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A composite immune signature parallels disease progression across T1D subjects

ABSTRACT: A composite immune signature parallels disease progression across T1D subjects

PROVIDER: PRJNA543890 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR9097730.fastq.gz	Fastqsanger.gz
	SRR9097740.fastq.gz	Fastqsanger.gz
	SRR9097750.fastq.gz	Fastqsanger.gz
	SRR9097760.fastq.gz	Fastqsanger.gz
	SRR9097770.fastq.gz	Fastqsanger.gz

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Project description:We used an array-based bioassay whereby serum is used to induce transcription in healthy “reporter” leukocytes. Serum samples were obtained from control-arm subjects enrolled in 3 clinical trials evaluating immunotherapies in T1D that were conducted by the Immune Tolerance Network. The included trials evaluated immune-modifying therapy in new-onset T1D, with similar trial timecourses, primary outcomes, and data and sample collection. Total RNA was isolated from the reporter cell line for gene expression analysis.

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Project description:At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting β cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool (DIFAcTO, Data Integration Flexible to Account for different Types of data and Outcomes) to identify a composite panel associated with decline in insulin secretion over 2 years following diagnosis. DIFAcTO uses multiple filtering steps to reduce data dimensionality, incorporates error estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome, and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a potentially novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D.

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A composite immune signature parallels disease progression across T1D subjects

Project description:This SuperSeries is composed of the SubSeries listed below.

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