Inside each of our cells, there is a kind of ‘user manual’: our DNA. However, DNA alone cannot do anything; RNA is necessary to turn those instructions into something tangible.
RNA molecules are made up of a sequence of chemical ‘letters’—A, U, C and G—arranged in a specific order. The order of these letters works much like a sentence: it changes the meaning, and therefore determines what the RNA can do and which molecules it can interact with.
Some RNAs are used to produce proteins, while others play a regulatory role, helping determine when, where and how much a gene should be active. To do this, they often need to interact with one another. However, the exact ‘rules’ that allow two RNAs to recognise and bind to each other are still largely unknown.
This is where a study entitled ‘The role of low-complexity repeats in RNA-RNA interactions and a deep learning framework for duplex prediction’, published in Nature Communications on 23 January 2026, becomes particularly relevant. By analysing large amounts of data, researchers discovered that even the simplest and most repetitive sequences—parts of RNA made up of repeating letters—can play a crucial role in enabling RNAs to recognise and interact with one another. In short, those parts that seemed boring or unimportant may actually act as the molecular glue that supports many cellular interactions.
Furthermore, the researchers developed an artificial intelligence tool called RIME, which can predict which specific RNAs are likely to interact with one another simply by analysing their sequence.
Important contributions to this scientific work were made by Alessio Colantoni, a lecturer in Molecular Biology at UniCamillus University, who specialises in predicting and characterising interactions between RNA and proteins or other RNAs using computational and machine-learning approaches. Colantoni supervised the study alongside Gian Gaetano Tartaglia of the Italian Institute of Technology. We asked Colantoni to explain the significance of these findings and how they might change the way we understand the RNA world.
How would you explain the objectives of this study to someone with no experience?
“RNAs are fundamental molecules that enable the information in our genes to be utilised by the cell. Each RNA is defined by a sequence of chemical ‘letters’ that represent genetic information and determine how the RNA behaves and which other molecules it can interact with. Many RNAs, known as messenger RNAs, function as instructions for producing proteins, which are the main ‘building blocks’ and regulators of cellular functions.
However, not all RNAs are used to produce proteins. There are also non-coding RNAs (ncRNAs) that play a regulatory role. One well-known example is microRNAs: small RNA molecules that control gene expression by binding to messenger RNAs and modulating their translation. The discovery of microRNAs was so significant that it won the Nobel Prize, highlighting the importance of understanding how RNAs interact with each other.
In recent years, it has emerged that long non-coding RNAs (lncRNAs) can interact directly with other RNAs, including messenger RNAs (mRNAs), thereby influencing their stability, localisation and/or activity. However, unlike microRNAs, the mechanisms underlying these interactions are still poorly understood.
This study aimed to understand the characteristics that enable long RNAs to recognise and interact with each other. In other words, we asked whether there are ‘rules’ in RNA sequences that guide their interactions, similar to those already known for microRNAs.
To address this question, we analysed numerous biological datasets using computational tools to identify recurring patterns in the sequences of RNAs that interact. This approach enabled us to identify common elements that may explain how and why certain long RNAs come into contact with each other. Understanding these mechanisms is important because such interactions represent an additional level of gene expression control and can contribute to both the proper functioning of cells and the development of diseases when altered”.
What is the most important and innovative result you have achieved?
“One of the key findings of our study is that so-called low-complexity repeats—very simple repeated sequences in RNAs—play a central role in interactions between long RNAs. Despite being extremely simple in terms of composition, we observed that these sequences are highly enriched at the points of contact between long RNAs.
In particular, we have demonstrated that these repetitive sequences form stable, multivalent interaction regions that can contact several different RNAs. This makes them central nodes within RNA interaction networks. Our work therefore suggests that the ability of RNAs to interact depends not only on complex or highly specific structures, but also on simple and flexible sequence modules that help coordinate fundamental processes such as gene expression regulation, development and RNA metabolism”.
Why are these repetitive sequences so important, and why were they not previously recognised as such?
