Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation.
Ontology highlight
ABSTRACT:
SUBMITTER: Hollbacher B
PROVIDER: S-EPMC7306512 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation.
Höllbacher Barbara B Balázs Kinga K Heinig Matthias M Uhlenhaut N Henriette NH
Computational and structural biotechnology journal 20200531
Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machin ...[more]
Similar Datasets
Project description:Epigenetic regulation plays an important role in controlling gene expression during complex processes, such as development of the human brain. Mutations in genes encoding chromatin modifying proteins and in the non-protein coding sequences of the genome can potentially alter transcription factor binding or chromatin accessibility. Such mutations can frequently cause neurodevelopmental disorders, therefore understanding how epigenetic regulation shapes brain development is of particular interest. While epigenetic regulation of neural development has been extensively studied in murine models, significant species-specific differences in both the genome sequence and in brain development necessitate human models. However, access to human fetal material is limited and these tissues cannot be grown or experimentally manipulated ex vivo. Therefore, models that recapitulate particular aspects of human fetal brain development, such as the in vitro differentiation of human pluripotent stem cells (hPSCs), are instrumental for studying the epigenetic regulation of human neural development. Here, we examine recent studies that have defined changes in the epigenomic landscape during fetal brain development. We compare these studies with analogous data derived by in vitro differentiation of hPSCs into specific neuronal cell types or as three-dimensional cerebral organoids. Such comparisons can be informative regarding which aspects of fetal brain development are faithfully recapitulated by in vitro differentiation models and provide a foundation for using experimentally tractable in vitro models of human brain development to study neural gene regulation and the basis of its disruption to cause neurodevelopmental disorders.
| S-EPMC8387070 | biostudies-literature
Project description:Transcription is the principal control point for bacterial gene expression, and it enables a global cellular response to an intracellular or environmental trigger. Transcriptional regulation is orchestrated by transcription factors, which activate or repress transcription of target genes by modulating the activity of RNA polymerase. Dissecting the nature and precise choreography of these interactions is essential for developing a molecular understanding of transcriptional regulation. While the contribution of X-ray crystallography has been invaluable, the 'resolution revolution' of cryo-electron microscopy has transformed our structural investigations, enabling large, dynamic and often transient transcription complexes to be resolved that in many cases had resisted crystallisation. In this review, we highlight the impact cryo-electron microscopy has had in gaining a deeper understanding of transcriptional regulation in bacteria. We also provide readers working within the field with an overview of the recent innovations available for cryo-electron microscopy sample preparation and image reconstruction of transcription complexes.
| S-EPMC8786299 | biostudies-literature
Project description:How cells convert the duration of signals into differential adaptation of gene expression is a poorly understood issue. Signal-induced immediate-early gene (IEG) expression couples early signals to late expression of downstream <target> genes. Here we study how kinetic features of the IEG-<target> system allow temporal integration of stimuli in a pancreatic beta cell model of metabolic stimulation. Gene expression profiling revealed that beta cells produce drastically different transcriptional outputs in response to different stimuli durations. Noteworthy, most genes (87%) regulated by a sustained stimulation (4 h) were not regulated by a transient stimulation (1 h followed by 3 h without stimulus). We analyzed the induction kinetics of several previously identified IEGs and <targets>. IEG expression persisted as long as stimulation was maintained, but was rapidly lost upon stimuli removal, abolishing the delayed <target> induction. The molecular mechanisms coupling the duration of stimuli to quantitative <target> transcription were demonstrated for the AP-1 transcription factor. In conclusion, we propose that the network composed of IEGs and their <targets> dynamically functions to convert signal inputs of different durations into quantitative differences in global transcriptional adaptation. These findings provide a novel and more comprehensive view of dynamic gene regulation.
| S-EPMC1636431 | biostudies-literature
Project description:IntroductionKawasaki disease (KD) is an acute febrile illness primarily affecting children and characterized by systemic inflammation and vasculitis that can lead to coronary artery complications. The aim of this study was to gain a comprehensive understanding of immune dysregulation in KD.MethodsTo this end, we employed integration of single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing (bulk RNA-Seq) data. Furthermore, we conducted flow cytometry analysis for a cohort of 82 KD patients.ResultsOur analysis revealed significant heterogeneity within immune cell populations in KD patients, with distinct clusters of T cells, B cells, and natural killer (NK) cells. Importantly, CD4+ naïve T cells in KD patients were found to predominantly differentiate into Treg cells and Th2 cells, potentially playing a role in the excessive inflammation and vascular damage characteristic of the disease. Dysregulated signaling pathways were also identified, including the mTOR signaling pathway, cardiomyopathy pathway, COVID-19 signaling pathway, and pathways involved in bacterial or viral infection.DiscussionThese findings provide insights into the immunopathogenesis of KD, emphasizing the importance of immune cell dysregulation and dysregulated signaling pathways. Integration of scRNA-Seq and bulk RNA-Seq data offers a comprehensive view of the molecular and cellular alterations in KD and highlights potential therapeutic targets for further investigation. Validation and functional studies are warranted to elucidate the roles of the identified immune cell types and pathways in KD pathogenesis and to develop targeted interventions to improve patient outcomes.
