scholarly journals The economy of chromosomal distances in bacterial gene regulation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Eda Cakir ◽  
Annick Lesne ◽  
Marc-Thorsten Hütt
Keyword(s):  
Transcriptional Regulatory Network ◽  
Real Space ◽  
Regulator Gene ◽  
Long Distance ◽  
Circular Chromosome ◽  
Chromosomal Structure ◽  
Bacterial Gene ◽  
Condensed Representation ◽  
Transcriptional Regulatory ◽  
Distance Regulation

AbstractIn the transcriptional regulatory network (TRN) of a bacterium, the nodes are genes and a directed edge represents the action of a transcription factor (TF), encoded by the source gene, on the target gene. It is a condensed representation of a large number of biological observations and facts. Nonrandom features of the network are structural evidence of requirements for a reliable systemic function. For the bacterium Escherichia coli we here investigate the (Euclidean) distances covered by the edges in the TRN when its nodes are embedded in the real space of the circular chromosome. Our work is motivated by ’wiring economy’ research in Computational Neuroscience and starts from two contradictory hypotheses: (1) TFs are predominantly employed for long-distance regulation, while local regulation is exerted by chromosomal structure, locally coordinated by the action of structural proteins. Hence long distances should often occur. (2) A large distance between the regulator gene and its target requires a higher expression level of the regulator gene due to longer reaching times and ensuing increased degradation (proteolysis) of the TF and hence will be evolutionarily reduced. Our analysis supports the latter hypothesis.

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 Antibiotic tolerance is associated with a broad and complex transcriptional response in E. coli

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Heather S. Deter ◽  
Tahmina Hossain ◽  
Nicholas C. Butzin
Keyword(s):  
Transcriptional Regulatory Network ◽  
Transcriptional Response ◽  
Regulatory Proteins ◽  
Health Concern ◽  
Transcriptional Networks ◽  
Antibiotic Tolerance ◽  
Multiple Gene ◽  
E Coli ◽  
Transcriptional Regulatory ◽  
Antibiotic Efficacy

AbstractAntibiotic treatment kills a large portion of a population, while a small, tolerant subpopulation survives. Tolerant bacteria disrupt antibiotic efficacy and increase the likelihood that a population gains antibiotic resistance, a growing health concern. We examined how E. coli transcriptional networks changed in response to lethal ampicillin concentrations. We are the first to apply transcriptional regulatory network (TRN) analysis to antibiotic tolerance by leveraging existing knowledge and our transcriptional data. TRN analysis shows that gene expression changes specific to ampicillin treatment are likely caused by specific sigma and transcription factors typically regulated by proteolysis. These results demonstrate that to survive lethal concentration of ampicillin specific regulatory proteins change activity and cause a coordinated transcriptional response that leverages multiple gene systems.

scholarly journals Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Matthew A. Wall ◽  
Serdar Turkarslan ◽  
Wei-Ju Wu ◽  
Samuel A. Danziger ◽  
David J. Reiss ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Chromosomal Abnormalities ◽  
Transcriptional Regulatory Network ◽  
Progression Free Survival ◽  
Cross Sectional ◽  
Free Survival ◽  
Extreme Risk ◽  
Transcriptional Regulatory ◽  
Transcription Regulators ◽  
Immunosuppressive Microenvironment

AbstractDespite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.

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

scholarly journals Long-distance regulation of Add2 pre-mRNA3′end processing

RNA Biology ◽  
2013 ◽  
Vol 10 (4) ◽  
pp. 516-527 ◽  
Author(s):  
Mirjana Nedeljkovic ◽  
Luisa Costessi ◽  
Alessandra Iaconcig ◽  
Fabiola Porro ◽  
Andrés F. Muro
Keyword(s):  
Long Distance ◽  
Distance Regulation

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

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