scholarly journals Reconstruction of a global gene regulatory network for Streptomyces coelicolor: Curation, inference, and assessment

2021 ◽  
Author(s):  
Andrea Zorro-Aranda ◽  
Juan Miguel Escorcia-Rodriguez ◽  
Jose Kenyi Gonzalez-Kise ◽  
Julio Augusto Freyre-Gonzalez
Keyword(s):  
Gene Regulatory Network ◽  
Dna Sequences ◽  
Regulatory Network ◽  
Sequence Data ◽  
Antibiotic Production ◽  
Streptomyces Coelicolor ◽  
System Level ◽  
Upstream Region ◽  
Regulatory Interactions ◽  
Gene Regulatory

Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. However, little effort to globally study its transcription has been made even though S. coelicolor has an outstanding variety of regulators among bacteria. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~41% of the total expected interactions). This provides the most extensive and up-to-date reconstruction available for the regulatory circuitry of this organism. We found a low level of direct experimental validation for the regulatory interactions reported in the literature and curated in this work. Only ~6% (533/9687) are supported by experiments confirming the binding of the transcription factor to the upstream region of the target gene, the so-called "strong" evidence. To tackle network incompleteness, we performed network inference using several methods (including two proposed here) for motif detection in DNA sequences and GRN inference from transcriptomics. Further, we contrasted the structural properties and functional architecture of the networks to assess the predictions' reliability, finding the inference from DNA sequence data to be the most trustworthy. Finally, we show two possible applications of the inferred and the curated network. The inferred one allowed us to identify putative novel transcription factors for the key Streptomyces antibiotic regulatory proteins (SARPs). The curated one allows us to study the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum. There we identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as Supplementary Material.

scholarly journals Reconstruction of a Global Gene Regulatory Network for Streptomyces Coelicolor: Curation, Inference, and Assessment

2021 ◽  
Author(s):  
Andrea Zorro-Aranda ◽  
Juan Miguel Escorcia-Rodríguez ◽  
José Kenyi González-Kise ◽  
Julio Augusto Freyre-González
Keyword(s):  
Gene Regulatory Network ◽  
Dna Sequences ◽  
Regulatory Network ◽  
Network Inference ◽  
Sequence Data ◽  
Antibiotic Production ◽  
Streptomyces Coelicolor ◽  
Community Integration ◽  
System Level ◽  
Gene Regulatory

Abstract Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. However, little effort to globally study its transcription has been made even though S. coelicolor has an outstanding variety of regulators among bacteria. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~41% of the total expected interactions). We performed network inference using several methods (including two proposed here) for motif detection in DNA sequences and GRN inference from transcriptomics to tackle network incompleteness, using a community integration approach to reduce false positives. Further, we contrasted the structural properties and functional architecture of the networks to assess the predictions’ reliability, finding the inference from DNA sequence data to be the most trustworthy. The inferences allowed us to identify putative novel transcription factors for key Streptomyces antibiotic regulatory proteins (SARPs). Finally, we studied the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum and identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as supplementary material.

scholarly journals Modeling a gene regulatory network of EMT hybrid states for mouse embryonic skin cells

2019 ◽  
Author(s):  
Dan Ramirez ◽  
Vivek Kohar ◽  
Ataur Katebi ◽  
Mingyang Lu
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Epithelial Mesenchymal Transition ◽  
Expression Patterns ◽  
Mesenchymal Transition ◽  
Hybrid State ◽  
Regulatory Interactions ◽  
Skin Cells ◽  
Embryonic Skin ◽  
Gene Regulatory

