Most RNAs present in the cell are mRNAs, but not all. There are also RNAs that are not a messenger between DNA and proteins but have direct catalytic or regulatory functions. One such group is long non-coding RNAs, even though they are not coding for a protein can be very large. Many carry catalytical functions or regulate cellular processes (Statello et al., 2021), one such process where they have been liked is leukemia (Dutta et al., 2024). The problem is that these RNAs often are of low abundance and often tightly associated with protein complexes. TRAPS-Pumby could be used to reliably detect these RNAs, due to the high binding affinity of the Pumbys (Cheong & Hall, 2006). This might lead to the system out competing already bound proteins to give the signal
of the presence of these RNAs.
Once TRAPS proves to be able to reliably sense the mCherry mRNA we want to continue validating the System. There are many variables that need testing and validation before we can confidently provide the system to the scientific community. Additionally, we want to determine the limitations of our system by a set of planned experiments. During expert talks multiple possible applications were communicated, that we also want to test.
Testing the limitations
Effect on translation
To determine the effect of our system on translation we already used a suitable first target. The effect on translation can be directly observed by the fluorescence of mCherry which corresponds to the amount of mCherry proteins present in the cell. We also took this already into consideration while designing the guide RNAs which determine the binding sited of Cas13. It was shown that binding of Cas13 to the translated region of RNA does not strongly influence translation whereas binding to the untranslated region (UTR) strongly reduces translation (Apostolopoulos et al., 2024). The translational machinery assembles on the UTR and then starts protein synthesis, proceeding over the translated region (Hinnebusch, 2014). One can imagine that the translational machinery cannot assemble correctly if the UTR is bound by Cas13, but once assembled it has a strong affinity to the RNA and can displace the Cas13 protein from the translated region which will then go back to bind the RNA after the translational machinery has passed, resulting in low disruption of translation. Taking all of this into account, we designed the first binding sites to be on the translated region to not interrupt translation and interfere with normal physiology, but all of this must be proven experimentally with the functional TRAPS system observing the mCherry fluorescence.
RNA detection threshold
One other important parameter to determine how effective the system is, is the RNA copy number needed for a condensate to form. To determine this sensitivity an RNA titration experiment is planned in which the amount of RNA copies present in the cell can be increased step by step (Feng et al., 2025). With this we will be able to determine how many RNAs are needed for reliable detection. First, we want to determine the threshold with the initial amount of four gRNAs. Once the threshold is determined we will increase the number of gRNAs which will theoretically decrease this threshold due to more possible binding sites on the target RNA. One target RNA can connect more Tetramers, if we increase the gRNA number, increasing the signal. With this the system can be refined to an optimal gRNA number.
Localization
The next uncertainty is, if localization of RNA will be possible with TRAPS. To analyse the capabilities of TRAPS, we already selected two targets to test localization. During cell division the mRNA for the ASH1 gene is specifically localized in the but tip of the daughter cell (Beach & Bloom, 2001) and can serve as a first target RNA for TRAPS to test localization. As a control the mRNA of the housekeeping gene ACT1 (Actin) which is present in the cytosol can be used (Corral-Debrinski et al., 2000).
Testing reversibility
We also don’t know if the condensates that form when the RNA is present will disintegrate after the RNA is not expressed anymore. For this to happen the RNA degradation must still function even if the RNA is bound by TRAPS. The RNA degradation mechanism is again, like most biological processes, quite complex. This complexity doesn’t allow us to take an educated guess on the dynamics of TRAPS condensate disintegration. The initial experiment to test this is quite straight forward. We induce galactose, activating the expression of the mCherry mRNA resulting in condensation of the TRAPS system, and then we reintroduce the cells into glucose media inactivating the promoter. Apon inactivation no novel RNAs should contribute to condensate formation, and we can observe if the condensates disintegrate.
Adaptation to new model organisms
We don’t want to limit TRAPS only to yeast cells, since it should be adaptable to all organisms or cells that are transparent and can be observed under a microscope. One first, very exciting experiment would be the introduction of TRAPS into zebrafish. Zebrafish allow quick experimentation since genes can be introduced via microinjection at the one cell stage (Rosen et al., 2009). As an initial experiment we want to codon optimise the system for zebrafish and switch the yeast optimised Cas13 to a in zebrafish functional Cas13. The respective sequence will be injected at the one cell stage together with gRNAs targeting an mRNA that is specific to a distinct developmental stage. This target has not yet been determined, but we are in close contact with Prof. Dr. Michael Brand trying to figure out a first suitable target. TRAPS should then condense once the developmental timepoint of the zebrafish embryo is reached.
