MotifLab: a tools and data integration workbench for motif discovery and regulatory sequence analysis

Genomic structure and transcript analysis of the Rapid Alkalinization Factor (RALF) gene family during host-pathogen crosstalk in Fragaria vesca and Fragaria x ananassa strawberry

Rapid Alkalinization Factors (RALFs) are cysteine-rich peptides ubiquitous within plant kingdom. They play multiple roles as hormonal signals in diverse processes, including root elongation, cell growth, pollen tube development, and fertilization. Their involvement in host-pathogen crosstalk as negative regulators of immunity in Arabidopsis has also been recognized. In addition, peptides homologous to RALF are secreted by different fungal pathogens as effectors during early stages of infection. Previous studies have identified nine RALF genes in the diploid strawberry (Fragaria vesca) genome. This work describes the genomic organization of the RALF gene families in commercial octoploid strawberry (Fragaria × ananassa) and the re-annotated genome of F. vesca, and then compares findings with orthologs in Arabidopsis thaliana. We reveal the presence of 15 RALF genes in F. vesca genotype Hawaii 4 and 50 in Fragaria x ananassa cv. Camarosa, showing a non-homogenous localization of genes among the different Fragaria x ananassa subgenomes. Expression analysis of Fragaria x ananassa RALF genes upon infection with Colletotrichum acutatum or Botrytis cinerea showed that FanRALF3-1 was the only fruit RALF gene upregulated after fungal infection. In silico analysis was used to identify distinct pathogen inducible elements upstream of the FanRALF3-1 gene. Agroinfiltration of strawberry fruit with deletion constructs of the FanRALF3-1 promoter identified a 5' region required for FanRALF3-1 expression in fruit, but failed to identify a region responsible for fungal induced expression.

MODSIDE: a motif discovery pipeline and similarity detector

Background

Previous studies demonstrate the usefulness of using multiple tools and methods for improving the accuracy of motif detection. Over the past years, numerous motif discovery pipelines have been developed. However, they typically report only the top ranked results either from individual motif finders or from a combination of multiple tools and algorithms.

Results

Here we present MODSIDE, a motif discovery pipeline and similarity detector. The pipeline integrated four de novo motif finders: ChIPMunk, MEME, Weeder, and XXmotif. It also incorporated a motif similarity detection tool MOTIFSIM. MODSIDE was designed for delivering not only the predictive results from individual motif finders but also the comparison results for multiple tools. The results include the common significant motifs from multiple tools, the motifs detected by some tools but not by others, and the best matches for each motif in the motif collection of multiple tools. MODSIDE also possesses other useful features for merging similar motifs and clustering motifs into motif trees.

Conclusions

We evaluated MODSIDE and its adopted motif finders on 16 benchmark datasets. The statistical results demonstrate MODSIDE achieves better accuracy than individual motif finders. We also compared MODSIDE with two popular motif discovery pipelines: MEME-ChIP and RSAT peak-motifs. The comparison results reveal MODSIDE attains similar performance as RSAT peak-motifs but better accuracy than MEME-ChIP. In addition, MODSIDE is able to deliver various comparison results that are not offered by MEME-ChIP, RSAT peak-motifs, and other existing motif discovery pipelines.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5148-1) contains supplementary material, which is available to authorized users.

Discovering Gene Regulatory Elements Using Coverage-Based Heuristics

Data mining algorithms and sequencing methods (such as RNA-seq and ChIP-seq) are being combined to discover genomic regulatory motifs that relate to a variety of phenotypes. However, motif discovery algorithms often produce very long lists of putative transcription factor binding sites, hindering the discovery of phenotype-related regulatory elements by making it difficult to select a manageable set of candidate motifs for experimental validation. To address this issue, the authors introduce the motif selection problem and provide coverage-based search heuristics for its solution. Analysis of 203 ChIP-seq experiments from the ENCyclopedia of DNA Elements project shows that our algorithms produce motifs that have high sensitivity and specificity and reveals new insights about the regulatory code of the human genome. The greedy algorithm performs the best, selecting a median of two motifs per ChIP-seq transcription factor group while achieving a median sensitivity of 77 percent.