AbstractEpithelial-mesenchymal transition (EMT) plays a crucial role in embryonic development and tumorigenesis. Although EMT has been extensively studied with both computational and experimental methods, the gene regulatory mechanisms governing the transition are not yet well understood. Recent investigations have begun to better characterize the complex phenotypic plasticity underlying EMT using a computational systems biology approach. Here, we analyzed recently published single-cell RNA sequencing data from E9.5 to E11.5 mouse embryonic skin cells and identified the gene expression patterns of both epithelial and mesenchymal phenotypes, as well as a clear hybrid state. By integrating the scRNA-seq data and gene regulatory interactions from the literature, we constructed a gene regulatory network model governing the decision-making of EMT in the context of the developing mouse embryo. We simulated the network using a recently developed mathematical modeling method, named RACIPE, and observed three distinct phenotypic states whose gene expression patterns can be associated with the epithelial, hybrid, and mesenchymal states in the scRNA-seq data. Additionally, the model is in agreement with published results on the composition of EMT phenotypes and regulatory networks. We identified Wnt signaling as a major pathway in inducing the EMT and its role in driving cellular state transitions during embryonic development. Our findings demonstrate a new method of identifying and incorporating tissue-specific regulatory interactions into gene regulatory network modeling.Author SummaryEpithelial-mesenchymal transition (EMT) is a cellular process wherein cells become disconnected from their surroundings and acquire the ability to migrate through the body. EMT has been observed in biological contexts including development, wound healing, and cancer, yet the regulatory mechanisms underlying it are not well understood. Of particular interest is a purported hybrid state, in which cells can retain some adhesion to their surroundings but also show mesenchymal traits. Here, we examine the prevalence and composition of the hybrid state in the context of the embryonic mouse, integrating gene regulatory interactions from published experimental results as well as from the specific single cell RNA sequencing dataset of interest. Using mathematical modeling, we simulated a regulatory network based on these sources and aligned the simulated phenotypes with those in the data. We identified a hybrid EMT phenotype and revealed the inducing effect of Wnt signaling on EMT in this context. Our regulatory network construction process can be applied beyond EMT to illuminate the behavior of any biological phenomenon occurring in a specific context, allowing better identification of therapeutic targets and further research directions.

scholarly journals Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Roan Eltigani Zaied ◽  
Tayaza Fadason ◽  
Murim Choi ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Target Genes ◽  
Specific Gene ◽  
Fat Percentage ◽  
Alcoholic Fatty Liver ◽  
Regulatory Interactions ◽  
Hepatic Diseases ◽  
Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

scholarly journals Odd-paired controls frequency doubling in Drosophila segmentation by altering the pair-rule gene regulatory network

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Erik Clark ◽  
Michael Akam
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Frequency Doubling ◽  
Drosophila Embryo ◽  
Anteroposterior Patterning ◽  
Regulatory Interactions ◽  
Pair Rule Gene ◽  
Gene Regulatory ◽  
Pair Rule

The Drosophila embryo transiently exhibits a double-segment periodicity, defined by the expression of seven 'pair-rule' genes, each in a pattern of seven stripes. At gastrulation, interactions between the pair-rule genes lead to frequency doubling and the patterning of 14 parasegment boundaries. In contrast to earlier stages of Drosophila anteroposterior patterning, this transition is not well understood. By carefully analysing the spatiotemporal dynamics of pair-rule gene expression, we demonstrate that frequency-doubling is precipitated by multiple coordinated changes to the network of regulatory interactions between the pair-rule genes. We identify the broadly expressed but temporally patterned transcription factor, Odd-paired (Opa/Zic), as the cause of these changes, and show that the patterning of the even-numbered parasegment boundaries relies on Opa-dependent regulatory interactions. Our findings indicate that the pair-rule gene regulatory network has a temporally modulated topology, permitting the pair-rule genes to play stage-specific patterning roles.

scholarly journals Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

2019 ◽  
Author(s):  
Daniel Morgan ◽  
Matthew Studham ◽  
Andreas Tjärnberg ◽  
Holger Weishaupt ◽  
Fredrik J. Swartling ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Validation Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

scholarly journals BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Patrick M. Staunton ◽  
Aleksandra A. Miranda-CasoLuengo ◽  
Brendan J. Loftus ◽  
Isobel Claire Gormley
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Likelihood Function ◽  
Computational Modelling ◽  
Regulatory Elements ◽  
Mycobacterium Abscessus ◽  
Sequence Information ◽  
Auxiliary Data ◽  
Regulatory Interactions ◽  
Gene Regulatory

Abstract Background Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to survive in hostile environments. Here, to computationally infer the gene regulatory network for Mycobacterium abscessus we propose a novel statistical computational modelling approach: BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER). In tandem with derived experimental coexpression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus.Inference on regulatory interactions is conducted by combining ‘primary’ and ‘auxiliary’ data strata. The data forming the primary and auxiliary strata are derived from RNA-seq experiments and sequence information in the primary organism Mycobacterium abscessus as well as ChIP-seq data extracted from a related proxy organism Mycobacterium tuberculosis. The primary and auxiliary data are combined in a hierarchical Bayesian framework, informing the apposite bivariate likelihood function and prior distributions respectively. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus. Results We implement BINDER on data relating to a collection of 167,280 regulator-target pairs resulting in the identification of 54 regulator-target pairs, across 5 transcription factors, for which there is strong probability of regulatory interaction. Conclusions The inferred regulatory interactions provide insight to, and a valuable resource for further studies of, transcriptional control in Mycobacterium abscessus, and in the family of Mycobacteriaceae more generally. Further, the developed BINDER framework has broad applicability, useable in settings where computational inference of a gene regulatory network requires integration of data sources derived from both the primary organism of interest and from related proxy organisms.

