How is the abundance of ncRNA currently managed? Which knowledge gaps and knowledge needs prevail? Which data are relevant for RA? What is key to data analysis and data evaluation?
- Slack: It is equally evident that ncRNAs have great potential to be used as biomarkers and that extensive knowledge gaps must be addressed before this goal may be met. Current investigations focus on analysing data to determine the best suitable ncRNAs and how they fit into specific pathways. Generally, knowledge on ncRNAs is just beginning to evolve, and new classes of RNAs, such as circular RNAs, have only been discovered very recently. Therefore, also an understanding on their tissue-specific presence or functionalities is still at its very beginning. It is very likely that further classes of RNAs remain to be discovered, just as miRNAs may not be the smallest RNAs present in organisms.
- Rasoulpour: The available knowledge on ncRNAs may indirectly or directly benefit substance RA, i.e. to concertedly design new molecules and to reveal specific ncRNA signatures in those animal species that are relevant for regulatory toxicology. In combining exposure and hazard assessment during RA, ncRNA profiling may provide biological explanations on mechanisms of toxicity that specific substances may affect. Eventually, such ncRNA profiling may provide opportunities to improve regulatory toxicity testing.
- Schmidt: The time- and dose-dependent up- and down-regulation in response to toxic compounds qualifies ncRNAs as useful biomarkers for toxicological studies. NcRNA expression profiles might supplement or even substitute conventional parameters, obtained by, e.g. haematology or clinical biochemistry. NcRNAs as new endpoints could be integrated into consolidated test vs. control group comparisons, and, basically, the familiar principles to statistically analyse potential apical effects and dose-response relationships are also adoptable to ncRNA expression profiling. In fact, such approaches are very similar to current toxicological testing; even though only single parameters are measured in the current toxicity tests, these parameters are combined for an assessment of complex endpoints. Challenges of quantification and data interpretation are not limited to the harmonisation of techniques to measure ncRNAs. Standardised procedures are also needed for data normalisation and referencing. Further, standard statistical estimators must be established to ensure comparability and to facilitate assessment, also of the biological relevance of effects. Historical control data and effect sizes of potential toxicological interest (e.g. in respect to the up- or down-regulation of genes) have to be established. It has to be clarified whether the test group sizes indicated in current test guidelines have sufficient power to detect ncRNA effect sizes. Finally, modern statistical methodologies and presentation methods should be implemented to enhance comprehensive analyses and interpretation of results (Schmidt et al, 2016).
- Gant: Research on ncRNAs is a technology-driven process that yields an abundance of data. In making use of ncRNA expression profiles for RA, interpretation of the data is the biggest challenge, as is also known for ‘-omics’ technologies. Some of the information that these technologies provide is not necessarily fully understood. Nevertheless, it is beneficial to collect all data, even though it may be challenging to manage large datasets. Knowledge gaps in respect to evaluating data by applied bioinformatics prevail. It is not yet understood which of the data are relevant for toxicological RA, which changes in ncRNA expression are causal and which are consequential, or how ncRNAs are involved in toxicological mechanisms. Such an understanding, however, is a prerequisite to selecting ncRNAs as biomarkers for RA. Presumably, different types of substances and different patterns of change are related to specific mechanisms of toxicity.