Same Fragments, Different Diseases: Analysis of Identical tRNA Fragments Across Diseases Utilizing Functional and...

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Transfer RNA-derived fragments (tRFs) are small non-coding RNAs involved in gene regulation and disease.
• Their target specificity and precise roles in various disease contexts are not fully understood.
• Investigating identical tRF sequences across different diseases is crucial for understanding their functional diversity.

Purpose of the Study:

• To explore the phenomenon of identical tRF sequences appearing in distinct disease contexts.
• To evaluate the consistency between experimentally validated tRF functions and predictions from existing databases.
• To assess the utility of target-based versus abundance-based tRF databases in predicting functional relevance.

Main Methods:

• Selected five tRFs with identical sequences from multiple disease studies.
• Extracted validated targets and disease associations from literature.
• Cross-referenced predicted targets using tatDB, tRFTar, and tsRFun databases.
• Assessed tRF abundance enrichment in OncotRF and MINTbase using TCGA data.

Main Results:

• Only one tRF (LeuAAG-001-N-3p-68-85) showed complete alignment between experimental data and target-based database predictions.
• Other tRFs exhibited partial overlaps in predicted binding regions with validated targets.
• tRF abundance data showed inconsistent enrichment, with limited concordance between abundance and experimentally validated disease associations.
• Functionally relevant tRFs were often poorly represented in abundance-only databases.

Conclusions:

• This pilot study suggests identical tRF sequences may have different functions across diseases.
• Target-based databases offer better mechanistic insights than abundance-based tools for tRF annotation.
• Integrated data sources are essential for accurate tRF functional inference, as expression data alone is insufficient.