given the over 300 million years of divergence between the Diptera and Coleoptera, we reasoned that DSCP-based reporter constructs will be useful when studying cis-regulation in a variety of insect models across the holometabola and possibly beyond. To this end, we sought to create a suite of new DSCP-based reporter vectors, leveraging dual compatibility with piggyBac and PhiC31-integration, the 3xP3 universal eye marker, GATEWAY cloning, different colors of reporters and markers, as well as Gal4-UAS binary expression. While all constructs functioned properly with a Tc-nub enhancer in Drosophila, complications arose with tissue-specific Gal4-UAS binary expression in Tribolium. Nevertheless, the functionality of these constructs across multiple holometabolous orders suggests a high potential compatibility with a variety of other insects. In addition, we present the piggyLANDR (piggyBac-LoxP AttP Neutralizable Destination Reporter) platform for the establishment of proper PhiC31 landing sites free from position effects. As a proof-of-principle, we demonstrated the workflow for piggyLANDR in Drosophila. The potential utility of these tools ranges from molecular biology research to pest and disease-vector management, and will help advance the study of gene regulation beyond traditional insect models.

Improved methodology for fixation and preparation of Aedes aegypti embryos.

The yellow fever mosquito Aedes aegypti is a major disease vector and an increasingly popular emerging model research organism. We present here an improved protocol for the collection, fixation, and preparation of A. aegypti embryos for immunohistochemical and in situ hybridization studies. The processing of A. aegypti embryos for such studies is complicated by the inability to easily remove the vitelline membrane, which prevents the reagents needed for staining from reaching their targets, and which furthermore obscures visualization of the embryo since the membrane is highly sclerotized. Previously described protocols for removal of the vitelline membrane are very low throughput, limiting the capacity of work that can be accomplished in a reasonable timeframe. Our adapted protocol increases the throughput capacity of embryos by an individual user, with experienced users able to prepare an average of 100-150 embryos per hour. The protocol provides high-quality intact embryos that can be used for morphological, immunohistochemical, and in situ hybridization studies. The protocol has been successfully tested on embryos of ages ranging from 14h after egg laying (AEL) at 27°C through to 55h AEL. Critical to the success of the optimized protocol is the selection, fabrication, and description of the tools required. To this end, a video-demonstrated protocol has been placed at protocols.io to clarify the protocol and provide easy access and training to anyone interested in the preparation of A. aegypti embryos for biological studies.

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.

Mechanisms of transcriptional regulation in Anopheles gambiae revealed by allele specific expression.

Malaria control relies on insecticides targeting the mosquito vector, but this is increasingly compromised by insecticide resistance, which can be achieved by elevated expression of detoxifying enzymes that metabolize the insecticide. In diploid organisms, gene expression is regulated both in cis, by regulatory sequences on the same chromosome, and by trans acting factors, affecting both alleles equally. Differing levels of transcription can be caused by mutations in cis-regulatory modules (CRM), but few of these have been identified in mosquitoes. We crossed bendiocarb resistant and susceptible Anopheles gambiae strains to identify cis-regulated genes that might be responsible for the resistant phenotype using RNAseq, and cis-regulatory module sequences controlling gene expression in insecticide resistance relevant tissues were predicted using machine learning. We found 115 genes showing allele specific expression in hybrids of insecticide susceptible and resistant strains, suggesting cis regulation is an important mechanism of gene expression regulation in Anopheles gambiae. The genes showing allele specific expression included a higher proportion of Anopheles specific genes on average younger than genes those with balanced allelic expression.

Evaluation of metric and representation learning approaches: Effects of representations driven by relative distance on the performance.

Several deep neural network architectures have emerged recently for metric learning. We asked which architecture is the most effective in measuring the similarity or dissimilarity among images. To this end, we evaluated six networks on a standard image set. We evaluated variational autoencoders, Siamese networks, triplet networks, and variational auto-encoders combined with Siamese or triplet networks. These networks were compared to a baseline network consisting of multiple separable convolutional layers. Our study revealed the following: (i) the triplet architecture proved the most effective one due to learning a relative distance - not an absolute distance; (ii) combining auto-encoders with networks that learn metrics (e.g., Siamese or triplet networks) is unwarranted; and (iii) an architecture based on separable convolutional layers is a reasonable simple alternative to triplet networks. These results can potentially impact our field by encouraging architects to develop advanced networks that take advantage of separable convolution and relative distance.

A novel role for trithorax in the gene regulatory network for a rapidly evolving fruit fly pigmentation trait.