Prediction and Validation of Transcription Factors Modulating the Expression of Sestrin3 Gene Using an Integrated Computational and Experimental Approach

SESN3 has been implicated in multiple biological processes including protection against oxidative stress, regulation of glucose and lipid metabolism. However, little is known about the factors and mechanisms controlling its gene expression at the transcriptional level. We performed in silico phylogenetic footprinting analysis of 5 kb upstream regions of a diverse set of human SESN3 orthologs for the identification of high confidence conserved binding motifs (BMo). We further analyzed the predicted BMo by a motif comparison tool to identify the TFs likely to bind these discovered motifs. Predicted TFs were then integrated with experimentally known protein-protein interactions and experimentally validated to delineate the important transcriptional regulators of SESN3. Our study revealed high confidence set of BMos (integrated with DNase I hypersensitivity sites) in the upstream regulatory regions of SESN3 that could be bound by transcription factors from multiple families including FOXOs, SMADs, SOXs, TCFs and HNF4A. TF-TF network analysis established hubs of interaction that include SMAD3, TCF3, SMAD2, HDAC2, SOX2, TAL1 and TCF12 as well as the likely protein complexes formed between them. We show using ChIP-PCR as well as over-expression and knock out studies that FOXO3 and SOX2 transcriptionally regulate the expression of SESN3 gene. Our findings provide an important roadmap to further our understanding on the regulation of SESN3.

Rewiring of the Ppr1 Zinc Cluster Transcription Factor from Purine Catabolism to Pyrimidine Biogenesis in the Saccharomycetaceae

Metabolic pathways are largely conserved in eukaryotes, but the transcriptional regulation of these pathways can sometimes vary between species; this has been termed "rewiring." Recently, it has been established that in the Saccharomyces lineage starting from Naumovozyma castellii, genes involved in allantoin breakdown have been genomically relocated to form the DAL cluster. The formation of the DAL cluster occurred along with the loss of urate permease (UAP) and urate oxidase (UOX), reducing the requirement for oxygen and bypassing the candidate Ppr1 inducer, uric acid. In Saccharomyces cerevisiae, this allantoin catabolism cluster is regulated by the transcription factor Dal82, which is not present in many of the pre-rearrangement fungal species. We have used ChIP-chip analysis, transcriptional profiling of an activated Ppr1 protein, bioinformatics, and nitrogen utilization studies to establish that in Candida albicans the zinc cluster transcription factor Ppr1 controls this allantoin catabolism regulon. Intriguingly, in S. cerevisiae, the Ppr1 ortholog binds the same DNA motif (CGG(N6)CCG) as in C. albicans but serves as a regulator of pyrimidine biosynthesis. This transcription factor rewiring appears to have taken place at the same phylogenetic step as the formation of the rearranged DAL cluster. This transfer of the control of allantoin degradation from Ppr1 to Dal82, together with the repositioning of Ppr1 to the regulation of pyrimidine biosynthesis, may have resulted from a switch to a metabolism that could exploit hypoxic conditions in the lineage leading to N. castellii and S. cerevisiae.

Identification of plant transcription factor target sequences

Regulation of gene expression depends on specific cis-regulatory sequences located in the gene promoter regions. These DNA sequences are recognized by transcription factors (TFs) in a sequence-specific manner, and their identification could help to elucidate the regulatory networks that underlie plant physiological responses to developmental programs or to environmental adaptation. Here we review recent advances in high throughput methodologies for the identification of plant TF binding sites. Several approaches offer a map of the TF binding locations in vivo and of the dynamics of the gene regulatory networks. As an alternative, high throughput in vitro methods provide comprehensive determination of the DNA sequences recognized by TFs. These advances are helping to decipher the regulatory lexicon and to elucidate transcriptional network hierarchies in plants in response to internal or external cues. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

DynaMIT: the dynamic motif integration toolkit

De-novo motif search is a frequently applied bioinformatics procedure to identify and prioritize recurrent elements in sequences sets for biological investigation, such as the ones derived from high-throughput differential expression experiments. Several algorithms have been developed to perform motif search, employing widely different approaches and often giving divergent results. In order to maximize the power of these investigations and ultimately be able to draft solid biological hypotheses, there is the need for applying multiple tools on the same sequences and merge the obtained results. However, motif reporting formats and statistical evaluation methods currently make such an integration task difficult to perform and mostly restricted to specific scenarios. We thus introduce here the Dynamic Motif Integration Toolkit (DynaMIT), an extremely flexible platform allowing to identify motifs employing multiple algorithms, integrate them by means of a user-selected strategy and visualize results in several ways; furthermore, the platform is user-extendible in all its aspects. DynaMIT is freely available at http://cibioltg.bitbucket.org.

