scholarly journals Machine Learning Uncovers a Data-Driven Transcriptional Regulatory Network for the Crenarchaeal Thermoacidophile Sulfolobus acidocaldarius

2021 ◽  
Vol 12 ◽  
Author(s):  
Siddharth M. Chauhan ◽  
Saugat Poudel ◽  
Kevin Rychel ◽  
Cameron Lamoureux ◽  
Reo Yoo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress Responses ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Cellular Responses ◽  
Sulfolobus Acidocaldarius ◽  
Network Discovery ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Export System

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that five modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of five uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophile’s responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap toward regulatory network discovery in archaea.

scholarly journals Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius

2021 ◽  
Author(s):  
Siddharth M Chauhan ◽  
Saugat Poudel ◽  
Kevin Rychel ◽  
Cameron Lamoureux ◽  
Reo Yoo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress Responses ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Cellular Responses ◽  
Sulfolobus Acidocaldarius ◽  
Network Discovery ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Export System

Dynamic cellular responses to environmental constraints are coordinated by the transcriptional regulatory network (TRN), which modulates gene expression. This network controls most fundamental cellular responses, including metabolism, motility, and stress responses. Here, we apply independent component analysis, an unsupervised machine learning approach, to 95 high-quality Sulfolobus acidocaldarius RNA-seq datasets and extract 45 independently modulated gene sets, or iModulons. Together, these iModulons contain 755 genes (32% of the genes identified on the genome) and explain over 70% of the variance in the expression compendium. We show that 5 modules represent the effects of known transcriptional regulators, and hypothesize that most of the remaining modules represent the effects of uncharacterized regulators. Further analysis of these gene sets results in: (1) the prediction of a DNA export system composed of 5 uncharacterized genes, (2) expansion of the LysM regulon, and (3) evidence for an as-yet-undiscovered global regulon. Our approach allows for a mechanistic, systems-level elucidation of an extremophile's responses to biological perturbations, which could inform research on gene-regulator interactions and facilitate regulator discovery in S. acidocaldarius. We also provide the first global TRN for S. acidocaldarius. Collectively, these results provide a roadmap towards regulatory network discovery in archaea.

scholarly journals Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin Rychel ◽  
Anand V. Sastry ◽  
Bernhard O. Palsson
Keyword(s):  
Machine Learning ◽  
Bacillus Subtilis ◽  
Biofilm Formation ◽  
Transcriptional Regulatory Network ◽  
Population Level ◽  
Cellular Functions ◽  
Fundamental Interest ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Dependent Activity

AbstractThe transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

scholarly journals Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

2020 ◽  
Author(s):  
Kevin Rychel ◽  
Anand V. Sastry ◽  
Bernhard O. Palsson
Keyword(s):  
Machine Learning ◽  
Bacillus Subtilis ◽  
Biofilm Formation ◽  
Transcriptional Regulatory Network ◽  
Population Level ◽  
Cellular Functions ◽  
Fundamental Interest ◽  
Gene Sets ◽  
Transcriptional Regulatory ◽  
Dependent Activity

AbstractThe transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression, and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover novel or recently discovered roles for at least 5 regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states, revealing a putative anaerobic metabolism role for SigG. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

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

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.

scholarly journals Topological and causal structure of the yeast transcriptional regulatory network

10.1038/ng873 ◽  
2002 ◽  
Vol 31 (1) ◽  
pp. 60-63 ◽  
Author(s):  
Nabil Guelzim ◽  
Samuele Bottani ◽  
Paul Bourgine ◽  
François Képès
Keyword(s):  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Causal Structure ◽  
Transcriptional Regulatory
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