Animal traits develop through the expression and action of numerous regulatory and realizator genes that comprise a gene regulatory network (GRN). For each GRN, its underlying patterns of gene expression are controlled by cis-regulatory elements (CREs) that bind activating and repressing transcription factors. These interactions drive cell-type and developmental stage-specific transcriptional activation or repression. Most GRNs remain incompletely mapped, and a major barrier to this daunting task is CRE identification. Here, we used an in silico method to identify predicted CREs (pCREs) that comprise the GRN which governs sex-specific pigmentation of Drosophila melanogaster. Through in vivo assays, we demonstrate that many pCREs activate expression in the correct cell-type and developmental stage. We employed genome editing to demonstrate that two CREs control the pupal abdomen expression of trithorax, whose function is required for the dimorphic phenotype. Surprisingly, trithorax had no detectable effect on this GRN's key trans-regulators, but shapes the sex-specific expression of two realizator genes. Comparison of sequences orthologous to these CREs supports an evolutionary scenario where these trithorax CREs predated the origin of the dimorphic trait. Collectively, this study demonstrates how in silico approaches can shed novel insights on the GRN basis for a trait's development and evolution.

Reporter gene assays and chromatin-level assays define substantially non-overlapping sets of enhancer sequences.

BACKGROUND: Transcriptional enhancers are essential for gene regulation, but how these regulatory elements are best defined remains a significant unresolved question. Traditional definitions rely on activity-based criteria such as reporter gene assays, while more recently, biochemical assays based on chromatin-level phenomena such as chromatin accessibility, histone modifications, and localized RNA transcription have gained prominence. RESULTS: We examine here whether these two types of definitions, activity-based and chromatin-based, effectively identify the same sets of sequences. We find that, concerningly, the overlap between the two groups is strikingly limited. Few of the data sets we compared displayed statistically significant overlap, and even for those, the degree of overlap was typically small (below 40% of sequences). Moreover, a substantial batch effect was observed in which experiment set rather than experimental method was a primary driver of whether or not chromatin-defined enhancers showed a strong overlap with reporter gene-defined enhancers. CONCLUSIONS: Our results raise important questions as to the appropriateness of both old and new enhancer definitions, and suggest that new approaches are required to reconcile the poor agreement among existing methods for defining enhancers.

EnhancerTracker: Comparing cell-type-specific enhancer activity of DNA sequence triplets via an ensemble of deep convolutional neural networks.

Motivation: Transcriptional enhancers - unlike promoters - are unrestrained by distance or strand orientation with respect to their target genes, making their computational identification a challenge. Further, there are insufficient numbers of confirmed enhancers for many cell types, preventing robust training of machine-learning-based models for enhancer prediction for such cell types. Results: We present EnhancerTracker , a novel tool that leverages an ensemble of deep separable convolutional neural networks to identify cell-type-specific enhancers with the need of only two confirmed enhancers. EnhancerTracker is trained, validated, and tested on 52,789 putative enhancers obtained from the FANTOM5 Project and control sequences derived from the human genome. Unlike available tools, which accept one sequence at a time, the input to our tool is three sequences; the first two are enhancers active in the same cell type. EnhancerTracker outputs 1 if the third sequence is an enhancer active in the same cell type(s) where the first two enhancers are active. It outputs 0 otherwise. On a held-out set (15%), EnhancerTracker achieved an accuracy of 64%, a specificity of 93%, a recall of 35%, a precision of 84%, and an F1 score of 49%. Availability and implementation: https://github.com/BioinformaticsToolsmith/EnhancerTracker. Contact: hani.girgis@tamuk.edu.

REDfly: An Integrated Knowledgebase for Insect Regulatory Genomics.

We provide here an updated description of the REDfly (Regulatory Element Database for Fly) database of transcriptional regulatory elements, a unique resource that provides regulatory annotation for the genome of Drosophila and other insects. The genomic sequences regulating insect gene expression-transcriptional cis-regulatory modules (CRMs, e.g., "enhancers") and transcription factor binding sites (TFBSs)-are not currently curated by any other major database resources. However, knowledge of such sequences is important, as CRMs play critical roles with respect to disease as well as normal development, phenotypic variation, and evolution. Characterized CRMs also provide useful tools for both basic and applied research, including developing methods for insect control. REDfly, which is the most detailed existing platform for metazoan regulatory-element annotation, includes over 40,000 experimentally verified CRMs and TFBSs along with their DNA sequences, their associated genes, and the expression patterns they direct. Here, we briefly describe REDfly's contents and data model, with an emphasis on the new features implemented since 2020. We then provide an illustrated walk-through of several common REDfly search use cases.

Common Themes and Future Challenges in Understanding Gene Regulatory Network Evolution.

