What is the main focus of your research?

We aim to explore the molecular mechanisms that contribute to cellular senescence, aging and age-associated diseases. We seek to translate our findings into novel biomedical applications for diagnosis, prognosis and eventually designing intervention or treatment strategies for human diseases, specifically age-associated diseases.

One of our main projects is the identification of miRNA biomarkers for early diagnosis of osteoporosis, which led to the foundation of our company TAmiRNA at the end of 2013.

How did you come to be interested in miRNAs?

Since some of the proteins that we identified as modulators of aging are RNA modifying enzymes, I stumbled upon miRNAs as novel regulators of gene expression in the field of aging quite early.

An Austrian research grant program called “GEN-AU” brought in the financial support to study miRNA expression in aging as a first step, followed by the Exiqon Grant Award in 2012 that allowed us to conduct the first proof-of-concept study of serum miRNAs in osteoporosis.

Which experiments had been performed leading up to this project?

Prior to our biomarker studies in osteoporosis, we have performed miRNA research projects on cellular senescence using LNA microarray technology, which led to the discovery of common miRNA signature of cellular and organismal aging (Hackl et al., 2010) and of miR-101 in UV induced photoaging of human skin cells (Greussing et al., 2013) or miR-663 in stress response of human fibroblasts (Waajer et al., 2014), which we reviewed as well (Grillari and Grillari-Voglauer, 2010; Schraml and Grillari, 2012; Weilner et al., 2013).

One of the thereby identified miRNAs, miR-21 was then the first miRNA that extended the cellular life span of normal human cells (Dellago et al., 2013). Afterwards we focused on miR-31, which was highly up-regulated in senescent endothelial cells, resulting in an enhanced secretion of this miRNA in vitro and in vivo.

Interestingly, this miRNA is a potent regulator of bone homeostasis as well, which is why we ended up studying miRNAs in age-related bone-loss, i.e. osteoporosis (Weilner et al., 2016).

What is the aim of your project involving miRNA qPCR?

There is an urgent clinical need to improve early diagnosis of osteoporosis, since the available screening tools lack sufficient specificity and sensitivity for early detection. This is likely due to the fact that osteoporosis is a multifactorial disease, including skeletal as well as extra-skeletal risk factors. We believe that we can improve early diagnosis of osteoporosis on the basis of circulating miRNAs derived from bone, muscle, immune and other cells in the body.

Therefore, our ultimate goal is to identify a signature of circulating miRNAs that is predictive of fracture-risk in post-menopausal women due to osteoporosis. We have depicted this idea of circulating miRNAs as biomarkers in complex multi-factorial diseases recently (Hackl et al., 2016).

What was your previous experience with miRNA qPCR?

We have started to work with miRNA qPCR experiments in 2007, and initially used the Taqman system. When we entered the circulating miRNA field, we switched to the miRCURY LNA miRNA PCR System, since it did not require pre-amplification to achieve sufficient sensitivity.

What were some specific challenges in your experiments?

When working with circulating miRNAs there are several challenges compared to tissue-based analysis. For us the low amounts of RNA, pre-analytical variance and lack of standardization/normalization tools were the most important ones.

How did you overcome them?

In collaboration with our company, TAmiRNA, we have implemented the miRCURY LNA miRNA PCR workflow in our lab to ensure maximum robustness. We have automated the analysis using liquid-handling robots and standardized pipetting volumes during RNA extraction.

We find the combination of spike-in controls very useful to monitor the efficiency of each step in the analytical workflow. In addition, we rely on the hemolysis indicator (miR-23a and miR-451a) to exclude serum specimens with poor quality from the analysis.

How do you feel about your results?

To this day we have used the miRCURY LNA miRNA PCR platform for the analysis of more than 1,500 serum samples from at least 5 different clinical centers.

We have been able to reproduce findings across these cohorts, and find very similar correlation structures in the miRNA data obtained from different cohorts at different time-points. This makes us confident that the data we generate is robust and can help us to identify biomarkers with clinical utility.

What was most important to you when choosing a miRNA qPCR system?

High sensitivity and specificity to discriminate very similar miRNA sequences, as found across miRNA families. In addition, we were looking for a system that offers universal cDNA synthesis, which is a pre-requisite for screening experiments.

What do you find to be the main advantage of the miRCURY LNA miRNA PCR system?

I believe LNA-enhanced primers in combination with miRNA-specific forward and reverse primers are a key advantage of the miRCURY technology over other platforms due to the higher affinity of LNA and optimization of melting temperatures.

