Genomic approaches towards finding cis-regulatory modules in animals

Gapped-kmer sequence modeling robustly identifies regulatory vocabularies and distal enhancers conserved between evolutionarily distant mammals

Gene regulatory elements drive complex biological phenomena and their mutations are associated with common human diseases. The impacts of human regulatory variants are often tested using model organisms such as mice. However, mapping human enhancers to conserved elements in mice remains a challenge, due to both rapid enhancer evolution and limitations of current computational methods. We analyze distal enhancers across 45 matched human/mouse cell/tissue pairs from a comprehensive dataset of DNase-seq experiments, and show that while cell-specific regulatory vocabulary is conserved, enhancers evolve more rapidly than promoters and CTCF binding sites. Enhancer conservation rates vary across cell types, in part explainable by tissue specific transposable element activity. We present an improved genome alignment algorithm using gapped-kmer features, called gkm-align, and make genome wide predictions for 1,401,803 orthologous regulatory elements. We show that gkm-align discovers 23,660 novel human/mouse conserved enhancers missed by previous algorithms, with strong evidence of conserved functional activity.

DMLS: an automated pipeline to extract the Drosophila modular transcription regulators and targets from massive literature articles

Transcription regulation in multicellular species is mediated by modular transcription factor (TF) binding site combinations termed cis-regulatory modules (CRMs). Such CRM-mediated transcription regulation determines the gene expression patterns during development. Biologists frequently investigate CRM transcription regulation on gene expressions. However, the knowledge of the target genes and regulatory TFs participating in the CRMs under study is mostly fragmentary throughout the literature. Researchers need to afford tremendous human resources to fully surf through the articles deposited in biomedical literature databases in order to obtain the information. Although several novel text-mining systems are now available for literature triaging, these tools do not specifically focus on CRM-related literature prescreening, failing to correctly extract the information of the CRM target genes and regulatory TFs from the literature. For this reason, we constructed a supportive auto-literature prescreener called Drosophila Modular transcription-regulation Literature Screener (DMLS) that achieves the following: (i) prescreens articles describing experiments on modular transcription regulation, (ii) identifies the described target genes and TFs of the CRMs under study for each modular transcription-regulation-describing article and (iii) features an automated and extendable pipeline to perform the task. We demonstrated that the final performance of DMLS in extracting the described target gene and regulatory TF lists of CRMs under study for given articles achieved test macro area under the ROC curve (auROC) = 89.7% and area under the precision-recall curve (auPRC) = 77.6%, outperforming the intuitive gene name-occurrence-counting method by at least 19.9% in auROC and 30.5% in auPRC. The web service and the command line versions of DMLS are available at https://cobis.bme.ncku.edu.tw/DMLS/  and  https://github.com/cobisLab/DMLS/, respectively. Database Tool URL: https://cobis.bme.ncku.edu.tw/DMLS/.

Systematic decoding of cis gene regulation defines context-dependent control of the multi-gene costimulatory receptor locus in human T cells

Cis-regulatory elements (CREs) interact with trans regulators to orchestrate gene expression, but how transcriptional regulation is coordinated in multi-gene loci has not been experimentally defined. We sought to characterize the CREs controlling dynamic expression of the adjacent costimulatory genes CD28, CTLA4 and ICOS, encoding regulators of T cell-mediated immunity. Tiling CRISPR interference (CRISPRi) screens in primary human T cells, both conventional and regulatory subsets, uncovered gene-, cell subset- and stimulation-specific CREs. Integration with CRISPR knockout screens and assay for transposase-accessible chromatin with sequencing (ATAC-seq) profiling identified trans regulators influencing chromatin states at specific CRISPRi-responsive elements to control costimulatory gene expression. We then discovered a critical CCCTC-binding factor (CTCF) boundary that reinforces CRE interaction with CTLA4 while also preventing promiscuous activation of CD28. By systematically mapping CREs and associated trans regulators directly in primary human T cell subsets, this work overcomes longstanding experimental limitations to decode context-dependent gene regulatory programs in a complex, multi-gene locus critical to immune homeostasis.

Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants

Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.

Towards a comprehensive regulatory map of Mammalian Genomes

Genome mapping studies have generated a nearly complete collection of genes for the human genome, but we still lack an equivalently vetted inventory of human regulatory sequences. Cis-regulatory modules (CRMs) play important roles in controlling when, where, and how much a gene is expressed. We developed a training data-free CRM-prediction algorithm, the Mammalian Regulatory MOdule Detector (MrMOD) for accurate CRM prediction in mammalian genomes. MrMOD provides genome position-fixed CRM models similar to the fixed gene models for the mouse and human genomes using only genomic sequences as the inputs with one adjustable parameter - the significance p-value. Importantly, MrMOD predicts a comprehensive set of high-resolution CRMs in the mouse and human genomes including all types of regulatory modules not limited to any tissue, cell type, developmental stage, or condition. We computationally validated MrMOD predictions used a compendium of 21 orthogonal experimental data sets including thousands of experimentally defined CRMs and millions of putative regulatory elements derived from hundreds of different tissues, cell types, and stimulus conditions obtained from multiple databases. In ovo transgenic reporter assay demonstrates the power of our prediction in guiding experimental design. We analyzed CRMs located in the chromosome 17 using unsupervised machine learning and identified groups of CRMs with multiple lines of evidence supporting their functionality, linking CRMs with upstream binding transcription factors and downstream target genes. Our work provides a comprehensive base pair resolution annotation of the functional regulatory elements and non-functional regions in the mammalian genomes.

Machine-guided design of synthetic cell type-specific cis-regulatory elements

Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose in vivo across vertebrates.

Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening

Investigating how chromatin organization determines cell-type-specific gene expression remains challenging. Experimental methods for measuring three-dimensional chromatin organization, such as Hi-C, are costly and have technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. We present C.Origami, a multimodal deep neural network that performs de novo prediction of cell-type-specific chromatin organization using DNA sequence and two cell-type-specific genomic features-CTCF binding and chromatin accessibility. C.Origami enables in silico experiments to examine the impact of genetic changes on chromatin interactions. We further developed an in silico genetic screening approach to assess how individual DNA elements may contribute to chromatin organization and to identify putative cell-type-specific trans-acting regulators that collectively determine chromatin architecture. Applying this approach to leukemia cells and normal T cells, we demonstrate that cell-type-specific in silico genetic screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulation circuits in both normal and disease-related biological systems.

CFA: An explainable deep learning model for annotating the transcriptional roles of cis-regulatory modules based on epigenetic codes

Metazoa gene expression is controlled by modular DNA segments called cis-regulatory modules (CRMs). CRMs can convey promoter/enhancer/insulator roles, generating additional regulation layers in transcription. Experiments for understanding CRM roles are low-throughput and costly. Large-scale CRM function investigation still depends on computational methods. However, existing in silico tools only recognize enhancers or promoters exclusively, thus accumulating errors when considering CRM promoter/enhancer/insulator roles altogether. Currently, no algorithm can concurrently consider these CRM roles. In this research, we developed the CRM Function Annotator (CFA) model. CFA provides complete CRM transcriptional role labeling based on epigenetic profiling interpretation. We demonstrated that CFA achieves high performance (test macro auROC/auPRC = 94.1%/90.3%) and outperforms existing tools in promoter/enhancer/insulator identification. CFA is also inspected to recognize explainable epigenetic codes consistent with previous findings when labeling CRM roles. By considering the higher-order combinations of the epigenetic codes, CFA significantly reduces false-positive rates in CRM transcriptional role annotation. CFA is available at https://github.com/cobisLab/CFA/.

