scholarly journals Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

2018 ◽  
Author(s):  
Emily R. Miraldi ◽  
Maria Pokrovskii ◽  
Aaron Watters ◽  
Dayanne M. Castro ◽  
Nicholas De Veaux ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Indirect Evidence ◽  
T Helper ◽  
Motif Analysis ◽  
T Helper 17 ◽  
Transcriptional Regulatory

AbstractTranscriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The Assay for Transposase Accessible Chromatin (ATAC)-seq, coupled with transcription-factor motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to influence gene expression modeling. We rigorously test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources (plentiful gene expression data, TF knock-outs and ChIP-seq experiments). In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF KO, ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs (“TF-TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that application of our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.

scholarly journals Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

2019 ◽  
Vol 29 (3) ◽  
pp. 449-463 ◽  
Author(s):  
Emily R. Miraldi ◽  
Maria Pokrovskii ◽  
Aaron Watters ◽  
Dayanne M. Castro ◽  
Nicholas De Veaux ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Chromatin Accessibility ◽  
T Helper ◽  
T Helper 17 ◽  
T Helper 17 Cells ◽  
Transcriptional Regulatory

scholarly journals Mutual information estimation for transcriptional regulatory network inference

2017 ◽  
Author(s):  
Jonathan Ish-Horowicz ◽  
John Reid
Keyword(s):  
Mutual Information ◽  
Regulatory Networks ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Estimation Method ◽  
Computation Time ◽  
Estimation Methods ◽  
Inference Algorithm ◽  
Inference Algorithms ◽  
Transcriptional Regulatory

AbstractMutual information-based network inference algorithms are an important tool in the reverse-engineering of transcriptional regulatory networks, but all rely on estimates of the mutual information between the expression of pairs of genes. Various methods exist to compute estimates of the mutual information, but none have been firmly established as optimal for network inference. The performance of 9 mutual information estimation methods are compared using three popular network inference algorithms: CLR, MRNET and ARACNE. The performance of the estimators is compared on one synthetic and two real datasets. For estimators that discretise data, the effect of discretisation parameters are also studied in detail. Implementations of 5 estimators are provided in parallelised C++ with an R interface. These are faster than alternative implementations, with reductions in computation time up to a factor of 3,500.ResultsThe B-spline estimator consistently performs well on real and synthetic datasets. CLR was found to be the best performing inference algorithm, corroborating previous results indicating that it is the state of the art mutual inference algorithm. It is also found to be robust to the mutual information estimation method and their parameters. Furthermore, when using an estimator that discretises expression data, using N1/3 bins for N samples gives the most accurate inferred network. This contradicts previous findings that suggested using N1/2 bins.

scholarly journals Revealing 29 sets of independently modulated genes inStaphylococcus aureus, their regulators, and role in key physiological response

2020 ◽  
Vol 117 (29) ◽  
pp. 17228-17239 ◽  
Author(s):  
Saugat Poudel ◽  
Hannah Tsunemoto ◽  
Yara Seif ◽  
Anand V. Sastry ◽  
Richard Szubin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Mueller Hinton ◽  
Transcriptional Regulatory ◽  
Its Gene ◽  
Low Dimensional ◽  
Regulatory Organization

The ability ofStaphylococcus aureusto infect many different tissue sites is enabled, in part, by its transcriptional regulatory network (TRN) that coordinates its gene expression to respond to different environments. We elucidated the organization and activity of this TRN by applying independent component analysis to a compendium of 108 RNA-sequencing expression profiles from twoS. aureusclinical strains (TCH1516 and LAC). ICA decomposed theS. aureustranscriptome into 29 independently modulated sets of genes (i-modulons) that revealed: 1) High confidence associations between 21 i-modulons and known regulators; 2) an association between an i-modulon and σS, whose regulatory role was previously undefined; 3) the regulatory organization of 65 virulence factors in the form of three i-modulons associated with AgrR, SaeR, and Vim-3; 4) the roles of three key transcription factors (CodY, Fur, and CcpA) in coordinating the metabolic and regulatory networks; and 5) a low-dimensional representation, involving the function of few transcription factors of changes in gene expression between two laboratory media (RPMI, cation adjust Mueller Hinton broth) and two physiological media (blood and serum). This representation of the TRN covers 842 genes representing 76% of the variance in gene expression that provides a quantitative reconstruction of transcriptional modules inS. aureus, and a platform enabling its full elucidation.

scholarly journals Regulus, a transcriptional regulatory networks inference tool based on Semantic Web technologies