Would you recommend the miRCURY LNA miRNA PCR system to colleagues?

Yes.

What would be your advice to colleagues about getting started with miRNA qPCR analysis?

Especially in respect to circulating miRNA analysis, we feel that it is important to include as many controls as possible in the experiments. This includes exogenous controls such as spike-ins but also endogenous controls, for example to monitor pre-analytical variance.

We have found that not only hemolysis poses a problem, but also variable platelet activation can bias experiments. Platelets can release a significant amount of miRNA into the blood upon activation. Variation in platelet activation can be caused by anti-platelet therapy e.g. aspirins (Kaudewitz et al., 2016) or by pre-analytical variation due to differences in blood collection tube or processing (Cheng et al., 2013). We seek to minimize the potential for variation in platelet activation by using completely standardized protocols for blood sampling and processing.

We find that the technical variance for cell-free miRNA analysis is higher than that of cellular miRNA analysis. Therefore, the number of replicates for cell-free miRNA experiments must be increased.

We are concerned that there is currently not enough evidence to consider specific miRNAs or other non-coding RNA as stable reference genes for data normalization. Consequently, we encourage the discovery of multivariate models or ratios of miRNAs that are regulated in the opposite direction.

What are the next steps in this project and how do you plan to perform them?

We are currently validating our serum miRNA signature for early diagnosis of osteoporosis in a longitudinal study including several hundred patients. In addition, we have started to look into the biological function of our candidates using a range of in vitro and in vivo models.

When and where will we read more about your studies?

A pilot study on circulating miRNAs as well as a review of circulating miRNAs as biomarkers in bone disease is published (Weilner et al., 2015). The mechanistic study of miR-31 is published in Aging Cell (Weilner et al., 2016) and the paper describing the discovery of a miRNA signature for diagnosis of osteoporosis will soon be published in the Journal of Bone and Mineral Research (Heilmeier et al., 2016, accepted).

References

1. Cheng et al., Plasma processing conditions substantially influence circulating miRNA biomarker levels. PLoS One. 2013, 8(6):e64795. PMID: 23762257.
2.  Dellago et al., High levels of oncomiR-21 contribute to the senescence-induced growth arrest in normal human cells and its knock-down increases the replicative lifespan. Aging Cell. 2013, 12(3):446-58. PMID: 23496142.
3. Grillari and Grillari-Voglauer, Novel modulators of senescence, aging, and longevity: Small non-coding RNAs enter the stage. Exp Gerontol. 2010, 45(4):302-11. PMID: 20080172.
4. Greussing et al., Identification of miRNA-mRNA functional interactions in UVB-induced senescence of human diploid fibroblasts. BMC Genomics. 2013, 14:224. PMID: 23557329.
5. Hackl et al., miR-17, miR-19b, miR-20a, and miR-106a are down-regulated in human aging. Aging Cell. 2010, 9(2):291-6. PMID: 20089119.
6. Hackl et al., Circulating miRNAs as novel biomarkers for bone diseases – Complex signatures for multifactorial diseases? Mol Cell Endocrinol. 2016, 432:83-95. PMID: 26525415.
7.  Heilmeier et al., Serum miRNAs are indicative of skeletal fractures in postmenopausal women with and without type 2 diabetes and influence osteogenic and adipogenic differentiation of human adipose tissue derived mesenchymal stem cells. JBMR 2016 (accepted).
8. Kaudewitz et al., Association of MiRNAs and YRNAs with Platelet Function. Circ Res. 2016, 118(3):420-32. PMID: 26646931.
9. Schraml and Grillari, From cellular senescence to age-associated diseases: the miRNA connection. Longev Healthspan. 2012, 1(1):10. PMID: 24472232.
10. Waaijer et al., MiRNA-663 induction upon oxidative stress in cultured human fibroblasts depends on the chronological age of the donor. Biogerontology. 2014, 15(3):269-78. PMID: 24664125.
11.  Weilner et al., Secretion of microvesicular miRNAs in cellular and organismal aging. Exp Gerontol. 2013, 48(7):626-33. PMID: 23283304.
12. Weilner et al., Differentially circulating miRNAs after recent osteoporotic fractures can influence osteogenic differentiation. Bone. 2015, 79:43-51. PMID: 26026730.
13. Weilner et al., Secreted microvesicular miR-31 inhibits osteogenic differentiation of mesenchymal stem cells. Aging Cell. 2016, May 4. PMID: 27146333.