Limited conservation in cross-species comparison of GLK transcription factor binding suggested wide-spread cistrome divergence

Non-coding cis-regulatory variants in animal genomes are an important driving force in the evolution of transcription regulation and phenotype diversity. However, cistrome dynamics in plants remain largely underexplored. Here, we compare the binding of GOLDEN2-LIKE (GLK) transcription factors in tomato, tobacco, Arabidopsis, maize and rice. Although the function of GLKs is conserved, most of their binding sites are species-specific. Conserved binding sites are often found near photosynthetic genes dependent on GLK for expression, but sites near non-differentially expressed genes in the glk mutant are nevertheless under purifying selection. The binding sites' regulatory potential can be predicted by machine learning model using quantitative genome features and TF co-binding information. Our study show that genome cis-variation caused wide-spread TF binding divergence, and most of the TF binding sites are genetically redundant. This poses a major challenge for interpreting the effect of individual sites and highlights the importance of quantitatively measuring TF occupancy.

Detecting clusters of transcription factors based on a nonhomogeneous poisson process model

BACKGROUND

Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation still lags behind.

RESULTS

In order to address cooperative TF binding and detect TF clusters with coordinative functions, we have developed novel computational methods based on clustering the sample paths of nonhomogeneous Poisson processes. Simulation studies demonstrated the capability of these methods to accurately detect TF clusters and uncover the hierarchy of TF interactions. A further application to the multiple-TF ChIP-seq data in mouse embryonic stem cells (ESCs) showed that our methods identified the cluster of core ESC regulators reported in the literature and provided new insights on functional implications of transcrisptional regulatory modules.

CONCLUSIONS

Effective analytical tools are essential for studying protein-DNA relations. Information derived from this research will help us better understand the orchestration of transcription factors in gene regulation processes.

Exploiting Genomic Features to Improve the Prediction of Transcription Factor-Binding Sites in Plants

The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performance of pattern matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows decision rule models to be learnt using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA sequence conservation, the chromatin state and various CRE footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TFs controlling metabolite biosynthesis at the organ level in Arabidopsis. We developed a tool-Wimtrap-to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports an R Shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa and Zea mays.

Structural and developmental dynamics of Matrix associated regions in Drosophila melanogaster genome

Background

Eukaryotic genome is compartmentalized into structural and functional domains. One of the concepts of higher order organization of chromatin posits that the DNA is organized in constrained loops that behave as independent functional domains. Nuclear Matrix (NuMat), a ribo-proteinaceous nucleoskeleton, provides the structural basis for this organization. DNA sequences located at base of the loops are known as the Matrix Attachment Regions (MARs). NuMat relates to multiple nuclear processes and is partly cell type specific in composition. It is a biochemically defined structure and several protocols have been used to isolate the NuMat where some of the steps have been critically evaluated. These sequences play an important role in genomic organization it is imperative to know their dynamics during development and differentiation.

Results

Here we look into the dynamics of MARs when the preparation process is varied and during embryonic development of D. melanogaster. A subset of MARs termed as “Core-MARs” present abundantly in pericentromeric heterochromatin, are constant unalterable anchor points as they associate with NuMat through embryonic development and are independent of the isolation procedure. Euchromatic MARs are dynamic and reflect the transcriptomic profile of the cell. New MARs are generated by nuclear stabilization, and during development, mostly at paused RNA polymerase II promoters. Paused Pol II MARs depend on RNA transcripts for NuMat association.

Conclusions

Our data reveals the role of MARs in functionally dynamic nucleus and contributes to the current understanding of nuclear architecture in genomic context.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08944-4.

Genome-wide identification and analysis of the cytokinin oxidase/dehydrogenase (ckx) gene family in finger millet (Eleusine coracana)

Cytokinin dehydrogenase/oxidase (CKX) enzymes play a key role in regulating cytokinin (CK) levels in plants by degrading the excess of this phytohormone. CKX genes have proven an attractive target for genetic engineering, as their silencing boosts cytokinin accumulation in various tissues, thereby contributing to a rapid increase in biomass and overall plant productivity. We previously reported a similar effect in finger millet (Eleusine coracana) somaclonal lines, caused by downregulation of EcCKX1 and EcCKX2. However, the CKX gene family has numerous representatives, especially in allopolyploid crop species, such as E. coracana. To date, the entire CKX gene family of E. coracana and its related species has not been characterized. We offer here, for the first time, a comprehensive genome-wide identification and analysis of a panel of CKX genes in finger millet. The functional genes identified in the E. coracana genome are compared with the previously-identified genes, EcCKX1 and EcCKX2. Exon-intron structural analysis and motif analysis of FAD- and CK-binding domains are performed. The phylogeny of the EcCKX genes suggests that CKX genes are divided into several distinct groups, corresponding to certain isotypes. Finally, the phenotypic effect of EcCKX1 and EcCKX2 in partially silencing the SE7 somaclonal line is investigated, showing that lines deficient in CKX-expression demonstrate increased grain yield and greater bushiness, enhanced biomass accumulation, and a shorter vegetation cycle.

Single-Cell Gene Expression Analysis Revealed Immune Cell Signatures of Delta COVID-19

The coronavirus disease 2019 (COVID-19) is accompanied by a cytokine storm with the release of many proinflammatory factors and development of respiratory syndrome. Several SARS-CoV-2 lineages have been identified, and the Delta variant (B.1.617), linked with high mortality risk, has become dominant in many countries. Understanding the immune responses associated with COVID-19 lineages may therefore aid the development of therapeutic and diagnostic strategies. Multiple single-cell gene expression studies revealed innate and adaptive immunological factors and pathways correlated with COVID-19 severity. Additional investigations covering host–pathogen response characteristics for infection caused by different lineages are required. Here, we performed single-cell transcriptome profiling of blood mononuclear cells from the individuals with different severity of the COVID-19 and virus lineages to uncover variant specific molecular factors associated with immunity. We identified significant changes in lymphoid and myeloid cells. Our study highlights that an abundant population of monocytes with specific gene expression signatures accompanies Delta lineage of SARS-CoV-2 and contributes to COVID-19 pathogenesis inferring immune components for targeted therapy.

