Evaluating biological activity of compounds by transcription factor activity profiling.
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ABSTRACT:
SUBMITTER: Medvedev A
PROVIDER: S-EPMC6157966 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Transcription factors (TFs) exert their regulatory influence through the binding of enhancers, resulting in coordination of gene expression programs. Active enhancers are often characterized by the presence of short, unstable transcripts termed enhancer RNAs (eRNAs). While their function remains unclear, we demonstrate that eRNAs are a powerful readout of TF activity. We infer sites of eRNA origination across hundreds of publicly available nascent transcription data sets and show that eRNAs initiate from sites of TF binding. By quantifying the colocalization of TF binding motif instances and eRNA origins, we derive a simple statistic capable of inferring TF activity. In doing so, we uncover dozens of previously unexplored links between diverse stimuli and the TFs they affect.
| S-EPMC5848612 | biostudies-literature
Project description:Live-cell assays to measure cellular function performed within 3D cultures have the potential to elucidate the underlying processes behind disease progression and tissue formation. Cells cultured in 3D interact and remodel their microenvironment and can develop into complex structures. We have developed a transcription factor (TF) activity array that uses bioluminescence imaging (BLI) of lentiviral delivered luminescent reporter constructs that allows for the non-invasive imaging of TF activity in both 2D and 3D culture. Imaging can be applied repeatedly throughout culture to capture dynamic TF activity, though appropriate normalization is necessary. We investigated in-well normalization using Gaussia or Renilla luciferase, and external well normalization using firefly luciferase. Gaussia and Renilla luciferase were each unable to provide consistent normalization for long-term measurement of TF activity. However, external well normalization provided low variability and accounted for changes in cellular dynamics. Using external normalization, dynamic TF activities were quantified for five TFs. The array captured expected changes in TF activity to stimuli, however the array also provided dynamic profiles within 2D and 3D that have not been previously characterized. The development of the technology to dynamically track TF activity within cells cultured in both 2D and 3D can provide greater understanding of complex cellular processes.
| S-EPMC4073638 | biostudies-literature
Project description:Several methods predict activity changes of transcription factors (TFs) from a given regulatory network and measured expression data. But available gene regulatory networks are incomplete and contain many condition-dependent regulations that are not relevant for the specific expression measurement. It is not known which combination of active TFs is needed to cause a change in the expression of a target gene. A method to systematically evaluate the inferred activity changes is missing. We present such an evaluation strategy that indicates for how many target genes the observed expression changes can be explained by a given set of active TFs. To overcome the problem that the exact combination of active TFs needed to activate a gene is typically not known, we assume a gene to be explained if there exists any combination for which the predicted active TFs can possibly explain the observed change of the gene. We introduce the i-score (inconsistency score), which quantifies how many genes could not be explained by the set of activity changes of TFs. We observe that, even for these minimal requirements, published methods yield many unexplained target genes, i.e. large i-scores. This holds for all methods and all expression datasets we evaluated. We provide new optimization methods to calculate the best possible (minimal) i-score given the network and measured expression data. The evaluation of this optimized i-score on a large data compendium yields many unexplained target genes for almost every case. This indicates that currently available regulatory networks are still far from being complete. Both the presented Act-SAT and Act-A* methods produce optimal sets of TF activity changes, which can be used to investigate the difficult interplay of expression and network data. A web server and a command line tool to calculate our i-score and to find the active TFs associated with the minimal i-score is available from https://services.bio.ifi.lmu.de/i-score.
| S-EPMC5056719 | biostudies-literature
Project description:Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.
| S-EPMC8782462 | biostudies-literature
Project description:BackgroundBinding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease. TFBSs are generally recognized by scanning a position weight matrix (PWM) against DNA using one of a number of available computer programs. Thus we set out to evaluate the best tools that can be used locally (and are therefore suitable for large-scale analyses) for creating PWMs from high-throughput ChIP-Seq data and for scanning them against DNA.ResultsWe evaluated a set of de novo motif discovery tools that could be downloaded and installed locally using ENCODE-ChIP-Seq data and showed that rGADEM was the best-performing tool. TFBS prediction tools used to scan PWMs against DNA fall into two classes - those that predict individual TFBSs and those that identify clusters. Our evaluation showed that FIMO and MCAST performed best respectively.ConclusionsSelection of the best-performing tools for generating PWMs from ChIP-Seq data and for scanning PWMs against DNA has the potential to improve prediction of precise transcription factor binding sites within regions identified by ChIP-Seq experiments for gene finding, understanding regulation and in evaluating the effects of single nucleotide variations in causing disease.
