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Aerobicity stimulon in Escherichia coli revealed using multi-scale computational systems biology of adapted respiratory variants

ABSTRACT: Aerobicity stimulon in Escherichia coli revealed using multi-scale computational systems biology of adapted respiratory variants

PROVIDER: PRJNA1235115 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR32663900_1.fastq.gz	Fastqsanger.gz
	SRR32663900_2.fastq.gz	Fastqsanger.gz
	SRR32663901_1.fastq.gz	Fastqsanger.gz
	SRR32663901_2.fastq.gz	Fastqsanger.gz
	SRR32663902_1.fastq.gz	Fastqsanger.gz

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Project description:Our group used rectal biopsies from patients with active ulcerative colitis (UC) and differential response to glucocorticoid treatment, to generate a predictive computational strategy based on systems biology. Contrasting mRNA and miRNAs intestinal expression profiles with updated molecular information related to UC and glucocorticoids, we were able to identify a mechanism of action (MoA) associated with corticosteroid failure in UC. ith the aim of transferring these results to clinical practice, the MoA-models created previously were implemented with plasmatic miRNAs profiles, to describe in silico non-invasive potential biomarkers of glucocorticoid refractoriness. Small RNA-seq analysis was perfomed using Illumina next generation sequencing and standard statistical criteria were applied to compare the miRNA profiles, incluidng paired samples before clucocorticoid treatment to the third day of the steroidal treatment. The selection these miRNA with predictive capacity was done using two complementary strategies: the first one using all the obtained miRNAs, and the second taking into account those miRNAs that are directly related to MoA, through various statistical restrictions (RELIEF, ENTROPY + CORRELATION, WILCOXON, ROC, and simple REGRESSION). These computational approaches from all miRNAs identified hsa-miR218-5p as the best to classify patients according their respond at basal time (Accuracy = 84.21%, Generalization Capability = 84.21%, Sensitivity = 100% and Specificity = 66.67%). In addition, the combination of this miRNA with hsa-miR6754-5p and hsa-miR4767, increases the diagnosis accuracy of the disease (Accuracy = 94.74%, Generalization Capability = 75.44%, Sensitivity = 70% and specificity = 81.48%). On the other hand, the combination of hsa-miR218-5p and the plasma levels of the Protein C Reactive increases the ability to classify patients towards their response to glucocorticoids up to 89%. Taking in account all this data, we can conclude that by means of a computer strategy based on Biology of Systems we have identified the plasmatic miR-218-5p as a potential biomarker to glucocorticoid response in UC before start the treatment.

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Aerobicity stimulon in Escherichia coli revealed using multi-scale computational systems biology of adapted respiratory variants

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