Gnocis: An integrated system for interactive and reproducible analysis and modelling of cis-regulatory elements in Python 3

Gene expression is regulated through cis-regulatory elements (CREs), among which are promoters, enhancers, Polycomb/Trithorax Response Elements (PREs), silencers and insulators. Computational prediction of CREs can be achieved using a variety of statistical and machine learning methods combined with different feature space formulations. Although Python packages for DNA sequence feature sets and for machine learning are available, no existing package facilitates the combination of DNA sequence feature sets with machine learning methods for the genome-wide prediction of candidate CREs. We here present Gnocis, a Python package that streamlines the analysis and the modelling of CRE sequences by providing extensible APIs and implementing the glue required for combining feature sets and models for genome-wide prediction. Gnocis implements a variety of base feature sets, including motif pair occurrence frequencies and the k-spectrum mismatch kernel. It integrates with Scikit-learn and TensorFlow for state-of-the-art machine learning. Gnocis additionally implements a broad suite of tools for the handling and preparation of sequence, region and curve data, which can be useful for general DNA bioinformatics in Python. We also present Deep-MOCCA, a neural network architecture inspired by SVM-MOCCA that achieves moderate to high generalization without prior motif knowledge. To demonstrate the use of Gnocis, we applied multiple machine learning methods to the modelling of D. melanogaster PREs, including a Convolutional Neural Network (CNN), making this the first study to model PREs with CNNs. The models are readily adapted to new CRE modelling problems and to other organisms. In order to produce a high-performance, compiled package for Python 3, we implemented Gnocis in Cython. Gnocis can be installed using the PyPI package manager by running ‘pip install gnocis’. The source code is available on GitHub, at https://github.com/bjornbredesen/gnocis.

Mitogen-activating protein kinase kinase kinase kinase-3, inhibited by Astragaloside IV through H3 lysine 4 monomethylation, promotes the progression of diabetic nephropathy by inducing apoptosis

Astragaloside IV (AS-IV) is a bioactive saponin extracted from the Astragalus root and has been reported to exert a protective effect on diabetic nephropathy (DN). However, the underlying mechanism remains unclear. Herein, we found that AS-IV treatment alleviated DN symptoms in DN mice accompanied by reduced metabolic parameters (body weight, urine microalbumin and creatinine, creatinine clearance, and serum urea nitrogen and creatinine), pathological changes, and apoptosis. Epigenetic histone modifications are closely related to diabetes and its complications, including H3 lysine 4 monomethylation (H3K4me1, a promoter of gene transcription). A ChIP-seq assay was conducted to identify the genes regulated by H3K4me1 in DN mice after AS-IV treatment and followed by a Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The results showed that there were 16 common genes targeted by H3K4me1 in normal and AS-IV-treated DN mice, 1148 genes were targeted by H3K4me1 only in DN mice. From the 1148 genes, we screened mitogen-activating protein kinase kinase kinase kinase-3 (MAP4K3) for the verification of gene expression and functional study. The results showed that MAP4K3 was significantly increased in DN mice and high glucose (HG)-treated NRK-52E cells, which was reversed by AS-IV. MAP4K3 silencing reduced the apoptosis of NRK-52E cells under HG condition, as evidenced by decreased cleaved caspase 3 and Bax (pro-apoptotic factors), and increased Bcl-2 and Bcl-xl (anti-apoptotic factors). Collectively, AS-IV may downregulate MAP4K3 expression by regulating H3K4me1 binding and further reducing apoptosis, which may be one of the potential mechanisms that AS-IV plays a protective effect on DN.

Engineering of the CMV promoter for controlled expression of recombinant genes in HEK293 cells

Expression of recombinant genes in HEK293 cells is frequently utilized for production of recombinant proteins and viral vectors. These systems frequently employ the cytomegalovirus (CMV) promoter to drive recombinant gene transcription. However, the mechanistic basis of CMV-mediated transcriptional activation in HEK293 cells is unknown and consequently there are no strategies to engineer CMV for controlled expression of recombinant genes. Extensive bioinformatic analyses of transcription factor regulatory elements (TFREs) within the human CMV sequence and transcription factor mRNAs within the HEK293 transcriptome revealed 80 possible regulatory interactions. Through in vitro functional testing using reporter constructs harboring discrete TFREs or CMV deletion variants we identified key TFRE components and clusters of TFREs (cis-regulatory modules) within the CMV sequence. Our data reveal that CMV activity in HEK293 cells is a function of the promoters various constituent TFREs including AhR:ARNT, CREB, E4F, Sp1, ZBED1, JunB, c-Rel, and NF-κB. We also identified critical Sp1-dependent upstream activator elements near the transcriptional start site that were required for efficient transcription and YY1 and RBP-Jκ binding sites that mediate transrepression. Our study shows for the first time that novel, compact CMV-derived promoters can be engineered that exhibit up to 50% higher transcriptional efficiency (activity per unit DNA sequence) or 14% increase in total activity compared to the wild-type counterpart.

Notch-dependent DNA cis-regulatory elements and their dose-dependent control of C. elegans stem cell self-renewal

A long-standing biological question is how DNA cis-regulatory elements shape transcriptional patterns during metazoan development. Reporter constructs, cell culture assays and computational modeling have made major contributions to answering this question, but analysis of elements in their natural context is an important complement. Here, we mutate Notch-dependent LAG-1 binding sites (LBSs) in the endogenous Caenorhabditis elegans sygl-1 gene, which encodes a key stem cell regulator, and analyze the consequences on sygl-1 expression (nascent transcripts, mRNA, protein) and stem cell maintenance. Mutation of one LBS in a three-element cluster approximately halved both expression and stem cell pool size, whereas mutation of two LBSs essentially abolished them. Heterozygous LBS mutant clusters provided intermediate values. Our results lead to two major conclusions. First, both LBS number and configuration impact cluster activity: LBSs act additively in trans and synergistically in cis. Second, the SYGL-1 gradient promotes self-renewal above its functional threshold and triggers differentiation below the threshold. Our approach of coupling CRISPR/Cas9 LBS mutations with effects on both molecular and biological readouts establishes a powerful model for in vivo analyses of DNA cis-regulatory elements.

Summary: Notch-dependent DNA cis-regulatory elements work together in their developmental context in C. elegans to shape a transcriptional gradient, control stem cell pool size, and govern differentiation onset.

Functional annotation of breast cancer risk loci: current progress and future directions

Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.

regCNN: identifying Drosophila genome-wide cis-regulatory modules via integrating the local patterns in epigenetic marks and transcription factor binding motifs

Transcription regulation in metazoa is controlled by the binding events of transcription factors (TFs) or regulatory proteins on specific modular DNA regulatory sequences called cis-regulatory modules (CRMs). Understanding the distributions of CRMs on a genomic scale is essential for constructing the metazoan transcriptional regulatory networks that help diagnose genetic disorders. While traditional reporter-assay CRM identification approaches can provide an in-depth understanding of functions of some CRM, these methods are usually cost-inefficient and low-throughput. It is generally believed that by integrating diverse genomic data, reliable CRM predictions can be made. Hence, researchers often first resort to computational algorithms for genome-wide CRM screening before specific experiments. However, current existing in silico methods for searching potential CRMs were restricted by low sensitivity, poor prediction accuracy, or high computation time from TFBS composition combinatorial complexity. To overcome these obstacles, we designed a novel CRM identification pipeline called regCNN by considering the base-by-base local patterns in TF binding motifs and epigenetic profiles. On the test set, regCNN shows an accuracy/auROC of 84.5%/92.5% in CRM identification. And by further considering local patterns in epigenetic profiles and TF binding motifs, it can accomplish 4.7% (92.5%–87.8%) improvement in the auROC value over the average value-based pure multi-layer perceptron model. We also demonstrated that regCNN outperforms all currently available tools by at least 11.3% in auROC values. Finally, regCNN is verified to be robust against its resizing window hyperparameter in dealing with the variable lengths of CRMs. The model of regCNN can be downloaded athttp://cobisHSS0.im.nuk.edu.tw/regCNN/.