c-Myb Binding Sites in Haematopoietic Chromatin Landscapes

Strict control of tissue-specific gene expression plays a pivotal role during lineage commitment. The transcription factor c-Myb has an essential role in adult haematopoiesis and functions as an oncogene when rearranged in human cancers. Here we have exploited digital genomic footprinting analysis to obtain a global picture of c-Myb occupancy in the genome of six different haematopoietic cell-types. We have biologically validated several c-Myb footprints using c-Myb knockdown data, reporter assays and DamID analysis. We show that our predicted conserved c-Myb footprints are highly dependent on the haematopoietic cell type, but that there is a group of gene targets common to all cell-types analysed. Furthermore, we find that c-Myb footprints co-localise with active histone mark H3K4me3 and are significantly enriched at exons. We analysed co-localisation of c-Myb footprints with 104 chromatin regulatory factors in K562 cells, and identified nine proteins that are enriched together with c-Myb footprints on genes positively regulated by c-Myb and one protein enriched on negatively regulated genes. Our data suggest that c-Myb is a transcription factor with multifaceted target regulation depending on cell type.

Genome Wide Binding Site Analysis Reveals Transcriptional Coactivation of Cytokinin-Responsive Genes by DELLA Proteins

The ability of plants to provide a plastic response to environmental cues relies on the connectivity between signaling pathways. DELLA proteins act as hubs that relay environmental information to the multiple transcriptional circuits that control growth and development through physical interaction with transcription factors from different families. We have analyzed the presence of one DELLA protein at the Arabidopsis genome by chromatin immunoprecipitation coupled to large-scale sequencing and we find that it binds at the promoters of multiple genes. Enrichment analysis shows a strong preference for cis elements recognized by specific transcription factor families. In particular, we demonstrate that DELLA proteins are recruited by type-B ARABIDOPSIS RESPONSE REGULATORS (ARR) to the promoters of cytokinin-regulated genes, where they act as transcriptional co-activators. The biological relevance of this mechanism is underpinned by the necessity of simultaneous presence of DELLAs and ARRs to restrict root meristem growth and to promote photomorphogenesis.

Plants respond to environmental cues by modulating transcriptional circuits. One mechanism for such modulation involves DELLA proteins. They are promiscuous interactors of transcription factors and, in most cases, this interaction impairs the recognition of the DNA target sequences. Here we show that DELLA proteins are also recruited to multiple locations of the genome where they act as transcriptional coactivators, and we demonstrate how physical interaction with type-B ARRs is relevant for the regulation of meristem maintenance and photomorphogenesis.

GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare

A wide array of biomedical data are generated and made available to healthcare experts. However, due to the diverse nature of data, it is difficult to predict outcomes from it. It is therefore necessary to combine these diverse data sources into a single unified dataset. This paper proposes a global unified data model (GUDM) to provide a global unified data structure for all data sources and generate a unified dataset by a “data modeler” tool. The proposed tool implements user-centric priority based approach which can easily resolve the problems of unified data modeling and overlapping attributes across multiple datasets. The tool is illustrated using sample diabetes mellitus data. The diverse data sources to generate the unified dataset for diabetes mellitus include clinical trial information, a social media interaction dataset and physical activity data collected using different sensors. To realize the significance of the unified dataset, we adopted a well-known rough set theory based rules creation process to create rules from the unified dataset. The evaluation of the tool on six different sets of locally created diverse datasets shows that the tool, on average, reduces 94.1% time efforts of the experts and knowledge engineer while creating unified datasets.

Metabolic regulation in model ascomycetes--adjusting similar genomes to different lifestyles

The related yeasts Saccharomyces cerevisiae and Candida albicans have similar genomes but very different lifestyles. These fungi have modified transcriptional and post-translational regulatory processes to adapt their similar genomes to the distinct biological requirements of the two yeasts. We review recent findings comparing the differences between these species, highlighting how they have achieved specialized metabolic capacities tailored to their lifestyles despite sharing similar genomes. Studying this transcriptional and post-transcriptional rewiring may improve our ability to interpret phenotype from genotype.