| S-EPMC6889335 | biostudies-literature
Project description:BackgroundExtracellular activation of signal transduction pathways and their downstream target transcription factors (TFs) are critical regulators of cellular processes and tissue development. The intracellular signaling network is complex, and techniques that quantify the activities of numerous pathways and connect their activities to the resulting phenotype would identify the signals and mechanisms regulating tissue development. The ability to investigate tissue development should capture the dynamic pathway activity and requires an environment that supports cellular organization into structures that mimic in vivo phenotypes. Taken together, our objective was to develop cellular arrays for dynamic, large-scale quantification of TF activity as cells organized into spherical structures within 3D culture.Methodology/principal findingsTF-specific and normalization reporter constructs were delivered in parallel to a cellular array containing a well-established breast cancer cell line cultured in Matrigel. Bioluminescence imaging provided a rapid, non-invasive, and sensitive method to quantify luciferase levels, and was applied repeatedly on each sample to monitor dynamic activity. Arrays measuring 28 TFs identified up to 19 active, with 13 factors changing significantly over time. Stimulation of cells with ?-estradiol or activin A resulted in differential TF activity profiles evolving from initial stimulation of the ligand. Many TFs changed as expected based on previous reports, yet arrays were able to replicate these results in a single experiment. Additionally, arrays identified TFs that had not previously been linked with activin A.Conclusions/significanceThis system provides a method for large-scale, non-invasive, and dynamic quantification of signaling pathway activity as cells organize into structures. The arrays may find utility for investigating mechanisms regulating normal and abnormal tissue growth, biomaterial design, or as a platform for screening therapeutics.
| S-EPMC2984444 | biostudies-literature
Project description:Mentha species are widely used as food, medicine, spices, and flavoring agents. Thus, chemical composition is an important parameter for assessing the quality of mints. In general, the contents of menthol, menthone, eucalyptol, and limonene comprise one of the major parameters for assessing the quality of commercially important mints. Building further on the phytochemical characterization of the quality of Mentha species, this work was focused on the composition of phenolic compounds in methanolic extracts. Thirteen Mentha species were grown under the same environmental conditions, and their methanolic extracts were subjected to the LC-MS/MS (liquid chromatography-tandem mass spectrometry) profiling of phenolics and the testing their biological activities, i.e., antioxidant and tyrosinase inhibition activities, which are important features for the cosmetic industry. The total phenolic content (TPC) ranged from 14.81 ± 1.09 mg GAE (gallic acid equivalents)/g for Mentha cervina to 58.93. ± 8.39 mg GAE/g for Mentha suaveolens. The antioxidant activity of examined Mentha related with the content of the phenolic compounds and ranged from 22.79 ± 1.85 to 106.04 ± 3.26 mg TE (Trolox equivalents)/g for M. cervina and Mentha x villosa, respectively. Additionally, Mentha pulegium (123.89 ± 5.64 mg KAE (kojic acid equivalents)/g) and Mentha x piperita (102.82 ± 15.16 mg KAE/g) showed a strong inhibition of the enzyme tyrosinase, which is related to skin hyperpigmentation. The most abundant compound in all samples was rosmarinic acid, ranging from 1363.38 ± 8323 to 2557.08 ± 64.21 μg/g. In general, the levels of phenolic acids in all examined mint extracts did not significantly differ. On the contrary, the levels of flavonoids varied within the species, especially in the case of hesperidin (from 0.73 ± 0.02 to 109. 39 ± 2.01 μg/g), luteolin (from 1.84 ± 0.11 to 31.03 ± 0.16 μg/g), and kaempferol (from 1.30 ± 0.17 to 33.68 ± 0.81 μg/g). Overall results indicated that all examined mints possess significant amounts of phenolic compounds that are responsible for antioxidant activity and, to some extent, for tyrosinase inhibition activity. Phenolics also proved to be adequate compounds, together with terpenoids, for the characterization of Mentha sp. Additionally, citrus-scented Mentha x villosa could be selected as a good candidate for the food and pharmaceutical industry, especially due its chemical composition and easy cultivation, even in winter continental conditions.
| S-EPMC8000339 | biostudies-literature
Project description:The Ras-responsive element binding protein 1(RREB1) is a member of zinc finger transcription factors, which is widely involved in biological processes including cell proliferation, transcriptional regulation and DNA damage repair. New findings reveal RREB1 functions as both transcriptional repressors and transcriptional activators for transcriptional regulation of target genes. The activation of RREB1 is regulated by MAPK pathway. We have summarized the target genes of RREB1 and discussed RREB1 roles in the cancer development. In addition, increasing evidences suggest that RREB1 is a potential risk gene for type 2 diabetes and obesity. We also review the current clinical application of RREB1 as a biomarker for melanoma detection. In conclusion, RREB1 is a promising diagnostic biomarker or new drug target for cancers and metabolic diseases.
| S-EPMC7085234 | biostudies-literature
Project description:BackgroundAn important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties.Principal findings(1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4). (2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression. (3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties. (4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter.ConclusionPostprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite.
| S-EPMC2441612 | biostudies-literature
Project description:Understanding the molecular mechanisms of gene regulation is pivotal for understanding how cells establish and modify their identities and functions. Multiple transcription factors (TFs) coordinate to alter gene expression in cells; however, a method to quantitatively analyze the activity of each TF is lacking, particularly in vivo. Here, we introduce a viral-vector-based TF reporter battery that can be used to simultaneously analyze the activity of multiple TFs, visualized as the TF activity profile (TFAP) obtained by qPCR. We show that the cells possess distinct TFAPs that dynamically change according to experimental manipulation or physiological activity. We report a practical method to obtain the TFAP of a defined cell population and their experience-dependent changes in the mouse brain in vivo. The TFAP obtained by our method will help bridge the information gap between the genome and transcriptome and aid the multi-omics view of understanding the gene regulation system.
| S-EPMC8904617 | biostudies-literature