YTLR: Extracting yeast transcription factor-gene associations from the literature using automated literature readers

Cells adapt to environmental stresses mainly via transcription reprogramming. Correct transcription control is mediated by the interactions between transcription factors (TF) and their target genes. These TF-gene associations can be probed by chromatin immunoprecipitation techniques and knockout experiments, revealing TF binding (TFB) and regulatory (TFR) evidence, respectively. Nevertheless, most evidence is still fragmentary in the literature and requires tremendous human resources to curate. We developed the first pipeline called YTLR (Yeast Transcription-regulation Literature Reader) to automate TF-gene relation extraction from the literature. YTLR first identifies articles with TFB and TFR information. Then TF-gene binding pairs are extracted from the TFB articles, and TF-gene regulatory associations are recognized from the TFR papers. On gathered test sets, YTLR achieves an AUC value of 98.8% in identifying articles with TFB evidence and AUC = 83.4% in extracting the detailed TF-gene binding pairs. And similarly, YTLR also obtains an AUC value of 98.2% in identifying TFR articles and AUC = 80.4% in extracting the detailed TF-gene regulatory associations. Furthermore, YTLR outperforms previous methods in both tasks. To facilitate researchers in extracting TF-gene transcriptional relations from large-scale queried articles, an automated and easy-to-use software tool based on the YTLR pipeline is constructed. In summary, YTLR aims to provide easier literature pre-screening for curators and help researchers gather yeast TF-gene transcriptional relation conclusions from articles in a high-throughput fashion. The YTLR pipeline software tool can be downloaded at https://github.com/cobisLab/YTLR/.

Aldehyde dehydrogenase 3I1 gene is recruited in conferring multiple abiotic stress tolerance in plants

Plant growth and productivity is restricted by a multitude of abiotic stresses. These stresses negatively affect physiological and metabolic pathways, leading to the production of many harmful substances like ROS, lipid peroxides and aldehydes. This study was conducted to investigate the role of Arabidopsis ALDH3I1 gene in multiple abiotic stress tolerance. Transgenic tobacco plants were generated that overexpress the ALDH3I1 gene driven by the CaMV35S promoter and evaluated under different abiotic stresses, namely salt, drought, cold and oxidative stress. Tolerance to stress was evaluated based on responses of various growth and physiological traits under stress condition. Transgenic plants displayed elevated ALDH3I1 transcript levels compared to WT plants. The constitutive ectopic expression of ALDH3I1 conferred increased tolerance to salt, drought, cold and oxidative stresses in transgenic plants, along with improved plant growth. Transgenic plants overexpressing ALDH3I1 had higher chlorophyll content, photosynthesis rate and proline, and less accumulation of ROS and malondialdehyde compared to the WT, which contributed to stress tolerance in transgenic plants. Our results further revealed that ALDH3I1 had a positive effect on CO₂ assimilation rate in plants under abiotic stress conditions. Overall, this study revealed that ALDH3I1 positively regulates abiotic stress tolerance in plants, and has future implications in producing transgenic cereal and horticultural plants tolerant to abiotic stresses.

Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin

Context-specific activities of transcription regulators (TRs) in the nucleus modulate spatiotemporal gene expression precisely. Using the largest ChIP-seq data and chromatin loops in the human K562 cell line, we initially interrogated TR cooperation in 3D chromatin via a graphical model and revealed many known and novel TRs manipulating context-specific pathways. To explore TR cooperation across broad tissue/cell types, we systematically leveraged large-scale open chromatin profiles, computational footprinting, and high-resolution chromatin interactions to investigate tissue/cell type-specific TR cooperation. We first delineated a landscape of TR cooperation across 40 human tissue/cell types. Network modularity analyses uncovered the commonality and specificity of TR cooperation in different conditions. We also demonstrated that TR cooperation information can better interpret the disease-causal variants identified by genome-wide association studies and recapitulate cell states during neural development. Our study characterizes shared and unique patterns of TR cooperation associated with the cell type specificity of gene regulation in 3D chromatin.

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Computational inference of transcriptional regulator (TR) cooperation in 3D chromatin

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A landscape of 3D TR cooperation across 40 human tissue/cell types

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TR cooperation can better interpret the disease-causal variants identified by GWAS

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Cooperation of certain TRs shapes context-specific gene regulation in cell development

Labile sex chromosomes in the Australian freshwater fish family Percichthyidae

Sex-specific ecology has management implications, but rapid sex-chromosome turnover in fishes hinders sex-marker development for monomorphic species. We used annotated genomes and reduced-representation sequencing data for two Australian percichthyids, Macquarie perch Macquaria australasica and golden perch M. ambigua, and whole genome resequencing for 50 Macquarie perch of each sex, to identify sex-linked loci and develop an affordable sexing assay. In silico pool-seq tests of 1,492,004 Macquarie perch SNPs revealed that a 275-kb scaffold was enriched for gametologous loci. Within this scaffold, 22 loci were sex-linked in a predominantly XY system, with females being homozygous for the X-linked allele at all 22, and males having the Y-linked allele at >7. Seven XY-gametologous loci (all males, but no females, are heterozygous or homozygous for the male-specific allele) were within a 146-bp region. A PCR-RFLP sexing assay targeting one Y-linked SNP, tested in 66 known-sex Macquarie perch and two of each sex of three confamilial species, plus amplicon sequencing of 400 bp encompassing the 146-bp region, revealed that the few sex-linked positions differ between species and between Macquarie perch populations. This indicates sex-chromosome lability in Percichthyidae, supported by nonhomologous scaffolds containing sex-linked loci for Macquarie- and golden perches. The present resources facilitate genomic research in Percichthyidae, including formulation of hypotheses about candidate genes of interest such as transcription factor SOX1b that occurs in the 275-kb scaffold ~38 kb downstream of the 146-bp region containing seven XY-gametologous loci. Sex-linked markers will be useful for determining genetic sex in some populations and studying sex chromosome turnover.