“Repetitive sequences are interesting because they vary greatly between individuals; their length and composition can easily differ from person to person. This variability means they are commonly used for genetic identification, for example in paternity tests or forensic genetics. However, their molecular function has long remained unclear from a biological point of view.
For many years, these sequences were considered uninformative or lacking a specific function. Furthermore, many computational analyses tend to exclude or simplify repetitive regions, treating them as noise rather than possible functional signals.
However, our study suggests that it is precisely these characteristics—simplicity, repetitiveness and flexibility—that make repetitive sequences particularly well-suited to mediating interactions between RNAs. Their composition favours pairing with multiple partners, enabling a single RNA to participate in numerous interactions. This leads to a new interpretation of their role: they are not just genetic markers, but also active components in the organisation and regulation of interactions between RNAs in the cell”.
You have developed RIME, an AI tool. What is it used for, how does it work, and in what ways does it surpass traditional methods?
“RIME is an AI tool that predicts which RNAs can interact with each other based on their sequence. It is therefore used to quickly and systematically identify potential RNA–RNA interactions that would be very difficult to detect using traditional experiments alone.
Traditional methods of predicting RNA interactions mainly rely on thermodynamic calculations to estimate the stability of pairing between two molecules based on binding energy. While these approaches are useful, they are most effective for short RNAs and often fail to capture the complexity of interactions between long RNAs, which depend on sequence context and many other biological factors.
RIME takes a different approach. It analyses RNA sequences using machine-learning models that recognise patterns in nucleotide sequences. This enables the tool to recognise recurring patterns in the sequence, including those related to repetitive sequences that favour RNA interactions, even when these are not easily predictable using energy rules alone.
In short, RIME does more than just calculate the theoretical possibility of RNA interaction; it also learns from experimental data how such interactions actually occur within cells. This makes it a powerful tool for exploring new layers of gene expression regulation”.
What are the implications of this discovery for medicine?
“The implications are mainly related to a better understanding of the molecular mechanisms that regulate cell function. Many diseases, particularly neurodegenerative diseases and certain cancers, depend not only on protein mutations, but also on alterations in RNA levels, structure and interactions.
The discovery that repetitive sequences play an active role in interactions between long RNAs is particularly relevant in medicine, as many of these sequences are associated with diseases such as amyotrophic lateral sclerosis, autism and Huntington’s disease. However, their molecular contribution was not entirely clear until now.
Furthermore, interactions mediated by repetitive sequences can promote the formation of RNA aggregates and abnormal cellular structures, such as stress granules, which are frequently observed in pathological conditions. Therefore, understanding how and why these interactions form helps clarify the mechanisms that lead to cellular dysfunction and neurodegeneration.
Finally, tools such as RIME enable us to identify and prioritise potentially relevant RNA–RNA interactions in pathological contexts, paving the way for new diagnostic and therapeutic approaches. While we are still in the early stages of basic research, these findings offer a valuable conceptual framework for better understanding the role of RNAs in disease and for developing strategies to intervene in RNA-mediated regulatory mechanisms in the future”.
What’s the next step in RNA interaction research?
“The next step will be to move from identifying RNA interactions to gaining a deeper understanding of their functional role in cells. Now that we know simple repetitive sequences can drive many of these interactions, understanding when, where and what the biological consequences of their occurrence might be becomes crucial.
A key aspect will be studying how interactions mediated by repetitive sequences change in different contexts, such as during development, in response to cellular stress or in pathological conditions. This will help distinguish physiological interactions from those that contribute to cellular dysfunction.
Another important area of research will be to integrate RNA–RNA interactions with other levels of regulation, such as interactions with proteins and the spatial organisation of RNA within cells.
Repetitive sequences appear to be located at the convergence points of different regulatory processes, suggesting that they may coordinate complex networks of gene expression regulation.
From a methodological point of view, it will also be crucial to develop new computational tools and combine them with experimental approaches that allow these interactions to be studied more precisely in conditions closer to cellular physiology. Our study therefore offers a new perspective, paving the way for a more integrated and dynamic view of how RNAs interact within the cell. This has potential implications for both basic biology and medicine”.