2021 ◽  
Author(s):  
Marine Louarn ◽  
Guillaume Collet ◽  
Eve Barre ◽  
Thierry Fest ◽  
Olivier Dameron ◽  
...  
Keyword(s):  
Gene Expression ◽  
Semantic Web ◽  
Regulatory Networks ◽  
Network Inference ◽  
Transcriptional Regulatory Networks ◽  
Semantic Web Technologies ◽  
Regulatory Regions ◽  
Web Technologies ◽  
Transcriptional Regulatory ◽  
Tf Gene

Motivation: Transcriptional regulation -a major field of investigation in life science- is performed by binding of specialized proteins called transcription factors (TF) to DNA in specific, context-dependent regulatory regions, leading to either activation or inhibition of gene expression. Relations between TF, regions and genes can be described as regulatory networks, which are basically knowledge graphs containing the relationships between the different entities. Current methods of transcriptional regulatory networks inference rarely use information about TF binding or regulatory regions, often require a large number of samples and most of time do not indicate if the TF-gene relation is an activation or an inhibition. The resulting networks may then contain inconsistent relations and the methods are not applicable for common experimental or clinical settings, where the number of samples is limited. Therefore, based on our previous experience of formalizing the Regulatory Circuits data-sets with Semantic Web Technologies, we decided to create a new tool for transcriptional networks inference, that could solve these issues. Results: Our tool, Regulus, provides candidate signed TF-gene relations computed from gene expressions, regulatory region activities and TF binding sites data, together with the genomic location of all entities. After creating expressions and activities patterns, data are integrated into a RDF endpoint. A dedicated SPARQL query retrieves all potential TF-region relations for a given gene expression pattern. These ternary TF-region-gene pattern relations are then filtered and signed using a logical consistency check translated from biological knowledge. Regulus compares favorably to its closest network inference method, provides signs which are consistent with public databases and, when applied to real biological data, identifies both known and potential new regulators. We also provide several means to more stringently filter the output regulators. Altogether, we propose a new tool devoted to transcriptional network inference in settings where samples are scarce and cell populations may be closely related.

scholarly journals Global transcriptional regulatory network forEscherichia colirobustly connects gene expression to transcription factor activities

2017 ◽  
Vol 114 (38) ◽  
pp. 10286-10291 ◽  
Author(s):  
Xin Fang ◽  
Anand Sastry ◽  
Nathan Mih ◽  
Donghyuk Kim ◽  
Justin Tan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Data Types ◽  
High Confidence ◽  
Specific Expression ◽  
Surrogate Variable Analysis ◽  
Transcriptional Regulatory

Transcriptional regulatory networks (TRNs) have been studied intensely for >25 y. Yet, even for theEscherichia coliTRN—probably the best characterized TRN—several questions remain. Here, we address three questions: (i) How complete is our knowledge of theE. coliTRN; (ii) how well can we predict gene expression using this TRN; and (iii) how robust is our understanding of the TRN? First, we reconstructed a high-confidence TRN (hiTRN) consisting of 147 transcription factors (TFs) regulating 1,538 transcription units (TUs) encoding 1,764 genes. The 3,797 high-confidence regulatory interactions were collected from published, validated chromatin immunoprecipitation (ChIP) data and RegulonDB. For 21 different TF knockouts, up to 63% of the differentially expressed genes in the hiTRN were traced to the knocked-out TF through regulatory cascades. Second, we trained supervised machine learning algorithms to predict the expression of 1,364 TUs given TF activities using 441 samples. The algorithms accurately predicted condition-specific expression for 86% (1,174 of 1,364) of the TUs, while 193 TUs (14%) were predicted better than random TRNs. Third, we identified 10 regulatory modules whose definitions were robust against changes to the TRN or expression compendium. Using surrogate variable analysis, we also identified three unmodeled factors that systematically influenced gene expression. Our computational workflow comprehensively characterizes the predictive capabilities and systems-level functions of an organism’s TRN from disparate data types.

scholarly journals Chromatin accessibility analysis reveals regulatory dynamics of developing human retina and hiPSC-derived retinal organoids

2020 ◽  
Vol 6 (6) ◽  
pp. eaay5247 ◽  
Author(s):  
Haohuan Xie ◽  
Wen Zhang ◽  
Mei Zhang ◽  
Tasneem Akhtar ◽  
Young Li ◽  
...  
Keyword(s):  
Transcriptional Regulatory Network ◽  
Retinal Development ◽  
Chromatin Accessibility ◽  
Mouse Retina ◽  
Transcriptional Dynamics ◽  
Transcriptional Regulatory ◽  
Regulatory Dynamics ◽  
Induced Pluripotent