Coordinated Gene Expression and Chromatin Regulation during Hydra Head Regeneration

The cnidarian model organism Hydra has long been studied for its remarkable ability to regenerate its head, which is controlled by a head organizer located near the hypostome. The canonical Wnt pathway plays a central role in head organizer function during regeneration and during bud formation, which is the asexual mode of reproduction in Hydra. However, it is unclear how shared the developmental programs of head organizer genesis are in budding and regeneration. Time-series analysis of gene expression changes during head regeneration and budding revealed a set of 298 differentially expressed genes during the 48-h head regeneration and 72-h budding time courses. In order to understand the regulatory elements controlling Hydra head regeneration, we first identified 27,137 open-chromatin elements that are open in one or more sections of the organism body or regenerating tissue. We used histone modification ChIP-seq to identify 9,998 candidate proximal promoter and 3,018 candidate enhancer-like regions respectively. We show that a subset of these regulatory elements is dynamically remodeled during head regeneration and identify a set of transcription factor motifs that are enriched in the enhancer regions activated during head regeneration. Our results show that Hydra displays complex gene regulatory structures of developmentally dynamic enhancers, which suggests that the evolution of complex developmental enhancers predates the split of cnidarians and bilaterians.

Intergenic spaces: a new frontier to improving plant health

To more sustainably mitigate the impact of crop diseases on plant health and productivity, there is a need for broader spectrum, long-lasting resistance traits. Defense response (DR) genes, located throughout the genome, participate in cellular and system-wide defense mechanisms to stave off infection by diverse pathogens. This multigenic resistance avoids rapid evolution of a pathogen to overcome host resistance. DR genes reside within resistance-associated quantitative trait loci (QTL), and alleles of DR genes in resistant varieties are more active during pathogen attack relative to susceptible haplotypes. Differential expression of DR genes results from polymorphisms in their regulatory regions, that includes cis-regulatory elements such as transcription factor binding sites as well as features that influence epigenetic structural changes to modulate chromatin accessibility during infection. Many of these elements are found in clusters, known as cis-regulatory modules (CRMs), which are distributed throughout the host genome. Regulatory regions involved in plant-pathogen interactions may also contain pathogen effector binding elements that regulate DR gene expression, and that, when mutated, result in a change in the plants' response. We posit that CRMs and the multiple regulatory elements that comprise them are potential targets for marker-assisted breeding for broad-spectrum, durable disease resistance.

Characterization of a functional endothelial super-enhancer that regulates ADAMTS18 and angiogenesis

Super-enhancers are clusters of enhancers associated with cell lineage. They can be powerful gene-regulators and may be useful in cell-type specific viral-vector development. Here, we have screened for endothelial super-enhancers and identified an enhancer from within a cluster that conferred 5–70-fold increase in transgene expression. Importantly, CRISPR/Cas9 deletion of enhancers demonstrated regulation of ADAMTS18, corresponding to evidence of chromatin contacts between these genomic regions. Cell division-related pathways were primarily affected by the enhancer deletions, which correlated with significant reduction in cell proliferation. Furthermore, we observed changes in angiogenesis-related genes consistent with the endothelial specificity of this SE. Indeed, deletion of the enhancers affected tube formation, resulting in reduced or shortened sprouts. The super-enhancer angiogenic role is at least partly due to its regulation of ADAMTS18, as siRNA knockdown of ADAMTS18 resulted in significantly shortened endothelial sprouts. Hence, functional characterization of a novel endothelial super-enhancer has revealed substantial downstream effects from single enhancer deletions and led to the discovery of the cis-target gene ADAMTS18 and its role in endothelial function.

ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements

Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIP-seq profiling of multiple TFs makes it feasible to infer functional TF modules. However, when inferring TF modules based on co-localization of ChIP-seq peaks, often many weak binding events are missed, especially for mediators, resulting in incomplete identification of modules. To address this problem, we develop a ChIP-seq data-driven Gibbs Sampler to infer Modules (ChIP-GSM) using a Bayesian framework that integrates ChIP-seq profiles of multiple TFs. ChIP-GSM samples read counts of module TFs iteratively to estimate the binding potential of a module to each region and, across all regions, estimates the module abundance. Using inferred module-region probabilistic bindings as feature units, ChIP-GSM then employs logistic regression to predict active regulatory elements. Validation of ChIP-GSM predicted regulatory regions on multiple independent datasets sharing the same context confirms the advantage of using TF modules for predicting regulatory activity. In a case study of K562 cells, we demonstrate that the ChIP-GSM inferred modules form as groups, activate gene expression at different time points, and mediate diverse functional cellular processes. Hence, ChIP-GSM infers biologically meaningful TF modules and improves the prediction accuracy of regulatory region activities.

AniProtDB: A Collection of Consistently Generated Metazoan Proteomes for Comparative Genomics Studies

To address the void in the availability of high-quality proteomic data traversing the animal tree, we have implemented a pipeline for generating de novo assemblies based on publicly available data from the NCBI Sequence Read Archive, yielding a comprehensive collection of proteomes from 100 species spanning 21 animal phyla. We have also created the Animal Proteome Database (AniProtDB), a resource providing open access to this collection of high-quality metazoan proteomes, along with information on predicted proteins and protein domains for each taxonomic classification and the ability to perform sequence similarity searches against all proteomes generated using this pipeline. This solution vastly increases the utility of these data by removing the barrier to access for research groups who do not have the expertise or resources to generate these data themselves and enables the use of data from non-traditional research organisms that have the potential to address key questions in biomedicine.

MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics

BACKGROUND

Cis-regulatory elements (CREs) are DNA sequence segments that regulate gene expression. Among CREs are promoters, enhancers, Boundary Elements (BEs) and Polycomb Response Elements (PREs), all of which are enriched in specific sequence motifs that form particular occurrence landscapes. We have recently introduced a hierarchical machine learning approach (SVM-MOCCA) in which Support Vector Machines (SVMs) are applied on the level of individual motif occurrences, modelling local sequence composition, and then combined for the prediction of whole regulatory elements. We used SVM-MOCCA to predict PREs in Drosophila and found that it was superior to other methods. However, we did not publish a polished implementation of SVM-MOCCA, which can be useful for other researchers, and we only tested SVM-MOCCA with IUPAC motifs and PREs.

RESULTS

We here present an expanded suite for modelling CRE sequences in terms of motif occurrence combinatorics-Motif Occurrence Combinatorics Classification Algorithms (MOCCA). MOCCA contains efficient implementations of several modelling methods, including SVM-MOCCA, and a new method, RF-MOCCA, a Random Forest-derivative of SVM-MOCCA. We used SVM-MOCCA and RF-MOCCA to model Drosophila PREs and BEs in cross-validation experiments, making this the first study to model PREs with Random Forests and the first study that applies the hierarchical MOCCA approach to the prediction of BEs. Both models significantly improve generalization to PREs and boundary elements beyond that of previous methods-including 4-spectrum and motif occurrence frequency Support Vector Machines and Random Forests-, with RF-MOCCA yielding the best results.

CONCLUSION

MOCCA is a flexible and powerful suite of tools for the motif-based modelling of CRE sequences in terms of motif composition. MOCCA can be applied to any new CRE modelling problems where motifs have been identified. MOCCA supports IUPAC and Position Weight Matrix (PWM) motifs. For ease of use, MOCCA implements generation of negative training data, and additionally a mode that requires only that the user specifies positives, motifs and a genome. MOCCA is licensed under the MIT license and is available on Github at https://github.com/bjornbredesen/MOCCA .