Large-scale identification of gibberellin-related transcription factors defines group VII ETHYLENE RESPONSE FACTORS as functional DELLA partners

DELLA proteins are the master negative regulators in gibberellin (GA) signaling acting in the nucleus as transcriptional regulators. The current view of DELLA action indicates that their activity relies on the physical interaction with transcription factors (TFs). Therefore, the identification of TFs through which DELLAs regulate GA responses is key to understanding these responses from a mechanistic point of view. Here, we have determined the TF interactome of the Arabidopsis (Arabidopsis thaliana) DELLA protein GIBBERELLIN INSENSITIVE and screened a collection of conditional TF overexpressors in search of those that alter GA sensitivity. As a result, we have found RELATED TO APETALA2.3, an ethylene-induced TF belonging to the group VII ETHYLENE RESPONSE FACTOR of the APETALA2/ethylene responsive element binding protein superfamily, as a DELLA interactor with physiological relevance in the context of apical hook development. The combination of transactivation assays and chromatin immunoprecipitation indicates that the interaction with GIBBERELLIN INSENSITIVE impairs the activity of RELATED TO APETALA2.3 on the target promoters. This mechanism represents a unique node in the cross regulation between the GA and ethylene signaling pathways controlling differential growth during apical hook development.

The catecholamine biosynthetic enzyme dopamine β-hydroxylase (DBH): first genome-wide search positions trait-determining variants acting additively in the proximal promoter

Dopamine beta-hydroxylase (DBH) is the biosynthetic enzyme catalyzing formation of norepinephrine. Changes in DBH expression or activity have been implicated in the pathogenesis of cardiovascular and neuropsychiatric disorders. Genetic determination of DBH enzymatic activity and its secretion are only incompletely understood. We began with a genome-wide association search for loci contributing to DBH activity in human plasma. Initially, in a population sample of European ancestry, we identified the proximal DBH promoter as a region harboring three common trait-determining variants (top hit rs1611115, P = 7.2 × 10(-51)). We confirmed their effects on transcription and showed that the three variants each acted additively on gene expression. Results were replicated in a population sample of Native American descent (top hit rs1611115, P = 4.1 × 10(-15)). Jointly, DBH variants accounted for 57% of DBH trait variation. We further identified a genome-wide significant SNP at the LOC338797 locus on chromosome 12 as trans-quantitative trait locus (QTL) (rs4255618, P = 4.62 × 10(-8)). Conditional analyses on DBH identified a third genomic region contributing to DBH variation: a likely cis-QTL adjacent to DBH in SARDH (rs7040170, P = 1.31 × 10(-14)) on chromosome 9q. We conclude that three common SNPs in the DBH promoter act additively to control phenotypic variation in DBH levels, and that two additional novel loci (SARDH and LOC338797) may also contribute to the expression of this catecholamine biosynthetic trait. Identification of DBH variants with strong effects makes it possible to take advantage of Mendelian randomization approaches to test causal effects of this intermediate trait on disease.

An intricate network of conserved DNA upstream motifs and associated transcription factors regulate the expression of uromodulin gene

PURPOSE

Uromodulin is a kidney specific glycoprotein whose expression can modulate kidney homeostasis. However, the set of sequence specific transcription factors that regulate the uromodulin gene UMOD and their upstream binding locations are not well characterized. We built a high resolution map of its transcriptional regulation.

MATERIALS AND METHODS

We applied in silico phylogenetic footprinting on the upstream regulatory regions of a diverse set of human UMOD orthologs to identify conserved binding motifs and corresponding position specific weight matrices. We further analyzed the predicted binding motifs by motif comparison, which identified transcription factors likely to bind these discovered motifs. Predicted transcription factors were then integrated with experimentally known protein-protein interactions available from public databases and tissue specific expression resources to delineate important regulators controlling UMOD expression.

