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ABSTRACT: Identification of differentially expressed miRNA in cervical cancer

PROVIDER: PRJNA1164482 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR30782021.fastq.gz	Fastqsanger.gz
	SRR30782022.fastq.gz	Fastqsanger.gz
	SRR30782023.fastq.gz	Fastqsanger.gz
	SRR30782024.fastq.gz	Fastqsanger.gz
	SRR30782025.fastq.gz	Fastqsanger.gz

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Palaniappan2021 - Cell free modelling of second generation Toehold switches

Project description:Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Poor awareness and access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative approach for a new kind of diagnosis has been developed, comprising identification of optimal biomarkers, design of the sensor, and simulation of the diagnostic circuit. Using miRNA expression data in the public domain, we identified circulating miRNA biomarkers specific to cervical cancer using multi-tier screening. Synthetic riboregulators called toehold switches specific for the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we developed a generic grammar of these switches, and built a multivariate linear regression model using thermodynamic features derived from RNA secondary structure and interaction. The model yielded predictions of toehold efficacy with an adjusted R2 = 0.59. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10nM and 100nM before saturation. Our study demonstrates an end-to-end workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic signatures that would be increasingly important in today’s world. The approach has the potential to direct experimental efforts and minimise costs. All resources are provided open-source (https://github.com/igem2019) under GNU GPLv3 licence.

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