| S-EPMC10568768 | biostudies-literature
Project description:MotivationExperimental and predicted data concerning gene transcriptional regulation are distributed among many heterogeneous sources. However, there are no resources to integrate these data automatically or to provide a 'one-stop shop' experience for users seeking information essential for deciphering and modeling gene regulatory networks.ResultsIntegromeDB, a semantic graph-based 'deep-web' data integration system that automatically captures, integrates and manages publicly available data concerning transcriptional regulation, as well as other relevant biological information, is proposed in this article. The problems associated with data integration are addressed by ontology-driven data mapping, multiple data annotation and heterogeneous data querying, also enabling integration of the user's data. IntegromeDB integrates over 100 experimental and computational data sources relating to genomics, transcriptomics, genetics, and functional and interaction data concerning gene transcriptional regulation in eukaryotes and prokaryotes.AvailabilityIntegromeDB is accessible through the integrated research environment BiologicalNetworks at http://www.BiologicalNetworks.orgContactbaitaluk@sdsc.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2887046 | biostudies-literature
Project description:Transcriptional regulation contributes to the maintenance of pluripotency, self-renewal and differentiation in embryonic cells and in stem cells. Therefore, control of gene expression at the level of transcription is crucial for embryonic development, as well as for organogenesis, functional adaptation, and regeneration in adult tissues and organs. In the past, most work has focused on how transcriptional regulation results from the complex interplay between chemical cues, adhesion signals, transcription factors and their co-regulators during development. However, chemical signaling alone is not sufficient to explain how three-dimensional (3D) tissues and organs are constructed and maintained through the spatiotemporal control of transcriptional activities. Accumulated evidence indicates that mechanical cues, which include physical forces (e.g. tension, compression or shear stress), alterations in extracellular matrix (ECM) mechanics and changes in cell shape, are transmitted to the nucleus directly or indirectly to orchestrate transcriptional activities that are crucial for embryogenesis and organogenesis. In this Commentary, we review how the mechanical control of gene transcription contributes to the maintenance of pluripotency, determination of cell fate, pattern formation and organogenesis, as well as how it is involved in the control of cell and tissue function throughout embryogenesis and adult life. A deeper understanding of these mechanosensitive transcriptional control mechanisms should lead to new approaches to tissue engineering and regenerative medicine.
| S-EPMC3434847 | biostudies-literature
Project description:BackgroundThe process of cellular differentiation is governed by complex dynamical biomolecular networks consisting of a multitude of genes and their products acting in concert to determine a particular cell fate. Thus, a systems level view is necessary for understanding how a cell coordinates this process and for developing effective therapeutic strategies to treat diseases, such as cancer, in which differentiation plays a significant role. Theoretical considerations and recent experimental evidence support the view that cell fates are high dimensional attractor states of the underlying molecular networks. The temporal behavior of the network states progressing toward different cell fate attractors has the potential to elucidate the underlying molecular mechanisms governing differentiation.ResultsUsing the HL60 multipotent promyelocytic leukemia cell line, we performed experiments that ultimately led to two different cell fate attractors by two treatments of varying dosage and duration of the differentiation agent all-trans-retinoic acid (ATRA). The dosage and duration combinations of the two treatments were chosen by means of flow cytometric measurements of CD11b, a well-known early differentiation marker, such that they generated two intermediate populations that were poised at the apparently same stage of differentiation. However, the population of one treatment proceeded toward the terminally differentiated neutrophil attractor while that of the other treatment reverted back toward the undifferentiated promyelocytic attractor. We monitored the gene expression changes in the two populations after their respective treatments over a period of five days and identified a set of genes that diverged in their expression, a subset of which promotes neutrophil differentiation while the other represses cell cycle progression. By employing promoter based transcription factor binding site analysis, we found enrichment in the set of divergent genes, of transcription factors functionally linked to tumor progression, cell cycle, and development.ConclusionSince many of the transcription factors identified by this approach are also known to be implicated in hematopoietic differentiation and leukemia, this study points to the utility of incorporating a dynamical systems level view into a computational analysis framework for elucidating transcriptional mechanisms regulating differentiation.
| S-EPMC2652435 | biostudies-literature
Project description:Given the role of transcriptional misregulation in the pathogenesis of human disease, there is enormous interest in the development of molecules that exogenously control transcription in a defined manner. The past decade has seen many exciting advancements in the identification of molecules that mimic or inhibit the interactions between natural transcriptional activators and their binding partners. In this minireview, we focus on four activator.target protein complexes, highlighting recent advances as well as challenges in the field.
| S-EPMC2856975 | biostudies-literature
Project description:Eukaryotic transcription generally occurs in bursts of activity lasting minutes to hours; however, state-of-the-art measurements have revealed that many of the molecular processes that underlie bursting, such as transcription factor binding to DNA, unfold on timescales of seconds. This temporal disconnect lies at the heart of a broader challenge in physical biology of predicting transcriptional outcomes and cellular decision-making from the dynamics of underlying molecular processes. Here, we review how new dynamical information about the processes underlying transcriptional control can be combined with theoretical models that predict not only averaged transcriptional dynamics, but also their variability, to formulate testable hypotheses about the molecular mechanisms underlying transcriptional bursting and control.
| S-EPMC8498946 | biostudies-literature
Project description:Monitoring transcription in living cells gives access to the dynamics of this complex fundamental process. It reveals that transcription is discontinuous, whereby active periods (bursts) are separated by one or several types of inactive periods of distinct lifetimes. However, decoding temporal fluctuations arising from live imaging and inferring the distinct transcriptional steps eliciting them is a challenge. We present BurstDECONV, a novel statistical inference method that deconvolves signal traces into individual transcription initiation events. We use the distribution of waiting times between successive polymerase initiation events to identify mechanistic features of transcription such as the number of rate-limiting steps and their kinetics. Comparison of our method to alternative methods emphasizes its advantages in terms of precision and flexibility. Unique features such as the direct determination of the number of promoter states and the simultaneous analysis of several potential transcription models make BurstDECONV an ideal analytic framework for live cell transcription imaging experiments. Using simulated realistic data, we found that our method is robust with regards to noise or suboptimal experimental designs. To show its generality, we applied it to different biological contexts such as Drosophila embryos or human cells.
| S-EPMC10484743 | biostudies-literature