A major driving force behind the evolution of species-specific traits and novel structures is alterations in gene regulatory networks (GRNs). Comprehending evolution therefore requires an understanding of the nature of changes in GRN structure and the responsible mechanisms. Here, we review two insect pigmentation GRNs in order to examine common themes in GRN evolution and to reveal some of the challenges associated with investigating changes in GRNs across different evolutionary distances at the molecular level. The pigmentation GRN in Drosophila melanogaster and other drosophilids is a well-defined network for which studies from closely related species illuminate the different ways co-option of regulators can occur. The pigmentation GRN for butterflies of the Heliconius species group is less fully detailed but it is emerging as a useful model for exploring important questions about redundancy and modularity in cis-regulatory systems. Both GRNs serve to highlight the ways in which redeployment of trans-acting factors can lead to GRN rewiring and network co-option. To gain insight into GRN evolution, we discuss the importance of defining GRN architecture at multiple levels both within and between species and of utilizing a range of complementary approaches.

Annotating the Insect Regulatory Genome.

An ever-growing number of insect genomes is being sequenced across the evolutionary spectrum. Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing. Driven by developments in sequencing technologies and in both empirical and computational discovery strategies, the past few decades have witnessed dramatic progress in our ability to identify cis-regulatory modules (CRMs), sequences such as enhancers that play a major role in regulating transcription. Nevertheless, providing a timely and comprehensive regulatory annotation of newly sequenced insect genomes is an ongoing challenge. We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw. These resources can be paired together in a powerful combination to facilitate insect regulatory annotation over a broad range of species, with an accuracy equal to or better than that of other state-of-the-art methods.

PeakMatcher facilitates updated Aedes aegypti embryonic cis-regulatory element map.

BACKGROUND: The Aedes aegypti mosquito is a threat to human health across the globe. The A. aegypti genome was recently re-sequenced and re-assembled. Due to a combination of long-read PacBio and Hi-C sequencing, the AaegL5 assembly is chromosome complete and significantly improves the assembly in key areas such as the M/m sex-determining locus. Release of the updated genome assembly has precipitated the need to reprocess historical functional genomic data sets, including cis-regulatory element (CRE) maps that had previously been generated for A. aegypti. RESULTS: We re-processed and re-analyzed the A. aegypti whole embryo FAIRE seq data to create an updated embryonic CRE map for the AaegL5 genome. We validated that the new CRE map recapitulates key features of the original AaegL3 CRE map. Further, we built on the improved assembly in the M/m locus to analyze overlaps of open chromatin regions with genes. To support the validation, we created a new method (PeakMatcher) for matching peaks from the same experimental data set across genome assemblies. CONCLUSION: Use of PeakMatcher software, which is available publicly under an open-source license, facilitated the release of an updated and validated CRE map, which is available through the NIH GEO. These findings demonstrate that PeakMatcher software will be a useful resource for validation and transferring of previous annotations to updated genome assemblies.

Behavior-related gene regulatory networks: A new level of organization in the brain.

Neuronal networks are the standard heuristic model today for describing brain activity associated with animal behavior. Recent studies have revealed an extensive role for a completely distinct layer of networked activities in the brain-the gene regulatory network (GRN)-that orchestrates expression levels of hundreds to thousands of genes in a behavior-related manner. We examine emerging insights into the relationships between these two types of networks and discuss their interplay in spatial as well as temporal dimensions, across multiple scales of organization. We discuss properties expected of behavior-related GRNs by drawing inspiration from the rich literature on GRNs related to animal development, comparing and contrasting these two broad classes of GRNs as they relate to their respective phenotypic manifestations. Developmental GRNs also represent a third layer of network biology, playing out over a third timescale, which is believed to play a crucial mediatory role between neuronal networks and behavioral GRNs. We end with a special emphasis on social behavior, discuss whether unique GRN organization and cis-regulatory architecture underlies this special class of behavior, and review literature that suggests an affirmative answer.

How to study enhancers in non-traditional insect models.

Transcriptional enhancers are central to the function and evolution of genes and gene regulation. At the organismal level, enhancers play a crucial role in coordinating tissue- and context-dependent gene expression. At the population level, changes in enhancers are thought to be a major driving force that facilitates evolution of diverse traits. An amazing array of diverse traits seen in insect morphology, physiology and behavior has been the subject of research for centuries. Although enhancer studies in insects outside of Drosophila have been limited, recent advances in functional genomic approaches have begun to make such studies possible in an increasing selection of insect species. Here, instead of comprehensively reviewing currently available technologies for enhancer studies in established model organisms such as Drosophila, we focus on a subset of computational and experimental approaches that are likely applicable to non-Drosophila insects, and discuss the pros and cons of each approach. We discuss the importance of validating enhancer function and evaluate several possible validation methods, such as reporter assays and genome editing. Key points and potential pitfalls when establishing a reporter assay system in non-traditional insect models are also discussed. We close with a discussion of how to advance enhancer studies in insects, both by improving computational approaches and by expanding the genetic toolbox in various insects. Through these discussions, this Review provides a conceptual framework for studying the function and evolution of enhancers in non-traditional insect models.