scholarly journals Reconstruction of Gene Regulatory Network using Modified Ant-based Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 443-448
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Graphical Representation ◽  
Gene Interaction ◽  
Biological Data ◽  
Swine Flu ◽  
Major Area ◽  
Regulatory Interactions ◽  
Nature Inspired Algorithm ◽  
Gene Regulatory

Healthcare is a major area of research since few years. Ample amount of biological data getting accumulated daily due to advancement in technologies. Microarray is such technology which captures expressions of thousands of genes at a time. Interactions occur among genes are represented in terms of special networkeknown as Gene Regulatory Network (GRN). It is constructed from Differentially Expressing Genes(DEFs). GRN is a graphical representation containing genes as nodes and regulatory interactions among them as edges. It helps in tracking pathways where usual gene interaction changes leading to malfunctioning of cells and results in illness. Also, now a day’s people are diagnosed with new diseases like dengue, swine flu, Nipah, Corona virus infection for which exact molecular pathways are yet to be invented through GRN. Therefore, in this paper, a nature inspired algorithm is used for reconstruction of GRN using differentially expressing genes.

scholarly journals Odd-paired controls frequency doubling in Drosophila segmentation by altering the pair-rule gene regulatory network

2016 ◽  
Author(s):  
Erik Clark ◽  
Michael Akam
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Frequency Doubling ◽  
Anteroposterior Patterning ◽  
Regulatory Interactions ◽  
Pair Rule Gene ◽  
Gene Regulatory ◽  
Pair Rule

ABSTRACTThe Drosophila embryo transiently exhibits a double segment periodicity, defined by the expression of seven “pair-rule” genes, each in a pattern of seven stripes. At gastrulation, interactions between the pair-rule genes lead to frequency doubling and the patterning of fourteen parasegment boundaries. In contrast to earlier stages of Drosophila anteroposterior patterning, this transition is not well understood. By carefully analysing the spatiotemporal dynamics of pair-rule gene expression, we demonstrate that frequency-doubling is precipitated by multiple coordinated changes to the network of regulatory interactions between the pair-rule genes. We identify the broadly expressed but temporally patterned transcription factor, Odd-paired (Opa/Zic), as the cause of these changes, and show that the patterning of the even-numbered parasegment boundaries relies on Opa-dependent regulatory interactions. Our findings indicate that the pair-rule gene regulatory network has a temporally-modulated topology, permitting the pair-rule genes to play stage-specific patterning roles.

scholarly journals A system-level gene regulatory network model for Plasmodium falciparum

2020 ◽  
Author(s):  
ML Neal ◽  
L Wei ◽  
E Peterson ◽  
ML Arrieta-Ortiz ◽  
SA Danziger ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Artemisinin Resistance ◽  
System Level ◽  
Data Sets ◽  
Regulatory Processes ◽  
Hedging Strategies ◽  
Level Model ◽  
Gene Regulatory

AbstractMany of the gene regulatory processes of Plasmodium falciparum, the deadliest malaria parasite, remain poorly understood. To develop a comprehensive guide for exploring this organism’s gene regulatory network, we generated a system-level model of Plasmodium falciparum gene regulation using a well-validated, machine-learning approach for predicting interactions between transcription regulators and their targets. The resulting network accurately predicts expression levels of transcriptionally coherent gene regulatory programs in independent transcriptomic data sets from parasites collected by different research groups in diverse laboratory and field settings. Thus, our results indicate that our gene regulatory model has predictive power and utility as a hypothesis-generating tool for illuminating clinically relevant gene regulatory mechanisms within Plasmodium falciparum. Using the set of regulatory programs we identified, we also investigated correlates of artemisinin resistance based on gene expression coherence. We report that resistance is associated with incoherent expression across many regulatory programs, including those controlling genes associated with erythrocyte-host engagement. These results suggest that parasite populations with reduced artemisinin sensitivity are more transcriptionally heterogenous. This pattern is consistent with a model where the parasite utilizes bet-hedging strategies to diversify the population, rendering a subpopulation more able to navigate drug treatment.

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