Retinal organoids (ROs) derived from human induced pluripotent stem cells (hiPSCs) provide potential opportunities for studying human retinal development and disorders; however, to what extent ROs recapitulate the epigenetic features of human retinal development is unknown. In this study, we systematically profiled chromatin accessibility and transcriptional dynamics over long-term human retinal and RO development. Our results showed that ROs recapitulated the human retinogenesis to a great extent, but divergent chromatin features were also discovered. We further reconstructed the transcriptional regulatory network governing human and RO retinogenesis in vivo. Notably, NFIB and THRA were identified as regulators in human retinal development. The chromatin modifications between developing human and mouse retina were also cross-analyzed. Notably, we revealed an enriched bivalent modification of H3K4me3 and H3K27me3 in human but not in murine retinogenesis, suggesting a more dedicated epigenetic regulation on human genome.

scholarly journals Synthetic gene regulatory networks in the opportunistic human pathogen Streptococcus pneumoniae

2019 ◽  
Author(s):  
Robin A. Sorg ◽  
Clement Gallay ◽  
Jan-Willem Veening
Keyword(s):  
Gene Expression ◽  
Streptococcus Pneumoniae ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Combinatorial Libraries ◽  
Regulatory Elements ◽  
Expression Control ◽  
Gene Expression Control

AbstractStreptococcus pneumoniae can cause disease in various human tissues and organs, including the ear, the brain, the blood and the lung, and thus in highly diverse and dynamic environments. It is challenging to study how pneumococci control virulence factor expression, because cues of natural environments and the presence of an immune system are difficult to simulate in vitro. Here, we apply synthetic biology methods to reverse-engineer gene expression control in S. pneumoniae. A selection platform is described that allows for straightforward identification of transcriptional regulatory elements out of combinatorial libraries. We present TetR- and LacI-regulated promoters that show expression ranges of four orders of magnitude. Based on these promoters, regulatory networks of higher complexity are assembled, such as logic AND and IMPLY gates. Finally, we demonstrate single-copy genome-integrated toggle switches that give rise to bimodal population distributions. The tools described here can be used to mimic complex expression patterns, such as the ones found for pneumococcal virulence factors, paving the way for in vivo investigations of the importance of gene expression control on the pathogenicity of S. pneumoniae.

scholarly journals Transcriptional regulatory networks that promote and restrict identities and functions of intestinal innate lymphoid cells

2018 ◽  
Author(s):  
Maria Pokrovskii ◽  
Jason A. Hall ◽  
David E. Ochayon ◽  
Ren Yi ◽  
Natalia S. Chaimowitz ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Inflammatory Diseases ◽  
Innate Lymphoid Cells ◽  
Chromatin Accessibility ◽  
Immune Defense ◽  
Lymphoid Cells ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

SummaryInnate lymphoid cells (ILCs) can be subdivided into several distinct cytokine-secreting lineages that promote tissue homeostasis and immune defense but also contribute to inflammatory diseases. Accumulating evidence suggests that ILCs, similarly to other immune populations, are capable of phenotypic and functional plasticity in response to infectious or environmental stimuli. Yet the transcriptional circuits that control ILC identity and function are largely unknown. Here we integrate gene expression and chromatin accessibility data to infer transcriptional regulatory networks within intestinal type 1, 2, and 3 ILCs. We predict the “core” sets of transcription-factor (TF) regulators driving each ILC subset identity, among which only a few TFs were previously known. To assist in the interpretation of these networks, TFs were organized into cooperative clusters, or modules that control gene programs with distinct functions. The ILC network reveals extensive alternative-lineage-gene repression, whose regulation may explain reported plasticity between ILC subsets. We validate new roles for c-MAF and BCL6 as regulators affecting the type 1 and type 3 ILC lineages. Manipulation of TF pathways identified here might provide a novel means to selectively regulate ILC effector functions to alleviate inflammatory disease or enhance host tolerance to pathogenic microbes or noxious stimuli. Our results will enable further exploration of ILC biology, while our network approach will be broadly applicable to identifying key cell state regulators in otherin vivocell populations.

What are Gene Regulatory Networks?

2010 ◽  
pp. 1-27 ◽  
Author(s):  
Alberto de la Fuente
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Models ◽  
Transcriptional Regulatory Networks ◽  
Biochemical Processes ◽  
Transcriptional Regulatory ◽  
Biochemical Systems ◽  
Gene Regulatory

This book deals with algorithms for inferring and analyzing Gene Regulatory Networks using mainly gene expression data. What precisely are the Gene Regulatory Networks that are inferred by such algorithms from this type of data? There is still much confusion in the current literature and it is important to start a book about computational methods for Gene Regulatory Networks with a definition that is as unambiguous as possible. In this chapter, I provide a definition and try to clearly explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. To do the latter in a formal way, I will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed’ into the more compact description of Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, such as Transcriptional Regulatory Networks and Co-Expression Networks, will be highlighted.

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