Silencers, Enhancers, and the Multifunctional Regulatory Genome.

Negative regulation of gene expression by transcriptional silencers has been difficult to study due to limited defined examples. A new study by Gisselbrecht et al. has dramatically increased the number of identified silencers and reveals that they are bifunctional regulatory sequences that also act as gene expression-promoting enhancers.

Computational enhancer prediction: evaluation and improvements.

BACKGROUND: Identifying transcriptional enhancers and other cis-regulatory modules (CRMs) is an important goal of post-sequencing genome annotation. Computational approaches provide a useful complement to empirical methods for CRM discovery, but it is critical that we develop effective means to evaluate their performance in terms of estimating their sensitivity and specificity. RESULTS: We introduce here pCRMeval, a pipeline for in silico evaluation of any enhancer prediction tools that are flexible enough to be applied to the Drosophila melanogaster genome. pCRMeval compares the result of predictions with the extensive existing knowledge of experimentally-validated Drosophila CRMs in order to estimate the precision and relative sensitivity of the prediction method. In the case of supervised prediction methods-when training data composed of validated CRMs are used-pCRMeval can also assess the sensitivity of specific training sets. We demonstrate the utility of pCRMeval through evaluation of our SCRMshaw CRM prediction method and training data. By measuring the impact of different parameters on SCRMshaw performance, as assessed by pCRMeval, we develop a more robust version of SCRMshaw, SCRMshaw_HD, that improves the number of predictions while maintaining sensitivity and specificity. Our analysis also demonstrates that SCRMshaw_HD, when applied to increasingly less well-assembled genomes, maintains its strong predictive power with only a minor drop-off in performance. CONCLUSION: Our pCRMeval pipeline provides a general framework for evaluation that can be applied to any CRM prediction method, particularly a supervised method. While we make use of it here primarily to test and improve a particular method for CRM prediction, SCRMshaw, pCRMeval should provide a valuable platform to the research community not only for evaluating individual methods, but also for comparing between competing methods.

Distinct roles and requirements for Ras pathway signaling in visceral versus somatic muscle founder specification.

Pleiotropic signaling pathways must somehow engender specific cellular responses. In the Drosophila mesoderm, Ras pathway signaling specifies muscle founder cells from among the broader population of myoblasts. For somatic muscles, this is an inductive process mediated by the ETS-domain downstream Ras effectors Pointed and Aop (Yan). We demonstrate here that for the circular visceral muscles, despite superficial similarities, a significantly different specification mechanism is at work. Not only is visceral founder cell specification not dependent on Pointed or Aop, but Ras pathway signaling in its entirety can be bypassed. Our results show that de-repression, not activation, is the predominant role of Ras signaling in the visceral mesoderm and that, accordingly, Ras signaling is not required in the absence of repression. The key repressor acts downstream of the transcription factor Lame duck and is likely a member of the ETS transcription factor family. Our findings fit with a growing body of data that point to a complex interplay between the Ras pathway, ETS transcription factors, and enhancer binding as a crucial mechanism for determining unique responses to Ras signaling.

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.

CRM Discovery Beyond Model Insects.

Although the number of sequenced insect genomes numbers in the hundreds, little is known about gene regulatory sequences in any species other than the well-studied Drosophila melanogaster. We provide here a detailed protocol for using SCRMshaw, a computational method for predicting cis-regulatory modules (CRMs, also "enhancers") in sequenced insect genomes. SCRMshaw is effective for CRM discovery throughout the range of holometabolous insects and potentially in even more diverged species, with true-positive prediction rates of 75% or better. Minimal requirements for using SCRMshaw are a genome sequence and training data in the form of known Drosophila CRMs; a comprehensive set of the latter can be obtained from the SCRMshaw download site. For basic applications, a user with only modest computational know-how can run SCRMshaw on a desktop computer. SCRMshaw can be run with a single, narrow set of training data to predict CRMs regulating a specific pattern of gene expression, or with multiple sets of training data covering a broad range of CRM activities to provide an initial rough regulatory annotation of a complete, newly-sequenced genome.