Applications
To see if the system will be applied by scientists, we met up with several research labs presenting our work and discussing possible applications. During these talks we were really delighted to see that TRAPS received a lot of support and attracted strong interest in the community. Several possible applications were derived.
Long non-Coding RNAs
Stem cell differentiation
Stem cells are undifferentiated cells that are the foundation for all different cell types of organisms. Understanding when and how these cells differentiate and classifying the factors involved can not only give insight into how organisms develop but also lead to therapeutic applications by, for example, inducing specifically programmed stem cells to cure diseases. TRAPS can be used to survey the differentiation process and determine the factors involved and the specific timepoints these are activated without disrupting the process.
Colocalization of RNAs and Proteins
RNAs are often bound to proteins. Most prominently they are bound by the ribosome during translation, but not only that. Regulating the translation of RNAs is also largely performed by proteins that bind to the untranslated region (UTR) (Szostak & Gebauer, 2013). This regulation can be quite simple but also very complex. This regulatory process of proteins in the UTR of RNAs was shown to be very complex for neuronal genes and disruption of the regulation was shown be the cause of diseases like Amyotrophic Lateral sclerosis (ALS) and Alzheimer’s (Conlon & Manley, 2017). To determine which proteins are involved in this, colocalization experiments of RNA with selected proteins are performed. Since TRAPS can sequester and locally up-concentrate specific RNAs this colocalization might become less difficult using our System.
Research on Condensation
The phenomenon of condensation that is the foundation of TRAPS is not only used to create this system but can also be studied by it. The drivers of naturally occurring biomolecular condensation processes like the formation of heat shock granules are protein-protein -, RNA-RNA - and Protein-RNA interactions (Banani et al., 2017). The dynamics behind this process are not fully understood. By targeting RNAs of different length and structure with TRAPS the influence of RNAs to the properties of condensates can be studied.
Transcriptome changes as Infection response
Viral and bacterial infections change normal cell physiology drastically. A virus basically programs the infected cell to produce proteins that are not cell intrinsic to multiply the virus and bacteria also drastically change the genetic profile of affected cells (Culver & Padmanabhan, 2007; Tran Van Nhieu & Arbibe, 2009). TRAPS can be used to survey these changes and even sequester the viral RNA and inactivate it. This can allow deep insight into the functioning of viruses and even be used as a sequestration method preventing virus replication.
Regeneration and development
During the development of organisms very complex genetic pathways are defining the cell fade from the first fertilized cell all the way to a full-grown organism. The same holds true for regeneration and it turns out that many developmental pathways are reused during regeneration of organisms that have high regeneration capacity, such as zebrafish (Gemberling et al., 2013). These pathways can be imagined as a well-orchestrated succession of genes becoming active and inactive. To understand these processes, understanding the timing of this orchestra is key. TRAPS can allow rapid sensing of the specific genes being activated in this process and can rapidly be adapted to check for the next gene in this symphony.
Low abundance RNAs
Many RNAs such as mRNAs for most genes are expressed at high copy numbers allowing reliable detection, but some RNAs are present at really low numbers sometimes even in single digits (Larson et al., 2009). These low abundance RNAs have been shown to still be of large importance in many processes and diseases like cancer but are generally hard to detect with conventional methos. The flexibility of TRAPS in increasing the number of gRNAs, resulting in a lower number of target RNA needed for a condensate to form, can allow detection of RNAs with really low copy numbers.
Gene profiling after drug treatment
Many drugs treat the respective disease by influencing gene expression, either lowering or increasing the activity of a gene. One process where this is often the case is the treatment of osteoporosis, where specific genes are less active resulting in bone loss (Yi et al., 2024). Drugs are currently being developed to increase the activity and prohibit the bone loss. In this drug development the immediate genetic changes after treatment need to be determined and explored. TRAPS can be used to directly see the effect of a drug on the treated tissue giving a direct readout of RNA expression and therefore gene activity.