Comparative transcriptomic analysis highlights contrasting levels of resistance of Vitis vinifera and Vitis amurensis to Botrytis cinerea

Botrytis cinerea is a major grapevine (Vitis spp.) pathogen, but some genotypes differ in their degree of resistance. For example, the Vitis vinifera cultivar Red Globe (RG) is highly susceptible, but V. amurensis Rupr Shuangyou (SY) is highly resistant. Here, we used RNA sequencing analysis to characterize the transcriptome responses of these two genotypes to B. cinerea inoculation at an early infection stage. Approximately a quarter of the genes in RG presented significant changes in transcript levels during infection, the number of which was greater than that in the SY leaves. The genes differentially expressed between infected leaves of SY and RG included those associated with cell surface structure, oxidation, cell death and C/N metabolism. We found evidence that an imbalance in the levels of reactive oxygen species (ROS) and redox homeostasis probably contributed to the susceptibility of RG to B. cinerea. SY leaves had strong antioxidant capacities and improved ROS homeostasis following infection. Regulatory network prediction suggested that WRKY and MYB transcription factors are associated with the abscisic acid pathway. Weighted gene correlation network analysis highlighted preinfection features of SY that might contribute to its increased resistance. Moreover, overexpression of VaWRKY10 in Arabidopsis thaliana and V. vinifera Thompson Seedless enhanced resistance to B. cinerea. Collectively, our study provides a high-resolution view of the transcriptional changes of grapevine in response to B. cinerea infection and novel insights into the underlying resistance mechanisms.

Parallel genetics of regulatory sequences using scalable genome editing in vivo

How regulatory sequences control gene expression is fundamental for explaining phenotypes in health and disease. Regulatory elements must ultimately be understood within their genomic environment and development- or tissue-specific contexts. Because this is technically challenging, few regulatory elements have been characterized in vivo. Here, we use inducible Cas9 and multiplexed guide RNAs to create hundreds of mutations in enhancers/promoters and 3' UTRs of 16 genes in C. elegans. Our software crispr-DART analyzes indel mutations in targeted DNA sequencing. We quantify the impact of mutations on expression and fitness by targeted RNA sequencing and DNA sampling. When applying our approach to the lin-41 3' UTR, generating hundreds of mutants, we find that the two adjacent binding sites for the miRNA let-7 can regulate lin-41 expression independently of each other. Finally, we map regulatory genotypes to phenotypic traits for several genes. Our approach enables parallel analysis of regulatory sequences directly in animals.

Amplicon genome fishing (AGF): a rapid and efficient method for sequencing target cis-regulatory regions in nonmodel organisms

Cis-regulatory sequences play a crucial role in regulating gene expression and are evolutionary hot spots that drive phenotypic divergence among organisms. Sequencing some cis-regulatory regions of interest in many different species is common in comparative genetic studies. For nonmodel organisms lacking genomic data, genome walking is often the preferred method for this type of application. However, applying genome walking will be laborious and time-consuming when the number of cis-regulatory regions and species to be analyzed is large. In this study, we propose a novel method called amplicon genome fishing (AGF), which can isolate and sequence cis-regulatory regions of interest for any organism. The main idea of the AGF method is to use fragments amplified from the target cis-regulatory regions as enrichment baits to capture and sequence the whole target cis-regulatory regions from genomic library pools. Unlike genome walking, the AGF method is based on hybridization capture and high-throughput sequencing, which makes this method rapid and efficient for projects where some cis-regulatory regions have to be sequenced for many species. We used human amplicons as capture baits and successfully sequenced five target enhancer regions of Homo sapiens, Mus musculus, Gallus gallus, and Xenopus tropicalis, proving the feasibility and repeatability of AGF. To show the utility of the AGF method in real studies, we used it to sequence the ZRS enhancer, a cis-regulatory region associated with the limb loss of snakes, for twenty-three vertebrate species (includes many limbless species never sequenced before). The newly obtained ZRS sequences provide new perspectives into the relationship between the ZRS enhancer's evolution and limb loss in major tetrapod lineages.

Modeling by disruption and a selected-for partner for the nude locus

A long‐standing problem in biology is how to dissect traits for which no tractable model exists. Here, we screen for genes like the nude locus (Foxn1)—genes central to mammalian hair and thymus development—using animals that never evolved hair, thymi, or Foxn1. Fruit flies are morphologically disrupted by the FOXN1 transcription factor and rescued by weak reductions in fly gene function, revealing molecules that potently synergize with FOXN1 to effect dramatic, chaotic change. Strong synergy/effectivity in flies is expected to reflect strong selection/functionality (purpose) in mammals; the more disruptive a molecular interaction is in alien contexts (flies), the more beneficial it will be in its natural, formative contexts (mammals). The approach identifies Aff4 as the first nude‐like locus, as murine AFF4 and FOXN1 cooperatively induce similar cutaneous/thymic phenotypes, similar gene expression programs, and the same step of transcription, pre‐initiation complex formation. These AFF4 functions are unexpected, as AFF4 also serves as a scaffold in common transcriptional‐elongation complexes. Most likely, the approach works because an interaction's power to disrupt is the inevitable consequence of its selected‐for power to benefit.

Mapping Regulatory Determinants in Plants

The domestication and improvement of many plant species have frequently involved modulation of transcriptional outputs and continue to offer much promise for targeted trait engineering. The cis-regulatory elements (CREs) controlling these trait-associated transcriptional variants however reside within non-coding regions that are currently poorly annotated in most plant species. This is particularly true in large crop genomes where regulatory regions constitute only a small fraction of the total genomic space. Furthermore, relatively little is known about how CREs function to modulate transcription in plants. Therefore understanding where regulatory regions are located within a genome, what genes they control, and how they are structured are important factors that could be used to guide both traditional and synthetic plant breeding efforts. Here, we describe classic examples of regulatory instances as well as recent advances in plant regulatory genomics. We highlight valuable molecular tools that are enabling large-scale identification of CREs and offering unprecedented insight into how genes are regulated in diverse plant species. We focus on chromatin environment, transcription factor (TF) binding, the role of transposable elements, and the association between regulatory regions and target genes.

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

Background

The binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently.

Results

In this work, we propose a PRobabilistic Enhancer PRedictIoN Tool (PREPRINT) that assumes characteristic coverage patterns of chromatin features at enhancers and employs a statistical model to account for their variability. PREPRINT defines probabilistic distance measures to quantify the similarity of the genomic query regions and the characteristic coverage patterns. The probabilistic scores of the enhancer and non-enhancer samples are utilised to train a kernel-based classifier. The performance of the method is demonstrated on ENCODE data for two cell lines. The predicted enhancers are computationally validated based on the transcriptional regulatory protein binding sites and compared to the predictions obtained by state-of-the-art methods.

Conclusion

PREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.

