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Related Concept Videos

Nuclear Export of mRNA02:31

Nuclear Export of mRNA

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Before mRNAs are exported to the cytoplasm, it is crucial to check each mRNA for structural and functional integrity. Eukaryotic cells use several different mechanisms, collectively known as mRNA surveillance, to look for irregularities in mRNAs. Irregular or aberrant mRNA are rapidly degraded by various enzymes. If a defective mRNA escapes the surveillance, it would be translated into a protein which would either be non-functional or not function properly. One of the primary irregularities in...
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mRNA Stability and Gene Expression02:51

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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability
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Nonsense-mediated mRNA Decay02:27

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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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Regulation of Expression Occurs at Multiple Steps02:24

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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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MicroRNAs01:22

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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mRNABench: A curated benchmark for mature mRNA property and function prediction.

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mRNABench provides a standardized benchmark for evaluating self-supervised models in mature messenger RNA (mRNA) biology. It identifies optimal models and training strategies for understanding mRNA regulation and function.

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Area of Science:

  • Computational Biology
  • Molecular Biology
  • Machine Learning

Background:

  • Messenger RNA (mRNA) plays a critical role in gene expression, influencing cellular phenotypes through its half-life, localization, and translation efficiency.
  • Self-supervised foundation models offer advanced capabilities for studying the mRNA regulatory code, surpassing limitations of traditional supervised learning.
  • The lack of standardized benchmarks hinders the evaluation and comparison of different self-supervised models in mRNA biology.

Purpose of the Study:

  • To introduce mRNABench, a comprehensive benchmarking suite for assessing the performance of self-supervised nucleotide foundation models in mature mRNA biology.
  • To evaluate the representational quality of mRNA embeddings generated by these models across diverse biological tasks.
  • To facilitate the identification of superior models and training methodologies for mRNA research.

Main Methods:

  • Curated ten diverse datasets and 59 prediction tasks covering key properties of mature mRNA.
  • Assessed the performance of 18 families of nucleotide foundation models, conducting a total of 135,000 experiments.
  • Investigated parameter scaling, compositional generalization, and the relationship between sequence compressibility and model performance.

Main Results:

  • Identified synergistic effects between specific self-supervised learning objectives.
  • Developed a novel Mamba-based model that achieves state-of-the-art performance with significantly reduced parameters (700x fewer).
  • Demonstrated the effectiveness of mRNABench in evaluating and comparing foundation models for mRNA biology.

Conclusions:

  • mRNABench provides a robust framework for benchmarking self-supervised models in mRNA biology.
  • The study highlights the potential of efficient, large-scale models like the new Mamba-based architecture.
  • This resource will accelerate the development and application of AI in understanding mRNA regulation and function.