Faculty Opinions recommendation of Genome-Scale Transcriptional Regulatory Network Models of Psychiatric and Neurodegenerative Disorders.

ABSTRACT Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism. Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean’s primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom’s metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels. This transcriptional regulatory network was integrated with a recently published genome-scale metabolic model of Phaeodactylum tricornutum to explore the connectivity of the regulatory network and shared metabolites. The integrated regulatory and metabolic model revealed highly connected modules within carbon and nitrogen metabolism. P. tricornutum’s response to rising carbon levels was analyzed by using the recent genome-scale metabolic model with cross comparison to experimental manipulations of carbon dioxide. IMPORTANCE Using a systems biology approach, we studied the response of the marine diatom Phaeodactylum tricornutum to changing atmospheric carbon concentrations on an ocean-wide scale. By integrating an available genome-scale metabolic model and a newly developed transcriptional regulatory network inferred from transcriptome sequencing expression data, we demonstrate that carbon metabolism and nitrogen metabolism are strongly connected and the genes involved are coregulated in this model diatom. These tight regulatory constraints could play a major role during the adaptation of P. tricornutum to increasing carbon levels. The transcriptional regulatory network developed can be further used to study the effects of different environmental perturbations on P. tricornutum’s metabolism.

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Bayesian Networks and Informative Priors: Transcriptional Regulatory Network Models

Bayesian Inference for Gene Expression and Proteomics ◽

10.1017/cbo9780511584589.022 ◽

2009 ◽

pp. 401-424 ◽

Cited By ~ 4

Author(s):

Alexander J. Hartemink ◽

Marina Vannucci

Keyword(s):

Bayesian Networks ◽

Regulatory Network ◽

Transcriptional Regulatory Network ◽

Network Models ◽

Informative Priors ◽

Transcriptional Regulatory

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Reconstructing blood stem cell regulatory network models from single-cell molecular profiles

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1610609114 ◽

2017 ◽

Vol 114 (23) ◽

pp. 5822-5829 ◽

Cited By ~ 34

Author(s):

Fiona K. Hamey ◽

Sonia Nestorowa ◽

Sarah J. Kinston ◽

David G. Kent ◽

Nicola K. Wilson ◽

...

Keyword(s):

Single Cell ◽

Regulatory Network ◽

Network Inference ◽

Transcriptional Regulatory Network ◽

Expression Profiles ◽

Network Models ◽

Cell Types ◽

Mature Cell ◽

Hematopoietic Stem ◽

Transcriptional Regulatory

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte–erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.

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Machine-learning from Pseudomonas putida transcriptomes reveals its transcriptional regulatory network

10.1101/2022.01.11.475908 ◽

2022 ◽

Author(s):

Hyun Gyu Lim ◽

Kevin Rychel ◽

Anand V. Sastry ◽

Joshua Mueller ◽

Wei Niu ◽

...

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Pseudomonas Putida ◽

Growth Rates ◽

Regulatory Network ◽

Transcriptional Regulatory Network ◽

Bacterial Gene ◽

Bacterial Physiology ◽

Transcriptional Regulatory ◽

Genome Scale

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1,993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

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