Identifying Regulatory Elements via Deep Learning

Deep neural networks have been revolutionizing the field of machine learning for the past several years. They have been applied with great success in many domains of the biomedical data sciences and are outperforming extant methods by a large margin. The ability of deep neural networks to pick up local image features and model the interactions between them makes them highly applicable to regulatory genomics. Instead of an image, the networks analyze DNA and RNA sequences and additional epigenomic data. In this review, we survey the successes of deep learning in the field of regulatory genomics. We first describe the fundamental building blocks of deep neural networks, popular architectures used in regulatory genomics, and their training process on molecular sequence data. We then review several key methods in different gene regulation domains. We start with the pioneering method DeepBind and its successors, which were developed to predict protein–DNA binding. We then review methods developed to predict and model epigenetic information, such as histone marks and nucleosome occupancy. Following epigenomics, we review methods to predict protein–RNA binding with its unique challenge of incorporating RNA structure information. Finally, we provide our overall view of the strengths and weaknesses of deep neural networks and prospects for future developments.

An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis

Thousands of epigenomic data sets have been generated in the past decade, but it is difficult for researchers to effectively use all the data relevant to their projects. Systematic integrative analysis can help meet this need, and the VISION project was established for validated systematic integration of epigenomic data in hematopoiesis. Here, we systematically integrated extensive data recording epigenetic features and transcriptomes from many sources, including individual laboratories and consortia, to produce a comprehensive view of the regulatory landscape of differentiating hematopoietic cell types in mouse. By using IDEAS as our integrative and discriminative epigenome annotation system, we identified and assigned epigenetic states simultaneously along chromosomes and across cell types, precisely and comprehensively. Combining nuclease accessibility and epigenetic states produced a set of more than 200,000 candidate cis-regulatory elements (cCREs) that efficiently capture enhancers and promoters. The transitions in epigenetic states of these cCREs across cell types provided insights into mechanisms of regulation, including decreases in numbers of active cCREs during differentiation of most lineages, transitions from poised to active or inactive states, and shifts in nuclease accessibility of CTCF-bound elements. Regression modeling of epigenetic states at cCREs and gene expression produced a versatile resource to improve selection of cCREs potentially regulating target genes. These resources are available from our VISION website to aid research in genomics and hematopoiesis.

Systematic integration of GATA transcription factors and epigenomes via IDEAS paints the regulatory landscape of hematopoietic cells

Members of the GATA family of transcription factors play key roles in the differentiation of specific cell lineages by regulating the expression of target genes. Three GATA factors play distinct roles in hematopoietic differentiation. In order to better understand how these GATA factors function to regulate genes throughout the genome, we are studying the epigenomic and transcriptional landscapes of hematopoietic cells in a model-driven, integrative fashion. We have formed the collaborative multi-lab VISION project to conduct ValIdated Systematic IntegratiON of epigenomic data in mouse and human hematopoiesis. The epigenomic data included nuclease accessibility in chromatin, CTCF occupancy, and histone H3 modifications for 20 cell types covering hematopoietic stem cells, multilineage progenitor cells, and mature cells across the blood cell lineages of mouse. The analysis used the Integrative and Discriminative Epigenome Annotation System (IDEAS), which learns all common combinations of features (epigenetic states) simultaneously in two dimensions-along chromosomes and across cell types. The result is a segmentation that effectively paints the regulatory landscape in readily interpretable views, revealing constitutively active or silent loci as well as the loci specifically induced or repressed in each stage and lineage. Nuclease accessible DNA segments in active chromatin states were designated candidate cis-regulatory elements in each cell type, providing one of the most comprehensive registries of candidate hematopoietic regulatory elements to date. Applications of VISION resources are illustrated for the regulation of genes encoding GATA1, GATA2, GATA3, and Ikaros. VISION resources are freely available from our website http://usevision.org.

Identification and Characterization of Cis-Regulatory Elements for Photoreceptor-Type-Specific Transcription in ZebraFish

Tissue-specific or cell-type-specific transcription of protein-coding genes is controlled by both trans-regulatory elements (TREs) and cis-regulatory elements (CREs). However, it is challenging to identify TREs and CREs, which are unknown for most genes. Here, we describe a protocol for identifying two types of transcription-activating CREs-core promoters and enhancers-of zebrafish photoreceptor type-specific genes. This protocol is composed of three phases: bioinformatic prediction, experimental validation, and characterization of the CREs. To better illustrate the principles and logic of this protocol, we exemplify it with the discovery of the core promoter and enhancer of the mpp5b apical polarity gene (also known as ponli), whose red, green, and blue (RGB) cone-specific transcription requires its enhancer, a member of the rainbow enhancer family. While exemplified with an RGB-cone-specific gene, this protocol is general and can be used to identify the core promoters and enhancers of other protein-coding genes.

Primate optogenetics: Progress and prognosis

Monkeys are a premier model organism for neuroscience research. Activity in the central nervous systems of monkeys can be recorded and manipulated while they perform complex perceptual, motor, or cognitive tasks. Conventional techniques for manipulating neural activity in monkeys are too coarse to address many of the outstanding questions in primate neuroscience, but optogenetics holds the promise to overcome this hurdle. In this article, we review the progress that has been made in primate optogenetics over the past 5 years. We emphasize the use of gene regulatory sequences in viral vectors to target specific neuronal types, and we present data on vectors that we engineered to target parvalbumin-expressing neurons. We conclude with a discussion of the utility of optogenetics for treating sensorimotor hearing loss and Parkinson's disease, areas of translational neuroscience in which monkeys provide unique leverage for basic science and medicine.

Exploring a Drosophila Transcription Factor Interaction Network to Identify Cis-Regulatory Modules

Multiple transcription factors (TFs) bind to specific sites in the genome and interact among themselves to form the cis-regulatory modules (CRMs). They are essential in modulating the expression of genes, and it is important to study this interplay to understand gene regulation. In the present study, we integrated experimentally identified TF binding sites collected from published studies with computationally predicted TF binding sites to identify Drosophila CRMs. Along with the detection of the previously known CRMs, this approach identified novel protein combinations. We determined high-occupancy target sites, where a large number of TFs bind. Investigating these sites revealed that Giant, Dichaete, and Knirp are highly enriched in these locations. A common TAG team motif was observed at these sites, which might play a role in recruiting other TFs. While comparing the binding sites at distal and proximal promoters, we found that certain regulatory TFs, such as Zelda, were highly enriched in enhancers. Our study has shown that, from the information available concerning the TF binding sites, the real CRMs could be predicted accurately and efficiently. Although we only may claim co-occurrence of these proteins in this study, it may actually point to their interaction (as known interaction proteins typically co-occur together). Such an integrative approach can, therefore, help us to provide a better understanding of the interplay among the factors, even though further experimental verification is required.

Identification of gene specific cis-regulatory elements during differentiation of mouse embryonic stem cells: An integrative approach using high-throughput datasets

Gene expression governs cell fate, and is regulated via a complex interplay of transcription factors and molecules that change chromatin structure. Advances in sequencing-based assays have enabled investigation of these processes genome-wide, leading to large datasets that combine information on the dynamics of gene expression, transcription factor binding and chromatin structure as cells differentiate. While numerous studies focus on the effects of these features on broader gene regulation, less work has been done on the mechanisms of gene-specific transcriptional control. In this study, we have focussed on the latter by integrating gene expression data for the in vitro differentiation of murine ES cells to macrophages and cardiomyocytes, with dynamic data on chromatin structure, epigenetics and transcription factor binding. Combining a novel strategy to identify communities of related control elements with a penalized regression approach, we developed individual models to identify the potential control elements predictive of the expression of each gene. Our models were compared to an existing method and evaluated using the existing literature and new experimental data from embryonic stem cell differentiation reporter assays. Our method is able to identify transcriptional control elements in a gene specific manner that reflect known regulatory relationships and to generate useful hypotheses for further testing.