RESULTS

Analysis allowed the identification of a reliable set of binding motifs in the upstream regulatory regions of UMOD to build a high confidence compendium of transcription factors that could bind these motifs, such as GATA3, HNF1B, SP1, SMAD3, RUNX2 and KLF4. ENCODE deoxyribonuclease I hypersensitivity sites in the UMOD upstream region of the mouse kidney confirmed that some of these binding motifs were open to binding by predicted transcription factors. The transcription factor-transcription factor network revealed several highly connected transcription factors, such as SP1, SP3, TP53, POU2F1, RARB, RARA and RXRA, as well as the likely protein complexes formed between them. Expression levels of these transcription factors in the kidney suggest their central role in controlling UMOD expression.

CONCLUSIONS

Our findings will form a map for understanding the regulation of uromodulin expression in health and disease.

Otx2 ChIP-seq reveals unique and redundant functions in the mature mouse retina

During mouse retinal development and into adulthood, the transcription factor Otx2 is expressed in pigment epithelium, photoreceptors and bipolar cells. In the mature retina, Otx2 ablation causes photoreceptor degeneration through a non-cell-autonomous mechanism involving Otx2 function in the supporting RPE. Surprisingly, photoreceptor survival does not require Otx2 expression in the neural retina, where the related Crx homeobox gene, a major regulator of photoreceptor development, is also expressed. To get a deeper view of mouse Otx2 activities in the neural retina, we performed chromatin-immunoprecipitation followed by massively parallel sequencing (ChIP-seq) on Otx2. Using two independent ChIP-seq assays, we identified consistent sets of Otx2-bound cis-regulatory elements. Comparison with our previous RPE-specific Otx2 ChIP-seq data shows that Otx2 occupies different functional domains of the genome in RPE cells and in neural retina cells and regulates mostly different sets of genes. To assess the potential redundancy of Otx2 and Crx, we compared our data with Crx ChIP-seq data. While Crx genome occupancy markedly differs from Otx2 genome occupancy in the RPE, it largely overlaps that of Otx2 in the neural retina. Thus, in accordance with its essential role in the RPE and its non-essential role in the neural retina, Otx2 regulates different gene sets in the RPE and the neural retina, and shares an important part of its repertoire with Crx in the neural retina. Overall, this study provides a better understanding of gene-regulatory networks controlling photoreceptor homeostasis and disease.

Bioinformatic analysis reveals an evolutional selection for DNA:RNA hybrid G-quadruplex structures as putative transcription regulatory elements in warm-blooded animals

Recently, we reported the co-transcriptional formation of DNA:RNA hybrid G-quadruplex (HQ) structure by the non-template DNA strand and nascent RNA transcript, which in turn modulates transcription under both in vitro and in vivo conditions. Here we present bioinformatic analysis on putative HQ-forming sequences (PHQS) in the genomes of eukaryotic organisms. Starting from amphibian, PHQS motifs are concentrated in the immediate 1000-nt region downstream of transcription start sites, implying their potential role in transcription regulation. Moreover, their occurrence shows a strong bias toward the non-template versus the template strand. PHQS has become constitutional in genes in warm-blooded animals, and the magnitude of the strand bias correlates with the ability of PHQS to form HQ, suggesting a selection based on HQ formation. This strand bias is reversed in lower species, implying that the selection of PHQS/HQ depended on the living temperature of the organisms. In comparison with the putative intramolecular G-quadruplex-forming sequences (PQS), PHQS motifs are far more prevalent and abundant in the transcribed regions, making them the dominant candidates in the formation of G-quadruplexes in transcription. Collectively, these results suggest that the HQ structures are evolutionally selected to function in transcription and other transcription-mediated processes that involve guanine-rich non-template strand.

A yeast one-hybrid and microfluidics-based pipeline to map mammalian gene regulatory networks

The comprehensive mapping of gene promoters and enhancers has significantly improved our understanding of how the mammalian regulatory genome is organized. An important challenge is to elucidate how these regulatory elements contribute to gene expression by identifying their trans-regulatory inputs. Here, we present the generation of a mouse-specific transcription factor (TF) open-reading frame clone library and its implementation in yeast one-hybrid assays to enable large-scale protein-DNA interaction detection with mouse regulatory elements. Once specific interactions are identified, we then use a microfluidics-based method to validate and precisely map them within the respective DNA sequences. Using well-described regulatory elements as well as orphan enhancers, we show that this cross-platform pipeline characterizes known and uncovers many novel TF-DNA interactions. In addition, we provide evidence that several of these novel interactions are relevant in vivo and aid in elucidating the regulatory architecture of enhancers.