Genetic reconstruction of a functional transcriptional regulatory network
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ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE4654 | GEO | 2007/04/01
SECONDARY ACCESSION(S): PRJNA95297
REPOSITORIES: GEO
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Project description:Although global analyses of transcription factor binding provide one view of potential transcriptional regulatory networks, regulation also occurs at levels distinct from transcription factor binding. Here, we use a genetic approach to identify targets of transcription factors in yeast and reconstruct a functional regulatory network. First, we profiled transcriptional responses in S. cerevisiae strains with individual deletions of 263 transcription factors. Then we used directed-weighted graph modeling and regulatory epistasis analysis to identify indirect regulatory relationships between these transcription factors, and from this we reconstructed a functional transcriptional regulatory network. The enrichment of promoter motifs and Gene Ontology annotations provide insight into the biological functions of the transcription factors. Data values are X scores and corresponding p-values derived using an error model. We profiled transcriptional responses upon the individual deletion of more than 260 TFs. We used a directed-weighted graph modelling approach and regulatory epistasis analysis to identify indirect regulatory relationships, and thus constructed a refined, functional transcriptional regulatory network.
2010-07-02 | E-GEOD-4654 | biostudies-arrayexpress
Project description:The importance of extrinsic regulation of hematopoietic stem cell activity is increasingly acknowledged. Here we report the generation of a new niche system, which supports expansion of mouse hematopoietic stem cells in vitro. Characterization of this niche revealed a transcriptional regulatory network including four critical factors, namely FOS, SPI1, KLF10 and TFEC. Interestingly, these factors are essential for osteoclastogenesis, thus revealing an osteoclastic network that supports hematopoietic stem cell self-renewal. Lentiviral vectors containing putative transcription factors regulating HSC expansion were transfected into GPE cells and gene expression values were compared to empty vector controls.
2013-12-31 | E-GEOD-39835 | biostudies-arrayexpress
Project description:Motivation: Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods.Methods: We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for dense graph recovery. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures.Results: Using in silico data, we show that ELMM has better performance than commonly used network inference algorithms including PC Algorithm, GeneNet, and ARACNE. We also apply ELMM to reconstruct a network among 5,400 genes expressed in human lung airway epithelium of healthy nonsmokers, healthy smokers, and smokers with pulmonary diseases assayed using microarrays. The analysis identifies dense subnetworks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress, wound response, and secretion.
2024-06-05 | GSE32030 | GEO
Project description:Endurance exercise in horses implies adaptive processes involving affective, physiological, biochemical, and cognitive-behavioral response in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome and miRNome during endurance exercise could provide significant insights into the molecular response to intense exercise or prediction of this response at basal status. In this perspective, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from 10 horses before and after a 160 km endurance competition. Results: We obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre- endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. This suggested that single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways. Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels. In an extended cohort of 39 animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses at T1. Multiple factorial analyses also identified potential biomarkers at T0 for an increased possibility of failure to finish an endurance competition.
2016-11-18 | ST000503 | MetabolomicsWorkbench
Project description:Boolean approaches and extensions thereof are becoming increasingly popular to model signaling and regulatory networks, including those controlling cell differentiation, pattern formation and embryonic development. Here, we describe a logical modeling framework relying on three steps: the delineation of a regulatory graph, the specification of multilevel components, and the encoding of Boolean rules specifying the behavior of model components depending on the levels or activities of their regulators. Referring to a non-deterministic, asynchronous updating scheme, we present several complementary methods and tools enabling the computation of stable activity patterns, the verification of the reachability of such patterns, as well as the generation of mean temporal evolution curves and the computation of the probabilities to reach distinct activity patterns. We apply this logical framework to the regulatory network controlling T lymphocyte specification. This process involves cross-regulations between specific T cell regulatory factors and factors driving alternative differentiation pathways, which remain accessible during the early steps of thymocyte development. Many transcription factors needed for T cell specification are required in other hematopoietic differentiation pathways and are combined in a fine-tuned, time-dependent fashion to achieve T cell commitment. Using the software GINsim, we integrated current knowledge into a dynamical model, which recapitulates the main developmental steps from early progenitors entering the thymus up to T cell commitment, as well as the impact of various documented environmental and genetic perturbations. Our model analysis further enabled the identification of several knowledge gaps. The model, software and whole analysis workflow are provided in computer-readable and executable form to ensure reproducibility and ease extensions.
