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 Functional Modules Analysis and Hub Gene Prognostic Values Evaluation Based on Co-Expression Network in Gastric Cancer

2021 ◽  
Author(s):  
Yujia Liu ◽  
Xiaoping Hu ◽  
Zongfu Pan ◽  
Yuchen Jiang ◽  
Dandan Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Transcriptional Regulatory Network ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Gene ◽  
Transcriptional Regulatory ◽  
Key Nodes

Abstract Background: Gastric cancer is one of the most common fatal disease worldwide, but its mechanism and therapeutic targets are still unclear. In this study, we have analyzed the differences in gene modules and key pathways in gastric cancer patients, then elaborated the mechanism and effective treatment of gastric cancer with microarray data from the gene expression omnibus(GEO) database. Methods: GEO2R tools were used to identify differential expression genes (DEGs), String database was employed to construct a protein-protein interaction (PPI) network. We imported the PPI network into the Cytoscape software to find key nodes, and employed statistical approach of MCODE to cluster genes. After that the ClueGO was used to enrich and annotate the pathways of key modules. To investigate the relationship between the upstream regulator and hub genes, the transcriptional regulatory network was built based on TFCAT database. Results: 63 characteristic genes of gastric cancer are involved in regulation of ECM-receptor interaction, focal adhesion and protein digestion and absorption. SPARC, FN1, BGN and COL1A2 are four key nodes relating to tumor proliferation and metastasis, and their expression were strongly associated with poor survival (p<0.05). 13 transcription factors including PRRX1 have remarkable changes in gastric cancer, which may play a key role in hub gene regulation. Conclusions: The present study defined the gene expression characteristics and transcriptional regulatory network that promote our understanding of the molecular mechanisms underlying the development of gastric cancer, and might provide new insights into targeted therapy and prognostic markers for the personalized treatment of gastric cancer.

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 Genetic and pharmacological inactivation of adenosine A2A receptor reveals an Egr-2-mediated transcriptional regulatory network in the mouse striatum

2005 ◽  
Vol 23 (1) ◽  
pp. 89-102 ◽  
Author(s):  
Liqun Yu ◽  
Peter M. Haverty ◽  
Juliana Mariani ◽  
Yumei Wang ◽  
Hai-Ying Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Mrna Level ◽  
Gene Expression Profiles ◽  
Bioinformatics Analyses ◽  
Mouse Striatum ◽  
Transcriptional Regulatory

The adenosine A2A receptor (A2AR) is highly expressed in the striatum, where it modulates motor and emotional behaviors. We used both microarray and bioinformatics analyses to compare gene expression profiles by genetic and pharmacological inactivation of A2AR and inferred an A2AR-controlled transcription network in the mouse striatum. A comparison between vehicle (VEH)-treated A2AR knockout (KO) mice (A2AR KO-VEH) and wild-type (WT) mice (WT-VEH) revealed 36 upregulated genes that were partially mimicked by treatment with SCH-58261 (SCH; an A2AR antagonist) and 54 downregulated genes that were not mimicked by SCH treatment. We validated the A2AR as a specific drug target for SCH by comparing A2AR KO-SCH and A2AR KO-VEH groups. The unique downregulation effect of A2AR KO was confirmed by comparing A2AR KO-SCH with WT-SCH gene groups. The distinct striatal gene expression profiles induced by A2AR KO and SCH should provide clues to the molecular mechanisms underlying the different phenotypes observed after genetic and pharmacological inactivation of A2AR. Furthermore, bioinformatics analysis discovered that Egr-2 binding sites were statistically overrepresented in the proximal promoters of A2AR KO-affected genes relative to the unaffected genes. This finding was further substantiated by the demonstration that the Egr-2 mRNA level increased in the striatum of both A2AR KO and SCH-treated mice and that striatal Egr-2 binding activity in the promoters of two A2AR KO-affected genes was enhanced in A2AR KO mice as assayed by chromatin immunoprecipitation. Taken together, these results strongly support the existence of an Egr-2-directed transcriptional regulatory network controlled by striatal A2ARs.

scholarly journals Identification of transcriptional regulatory network associated with response of host epithelial cells to SARS-CoV-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chen Su ◽  
Simon Rousseau ◽  
Amin Emad
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Organ Damage ◽  
Host Cells ◽  
Regulatory Mechanisms ◽  
Transcriptional Regulatory ◽  
Transcriptional Regulatory Mechanisms ◽  
The Impact

AbstractIdentification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19.

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.

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