References
- Apostolopoulos, A., Kawamoto, N., Chow, S. Y. A., Tsuiji, H., Ikeuchi, Y., Shichino, Y., & Iwasaki, S. (2024). dCas13-mediated translational repression for accurate gene silencing in mammalian cells. Nature Communications, 15(1), 2205. https://doi.org/10.1038/s41467-024-46412-7
- Banani, S. F., Lee, H. O., Hyman, A. A., & Rosen, M. K. (2017). Biomolecular condensates: Organizers of cellular biochemistry. Nature Reviews Molecular Cell Biology, 18(5), 285–298. https://doi.org/10.1038/nrm.2017.7
- Beach, D. L., & Bloom, K. (2001). ASH1 mRNA Localization in Three Acts. Molecular Biology of the Cell, 12(9), 2567–2577. https://doi.org/10.1091/mbc.12.9.2567
- Cheong, C.-G., & Hall, T. M. T. (2006). Engineering RNA sequence specificity of Pumilio repeats. Proceedings of the National Academy of Sciences, 103(37), 13635–13639. https://doi.org/10.1073/pnas.0606294103
- Conlon, E. G., & Manley, J. L. (2017). RNA-binding proteins in neurodegeneration: Mechanisms in aggregate. Genes & Development, 31(15), 1509–1528. https://doi.org/10.1101/gad.304055.117
- Corral-Debrinski, M., Blugeon, C., & Jacq, C. (2000). In Yeast, the 3′ Untranslated Region or the Presequence of ATM1 Is Required for the Exclusive Localization of Its mRNA to the Vicinity of Mitochondria. Molecular and Cellular Biology, 20(21), 7881–7892. https://doi.org/10.1128/MCB.20.21.7881-7892.2000
- Culver, J. N., & Padmanabhan, M. S. (2007). Virus-Induced Disease: Altering Host Physiology One Interaction at a Time. Annual Review of Phytopathology, 45(1), 221–243. https://doi.org/10.1146/annurev.phyto.45.062806.094422
- Dutta, S., Akhade, V. S., Choudhury, S. R., & Rao, M. R. S. (2024). Long non-coding RNAs: Emerging functional players in the pathobiology and progression of myeloid leukemia. Frontiers in RNA Research, 2, 1334464. https://doi.org/10.3389/frnar.2024.1334464
- Feng, J., Osmekhina, E., Timonen, J. V. I., & Linder, M. B. (2025). Effects of Sup35 overexpression on the formation, morphology, and physiological functions of intracellular Sup35 assemblies. Applied and Environmental Microbiology, 91(3), e01703-24. https://doi.org/10.1128/aem.01703-24
- Gemberling, M., Bailey, T. J., Hyde, D. R., & Poss, K. D. (2013). The zebrafish as a model for complex tissue regeneration. Trends in Genetics, 29(11), 611–620. https://doi.org/10.1016/j.tig.2013.07.003
- Hinnebusch, A. G. (2014). The Scanning Mechanism of Eukaryotic Translation Initiation. Annual Review of Biochemistry, 83(1), 779–812. https://doi.org/10.1146/annurev-biochem-060713-035802
- Larson, D. R., Singer, R. H., & Zenklusen, D. (2009). A single molecule view of gene expression. Trends in Cell Biology, 19(11), 630–637. https://doi.org/10.1016/j.tcb.2009.08.008
- Rosen, J. N., Sweeney, M. F., & Mably, J. D. (2009). Microinjection of Zebrafish Embryos to Analyze Gene Function. Journal of Visualized Experiments, 25, 1115. https://doi.org/10.3791/1115
- Statello, L., Guo, C.-J., Chen, L.-L., & Huarte, M. (2021). Gene regulation by long non-coding RNAs and its biological functions. Nature Reviews Molecular Cell Biology, 22(2), 96–118. https://doi.org/10.1038/s41580-020-00315-9
- Szostak, E., & Gebauer, F. (2013). Translational control by 3’-UTR-binding proteins. Briefings in Functional Genomics, 12(1), 58–65. https://doi.org/10.1093/bfgp/els056
- Tran Van Nhieu, G., & Arbibe, L. (2009). Genetic reprogramming of host cells by bacterial pathogens. F1000 Biology Reports, 1. https://doi.org/10.3410/B1-80
- Yi, S.-J., Lim, J., & Kim, K. (2024). Exploring epigenetic strategies for the treatment of osteoporosis. Molecular Biology Reports, 51(1), 398. https://doi.org/10.1007/s11033-024-09353-4