Causal Gene Regulatory Network Modeling and Genomics: Second-Generation Challenges

Gene regulatory network modeling has played a major role in advancing the understanding of developmental systems, by crystallizing structures of relevant extant information, by formally posing hypothetical functional relationships between network elements, and by offering clear predictive tests to improve understanding of the mechanisms driving developmental progression. Both ordinary differential equation (ODE)-based and Boolean models have also been highly successful in explaining dynamics within subcircuits of more complex processes. In a very small number of cases, gene regulatory network models of much more global scope have been proposed that successfully predict the dynamics of the processes establishing most of an embryonic body plan. Can such successes be expanded to very different developmental systems, including post-embryonic mammalian systems? This perspective discusses several problems that must be solved in more quantitative and predictive theoretical terms, to make this possible. These problems include: the effects of cellular history on chromatin state and how these affect gene accessibility; the dose dependence of activities of many transcription factors (a problem for Boolean models); stochasticity of some transcriptional outputs (a problem for simple ODE models); response timing delays due to epigenetic remodeling requirements; functionally different kinds of repression; and the regulatory syntax that governs responses of genes with multiple enhancers.

Changes to the core and flanking sequences of G-box elements lead to increases and decreases in gene expression in both native and synthetic soybean promoters

Cis-regulatory elements in promoters are major determinants of binding specificity of transcription factors (TFs) for transcriptional regulation. To improve our understanding of how these short DNA sequences regulate gene expression, synthetic promoters consisting of both classical (CACGTG) and variant G-box core sequences along with different flanking sequences derived from the promoters of three different highly expressing soybean genes, were constructed and used to regulate a green fluorescent protein (gfp) gene. Use of the classical 6-bp G-box provided information on the base level of GFP expression while modifications to the 2-4 flanking bases on either side of the G-box influenced the intensity of gene expression in both transiently transformed lima bean cotyledons and stably transformed soybean hairy roots. The proximal 2-bp sequences on either flank of the G-box significantly affected G-box activity, while the distal 2-bp flanking nucleotides also influenced gene expression albeit with a decreasing effect. Manipulation of the upstream 2- to 4-bp flanking sequence of a G-box variant (GACGTG), found in the proximal region of a relatively weak soybean glycinin promoter, significantly enhanced promoter activity using both transient and stable expression assays, if the G-box variant was first converted into a classical G-box (CACGTG). In addition to increasing our understanding of regulatory element composition and structure, this study shows that minimal targeted changes in native promoter sequences can lead to enhanced gene expression, and suggests that genome editing of the promoter region can result in useful and predictable changes in native gene expression.

Variants in the CYP19A1 gene can affect in vitro embryo production traits in cattle

PURPOSE

This study aimed to associate DNA variants in promoter and exon flanking regions of the CYP19A1 gene with in vitro embryo production traits in cattle. The role of transcription factor binding sites created or lost due to DNA sequence variation and their possible effect on gene expression was also evaluated.

METHODS

We collected date from Gyr dairy oocyte donor cows (Bos taurus indicus) at a commercial in vitro embryo production farm and analyzed the genotype-phenotype association with in vitro production traits. Using Sanger sequencing and web-based software, we assessed important CYP19A1 gene regions in oocyte donor cows and analyzed the effects of variants on the transcription factor binding sites.

RESULTS

Two SNP mutations significantly associated with oocyte production, oocyte viability, embryo development, and pregnancies were found (T > C in the untranslated exon 1 flanking region ([GenBank: AJ250379.1]: rs718446508 T > C), and a T > C in the 5'-upstream region (1.1 promoter) ([GenBank: AC_000167.1]: rs41651668 T > C). Six new transcription factor binding sites were created. A binding site for transcription factors associated with the development of the placenta and embryo implantation was eliminated due to variations in the DNA sequence identified.

CONCLUSIONS

The CYP19A1 gene contributes to genetic variation of in vitro embryo production traits in cattle. The complexity of the physiological phenomena related to estrogen pathways and their influence on reproduction in cattle allow indication of the mutations evaluated here as possible genetic markers for embryo production traits, which should be validated in the next steps of marker-assisted selection.

Genome-wide identification of enhancer elements in the placenta

Normal placental development is essential for a healthy pregnancy, and is contingent upon tight spatiotemporal regulation of gene expression. One level of transcriptional control is via enhancer elements in the genome. Enhancers are distal cis-regulatory elements that can impact gene expression regardless of their position or orientation. The study of enhancers in the placenta is usually focused on one or two at a time, and the simultaneous identification of all enhancers has been limited. However, such a holistic approach is necessary if we are to gain a systems-level understanding of gene expression regulation in the placenta. Here, we review current methods for genome-scale enhancer identification, as well as studies that have applied those techniques in the placenta, with the aim of guiding future research.

Towards a map of cis-regulatory sequences in the human genome

Accumulating evidence indicates that transcription factor (TF) binding sites, or cis-regulatory elements (CREs), and their clusters termed cis-regulatory modules (CRMs) play a more important role than do gene-coding sequences in specifying complex traits in humans, including the susceptibility to common complex diseases. To fully characterize their roles in deriving the complex traits/diseases, it is necessary to annotate all CREs and CRMs encoded in the human genome. However, the current annotations of CREs and CRMs in the human genome are still very limited and mostly coarse-grained, as they often lack the detailed information of CREs in CRMs. Here, we integrated 620 TF ChIP-seq datasets produced by the ENCODE project for 168 TFs in 79 different cell/tissue types and predicted an unprecedentedly completely map of CREs in CRMs in the human genome at single nucleotide resolution. The map includes 305 912 CRMs containing a total of 1 178 913 CREs belonging to 736 unique TF binding motifs. The predicted CREs and CRMs tend to be subject to either purifying selection or positive selection, thus are likely to be functional. Based on the results, we also examined the status of available ChIP-seq datasets for predicting the entire regulatory genome of humans.

Widespread Enhancer Activity from Core Promoters

Gene expression in higher eukaryotes is precisely regulated in time and space through the interplay between promoters and gene-distal regulatory regions, known as enhancers. The original definition of enhancers implies the ability to activate gene expression remotely, while promoters entail the capability to locally induce gene expression. Despite the conventional distinction between them, promoters and enhancers share many genomic and epigenomic features. One intriguing finding in the gene regulation field comes from the observation that many core promoter regions display enhancer activity. Recent high-throughput reporter assays along with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-related approaches have indicated that this phenomenon is common and might have a strong impact on our global understanding of genome organisation and gene expression regulation.