2021-04-06 | MODEL2002170001 | BioModels
Project description:Pseudohyphal growth is a developmental pathway seen in some strains of yeast, in which cells elongate and form multicellular filaments in response to environmental stresses. Regulation of this process is complex and involves multiple signaling pathways and a large number of transcription factors (TFs). We used multiplexed transposon M-bM-^@M-^\Calling CardsM-bM-^@M-^] to simultaneously record the genome-wide binding patterns of 28 TFs in nitrogen-starved yeast. We were able to identify TF targets relevant for pseudohyphal growth, producing a detailed map of the regulatory network that governs this process. Using tools from graph theory, we identified 8 transcription factors that lie at the center of this network, including Flo8, Mss11, and Mfg1, which bind as a complex. Surprisingly, the DNA binding preferences for these key transcription factors were not known. Using Calling Card data, we predicted the in vivo DNA binding motif for the Flo8-Mss11-Mfg1 complex and validated it using a reporter assay. We found that this complex binds several important targets, including FLO11, at both their promoter and termination sequences. We demonstrated that this binding pattern is the result of DNA-looping, which regulates the transcription of these targets and is stabilized by an interaction with the nuclear pore complex. This looping provides yeast cells with a transcriptional memory, enabling them to more rapidly execute the filamentous growth program when nitrogen-starved if they have been previously exposed to this condition. These data contain mapped insertion sites from Ty5 transposon "Calling Cards" in M-NM-#1278b yeast sequenced using an Illumina Hiseq. Other sequencing data present are RNA-seq reads.
2014-02-11 | E-GEOD-54831 | biostudies-arrayexpress
Project description:Comparison of Genotyping using pooled DNA samples (Allelotyping) and Individual Genotyping using the Affymetrix Genome-Wide Human SNP Array 6.0 In this study, data from 100 DNA samples individually genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 were used to estimate the error of the pooling approach by comparing the results with those obtained using the same array type but DNA pools each composed of 50 of the same samples. Newly developed and established methods for signal intensity correction were applied. Furthermore, the relative allele intensity signals (RAS) obtained by allelotyping were compared to the corresponding values derived from individual genotyping. Similarly, differences in RAS values between pools were determined and compared.
2013-06-22 | GSE48190 | GEO
Project description:Transcription factors (TFs) play a central role in regulating gene expression by interacting with cis regulatory DNA elements associated with their target genes. Recent surveys have examined the DNA binding specificities of most Saccharomyces cerevisiae transcription factors but a comprehensive evaluation of their data has been lacking. Results: We analyzed in vitro and in vivo TF-DNA binding data reported in previous large-scale studies to generate a comprehensive, curated resource of DNA binding specificity data for all characterized S. cerevisiae transcription factors. Our collection comprises DNA binding site motifs and comprehensive in vitro DNA binding specificity data for all possible 8 bp sequences. Included in this database is DNA binding specificity data for 27 TFs independently generated by PBM analysis in this current study. Investigation of the DNA binding specificities within the basic leucine zipper (bZIP) and VHR transcription factor families revealed unexpected plasticity in TF-DNA recognition: intriguingly, the VHR transcription factors, newly characterized by protein binding microarrays in this study, recognize bZIP like DNA motifs, while the bZIP transcription factor Hac1 recognizes a motif highly similar to the canonical E-box motif of basic helix-loop-helix (bHLH) transcription factors. We identified several transcription factors with distinct primary and secondary motifs, which might be associated with different regulatory functions. Finally, integrated analysis of in vivo transcription factor binding data with protein binding microarray data lends further support for indirect DNA binding in vivo by sequence-specific transcription factors. 27 Protein binding microarray (PBM) experiments of Saccharomyces cerevisiae transcription factors were performed. Briefly, the PBMs involved binding GST-tagged yeast transcription factors to double-stranded 44K Agilent microarrays in order to determine their sequence preferences. The method is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473). A key feature is that the microarrays are composed of de Bruijn sequences that contain each 10-base sequence once and only once, providing an evenly balanced sequence distribution. Individual de Bruijn sequences have different properties, including representation of gapped patterns. The array probe sequences on the custom array design used in this study were reported previously in Berger et al., Cell 2008 (PMID 18585359) and are available via an academic research use license. Here we provide the data transformed into median signal intensities (after normalization and detrending of the original array data) for all 32,896 8-base sequences, Z-scores for these intensities, and E-scores. E-scores are a modified version of AUC and describe how well each 8-mer ranks the intensities of the spots. In general, the E-scores are slightly more reproducible than Z-scores, but contain less information about relative binding affinity. Additional experimental details are found in Berger et al., Nature Biotechnology 2006, Gordan et al., Genome Biology (in press), and the accompanying Supplementary information.
2011-12-10 | E-GEOD-34306 | biostudies-arrayexpress