Population-Level Profiling of Plasma miRNAs

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Dr. Sabine Ameling, Tim Kacprowski, Dr. Georg Homuth, and Dr. Elke Hammer work at the University Medicine Greifswald (UMG) in the Department of Functional Genomics, which is led by Prof. Dr. Uwe Völker. In collaboration with other partners at the UMG, the team is analyzing plasma miRNA profiles as biomarkers for common diseases.
What is the focus of the research conducted in your lab?
We investigate the molecular mechanisms underlying common diseases using multiple omics techniques, such as genomics, proteomics, transcriptomics, and more. Our areas of focus include cardiovascular as well as infectious diseases. In addition, we are involved in many genetic and molecular epidemiological studies based on two large cohort studies, the Study of Health in Pomerania (SHIP/SHIP-TREND) (1) and the Greifswald Approach to Individualized Medicine (GANI_MED) (2).

How did you come to be interested in miRNAs?
miRNAs have been discussed as promising diagnostic and predictive biomarkers. Non-cellular circulating plasma miRNAs are easy to sample, highly stable, and simple to quantify by standard techniques such as RT-qPCR. This makes them ideally suited for large-scale association and case-control studies for the discovery of molecular signatures of disease-related phenotypes.

"[miRNAs are] ideally suited for large-scale association and case-control studies for the discovery of molecular signatures of disease-related phenotypes."


What is the aim of projects currently using miRNA qPCR?
We are investigating plasma miRNAs of several hundred individuals from the population-based SHIP-TREND cohort. Our goal is to detect plasma miRNA signatures associated with phenotypes related to common human diseases.

What were some specific challenges in your experiments?
The main challenge in our experiments has been to extract and detect plasma miRNAs reproducibly and reliably. Our plasma samples must be standardized in quality and amount, as demonstrably high consistency is mandatory when you are working with several hundred samples. The miRCURY workflow uses spike-ins at different stages of the total RNA extraction and cDNA synthesis procedure. That, combined with the miRCURY LNA miRNA QC PCR Panel, allows for very precise quality control of samples and streamlined data pre-processing.

What was most important to you when choosing an miRNA qPCR system?
We started large-scale studies on plasma miRNAs in 2011 and required a highly efficient approach to detect and distinguish RNAs approximately 22 nucleotides in length. We needed a method with high specificity and sensitivity. Your miRNA Panels using LNA-modified RT-qPCR assays represented an attractive approach that fulfilled these criteria.

"We needed a method with high specificity and sensitivity. Your miRNA Panels using LNA-modified RT-qPCR assays represented an attractive approach that fulfilled these criteria."

What would be your advice to colleagues about getting started on miRNA qPCR analysis?
The efficiency of miRNA profiling in individual samples depends on plasma type (e.g., EDTA, citrate) and the quantification protocol. Stringent quality control must be an integral part of your workflow to ensure comparability of individual samples. Also, in studies associating plasma miRNA levels with specific clinical phenotypes, special attention must be paid to sex and blood cell parameter differences between samples. (3)

"Stringent quality control must be an integral part of your workflow to ensure comparability of individual samples."

What are the next steps in this project and how do you plan to perform them?
We plan to use the procedure established in our lab to profile additional samples from SHIP, as well as samples from the patient cohorts in GANI_MED. Then, we will investigate the relationship between plasma miRNAs and disease-relevant phenotypes via association studies, classification-based, and integrative computational approaches.

When and where will we read more about your studies?
The results of our pilot study have been published (3) and the first data on disease cohorts are being prepared for publication.

References
1. Völzke et al. Cohort profile: the study of health in Pomerania. Int J Epidemiol. 2011 April;40(2):294-307. PMID: 20167617.
2. Grabe et al. Cohort profile: Greifswald approach to individualized medicine (GANI_MED). J Transl Med 2014 May 23;12:144. PMID: 24886498.
3. Ameling et al. Associations of circulating plasma miRNAs with age, body mass index and sex in a population-based study. BMC Med Genomics 2015 Oct 14;8:61. PMID: 26462558.

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